Expert Review of Proteomics最新文献

Introduction: Acute myeloid leukemia (AML) is an aggressive and poor-prognosis blood cancer. Despite a low mutation burden compared to other cancers, AML is heterogenous and identifying robust therapeutic targets has been difficult. Genomic profiling has greatly advanced our understanding of AML, and has revealed targets for AML therapy. However, only 50% of AML patients have gene mutations that are currently druggable, and relapse rates remain high. The addition of proteomic profiling is emerging to address these challenges.

Areas covered: Using references collected through Pubmed, we review recent studies that have combined genomic and proteomic profiling (i.e. proteogenomic profiling), as well as studies that have additionally integrated other omics approaches, such as phosphoproteomics. We highlight how proteogenomic profiling promises to deconvolve the cellular pathways driving leukemogenesis, uncover novel therapeutic targets, and identify biomarkers of response to novel and existing therapies.

Expert opinion: Proteogenomic profiling is providing unparalleled insight into AML, and is beginning to identify robust biomarkers. Standardization of workflows will be required before mass spectrometry-based proteomic assays can be integrated into routine clinical use. However, the demonstrated ability to adapt signatures into biomarker panels that can be assayed by existing clinical workflows is enabling current clinical translation.

Introduction: The extracellular matrix (ECM) is a highly organized and dynamic network of proteins and glycosaminoglycans that provides critical structural, mechanical, and biochemical support to cells. The functions of the ECM are directly influenced by the conformation of the proteins that compose it. ECM proteoforms, which can result from genetic, transcriptional, and/or post-translational modifications, adopt different conformations and, consequently, confer different structural properties and functionalities to the ECM in both physiological and pathological contexts.

Areas covered: In this review, we discuss how bottom-up proteomics has been applied to identify, map, and quantify post-translational modifications (e.g. additions of chemical groups, proteolytic cleavage, or cross-links) and ECM proteoforms arising from alternative splicing or genetic variants. We further illustrate how proteoform-level information can be leveraged to gain novel insights into ECM protein structure and ECM functions in health and disease.

Expert opinion: In the Expert opinion section, we discuss remaining challenges and opportunities with an emphasis on the importance of devising experimental and computational methods tailored to account for the unique biochemical properties of ECM proteins with the goal of increasing sequence coverage and, hence, accurate ECM proteoform identification.

Introduction: Periodontitis is a chronic inflammatory disease characterized by progressive soft tissue and alveolar bone loss due to interactions between microbial dental plaque and the host response. Despite extensive research on biomarkers from saliva or gingival crevicular fluid (GCF) for diagnosing periodontitis, clinical and radiological parameters remain the primary diagnostic tools.

Areas covered: This review discusses the ongoing research into salivary biomarkers for periodontitis diagnosis, emphasizing the need for reliable biomarkers to differentiate between periodontal health and disease. Salivary biomarker research has gained momentum with advancements in proteomic technologies, enabling noninvasive sample collection and revealing potential candidate biomarkers.

Expert opinion: Proteomic research since the early 2000s has identified promising biomarkers and provided insights into the pathogenesis of periodontitis. Bioinformatic analysis of proteomic data elucidates the underlying biological mechanisms. This review summarizes key findings and highlights common potential biomarkers identified through proteomic research in periodontology.

Objectives: Oral squamous cell carcinoma poses a substantial global health challenge marked by rising mortality rate. Recently, immunotherapy has shown promising results in cancer management by enhancing immune response. Thus, identifying additional immune-related markers is critical for advancing immunotherapy treatments.

Methods: Data-independent acquisition mass spectrometry approach was used to explore differentially expressed immune-related proteins in oral cancer tissues compared to adjacent non-cancerous tissues. Functional significance was identified through Gene Ontology, pathway, and network analysis. Gene expression of identified proteins was validated using transcriptomic data.

Results: DIA analysis identified 29,459 precursors corresponding to 3429 proteins. Among these, 1060 proteins were differentially expressed, with 166 being immune-related. Differentially regulated proteins were involved in innate immune response, mitochondrial ATP synthesis, and neutrophil degranulation. Pathway analysis of immune-related proteins showed perturbation in anti-tumor immunity-related pathways such as interferon signaling, TCR signaling, PD-1 signaling and antigen processing and presentation. Significance of these pathways was further reinforced by the strong interactions identified in the protein-protein interaction network analysis. Additionally, gene expression analysis showed similar mRNA expression patterns for key proteins involved in altered pathways, including ISG15, IFIT1/3, HLA-A/C and OAS2/3.

Conclusions: Further validation of these proteins could establish them as potential targets for personalized therapy.

Introduction: The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.

Areas covered: This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.

Expert opinion: The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.

Introduction: Understanding the metabolic regulatory mechanisms leading to antibacterial resistance is important to develop effective control measures.

Areas covered: In this review, we summarize the progress on metabolic mechanisms of antibiotic resistance in clinically isolated bacteria, as revealed using proteomic approaches.

Expert opinion: Proteomic approaches are effective tools for uncovering clinically significant bacterial metabolic responses to antibiotics. Proteomics can disclose the associations between metabolic proteins, pathways, and networks with antibiotic resistance, and help identify their functional impact. The mechanisms by which metabolic proteins control the four generally recognized resistance mechanisms (decreased influx and targets, and increased efflux and enzymatic degradation) are particularly important. The proposed mechanism of reprogramming proteomics via key metabolites to enhance the killing efficiency of existing antibiotics needs attention.

Introduction: Breast cancer subtyping is used clinically for diagnosis, prognosis, and treatment decisions. Subtypes are categorized by cell of origin, histomorphology, gene expression signatures, hormone receptor status, and/or protein levels. Categorizing breast cancer based on gene expression signatures aids in assessing a patient's recurrence risk. Protein biomarkers, on the other hand, provide functional data for selecting therapies for primary and recurrent tumors. We provide an update on protein biomarkers in breast cancer subtypes and their application in prognosis and therapy selection.

Areas covered: Protein pathways in breast cancer subtypes are reviewed in the context of current protein-targeted treatment options. PubMed, Science Direct, Scopus, and Cochrane Library were searched for relevant studies between 2017 and 17 August 2024.

Expert opinion: Post-translationally modified proteins and their unmodified counterparts have become clinically useful biomarkers for defining breast cancer subtypes from a therapy perspective. Tissue heterogeneity influences treatment outcomes and disease recurrence. Spatial profiling has revealed complex cellular subpopulations within the breast tumor microenvironment. Deciphering the functional relationships between and within tumor clonal cell populations will further aid in defining breast cancer subtypes and create new treatment paradigms for recurrent, drug resistant, and metastatic disease.

Introduction: Biomarker discovery is increasingly moving from single omics to multiomics, as well as from multi-cell omics to single-cell omics. These transitions have increasingly adopted digital transformation technologies to accelerate the progression from data to insight. Here, we will discuss the concept of 'digitalomics' and how digital transformation directly impacts biomarker discovery. This will ultimately assist clinicians in personalized therapy and precision-medicine treatment decisions.

Areas covered: Genotype-to-phenotype-based insight generation involves integrating large amounts of complex multiomic data. This data integration and analysis is aided through digital transformation, leading to better clinical outcomes. We also highlight the challenges and opportunities of Digitalomics, and provide examples of the application of Artificial Intelligence, cloud- and high-performance computing, and use of tensors for multiomic analysis workflows.

Expert opinion: Biomarker discovery, aided by digital transformation, is having a significant impact on cancer, cardiovascular, infectious, immunological, and neurological diseases, among others. Data insights garnered from multiomic analyses, combined with patient meta data, aids patient stratification and targeted treatment across a broad spectrum of diseases. Digital transformation offers time and cost savings while leading to improved patent healthcare. Here, we highlight the impact of digital transformation on multiomics- based biomarker discovery with specific applications related to oncology.

Introduction: A comprehensive and global knowledge of protein target engagement is of vital importance for mechanistic studies and in drug development. Since its initial introduction, the cellular thermal shift assay (CETSA) has proven to be a reliable and flexible technique that can be widely applied to multiple contexts and has profound applications in facilitating the identification and assessment of protein target engagement.

Areas covered: This review introduces the principle of CETSA, elaborates on western blot-based CETSA and MS-based thermal proteome profiling (TPP) as well as the major applications and prospects of these approaches.

Expert opinion: CETSA primarily evaluates a given ligand binding to a particular target protein in cells and tissues with the protein thermal stabilities analyzed by western blot. When coupling mass spectrometry with CETSA, thermal proteome profiling allows simultaneous proteome-wide experiment that greatly increased the efficiency of target engagement evaluation, and serves as a promising strategy to identify protein targets and off-targets as well as protein-protein interactions to uncover the biological effects. The CETSA approaches have broad applications and potentials in drug development and clinical research.

Introduction: Glycosylation, the process of glycan synthesis and attachment to target molecules, is a crucial and common post-translational modification (PTM) in mammalian cells. It affects the protein's hydrophilicity, charge, solubility, structure, localization, function, and protection from proteolysis. Aberrant glycosylation in proteins can reveal new detection and therapeutic Glyco-biomarkers, which help to improve accurate early diagnosis and personalized treatment. This review underscores the pivotal role of glycans and glycoproteins as a source of biomarkers in human diseases, particularly cancer.

Areas covered: This review delves into the implications of glycosylation, shedding light on its intricate roles in cancer-related cellular processes influencing biomarkers. It is underpinned by a thorough examination of literature up to June 2024 in PubMed, Scopus, and Google Scholar; concentrating on the terms: (Glycosylation[Title/Abstract]) OR (Glycan[Title/Abstract]) OR (glycoproteomics[Title/Abstract]) OR (Proteoglycans[Title/Abstract]) OR (Glycomarkers[Title/Abstract]) AND (Cancer[Title/Abstract]) AND ((Diagno*[Title/Abstract]) OR (Progno*[Title/Abstract])).

Expert opinion: Glyco-biomarkers enhance early cancer detection, allow early intervention, and improve patient prognoses. However, the abundance and complex dynamic glycan structure may make their scientific and clinical application difficult. This exploration of glycosylation signatures in cancer biomarkers can provide a detailed view of cancer etiology and instill hope in the potential of glycosylation to revolutionize cancer research.