Expert Review of Proteomics最新文献

Introduction: Determination of urinary gluten immunogenic peptides (GIP) has emerged as one of the most attractive test to monitor the adherence to the gluten-free diet (GFD) of patients with celiac disease (CD), being a simple, noninvasive and direct method to detect gluten contamination of the GFD.

Areas covered: We conducted a scoping review in Medline (PubMed) of articles published up to April 2023 that analyzed any aspect of the clinical relevance of the use of urinary GIP measurement in patients with CD. A total of 17 articles reporting the clinical use of urinary peptidomics for the follow-up of CD patients were finally included.

Expert opinion: Available data suggest that a negative urinary GIP result is a reliable noninvasive predictor of intestinal mucosa healing in CD patients treated with the GFD, especially if testing three urine samples on different days including the weekend. Due to conflicting results about the sensitivity and the specificity of the urinary GIP determination, additional in-depth information is needed, particularly related to (1) the relationship between the amount of ingested gluten and the quantity of urinary GIP excreted in treated CD patients, (2) the GIP kinetics and best timing for sample collection.

Introduction: Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software.

Areas covered: The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses.

Expert opinion: The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

Introduction: Brain tumors are complex and heterogeneous malignancies with significant challenges in diagnosis, prognosis, and therapy. Proteomics, the large-scale study of proteins and their functions, has emerged as a powerful tool to comprehensively investigate the molecular mechanisms underlying brain tumor regulation.

Areas covered: This review explores brain tumors from a proteomic standpoint, highlighting recent progress and insights gained through proteomic methods. It delves into the proteomic techniques employed and underscores potential biomarkers for early detection, prognosis, and treatment planning. Recent PubMed Central proteomic studies (2017-present) are discussed, summarizing findings on altered protein expression, post-translational changes, and protein interactions. This sheds light on brain tumor signaling pathways and their significance in innovative therapeutic approaches.

Expert opinion: Proteomics offers immense potential for revolutionizing brain tumor diagnosis and therapy. To unlock its full benefits, further translational research is crucial. Combining proteomics with other omics data enhances our grasp of brain tumors. Validating and translating proteomic biomarkers are vital for better patient results. Challenges include tumor complexity, lack of curated proteomic databases, and the need for collaboration between researchers and clinicians. Overcoming these challenges requires investment in technology, data sharing, and translational research.

Introduction: Diabetes Mellitus (DM) is a chronic heterogeneous metabolic disorder characterized by hyperglycemia due to the destruction of insulin-producing pancreatic β cells and/or insulin resistance. It is now considered a global epidemic disease associated with serious threats to a patient's life. Understanding the metabolic pathways involved in disease pathogenesis and progression is important and would improve prevention and management strategies. Metabolomics is an emerging field of research that offers valuable insights into the metabolic perturbation associated with metabolic diseases, including DM.

Area covered: Herein, we discussed the metabolomics in type 1 and 2 DM research, including its contribution to understanding disease pathogenesis and identifying potential novel biomarkers clinically useful for disease screening, monitoring, and prognosis. In addition, we highlighted the metabolic changes associated with treatment effects, including insulin and different anti-diabetic medications.

Expert opinion: By analyzing the metabolome, the metabolic disturbances involved in T1DM and T2DM can be explored, enhancing our understanding of the disease progression and potentially leading to novel clinical diagnostic and effective new therapeutic approaches. In addition, identifying specific metabolites would be potential clinical biomarkers for predicting the disease and thus preventing and managing hyperglycemia and its complications.

Introduction: Clinical proteomics studies of Alzheimer's disease (AD) research aim to identify biomarkers useful for clinical research, diagnostics, and improve our understanding of the pathological processes involved in the disease. The rapidly increasing performance of proteomics technologies is likely to have great impact on AD research.

Areas covered: We review recent proteomics approaches that have advanced the field of clinical AD research. Specifically, we discuss the application of targeted mass spectrometry (MS), labeling-based and label-free MS-based as well as affinity-based proteomics to AD biomarker development, underpinning their importance with the latest impactful clinical studies. We evaluate how proteomics technologies have been adapted to meet current challenges. Finally, we discuss the limitations and potential of proteomics techniques and whether their scope might extend beyond current research-based applications.

Expert opinion: To date, proteomics technologies in the AD field have been largely limited to AD biomarker discovery. The recent development of the first successful disease-modifying treatments of AD will further increase the need for blood biomarkers for early, accurate diagnosis, and CSF biomarkers that reflect specific pathological processes. Proteomics has the potential to meet these requirements and to progress into clinical routine practice, provided that current limitations are overcome.

Introduction: Positional proteomics provides proteome-wide information on protein termini and their modifications, uniquely enabling unambiguous identification of site-specific, limited proteolysis. Such proteolytic cleavage irreversibly modifies protein sequences resulting in new proteoforms with distinct protease-generated neo-N and C-termini and altered localization and activity. Misregulated proteolysis is implicated in a wide variety of human diseases. Protein termini, therefore, constitute a huge, largely unexplored source of specific analytes that provides a deep view into the functional proteome and a treasure trove for biomarkers.

Areas covered: We briefly review principal approaches to define protein termini and discuss recent advances in method development. We further highlight the potential of positional proteomics to identify and trace specific proteoforms, with a focus on proteolytic processes altered in disease. Lastly, we discuss current challenges and potential for applying positional proteomics in biomarker and pre-clinical research.

Expert opinion: Recent developments in positional proteomics have provided significant advances in sensitivity and throughput. In-depth analysis of proteolytic processes in clinical cohorts thus appears feasible in the near future. We argue that this will provide insights into the functional state of the proteome and offer new opportunities to utilize proteolytic processes altered or targeted in disease as specific diagnostic, prognostic and companion biomarkers.

Introduction: Tissue-based proteomic studies of colorectal cancer (CRC) metastasis have delivered fragmented results, with very few therapeutic targets and prognostic biomarkers moving beyond the discovery phase. This situation is likely due to the difficulties in obtaining and analyzing large numbers of patient-derived metastatic samples, the own heterogeneity of CRC, and technical limitations in proteomics discovery. As an alternative, metastatic CRC cell lines provide a flexible framework to investigate the underlying mechanisms and network biology of metastasis for target discovery.

Areas covered: In this perspective, we comment on different in-depth proteomic studies of metastatic versus non-metastatic CRC cell lines. Identified metastasis-related proteins are introduced and discussed according to the spatial location in different cellular fractions, with special emphasis on membrane/adhesion proteins, secreted proteins, and nuclear factors, including miRNAs associated with liver metastasis. Moreover, we analyze the biological significance and potential therapeutic applications of the identified liver metastasis-related proteins.

Expert opinion: The combination of protein discovery and functional analysis is the only way to accelerate the progress to clinical translation of the proteomic-derived findings in a relatively fast pace. Patient-derived organoids represent a promising alternative to patient tissues and cell lines, but further optimizations are still required for achieving solid and reproducible results.

Introduction: Female fertility has been a field of interest for the scientific community throughout the years. The contribution of proteomics in the study of female fertility as well as female infertility and in vitro fertilization (IVF) has been significant. Proteomics is a recently developed field, extensively applied to the identification and quantification of proteins, which could be used as potential biomarkers in a diagnostic, prognostic, or predictive manner in a variety of medical conditions.

Areas covered: The present review focuses on proteomic studies of the oocyte and endometrial environment as well as on conditions related to infertility, such as polycystic ovarian syndrome, endometriosis, obesity, and unexplained infertility. Moreover, this review presents studies that have been done in an effort to search for fertility biomarkers in individuals following the IVF procedure.

Expert opinion: The comprehension of the molecular pathways behind female fertility and infertility could contribute to the diagnosis, prognosis, and prediction of infertility. Moreover, the identification of proteomic biomarkers for IVF cycles could predict the possible outcome of an IVF cycle, prevent an unsuccessful IVF, and monitor the IVF cycle in a personalized manner, leading to increased success rates. [Figure: see text].