Expert Review of Proteomics最新文献

Introduction: Exogenous Cushing's syndrome is the result of long-term exposure to glucocorticoids, while endogenous Cushing's syndrome occurs due to excessive production of glucocorticoids within the body. Cushing's syndrome remains a diagnostic challenge for the treating physician.Mass spectrometry, with its better resolution, detectability, and specificity, paved the way to understanding the cellular and molecular mechanisms involved in several diseases that facilitated the evolution of biomarkers and personalized medicine, which can be applicable to manage Cushing's syndrome as well.

Areas covered: There are only a few reports of mass spectrometry-based metabolomic approaches to endogenous Cushing's syndrome of certain etiologies. However, the application of this approach in the diagnosis of exogenous Cushing has not been explored much. This review attempts to discuss the application of the mass spectrometry-based metabolomic approach in the evaluation of Cushing's syndrome.

Expert opinion: Global metabolomics has the potential to discover altered metabolites and associated signaling and metabolic pathways, which may serve as potential biomarkers that would help in developing tools to accelerate precision medicine. Multi-omics approaches will provide innovative solutions to develop molecular tests for multi-molecule panel assays.

Objective: Our study presents a novel analysis of the oncogenes and tumor suppressor proteins directly modulated by E6/E7 of high-risk HPV types 16 and 18, in colorectal cancer (CRC).

Methods: HCT 116 (KRAS mutant) & HT-29 (TP53 mutant) cell models of CRC were transduced with E6/E7 of HPV16 and HPV18, individually and in combination. Further, we utilized a liquid chromatography mass spectrometry (LC-MS/MS) approach to analyze and compare the proteomes of both CRC cell models.

Results: We generated six stably transduced cell lines. Our data revealed a significantly higher, HPV-induced modulation of oncogenes and tumor suppressor proteins in the TP53 mutant model, as compared to the KRAS mutant model (p ≤ 0.01). Less than 1% of the genes were commonly modulated by HPV, between both models. We also report that HT-29 cells, expressing E6/E7 of both HPV types, significantly reduced the suppression of oncogenes as compared to cells expressing E6/E7 of either HPV types individually (p-value ≤0.00001).

Conclusion: Our data imply that HPV coinfections leads to the sustenance of a pro-oncogenic environment in CRC. HPV modulates different oncogenes/tumor suppressor proteins in CRC of varying mutational backgrounds, thus highlighting the importance of personalized therapies for such diseases with mutational heterogeneity.

Introduction: The DeepMind's AlphaFold (AF) has revolutionized biomedical research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective for predicting structures of rigid and globular proteins, it is not able to fully capture the dynamics, conformational variability, and interactions of proteins with ligands and other biomacromolecules.

Areas covered: In this review, we present a comprehensive overview of the latest advancements in 3D model predictions for biomacromolecules using AF. We also provide a detailed analysis its of strengths and limitations, and explore more recent iterations, modifications, and practical applications of this strategy. Moreover, we map the path forward for expanding the landscape of AF toward predicting structures of every protein and peptide in the proteome in the most physiologically relevant form. This discussion is based on an extensive literature search performed using PubMed and Google Scholar.

Expert opinion: While significant progress has been made to enhance AF's modeling capabilities, we argue that a combined approach integrating both various in silico and in vitro methods will be most beneficial for the future of structural biology, bridging the gaps between static and dynamic features of proteins and their functions.

Introduction: Identifying early risks of developing Alzheimer's disease (AD) is a major challenge as the number of patients with AD steadily increases and requires innovative solutions. Current molecular diagnostic modalities, such as cerebrospinal fluid (CSF) testing and positron emission tomography (PET) imaging, exhibit limitations in their applicability for large-scale screening. In recent years, there has been a marked shift toward the development of blood plasma-based diagnostic tests, which offer a more accessible and clinically viable alternative for widespread use. Furthermore, advances in large-scale proteomics technologies have boosted an interest in identifying novel biomarkers and developing panels of AD-associated proteins.

Areas covered: This review mainly examines the results of recent searches for proteomic markers of AD in blood plasma (from 2022-2024 PubMed), focuses on some aspects for special attention in further studies, and discusses the prospects for their further application.

Expert opinion: Recent advances in AD plasma/serum proteomic studies are largely driven using novel Olink/PEA and SomaScan/aptamer technologies, which complement the 'gold standard' of MS-based quantitative proteomics (MRM/SRM), and particularly expand the capabilities for studying low-abundant proteins.

Introduction: Recent work identified members of the evolutionarily conserved coronin protein family as key regulators of cell population size. This work originated ~25 years ago through the identification, by two-dimensional gel electrophoresis, of coronin 1 as a host protein involved in the virulence of Mycobacterium tuberculosis. We here describe the journey from a spot on a 2D gel to the recent realization that coronin proteins represent key controllers of eukaryotic cell population sizes, using ever more sophisticated proteomic techniques.

Areas covered: We discuss the value of 'old school' proteomics using relatively simple and cost-effective technologies that allowed to gain insights into subcellular proteomes and describe how label-free quantitative (phospho)proteomics using mass spectrometry allowed to disentangle the role for coronin 1 in eukaryotic cell population size control. Finally, we mention potential implications of coronin-mediated cell population size control for health and disease.

Expert opinion: Proteome analysis has been revolutionized by the advent of modern-day mass spectrometers and is indispensable for a better understanding of biology. Here, we discuss how careful dissection of physio-pathological processes by a combination of proteomics, genomics, biochemistry and cell biology may allow to zoom in on the unexplored, thereby possibly tackling hitherto unasked questions and defining novel mechanisms.

Introduction: Molecular recognition features (MoRFs) are regions in protein sequences that undergo induced folding upon binding partner molecules. MoRFs are common in nature and can be predicted from sequences based on their distinctive sequence signatures.

Areas covered: We overview 20 years of progress in the sequence-based prediction of MoRFs which resulted in the development of 25 predictors of MoRFs that interact with proteins, peptides, and lipids. These methods range from simple discriminant analysis to sophisticated deep transformer networks that use protein language models. They generate relatively accurate predictions as evidenced by the results of a recently published community-driven assessment.

Expert opinion: MoRFs prediction is a mature field of research that is poised to continue at a steady pace in the foreseeable future. We anticipate further expansion of the scope of MoRF predictions to additional partner molecules, such as nucleic acids, and continued use of recent machine learning advances. Other future efforts should concentrate on improving availability of MoRF predictions by releasing, maintaining, and popularizing web servers and by depositing MoRF predictions to large databases of protein structure and function predictions. Furthermore, accurate MoRF predictions should be coupled with the equally accurate prediction and modeling of the resulting structures of complexes.

Introduction: Mitochondria contain multiple pathways including energy metabolism and several signaling and synthetic pathways. Mitochondrial proteomics is highly valuable for studying diseases including inherited metabolic disorders, complex and common disorders like neurodegeneration, diabetes, and cancer, since they all to some degree have mitochondrial underpinnings.

Areas covered: The main mitochondrial functions and pathways are outlined, and systematic protein lists are presented. The main energy metabolic pathways are as follows: iron-sulfur cluster synthesis, one carbon metabolism, catabolism of hydrogen sulfide, kynurenines and reactive oxygen species (ROS), and others, described with the aim of laying a foundation for systematic mitochondrial pathway analysis based on proteomics data. The links of the proteins and pathways to functional effects and diseases are discussed. The disease examples are focussed on inherited metabolic disorders, cancer, neurological, and cardiovascular disorders.

Expert opinion: To elucidate the role of mitochondria in health and disease, there is a need for comprehensive proteomics analyses with stringent, systematic data treatment for proper interpretation of mitochondrial pathway data. In that way, comprehensive hypothesis-based research can be performed based on proteomics data.

Introduction: Spatial biology is an emerging interdisciplinary field facilitating biological discoveries through the use of spatial omics technologies. Recent advancements in spatial transcriptomics, spatial genomics (e.g. genetic mutations and epigenetic marks), multiplexed immunofluorescence, and spatial metabolomics/lipidomics have enabled high-resolution spatial profiling of gene expression, genetic variation, protein expression, and metabolites/lipids profiles in tissue. These developments contribute to a deeper understanding of the spatial organization within tissue microenvironments at the molecular level.

Areas covered: This report provides an overview of the untargeted, bottom-up mass spectrometry (MS)-based spatial proteomics workflow. It highlights recent progress in tissue dissection, sample processing, bioinformatics, and liquid chromatography (LC)-MS technologies that are advancing spatial proteomics toward cellular resolution.

Expert opinion: The field of untargeted MS-based spatial proteomics is rapidly evolving and holds great promise. To fully realize the potential of spatial proteomics, it is critical to advance data analysis and develop automated and intelligent tissue dissection at the cellular or subcellular level, along with high-throughput LC-MS analyses of thousands of samples. Achieving these goals will necessitate significant advancements in tissue dissection technologies, LC-MS instrumentation, and computational tools.

Objectives: Cartilage defects (CDs) are regarded as early manifestation of osteoarthritis (OA). The infrapatellar fat pad (IPFP) is an important mediator in maintaining joint homeostasis, disease progression and tissue repair, with a crucial role of its secreted proteins. Here, we investigate the proteome of the IPFP in relation to clinical status and response to surgical treatment of CDs.

Methods: In order to characterize the proteome of the IPFP, samples from a cohort of 53 patients who received surgical treatment for knee CDs were analyzed with label-free proteomics. Patients were divided based on validated outcome scores for pain and knee function, preoperatively and at 1-year postoperatively, and on MRI assessment of the defect severity, fibrosis and synovitis.

Results: Specific proteins were differentially abundant in patients with MRI features and better clinical outcome after CD surgery, including a downregulation of cartilage intermediate layer protein 2 (CILP-2) and microsomal glutathione s-transferase 1 (MGST1), and an upregulation of aggrecan (ACAN), and proteoglycan 4 (PRG4). Pathways related to cell interaction, oxidation and matrix remodeling were altered.

Conclusion: Proteins in the IPFP that have a function in extracellular matrix, inflammation and immunomodulation were identified as potentially relevant markers for cartilage repair monitoring.