Proteomics最新文献

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to dementia. Many cases are diagnosed annually and there is no currently available cure. Understanding the underlying disease biology of AD through the study of molecular networks, particularly by mapping clinical variants to tissue-specific interactomes and regulatory macromolecular assemblies, offers a promising avenue to elucidate altered disease pathways. In this study, we applied differential interactome analysis using a manually curated AD dataset to identify how disease-associated mutations alter both transient and stable protein interactions. By focussing on variant-specific associations detected in brain-relevant tissues, we mapped disruptions in stable macromolecular assemblies and performed Reactome enrichment analysis to uncover perturbed pathways unique to each variant. Additionally, we explored therapeutic insights through the analysis of amyloid precursor protein (APP) physical interactors, identifying potential intervention points that influence amyloidogenic processing. Complementing protein-level data, we integrated microRNA (miRNA)-mediated regulatory interactions, revealing an additional layer of posttranscriptional control over key AD genes. Together, this multilayered strategy provides a framework for precision therapeutics in AD.

Lipids, indispensable yet structurally intricate biomolecules, serve as critical regulators of cellular function and disease progression. Conventional lipidomics, constrained by limited resolution for isomeric and low-abundance species, has been transformed by ion mobility-mass spectrometry (IM-MS). This technology augments analytical power through enhanced orthogonal separation, collision cross-section (CCS)-based identification, and improved sensitivity. This review examines the transformative advances in IM-MS-driven lipidomics, focusing on three major pillars: (1) a critical evaluation of leading ion mobility spectrometry (IMS) platforms, emphasizing innovative instrument geometries and breakthroughs in resolving lipid isomers; (2) an exploration of lipid CCS databases and predictive frameworks, spotlighting computational modeling and machine learning strategies that synergize experimental data with molecular representations for high-confidence lipid annotation; (3) emerging multi-dimensional lipidomics workflows integrating CCS with liquid chromatography-MS/MS to boost identification and depth, alongside mass spectrometry imaging for spatially resolved lipidomics. By unifying cutting-edge instrumentation, computational advances, and biological insights, this review outlines a roadmap for leveraging IM-MS to unravel lipidome complexity, catalyzing biomarker discovery and precision medicine innovation.

Synaptosomes (Syn) and synaptic junctions (SJ) are key neuronal compartments that have been widely characterized in omics studies to understand neurotransmitter- and signal transduction-related events. While synapses are lipid-rich, multiomics approaches integrating lipids and proteins remain largely underexplored. Liquid–liquid extraction (LLE), commonly used in lipidomics, offers significant potential for multiomics analyses by enabling the extraction of diverse molecular classes from a single sample. However, its impact on protein and phosphoprotein analysis in membrane-enriched samples has not been thoroughly investigated or compared to one-phase extraction methods. In this study, we assessed SIMPLEX (Simultaneous Metabolite, Protein, Lipid Extraction), an LLE-based method, against conventional acetone protein precipitation for mass spectrometry-based protein identification. SIMPLEX proved superior for proteomics and phosphoproteomics of SJ, achieving a 42% enrichment in membrane proteins compared to acetone precipitation. It enriched not only transmembrane proteins but also S-palmitoylated proteins. Enriched phosphoproteins included those with beta-transducin repeats (WD40), Armadillo repeats (ARM), and various transmembrane domains, highlighting the SIMPLEX potential and enhanced performance for multiomics analyses.

Multiple myeloma (MM) remains incurable; gaps in our understanding of MM molecular pathogenesis and drugs’ resistance mechanisms are involved in the failure of therapies. This study aims to identify proteins significantly impacting MM patients’ response to commonly used therapeutic regimens. Bone marrow CD138+ selected plasma cells were isolated from patients who had achieved Response (Responders, R) and those who were Non-Responders (NR) to their primary MM therapy. We used LC-MS/MS to investigate the proteomic profile of MM samples, followed by bioinformatics analysis. We identified 1190 proteins, of which 230 showed a statistically significant difference between R and NR, with 27 proteins being upregulated and 203 downregulated in R compared to NR. Pathway enrichment analysis identified pathways related to the immune response and protein synthesis regulation, closely associated with MM progression and response to therapy. The results were validated through individual RNA dataset analysis, corroborating the differential expression of several proteins, including proteins associated with MM (e.g., MIF, ILF3) as well as novel findings (e.g., DCPS and SET). Collectively, proteomics data obtained from R and NR to MM therapy displayed significant changes in the immune system and protein synthesis regulation, supporting their potential role in progression and therapeutic response of MM.