The quantification of key protein variants can guide precision oncology to enable better identification of driver mutations. Bottom-up assays infer parent protein levels through the quantification of surrogate peptides. Assay methods designed for absolute quantification of protein variants, such as multiple reaction monitoring mass spectrometry (MRM-MS), can achieve quantitation with high precision and specificity. Bottom-up approaches rely on proper selection of suitable surrogate proteogenotypic peptide targets to quantify protein variants of interest. To this end, we developed an R-based bioinformatics pipeline to predict and evaluate variant-specific peptides. The workflow generates variant protein sequences from wild-type sequences and mutations encoded in the Human Genome Variation Society (HGVS) recommended nomenclature, performs in-silico tryptic digestion, and identifies both variant and corresponding wild-type peptides. Each peptide is evaluated by 37 selection criteria to determine suitability as MRM targets. To assess the strictness of these criteria, we applied all protein-altering mutations from the NCI-Genomic Data Commons (GDC) and COSMIC to our pipeline. Of the peptides outputted, 5% satisfied all defined criteria, representing the highest confidence candidates for assay development. We developed a database and web application from NCI-GDC generated peptides for searching, filtering, and downloading. Another web application was developed to provide access to our pipeline.

Absence of the endosomal SNAREs vti1a and vti1b results in perinatal death and severe neuronal phenotypes in mice, while lack of one of these proteins results in minor phenotypes. Proteomic differences were investigated to obtain a deeper insight into processes in which vti1a and vti1b are involved. Here we applied a bottom-up shotgun proteomic approach to investigate the differences in wild-type, double heterozygous (DHET), vti1a-/- vti1b-/- double knockout (DKO), vti1a-/- knockout and vti1b-/- knockout cerebral cortices. Single deletions did not affect protein levels significantly. A total of 1725 proteins were detected of which 69 were less abundant and 191 proteins were more abundant in DKO cortices. Many less abundant proteins belonged to cellular components and reactome pathways synapse, synaptic vesicle cycle, vesicle mediated transport, L1CAM interaction, and cholesterol biosynthesis in pathway enrichment analysis. More abundant proteins were enriched in cellular components and Kyoto Encyclopaedia of Genes and Genomes (KEGG)-pathways such as spliceosome, ribosome, carbon metabolism, and ribonucleoprotein complex. Immunoblotting validated reduced expression levels of the tested synaptic vesicle proteins as well as increased amounts of lysophosphatidylcholine acyltransferase 1 (Lpcat1) and neuron-specific gene 2 (Nsg2), which is involved in postsynaptic AMPA-receptor recycling. These data indicate that the synapse and cell adhesion were strongly affected in DKO brains. STATEMENT OF SIGNIFICANCE OF THE STUDY: Distinct populations of neurons and glia cells are generated and organize into layers during brain development. Neurons develop an elaborate morphology to transmit information via axons and synapses to dendrites in receiving neurons. These neurites form via several specialized pathways of vesicle secretion and endocytosis. Fusion between these membranes requires members of the SNARE protein family. Double knockouts of the endosomal SNAREs vti1a and vti1b (DKO) result in perinatal lethality in mice with massive defects in the brain. In this study we compared the proteome of DKO brain cortices with double heterozygous controls to obtain insights into the molecular alterations and affected pathways. DKO brains contained lower amounts of synaptic proteins and proteins involved in cell adhesion, membrane trafficking and cholesterol biosynthesis. Several proteins of spliceosomes, ribosomes and carbon metabolism were more abundant in DKO brains, which may be a consequence of the reduced amounts of synaptic proteins or a shift in cell populations. Lysophosphatidylcholine acyltransferase 1 (Lpcat1) and neuron-specific gene 2 (Nsg2), which is involved in α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) recycling, were confirmed to be more abundant by Western blotting. These data point to defects in trafficking especially in the synapse and in cell adhesion, which is required for neurite outgrowth.

Stem cell-derived insulin-producing cells (Ins-PCs) hold great promise for diabetes treatment. Placenta-derived multipotent stem cells (PMSCs) are considered an ideal source of Ins-PC generation due to their immunomodulatory and differentiation properties. However, the cellular and molecular pathways underlying PMSC differentiation to Ins-PCs have not been fully elucidated. In this study, PMSCs were isolated from human placenta and successfully differentiated into Ins-PCs using miRNA-181a mimics. Differentiated Ins-PCs produced a significant amount of insulin and upregulation of C-peptide, insulin, and MAFA expression, compared to undifferentiated control PMSCs. RNA sequencing and LC-MS/MS were performed to uncover the pathways involved in the Ins-PC differentiation process. RNA sequencing revealed the transcriptional landscape of PMSC-derived Ins-PC differentiation. Pathway analysis identified important pathways involved in the differentiation process, including Notch and Wnt/ß-catenin, and so forth. Proteomics analysis further affirmed the presence of key insulin pathway-related proteins involved in the differentiation of PMSCs into Ins-PCs, including LEPR, STC2, MAP2K2, and so forth. Moreover, integrated transcriptomic and proteomic analyses further highlighted LEPR as a potential key regulator for Ins-PC differentiation. These findings demonstrated the feasibility of generating Ins-PCs from PMSCs and identified potential signaling pathways and regulators underlying Ins-PC differentiation, supporting PMSCs as a promising stem cell source of cell-based therapy for diabetes treatment.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by high metastatic potential, poor prognosis, and limited effective therapeutic options. In this study, we investigated the molecular mechanisms underlying the anticancer effects of the nucleolin (NCL)-targeting DNA aptamer AS1411 using label-free quantitative proteomic profiling. AS1411 treatment significantly reduced TNBC cell viability and migration. To uncover the underlying mechanisms, we performed global proteomic analysis of AS1411-treated TNBC cells. Bioinformatic analysis of differentially expressed proteins (DEPs) revealed enrichment of tumor-associated signaling pathways and protein-protein interaction networks regulated by AS1411. Among the DEPs, ATPase H+-transporting accessory protein 1 (ATP6AP1) was markedly downregulated in AS1411-treated TNBC cells. Functional studies demonstrated that ATP6AP1 knockdown suppressed TNBC cell proliferation and migration, whereas its overexpression enhanced tumorigenic phenotypes. Importantly, modulation of ATP6AP1 expression showed minimal effects on normal breast epithelial cells. Collectively, these findings identify ATP6AP1 as a key downstream mediator of AS1411 and support its potential as a therapeutic target in TNBC.

It is widely established that the layers of proteins bound to the surface of nanoparticles (NPs), also known as the protein corona, critically influence their behavior in biological environments. Despite numerous proteomics studies performed in different NP-corona systems, the impact of proteomic sample preparation methods on corona profiling remains poorly understood. In this study, we systematically compared five digestion protocols, namely S-TrapTM, iSTTM, ProteaseMAXTM, RapiGestTM, and a routine in-gel digestion protocol commonly used in several laboratories. Protein corona samples were isolated from silica NPs incubated in 80% human plasma in phosphate-buffered saline. Protocol performance was assessed in terms of digestion efficiency, protein and peptide yield, reproducibility, and identification bias. Proteomic characterization revealed marked differences across protocols, highlighting the influence of protocol on protein digestion, recovery, and representation. Our findings emphasize the need for method standardization and tailored protocol selection in corona studies to ensure accurate and reproducible proteomic profiling.

ABSTRACTMisfolding and aggregation of α‐Synuclein (α‐Syn) play a central role in Parkinson's disease (PD), with oligomeric intermediates implicated as key toxic species. Here, we investigate the aggregation of two α‐Syn segments, the NACore (68GAVVTGVTAVA78, WT‐PD1) and the preNAC region (47GVVHGVATVA56, WT‐PD2), using high‐resolution trapped ion‐mobility mass spectrometry (TIMS‐Qq‐ToF) and Thioflavin T fluorescence spectroscopy. The NACore is the minimal sequence required for α‐Syn aggregation, whereas most mutations affecting the onset of PD appear to be in the preNAC region, therefore modulating aggregation dynamics and toxicity. Our results demonstrate that TIMS‐Qq‐ToF effectively resolves and identifies oligomeric species, revealing distinct aggregation pathways for both peptide segments. Fluorescence assays confirm differences in aggregation kinetics and morphology, highlighting the different oligomer formation pathways observed with ion‐mobility mass spectrometry. Despite the oligomer‐preserving nature of the TIMS itself, ion transmission remains too harsh for the fragile oligomers, leading to fragmentation of fragile non‐covalent assemblies. However, collisional cross‐section (N2CCSTIMS) values support the presence of large oligomers. This study highlights the potential of TIMS‐Qq‐ToF for mapping α‐Syn aggregation and underscores the need for optimised, soft ion transmission to better preserve fragile transient intermediates, ultimately contributing to a deeper understanding of PD.SummaryUnderstanding early‐stage protein aggregation is essential for unravelling the molecular mechanisms of Parkinson's disease, a neurodegenerative disorder that currently lacks effective therapeutic solutions. This study employs high‐resolution Trapped Ion Mobility Mass Spectrometry combined with fluorescence spectroscopy to elucidate the oligomerisation of two α‐synuclein segments, revealing distinct aggregation pathways and associated structural characteristics. This study underscores the value of peptide models in advancing our understanding of protein aggregation behaviour. This multifaceted approach provides detailed structural insights into the unexplored, transient early‐stage oligomers of key α‐Syn segments, contributing to a deeper understanding of aggregation mechanisms and providing valuable insights for therapeutic strategies against Parkinson's disease.

ABSTRACTThe EmDia trial, designed to study the effects of the sodium glucose cotransporter‐2 (SGLT2) inhibitor empagliflozin on cardiovascular comorbidities in type 2 diabetes mellitus (T2DM) patients, has been investigated for short‐term metabolic alterations by a limited set of clinical assays. To expand on this data, we report on the development of a liquid chromatography‐mass spectrometry (LC‐MS)‐based metabolomics approach employing an optimized metabolite separation by pentafluorophenyl chromatography. High‐confidence metabolite annotation based on reference standards allows for fast and robust metabolic characterization of large plasma cohorts due to scalability. Applied to EmDia, we show the high predictive power of our methodology for several clinical parameters, including a near‐perfect prediction of fasting blood glucose (R2 = 0.97), and demonstrate how empagliflozin leads to reduced plasma levels of deoxyhexoses, such as 1,5‐anhydroglucitol, a short‐term biomarker for glycemic control.SummaryClinical metabolomics studies continue to gain interest due to their comprehensive metabolite coverage, offering insights into metabolic alterations in health and disease.In this study, we present a robust data‐independent acquisition liquid chromatography‐mass spectrometry‐based metabolomics workflow employing an optimized metabolite separation by pentafluorophenyl chromatography that showcases a comprehensive coverage of plasma metabolites.Applied to characterize plasma metabolite profiles in samples of EmDia, a placebo controlled study investigating the effect of the SGLT2 inhibitor empagliflozin, we assess the predictive power of metabolite signals for clinical parameters describing organ physiologies and pathophysiologies.Descriptive statistics are applied to the metabolite profiles to identify empagliflozin intake‐associated metabolite markers.

Extracellular vesicles (EVs) are membrane-bound vesicles secreted by various cell types into the extracellular space and play a role in intercellular communication. Their molecular cargo varies depending on the cell of origin and its functional state. As a result, EVs serve as representatives of their parent cells and reservoirs of disease biomarkers. Their presence in diverse bodily fluids has fueled interest in their potential for biomarker discovery and signaling research. Advances in mass spectrometry, high-throughput sequencing, and bioinformatics have expanded the molecular characterization of EVs, while emerging tools, including artificial intelligence (AI), image-based systems biology, and curated EV repositories, are driving exploration of disease-associated molecular signatures. Omics technologies generate extensive, multidimensional datasets that can be analyzed using bioinformatics techniques in conjunction with traditional statistical methods. Systems-based approaches, such as network analysis, computer modeling, and AI, are particularly effective for interpreting these complex datasets. However, their application in EV studies requires a solid understanding of EV-specific biological principles and analytical tools to ensure accuracy. By leveraging these analytical strategies, systems biology aims to unravel the intricate organization of biological processes, providing insights into how EVs interact within cells and organisms, and how they can be utilized to advance disease diagnostics, monitor disease progression, and develop novel therapeutic strategies. This review aims to elucidate the state-of-the-art in EV research, integrating multiomics, modeling, and disease-specific insights. EV-specific data repositories and the future of EVs in systems biology will also be highlighted.