Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.

Metformin (MetF) is used worldwide as a first-line therapy for type 2 diabetes. Recently, interest in the pleiotropic effects of MetF, such as its anticancer and antiaging properties, has increased. However, the molecular target of MetF and the detailed mechanism underlying its ability to inhibit cell growth through autophagy induction remain incompletely understood. In this study, using an innovative label-free drug affinity responsive target stability (DARTS)-LC-MS/MS method, we discovered that mitochondrial voltage-dependent anion channel 1 (VDAC1) is a novel binding protein involved in the induction of autophagy-related cell death by high-dose MetF in hepatocellular carcinoma (HCC). Computational alanine scanning mutagenesis revealed that MetF and VDAC1 (D9, E203) interact electrostatically. MetF disrupts the IP₃R-GRP75-VDAC1 complex, which plays a key role in stabilizing mitochondria-associated ER membranes (MAMs), by binding to VDAC1. This disruption leads to increased cytosolic calcium levels, thereby contributing to autophagy induction. MetF also decreased the AMP/ATP ratio and activated the AMPK pathway. Cells with genetic knockdown of VDAC1 mimicked the activity of MetF. In conclusion, this study provides new insights into the involvement of MetF in ionic interactions with VDAC1, contributing to its anticancer effects in HCC. These findings help elucidate the diverse biological and pharmacological effects of MetF, particularly its influence on autophagy, as well as the potential of MetF as a therapeutic agent for diseases characterized by VDAC1 overexpression.

LAR-RPTPs are evolutionarily conserved presynaptic cell-adhesion molecules that orchestrate multifarious synaptic adhesion pathways. Extensive alternative splicing of LAR-RPTP mRNAs may produce innumerable LAR-RPTP isoforms that act as regulatory “codes” for determining the identity and strength of specific synapse signaling. However, no direct evidence for this hypothesis exists. Here, using targeted RNA sequencing, we detected LAR-RPTP mRNAs in diverse cell types across adult male mouse brain areas. We found pronounced cell-type–specific patterns of two microexons, meA and meB, in Ptprd mRNAs. Moreover, diverse neural circuits targeting the same neuronal populations were dictated by the expression of different Ptprd variants with distinct inclusion patterns of microexons. Furthermore, conditional ablation of Ptprd meA⁺ variants at presynaptic loci of distinct hippocampal circuits impaired distinct modes of synaptic transmission and object-location memory. Activity-triggered alterations of the presynaptic Ptprd meA code in subicular neurons mediates NMDA receptor-mediated postsynaptic responses in CA1 neurons and object-location memory. Our data provide the evidence of cell-type- and/or circuit-specific expression patterns in vivo and physiological functions of LAR-RPTP microexons that are dynamically regulated.

Background: Nanoparticles have been utilized in brain research and therapeutics, including imaging, diagnosis, and drug delivery, owing to their versatile properties compared to bulk materials. However, exposure to nanoparticles leads to their accumulation in the brain, but drug development to counteract this nanotoxicity remains challenging. To date, concerns have risen about the potential toxicity to the brain associated with nanoparticles exposure via penetration of the brain blood barrier to address this issue.Methods: Here the effect of silica-coated-magnetic nanoparticles containing the rhodamine B isothiocyanate dye [MNPs@SiO₂(RITC)] were assessed on microglia through toxicological investigation, including biological analysis and integration of transcriptomics, proteomics, and metabolomics. MNPs@SiO₂(RITC)-induced biological changes, such as morphology, generation of reactive oxygen species, intracellular accumulation of MNPs@SiO₂(RITC) using transmission electron microscopy, and glucose uptake efficiency, were analyzed in BV2 murine microglial cells. Each omics data was collected via RNA-sequencing-based transcriptome analysis, liquid chromatography-tandem mass spectrometry-based proteome analysis, and gas chromatography- tandem mass spectrometry-based metabolome analysis. The three omics datasets were integrated and generated as a single network using a machine learning algorithm. Nineteen compounds were screened and predicted their effects on nanotoxicity within the triple-omics network.Results: Intracellular reactive oxygen species production, an inflammatory response, and morphological activation of cells were greater, but glucose uptake was lower in MNPs@SiO₂(RITC)-treated BV2 microglia and primary rat microglia in a dose-dependent manner. Expression of 121 genes (from 41,214 identified genes), and levels of 45 proteins (from 5918 identified proteins) and 17 metabolites (from 47 identified metabolites) related to the above phenomena changed in MNPs@SiO₂(RITC)-treated microglia. A combination of glutathione and citrate attenuated nanotoxicity induced by MNPs@SiO₂(RITC) and ten other nanoparticles in vitro and in the murine brain, protecting mostly the hippocampus and thalamus.Conclusions: Combination of glutathione and citrate can be one of the candidates for nanotoxicity alleviating drug against MNPs@SiO₂(RITC) induced detrimental effect, including elevation of intracellular reactive oxygen species level, activation of microglia, and reduction in glucose uptake efficiency. In addition, our findings indicate that an integrated triple omics approach provides useful and sensitive toxicological assessment for nanoparticles and screening of drug for nanotoxicity.Graphical

Although natural products are an important source of drugs and drug leads, identification and validation of their target proteins have proven difficult. Here, we report the development of a systematic strategy for target identification and validation employing drug affinity responsive target stability (DARTS) and mass spectrometry imaging (MSI) without modifying or labeling natural compounds. Through a validation step using curcumin, which targets aminopeptidase N (APN), we successfully standardized the systematic strategy. Using label-free voacangine, an antiangiogenic alkaloid molecule as the model natural compound, DARTS analysis revealed vascular endothelial growth factor receptor 2 (VEGFR2) as a target protein. Voacangine inhibits VEGFR2 kinase activity and its downstream signaling by binding to the kinase domain of VEGFR2, as was revealed by docking simulation. Through cell culture assays, voacangine was found to inhibit the growth of glioblastoma cells expressing high levels of VEGFR2. Specific localization of voacangine to tumor compartments in a glioblastoma xenograft mouse was revealed by MSI analysis. The overlap of histological images with the MSI signals for voacangine was intense in the tumor regions and showed colocalization of voacangine and VEGFR2 in the tumor tissues by immunofluorescence analysis of VEGFR2. The strategy employing DARTS and MSI to identify and validate the targets of a natural compound as demonstrated for voacangine in this study is expected to streamline the general approach of drug discovery and validation using other biomolecules including natural products.

The distribution of differential extracellular matrix (ECM) in the lateral and medial menisci can contribute to knee instability, and changes in the meniscus tissue can lead to joint disease. Thus, deep proteomic identification of the lateral and medial meniscus cartilage is expected to provide important information for treatment and diagnosis of various knee joint diseases. We investigated the proteomic profiles of 12 lateral/medial meniscus pairs obtained from excess tissue of osteoarthritis patients who underwent knee arthroscopy surgery using mass spectrometry-based techniques and measured 75 ECM protein levels in the lesions using a multiple reaction monitoring (MRM) assay we developed. A total of 906 meniscus proteins with a 1% false discovery rate (FDR) was identified through a tandem mass tag (TMT) analysis showing that the lateral and medial menisci had similar protein expression profiles. A total of 131 ECM-related proteins was included in meniscus tissues such as collagen, fibronectin, and laminin. Our data showed that 14 ECM protein levels were differentially expressed in lateral and medial lesions (p < 0.05). We present the proteomic characterization of meniscal tissue with mass spectrometry-based comparative proteomic analysis and developed an MRM-based assay of ECM proteins correlated with tissue regeneration. The mass spectrometry dataset has been deposited to the MassIVE repository with the dataset identifier MSV000087753.