Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis.

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

Spatial and temporal diversity of glycome expression in mammalian brain.

Mammalian brain glycome remains a relatively poorly understood area compared to other large-scale "omics" studies, such as genomics and transcriptomics due to the inherent complexity and heterogeneity of glycan structure and properties. Here, we first performed spatial and temporal analysis of glycome expression patterns in the mammalian brain using a cutting-edge experimental tool based on liquid chromatography-mass spectrometry, with the ultimate aim to yield valuable implications on molecular events regarding brain functions and development. We observed an apparent diversity in the glycome expression patterns, which is spatially well-preserved among nine different brain regions in mouse. Next, we explored whether the glycome expression pattern changes temporally during postnatal brain development by examining the prefrontal cortex (PFC) at different time point across six postnatal stages in mouse. We found that glycan expression profiles were dynamically regulated during postnatal developments. A similar result was obtained in PFC samples from humans ranging in age from 39 d to 49 y. Novel glycans unique to the brain were also identified. Interestingly, changes primarily attributed to sialylated and fucosylated glycans were extensively observed during PFC development. Finally, based on the vast heterogeneity of glycans, we constructed a core glyco-synthesis map to delineate the glycosylation pathway responsible for the glycan diversity during the PFC development. Our findings reveal high levels of diversity in a glycosylation program underlying brain region specificity and age dependency, and may lead to new studies exploring the role of glycans in spatiotemporally diverse brain functions.

Flashlight into the Function of Unannotated C11orf52 using Affinity Purification Mass Spectrometry.

For an enhanced understanding of the biological mechanisms of human disease, it is essential to investigate protein functions. In a previous study, we developed a prediction method of gene ontology (GO) terms by the I-TASSER/COFACTOR result, and we applied this to uPE1 in chromosome 11. Here, to validate the bioinformatics prediction of C11orf52, we utilized affinity purification and mass spectrometry to identify interacting partners of C11orf52. Using immunoprecipitation methods with three different peptide tags (Myc, Flag, and 2B8) in HEK 293T cell lines, we identified 79 candidate proteins that are expected to interact with C11orf52. The results of a pathway analysis of the GO and STRING database with candidate proteins showed that C11orf52 could be related to signaling receptor binding, cell-cell adhesion, and ribosome biogenesis. Then, we selected three partner candidates of DSG1, JUP, and PTPN11 for verification of the interaction with C11orf52 and confirmed them by colocalization at the cell-cell junctions by coimmunofluorescence experiments. On the basis of this study, we expect that C11orf52 is related to the Wnt signaling pathway via DSG1 from the protein-protein interactions, given the results of a comprehensive analysis of the bioinformatic predictions. The data set is available at the ProteomeXchange consortium via PRIDE repository (PXD026986).

Depletion of ST6GALNACIII retards A549 non-small cell lung cancer cell proliferation by downregulating transferrin receptor protein 1 expression.

Alterations in sialylation of terminal residues of glycoproteins have been implicated in forming tumor-associated glycans. ST6GALNAC transfers sialyl moiety to N-acetylgalactosamine residue via α2,6 linkage. Although the oncogenic characteristics of ST6GALNACI or II have been demonstrated in various cancer cells, the impact of ST6GALNACIII on tumor progression remains undefined. In this study, we evaluated the effect of ST6GALNACIII knockdown on the growth of A549 non-small cell lung cancer cells. ST6GALNACIII depletion resulted in significant retardation in growth of A549 cells under various culture conditions, including collagen-supported 3D culture and anchorage-independent soft agar culture conditions. Liquid chromatography with tandem mass spectrometry revealed that two glycopeptides of transferrin receptor protein 1 (TFR1) containing N-acetylhexosamine-sialic acid were not detected in ST6GALNACIII-depleted A549 cells compared with control cells. Subsequent lectin binding assay, western blotting, and real-time RT-PCR indicated that TFR1 sialylation was not significantly changed, but TFR1 protein and mRNA expressions were decreased after ST6GALNACIII knockdown. However, cell growth retardation by ST6GALNACIII knockdown was partially rescued by TFR1 overexpression. Additionally, TFR1 mRNA degradation was accelerated following ST6GALNACIII knockdown with concomitant reduction in mRNA levels of iron regulatory protein 1 and 2, the upstream regulators of TFR1 mRNA stability. Therefore, our results indicated an important role of ST6GALNACIII in promoting A549 cell growth through quantitative regulation of TFR1 expression and provided therapeutic implications for ST6GALNACIII targeting in tumor growth suppression in vivo.

Bioinformatic Prediction of Gene Ontology Terms of Uncharacterized Proteins from Chromosome 11.

In chromosome 11, 71 out of its 1254 proteins remain functionally uncharacterized on the basis of their existence evidence (uPE1s) following the latest version of neXtProt (release 2020-01-17). Because in vivo and in vitro experimental strategies are often time-consuming and labor-intensive, there is a need for a bioinformatics tool to predict the function annotation. Here, we used I-TASSER/COFACTOR provided on the neXtProt web site, which predicts gene ontology (GO) terms based on the 3D structure of the protein. I-TASSER/COFACTOR predicted 2413 GO terms with a benchmark dataset of the 22 proteins belonging to PE1 of chromosome 11. In this study, we developed a filtering algorithm in order to select specific GO terms using the GO map generated by I-TASSER/COFACTOR. As a result, 187 specific GO terms showed a higher average precision-recall score at the least cellular component term compared to 2413 predicted GO terms. Next, we applied 65 proteins belonging to uPE1s of chromosome 11, and then 409 out of 6684 GO terms survived, where 103 and 142 GO terms of molecular function and biological process, respectively, were included. Representatively, the cellular component GO terms of CCDC90B, C11orf52, and the SMAP were predicted and validated using the overexpression system into 293T cells and immunofluorescence staining. We will further study their biological and molecular functions toward the goal of the neXt-CP50 project as a part of C-HPP. We shared all results and programs in Github (https://github.com/heeyounh/I-TASSER-COFACTOR-filtering.git).

Absolute Quantification of N-Glycosylation of Alpha-Fetoprotein Using Parallel Reaction Monitoring with Stable Isotope-Labeled N-Glycopeptide as an Internal Standard.

Alpha-fetoprotein (AFP) is a well-established serum biomarker for hepatocellular carcinoma (HCC) in clinical laboratories. However, AFP levels can often be high in benign liver diseases such as liver cirrhosis. For this reason, specifically, the level of the aberrant N-glycosylation of AFP has been proposed as a HCC biomarker to improve diagnostic performance using targeted mass spectrometry (MS). In this study, we developed an endoglycosidase-assisted absolute quantification (AQUA) method by which to measure N-glycosylated AFP levels in serum using liquid chromatography-parallel reaction monitoring with immunoprecipitation. Especially, an isotopically labeled synthetic N-glycopeptide with N-acetylhexosamine (HexNAc) attached to asparagine (N) was used as an internal standard. The efficacy of this method was demonstrated by quantifying the N-glycosylation of AFP in human serum. As a result, we showed that the lower limit of the quantification of a stable isotope-labeled N-glycopeptide reached an attomolar level. Our method also had a linear dynamic range from 2 to 6000 ng/mL for N-glycosylated AFP levels. Finally, the N-glycosylation levels of AFP were measured in HCC patients and in healthy donors with the coefficient of variation in both cases (<10% CV). To the best of our knowledge, this is the first report of the AQUA of N-glycosylated AFP in human sera using a stable isotope-labeled glycopeptide as an internal standard. The results demonstrate that our method can facilitate the discovery and verification of aberrant glycoprotein biomarkers in human serum and plasma through sensitive and precise quantification.

Classification of Mucin-Type O-Glycopeptides Using Higher-Energy Collisional Dissociation in Mass Spectrometry.

Changes in mucin-type O-glycosylation of human proteins affect protein function, immune response, and cancer progression. Since O-glycoproteins are characterized by the microheterogeneity of diverse O-glycans with no conserved sequence and the macroheterogeneity of multiple glycosylation sites on serine and/or threonine in human proteins, the assessment of different mucin types, such as Tn-antigen, core 1, and core 2, and their extended core types in O-glycopeptides, is extremely challenging. Here, we present an O-GlycoProteome Analyzer (O-GPA) that automatically classifies mucin-type O-glycosylation using higher-energy collisional dissociation (HCD) in mass spectrometry. First, we estimated the number of GlcNAc residues using the intensity ratio of GlcNAc-specific fragment ions (HexNAc-CH6O3 and HexNAc-2H2O) over GalNAc-specific fragment ions (HexNAc-C2H6O3 and HexNAc-C2H4O2) in the HCD spectrum. Furthermore, we classified the different mucin types of O-glycopeptides from characteristic B2 (HexNAc2) or Y2α (PEP + HexNAc2), and Y2ß (PEP + HexNAcHex) fragment ions, along with the number of GlcNAc. Furthermore, O-GPA automatically determined single or multiple O-glycosylation, regardless of the mucin types. The mucin type of O-glycopeptides from human urine and plasma was confirmed with an overall accuracy of 96%. We found 97 core 1, 56 core 2, 13 extended core 1, and 12 extended core 2 glycopeptides from urine; and 22 core 1, 13 core 2, 7 extended core 1, 1 extended core 2, and 1 Tn-antigen from plasma. Our strategy can be used to successfully characterize specific mucin types of O-glycoproteins in human biological samples.

Selective Identification of α-Galactosyl Epitopes in N-Glycoproteins Using Characteristic Fragment Ions from Higher-Energy Collisional Dissociation.

The α-galactosyl epitope is a terminal N-glycan moiety of glycoproteins found in mammals except in humans, and thus, it is recognized as an antigen that provokes an immunogenic response in humans. Accordingly, it is necessary to analyze the α-galactosyl structure in biopharmaceuticals or organ transplants. Due to an identical glycan composition and molecular mass between α-galactosyl N-glycans and hybrid/high-mannose-type N-glycans, it is challenging to characterize α-galactosyl epitopes in N-glycoproteins using mass spectrometry. Here, we describe a method to identify α-galactosyl N-glycopeptides in mice glycoproteins using liquid chromatography with tandem mass spectrometry with higher-energy collisional dissociation (HCD). The first measure was an absence of [YHM] ion peaks in the HCD spectra, which was exclusively observed in hybrid and/or high-mannose-type N-glycopeptides. The second complementary criterion was the ratio of an m/z 528.19 (Hex2HexNAc1) ion to m/z 366.14 (Hex1HexNAc1) ion (Im/z528/Im/z366). The measure of [Im/z528/Im/z366 > 0.3] enabled a clear-cut determination of α-galactosyl N-glycopeptides with high accuracy. In Ggta1 knockout mice, we could not find any α-galactosyl N-glycoproteins identified in WT mice plasma. Using this method, we could screen for α-galactosyl N-glycoproteins from mice spleen, lungs, and plasma samples in a highly sensitive and specific manner. Conclusively, we suggest that this method will provide a robust analytical tool for determination of α-galactosyl epitopes in pharmaceuticals and complex biological samples.

Computational classification of core and outer fucosylation of N-glycoproteins in human plasma using collision-induced dissociation in mass spectrometry.

RATIONALE: Glycoprotein fucosylation, one of the major posttranslational modifications, is known to be highly involved in proteins related to various cancers. Fucosylation occurs in the core and/or outer sites of N-glycopeptides. Elucidation of the fucosylation type of N-glycoproteins is therefore important. However, it has remained a challenge to classify the fucosylation types of N-glycopeptides using collision-induced dissociation (CID) tandem mass (MS/MS) spectra. METHODS: The relative intensities of the Y1 F, Y2 F, Y3 F, and Y4 F product ions in the CID-MS/MS spectra of the IgG N-glycopeptides were measured for core fucosylation. The Core Fucose Index (CFI) was then calculated by multiplication of the relative intensities with a weight factor from logistic regression to differentiate between the core and none fucosylation. From the relative intensities of the B2 F and B3 SF ions of the MS/MS spectra of the AGP N-glycopeptides for outer fucosylation, the Outer Fucose Index (OFI) was calculated to differentiate between the outer and none fucosylation. RESULTS: In order to classify core and/or outer fucosylation of N-glycoproteins, we defined the fucosylation score (F-score) by a sigmoidal equation using a combination of the CFI and the OFI. For application, we classified the fucosylation types of N-glycoproteins in human plasma with 99.7% accuracy from the F-score. Human plasma samples showed 54.4%, 33.3%, 10.3%, and 1.6% for none, core, outer, and dual fucosylated N-glycopeptides, respectively. Core fucosylation was abundant at mono- and bi-antennary N-glycopeptides. Outer fucosylation was abundant at tri- and tetra-antennary N-glycopeptides. In total, 113 N-glycopeptides of 29 glycoproteins from 3365 glycopeptide spectral matches (GPSMs) were classified for different types of fucosylation. CONCLUSIONS: We established an F-score to classify three different fucosylation types: core, outer, and dual types of N-glycopeptides. The fucosylation types of 20 new N-glycopeptides from 11 glycoproteins in human plasma were classified using the F-score. Therefore, the F-score can be useful for the automatic classification of different types of fucosylation in N-glycoproteins of biological fluids including plasma, serum, and urine.

Cyclic Peptide Mimotopes for the Detection of Serum Anti-ATIC Autoantibody Biomarker in Hepato-Cellular Carcinoma.

Tumor-associated (TA) autoantibodies have been identified at the early tumor stage before developing clinical symptoms, which holds hope for early cancer diagnosis. We identified a TA autoantibody from HBx-transgenic (HBx-tg) hepatocellular carcinoma (HCC) model mouse, characterized its target antigen, and examined its relationship to human HCC. The mimotopes corresponding to the antigenic epitope of TA autoantibody were screened from a random cyclic peptide library and used for the detection of serum TA autoantibody. The target antigen of the TA autoantibody was identified as an oncogenic bi-functional purine biosynthesis protein, ATIC. It was upregulated in liver cancer tissues of HBx-tg mouse as well as human HCC tissues. Over-expressed ATIC was also secreted extracellularly via the cancer-derived exosomes, which might cause auto-immune responses. The cyclic peptide mimotope with a high affinity to anti-ATIC autoantibody, CLPSWFHRC, distinguishes between serum samples from HCC patients and healthy subjects with 70.83% sensitivity, 90.68% specificity (AUC = 0.87). However, the recombinant human ATIC protein showed a low affinity to anti-ATIC autoantibody, which may be incompatible as a capture antigen for serum TA autoantibody. This study indicates that anti-ATIC autoantibody can be a potential HCC-associated serum biomarker and suggests that autoantibody biomarker's efficiency can be improved by using antigenic mimicry to native antigens present in vivo.

SAAVpedia: Identification, Functional Annotation, and Retrieval of Single Amino Acid Variants for Proteogenomic Interpretation.

Next-generation genome sequencing has enabled the discovery of numerous disease- or drug-response-associated nonsynonymous single nucleotide variants (nsSNVs) that alter the amino acid sequences of a protein. Although several studies have attempted to characterize pathogenic nsSNVs, few have been confirmed as single amino acid variants (SAAVs) at the protein level. Here we developed the SAAVpedia platform to identify, annotate, and retrieve pathogenic SAAV candidates from proteomic and genomic data. The platform consists of four modules: SAAVidentifier, SAAVannotator, SNV/SAAVretriever, and SAAVvisualizer. The SAAVidentifier provides a reference database containing 18 206 090 SAAVs and performs the identification and quality assessment of SAAVs. The SAAVannotator provides functional annotation with biological, clinical, and pharmacological information for the interpretation of condition-specific SAAVs. The SNV/SAAVretriever module enables bidirectional navigation between relevant SAAVs and nsSNVs with diverse genomic and proteomic data. SAAVvisualizer provides various statistical plots based on functional annotations of detected SAAVs. To demonstrate the utility of SAAVpedia, the proteogenomic pipeline with protein-protein interaction network analysis was applied to proteomic data from breast cancer and glioblastoma patients. We identified 1326 and 12 breast-cancer- and glioblastoma-related genes that contained one or more SAAVs, including BRCA2 and FAM49B, respectively. SAAVpedia is a suitable platform for confirming whether a genomic variant is maintained in an amino acid sequence. Furthermore, as a result of the SAAV discovery of these positive controls, the SAAVpedia could play a key role in the protein functional study for the Human Proteome Project (HPP).

Mass spectrometry analysis of glycoprotein biomarkers in human blood of hepatocellular carcinoma.

Introduction: Diagnosis of hepatocellular carcinoma (HCC) is important for improving the survival rate and selecting the optimum therapeutic option. However, some patients with HCC are not diagnosed until after symptoms appear, when the tumor is already advanced. Thus, biomarkers associated with HCC and novel diagnostic methods are required to improve the diagnosis of HCC. Mass spectrometry (MS) is one of the most widely used analytical tools in proteomic research. Furthermore, tandem MS (MS/MS) has been applied for the discovery and verification of protein biomarkers for clinical use. Areas covered: We review candidate glycoprotein biomarkers, including their aberrant glycosylation discovered by MS-based proteomics techniques and their diagnostic strategies using human blood samples. Finally, we discuss the limitations and prospects of MS-based approaches for clinical applications. Expert commentary: The development of biomarkers with high sensitivity and specificity is essential for optimizing the management of HCC. Various glycoprotein biomarkers of HCC have been identified using MS-based techniques. MS-based assays will continue to play an important role in clinical applications for discovery and verification of biomarkers. Furthermore, combination of multibiomarker, improvements in sample enrichment and the development of highly sensitive MS methods will facilitate more rapid adoption of MS for the diagnosis of HCC.

Parallel reaction monitoring with multiplex immunoprecipitation of N-glycoproteins in human serum for detection of hepatocellular carcinoma.

The N-glycosylation of proteins is one of the most important post-translational modifications relevant to various biological functions. The identification and quantification of N-glycoproteins in liquid chromatography-mass spectrometry (LC-MS) is challenging because of their low analytical sensitivity and selectivity. This is due to their microheterogeneity and the difficulty of synthesizing N-glycopeptides as an internal standard. Parallel reaction monitoring (PRM) is widely used in targeted LC-MS. The key advantage of LC-PRM is that it can identify N-glycopeptides using tandem mass spectrometry (MS/MS) fragmentation, even without an internal standard. We investigated the feasibility of analyzing N-glycoproteins using multiplex immunoprecipitation to improve sensitivity and selectivity. We targeted N-glycoproteins [α-fetoprotein (AFP), vitronectin (VTN), and α-1-antichymotrypsin (AACT)] that are abnormally glycosylated in hepatocellular carcinoma (HCC). Their tryptic N-glycopeptides were selected to determine the percentages of fucosylated N-glycopeptides using Y ions, which include glycopeptide fragments with amino acid sequences. Finally, we confirmed that the area under the receiver operating characteristic curve (AUC = 0.944) for the combination of AFP and VTN increased more so than for a single glycopeptide (AUC = 0.889 for AFP and 0.792 for VTN) with respect to discriminating between HCC and cirrhosis serum. This study shows that an LC-PRM method using multiplex N-glycoproteins immunoprecipitated from serum could be applied to develop and verify cancer biomarkers. Graphical abstract.

Identification of Missing Proteins in Human Olfactory Epithelial Tissue by Liquid Chromatography-Tandem Mass Spectrometry.

We performed proteomic analyses of human olfactory epithelial tissue to identify missing proteins using liquid chromatography-tandem mass spectrometry. Using a next-generation proteomic pipeline with a < 1.0% false discovery rate at the peptide and protein levels, we identified 3731 proteins, among which five were missing proteins (P0C7M7, P46721, P59826, Q658L1, and Q8N434). We validated the identified missing proteins using the corresponding synthetic peptides. No olfactory receptor (OR) proteins were detected in olfactory tissue, suggesting that detection of ORs would be very difficult. We also identified 49 and 50 alternative splicing variants mapped at the neXtProt and GENCODE databases, respectively, and 2000 additional single amino acid variants. This data set is available at the ProteomeXchange consortium via PRIDE repository (PXD010025).

Launching the C-HPP neXt-CP50 Pilot Project for Functional Characterization of Identified Proteins with No Known Function.

An important goal of the Human Proteome Organization (HUPO) Chromosome-centric Human Proteome Project (C-HPP) is to correctly define the number of canonical proteins encoded by their cognate open reading frames on each chromosome in the human genome. When identified with high confidence of protein evidence (PE), such proteins are termed PE1 proteins in the online database resource, neXtProt. However, proteins that have not been identified unequivocally at the protein level but that have other evidence suggestive of their existence (PE2-4) are termed missing proteins (MPs). The number of MPs has been reduced from 5511 in 2012 to 2186 in 2018 (neXtProt 2018-01-17 release). Although the annotation of the human proteome has made significant progress, the "parts list" alone does not inform function. Indeed, 1937 proteins representing ∼10% of the human proteome have no function either annotated from experimental characterization or predicted by homology to other proteins. Specifically, these 1937 "dark proteins" of the so-called dark proteome are composed of 1260 functionally uncharacterized but identified PE1 proteins, designated as uPE1, plus 677 MPs from categories PE2-PE4, which also have no known or predicted function and are termed uMPs. At the HUPO-2017 Annual Meeting, the C-HPP officially adopted the uPE1 pilot initiative, with 14 participating international teams later committing to demonstrate the feasibility of the functional characterization of large numbers of dark proteins (CP), starting first with 50 uPE1 proteins, in a stepwise chromosome-centric organizational manner. The second aim of the feasibility phase to characterize protein (CP) functions of 50 uPE1 proteins, termed the neXt-CP50 initiative, is to utilize a variety of approaches and workflows according to individual team expertise, interest, and resources so as to enable the C-HPP to recommend experimentally proven workflows to the proteome community within 3 years. The results from this pilot will not only be the cornerstone of a larger characterization initiative but also enhance understanding of the human proteome and integrated cellular networks for the discovery of new mechanisms of pathology, mechanistically informative biomarkers, and rational drug targets.

Identification of anti-SF3B1 autoantibody as a diagnostic marker in patients with hepatocellular carcinoma.

BACKGROUND: Tumor-associated (TA) autoantibodies, which are generated by the immune system upon the recognition of abnormal TA antigens, are promising biomarkers for the early detection of tumors. In order to detect autoantibody biomarkers effectively, antibody-specific epitopes in the diagnostic test should maintain the specific conformations that are as close as possible to those presenting in the body. However, when using patients' serum as a source of TA autoantibodies the characterization of the autoantibody-specific epitope is not easy due to the limited amount of patient-derived serum. METHODS: To overcome these limits, we constructed a B cell hybridoma pool derived from a hepatocellular carcinoma (HCC) model HBx-transgenic mouse and characterized autoantibodies derived from them as tumor biomarkers. Their target antigens were identified by mass spectrometry and the correlations with HCC were examined. With the assumption that TA autoantibodies generated in the tumor mouse model are induced in human cancer patients, the enzyme-linked immunosorbent assays (ELISA) based on the characteristics of mouse TA autoantibodies were developed for the detection of autoantibody biomarkers in human serum. To mimic natural antigenic structures, the specific epitopes against autoantibodies were screened from the phage display cyclic random heptapeptide library, and the streptavidin antigens fused with the specific epitopes were used as coating antigens. RESULTS: In this study, one of HCC-associated autoantibodies derived from HBx-transgenic mouse, XC24, was characterized. Its target antigen was identified as splicing factor 3b subunit 1 (SF3B1) and the high expression of SF3B1 was confirmed in HCC tissues. The specific peptide epitopes against XC24 were selected and, among them, XC24p11 cyclic peptide (-CDATPPRLC-) was used as an epitope of anti-SF3B1 autoantibody ELISA. With this epitope, we could effectively distinguish between serum samples from HCC patients (n = 102) and healthy subjects (n = 85) with 73.53% sensitivity and 91.76% specificity (AUC = 0.8731). Moreover, the simultaneous detection of anti-XC24p11 epitope autoantibody and AFP enhanced the efficiency of HCC diagnosis with 87.25% sensitivity and 90.59% specificity (AUC = 0.9081). CONCLUSIONS: ELISA using XC24p11 peptide epitope that reacts against anti-SF3B1 autoantibody can be used as a novel test to enhance the diagnostic efficiency of HCC.

FK506, an Immunosuppressive Drug, Induces Autophagy by Binding to the V-ATPase Catalytic Subunit A in Neuronal Cells.

The drug FK506 (tacrolimus, fujimycin) exerts its immunosuppressive effects by regulating the nuclear factor of the activated T-cell (NFAT) family of transcription factors. However, FK506 also exhibits neuroprotective effects, but its direct target proteins that mediate these effects have not been determined. To identify the target proteins responsible for FK506's neuroprotective effects, the drug affinity responsive target stability (DARTS) method was performed using label-free FK506, and LC-MS/MS analysis of the FK506-treated proteome was also performed. Using DARTS and LC-MS/MS analyses in combination with reference studies, V-ATPase catalytic subunit A (ATP6V1A) was identified as a new target protein of FK506. The biological relevance of ATP6V1A in mediating the neuroprotective effects of FK506 was validated by analyzing FK506 activity with respect to autophagy via acridine orange staining and transcription factor EB (TFEB) translocation assay. These analyses demonstrated that the binding of FK506 with ATP6V1A induces autophagy by activating the translocation of TFEB from the cytosol into the nucleus. Because autophagy has been identified as a mechanism for treating neurodegenerative diseases and because we have demonstrated that FK506 induces autophagy, this study demonstrates that FK506 is a possible new therapy for treating neurodegenerative diseases.

Next Generation Proteomic Pipeline for Chromosome-Based Proteomic Research Using NeXtProt and GENCODE Databases.

Human Proteome Project aims to map all human proteins including missing proteins as well as proteoforms with post translational modifications, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). neXtProt and Ensemble databases are usually used to provide curated information on human coding genes. However, to find these proteoforms, we (Chr #11 team) first introduce a streamlined pipeline using customized and concatenated neXtProt and GENCODE originated from Ensemble, with controlled false discovery rate (FDR). Because of large sized databases used in this pipeline, we found more stringent FDR filtering (0.1% at the peptide level and 1% at the protein level) to claim novel findings, such as GENCODE ASVs and missing proteins, from human hippocampus data set (MSV000081385) and ProteomeXchange (PXD007166). Using our next generation proteomic pipeline (nextPP) with neXtProt and GENCODE databases, two missing proteins such as activity-regulated cytoskeleton-associated protein (ARC, Chr 8) and glutamate receptor ionotropic, kainite 5 (GRIK5, Chr 19) were additionally identified with two or more unique peptides from human brain tissues. Additionally, by applying the pipeline to human brain related data sets such as cortex (PXD000067 and PXD000561), spinal cord, and fetal brain (PXD000561), seven GENCODE ASVs such as ACTN4-012 (Chr.19), DPYSL2-005 (Chr.8), MPRIP-003 (Chr.17), NCAM1-013 (Chr.11), EPB41L1-017 (Chr.20), AGAP1-004 (Chr.2), and CPNE5-005 (Chr.6) were identified from two or more data sets. The identified peptides of GENCODE ASVs were mapped onto novel exon insertions, alternative translations at 5'-untranslated region, or novel protein coding sequence. Applying the pipeline to male reproductive organ related data sets, 52 GENCODE ASVs were identified from two testis (PXD000561 and PXD002179) and a spermatozoa (PXD003947) data sets. Four out of 52 GENCODE ASVs such as RAB11FIP5-008 (Chr. 2), RP13-347D8.7-001 (Chr. X), PRDX4-002 (Chr. X), and RP11-666A8.13-001 (Chr. 17) were identified in all of the three samples.