Yoshitoshi Hirao

Glycoproteomics Approach for Identifying Glycobiomarker Candidate Molecules for Tissue Type Classification of Non-small Cell Lung Carcinoma

Histopathological classification of lung cancer has important implications in the application of clinical practice guidelines and the prediction of patient prognosis. Thus, we focused on discovering glycobiomarker candidates to classify the types of lung cancer tissue. First, we performed lectin microarray analysis of lung cancer tissue specimens and cell lines and identified Aleuria aurantia lectin (AAL), Hippeastrum hybrid lectin (HHL), and Concanavalia ensiformis agglutinin (ConA) as lectin probes specific to non-small cell lung carcinoma (NSCLC). LC-MS-based analysis was performed for the comprehensive identification of glycoproteins and N-linked glycosylation sites using lectin affinity capture of NSCLC-specific glycoforms of glycoproteins. This analysis identified 1092 AAL-bound glycoproteins (316 gene symbols) and 948 HHL/ConA-bound glycoproteins (279 gene symbols). The lectin microarray-assisted verification using 15 lung cancer cell lines revealed the NSCLC-specific expression of fibronectin. The glycosylation profiling of fibronectin indicated that the peanut agglutinin (PNA) signal appeared to differentiate two NSCLC types, adenocarcinoma and large cell carcinoma, whereas the protein expression level was similar between these types. Our glycoproteomics approach together with the concurrent use of an antibody and lectin is applicable to the quantitative and qualitative monitoring of variations in glycosylation of fibronectin specific to certain types of lung cancer tissue.

Complementary Protein and Peptide OFFGEL Fractionation for High-Throughput Proteomic Analysis

OFFGEL fractionation of mouse kidney protein lysate and its tryptic peptide digest has been examined in this study for better understanding the differences between protein and peptide fractionation methods and attaining maximum recruitment of this modern methodology for in-depth proteomic analysis. With the same initial protein/peptide load for both fractionation methods, protein OFFGEL fractionation showed a preponderance in terms of protein identification, fractionation efficiency, and focusing resolution, while peptide OFFGEL was better in recovery, number of peptide matches, and protein coverage. This result suggests that the protein fractionation method is more suitable for shotgun analysis while peptide fractionation suits well quantitative peptide analysis [isobaric tags for relative and absolute quantitation (iTRAQ) or tandem mass tags (TMT)]. Taken together, utilization of the advantages of both fractionation approaches could be attained by coupling both methods to be applied on complex biological tissue. A typical result is shown in this article by identification of 8262 confident proteins of whole mouse kidney under stringent condition. We therefore consider OFFGEL fractionation as an effective and efficient addition to both label-free and quantitative label proteomics workflow.

Why are they missing? : Bioinformatics characterization of missing human proteins.

NeXtProt is a web-based protein knowledge platform that supports research on human proteins. NeXtProt (release 2015-04-28) lists 20,060 proteins, among them, 3373 canonical proteins (16.8%) lack credible experimental evidence at protein level (PE2:PE5). Therefore, they are considered as missing proteins. A comprehensive bioinformatic workflow has been proposed to analyze these missing proteins. The aims of current study were to analyze physicochemical properties, existence and distribution of the tryptic cleavage sites, and to pinpoint the signature peptides of the missing proteins. Our findings showed that 23.7% of missing proteins were hydrophobic proteins possessing transmembrane domains (TMD). Also, forty missing entries generate tryptic peptides were either out of mass detection range (>30aa) or mapped to different proteins (<9aa). Additionally, 21% of missing entries didnt generate any unique tryptic peptides. In silico endopeptidase combination strategy increased the possibility of missing proteins identification. Coherently, using both mature protein database and signal peptidome database could be a promising option to identify some missing proteins by targeting their unique N-terminal tryptic peptide from mature protein database and or C-terminus tryptic peptide from signal peptidome database. In conclusion, Identification of missing protein requires additional consideration during sample preparation, extraction, digestion and data analysis to increase its incidence of identification.

A proteomic glimpse into human ureter proteome

Urine has evolved as one of the most important biofluids in clinical proteomics due to its noninvasive sampling and its stability. Yet, it is used in clinical diagnostics of several disorders by detecting changes in its components including urinary protein/polypeptide profile. Despite the fact that majority of proteins detected in urine are primarily originated from the urogenital (UG) tract, determining its precise source within the UG tract remains elusive. In this article, we performed a comprehensive analysis of ureter proteome to assemble the first unbiased ureter dataset. Next, we compared these data to urine, urinary exosome, and kidney mass spectrometric datasets. Our result concluded that among 2217 nonredundant ureter proteins, 751 protein candidates (33.8%) were detected in urine as urinary protein/polypeptide or exosomal protein. On the other hand, comparing ureter protein hits (48) that are not shown in corresponding databases to urinary bladder and prostate human protein atlas databases pinpointed 21 proteins that might be unique to ureter tissue. In conclusion, this finding offers future perspectives for possible identification of ureter disease‐associated biomarkers such as ureter carcinoma. In addition, the ureter proteomic dataset published in this article will provide a valuable resource for researchers working in the field of urology and urine biomarker discovery. All MS data have been deposited in the ProteomeXchange with identifier PXD002620 (http://proteomecentral.proteomexchange.org/dataset/PXD002620).

Glycobiomarker, Fucosylated Short-Form Secretogranin III Levels Are Increased in Serum of Patients with Small Cell Lung Carcinoma

Secretogranin III (SgIII) is a member of the chromogranin/secretogranin family of neuroendocrine secretory proteins. Granins are expressed in endocrine and neuroendocrine cells and subsequently processed into bioactive hormones. Although granin-derived peptide expression is correlated with neuroendocrine carcinomas, little is known about SgIII. We previously identified SgIII by a comparative glycoproteomics approach for elucidation of glycobiomarker candidates in lung carcinoma. Here, we examined the expression, secretion, and glycosylation of SgIII to identify novel biomarkers of small cell lung carcinoma (SCLC). In comparative immunohistochemical analysis and secretion profiling, SgIII was observed in all types of lung cancer. However, low-molecular-weight SgIII (short-form SgIII) was specifically found in SCLC culture medium. Glycoproteomics analysis showed that a fucosylated glycan was attached to the first of three potential N-glycosylation sites and an unfucosylated glycan was detected on the second site; however, the third site was not glycosylated. Next, we performed lectin capture with a fucose-binding lectin and detected short-form SgIII specifically in the sera of patients with SCLC. The results suggested an association between the fucosylated glycoform of short-form SgIII and SCLC. Thus, fucosylated short-form SgIII may be a valuable biomarker for SCLC and could be used to monitor development of the disease. All MS data are available via ProteomeXchange and jPOST with identifiers PXD007626 and JPST000313, respectively.

Identification and Validation of Human Missing Proteins and Peptides in Public Proteome Databases: Data Mining Strategy

In an attempt to complete human proteome project (HPP), Chromosome-Centric Human Proteome Project (C-HPP) launched the journey of missing protein (MP) investigation in 2012. However, 2579 and 572 protein entries in the neXtProt (2017-1) are still considered as missing and uncertain proteins, respectively. Thus, in this study, we proposed a pipeline to analyze, identify, and validate human missing and uncertain proteins in open-access transcriptomics and proteomics databases. Analysis of RNA expression pattern for missing proteins in Human protein Atlas showed that 28% of them, such as Olfactory receptor 1I1 ( O60431 ), had no RNA expression, suggesting the necessity to consider uncommon tissues for transcriptomic and proteomic studies. Interestingly, 21% had elevated expression level in a particular tissue (tissue-enriched proteins), indicating the importance of targeting such proteins in their elevated tissues. Additionally, the analysis of RNA expression level for missing proteins showed that 95% had no or low expression level (0-10 transcripts per million), indicating that low abundance is one of the major obstacles facing the detection of missing proteins. Moreover, missing proteins are predicted to generate fewer predicted unique tryptic peptides than the identified proteins. Searching for these predicted unique tryptic peptides that correspond to missing and uncertain proteins in the experimental peptide list of open-access MS-based databases (PA, GPM) resulted in the detection of 402 missing and 19 uncertain proteins with at least two unique peptides (≥9 aa) at <(5 × 10-4)% FDR. Finally, matching the native spectra for the experimentally detected peptides with their SRMAtlas synthetic counterparts at three transition sources (QQQ, QTOF, QTRAP) gave us an opportunity to validate 41 missing proteins by ≥2 proteotypic peptides.

Identification of Core Proteins Carrying the Sialyl Lewis a Epitope in Pancreatic Cancers

Identification of core proteins carrying the CA19-9 (carbohydrate antigen, sialyl Lewis a) epitope from various tissues will improve the diagnosis of pancreatic cancer in terms of specificity and sensitivity. In this study, we attempted to identify sialyl Lewis a-carrier proteins specifically expressed in pancreatic cancer. Pancreatic cancer is difficult to detect in the early stages of the disease, resulting in a high level of mortality. Therefore, in order to determine the correct course of treatment, it is vital to distinguish cancer from obstruction of the bile duct or other diseases. Our strategy to identify the carrier proteins was as follows: glycoproteins carrying sialyl Lewis a antigen were enriched from pancreatic cancer cell lines using anti-sialyl Lewis a antibody and then subjected to Peptide Mass Fingerprinting analysis. Based on these studies we identified nine glycoproteins carrying the sialyl Lewis a epitope. We evaluated candidate molecules by biochemical analyses of culture supernatants and human sera. In particular, we focused on one candidate molecule carrying a sialyl Lewis a epitope, Galectin-3BP/MAC2BP; M2BP, which was analyzed in detail. These results verified that our candidate molecule is a core protein carrying the sialyl Lewis a epitope. Furthermore, we demonstrated sandwich ELISA, which showed that the glycoprotein was able to detect CA19-9 antigen in culture supernatants. Our approach facilitated the identification of the core protein carrying the sialyl Lewis a epitope. We believe our approach will enable future developments in cancer glycobiomarker identification.

Proteome Profiling of Diabetic Mellitus Patient Urine for Discovery of Biomarkers by Comprehensive MS-Based Proteomics

Diabetic mellitus (DM) is a disease that affects glucose homeostasis and causes complications, such as diabetic nephropathy (DN). For early diagnosis of DN, microalbuminuria is currently one of the most frequently used biomarkers. However, more early diagnostic biomarkers are desired in addition to microalbuminuria. In this study, we performed comprehensive proteomics analysis of urine proteomes of diabetic mellitus patients without microalbuminuria and healthy volunteers to compare the protein profiles by mass spectrometry. With high confidence criteria, 942 proteins in healthy volunteer urine and 645 proteins in the DM patient urine were identified with label-free semi-quantitation, respectively. Gene ontology and pathway analysis were performed with the proteins, which were up- or down-regulated in the DM patient urine to elucidate significant changes in pathways. The discovery of a useful biomarker for early DN discovery is expected.

Proteomic Analysis of Rice Golgi Membranes Isolated by Floating Through Discontinuous Sucrose Density Gradient

The Golgi apparatus is an endomembrane system organelle and has roles in glycosylation, sorting, and secretion of proteins in the secretory pathway. It has a central function in living organism and is also essential for plant growth. Proteomic approaches to identify the Golgi membrane proteins have been performed in cell suspension cultures and many Golgi membrane-associated proteins were found, whereas it has well established in rice seedling yet. In this chapter, our recent improving published methods for isolated rice Golgi membranes by floating through a discontinuous sucrose density gradient are provided in detail with proteomic analyses.

Comprehensive data analysis of human ureter proteome

Comprehensive human ureter proteome dataset was generated from OFFGel fractionated ureter samples. Our result showed that among 2217 non-redundant ureter proteins, 751 protein candidates (33.8%) were detected in urine as urinary protein/polypeptide or exosomal protein. On the other hand, comparing ureter protein hits (48) that are not shown in corresponding databases to urinary bladder and prostate human protein atlas databases pinpointed 21 proteins that might be unique to ureter tissue. In conclusion, this finding offers future perspectives for possible identification of ureter disease-associated biomarkers such as ureter carcinoma. In addition, Cytoscape GO annotation was examined on the final ureter dataset to better understand proteins molecular function, biological processes, and cellular component. The ureter proteomic dataset published in this article will provide a valuable resource for researchers working in the field of urology and urine biomarker discovery.