Serum proteins are often expressed as multiple isoforms with different isoelectric points (pI) and molecular masses. These diverse molecular forms result not only from genetic variations but also from post-translational modifications (PTM) such as phosophorylation, acetylation, glycosylation and oxidation. However, it is not known whether each isoform plays a distinct physiological role, nor whether differential expression of protein isoform correlates with a certain disease state. In order to investigate the relationship between protein modifications and diseases, a rapid detection system which enables us to distinguish modified from unmodified forms is critically important. We reported in the 4th JHUPO meeting a new isoelectric focusing chip in which micro-channels equipped with nano-pillar structure are fabricated on silicon substrates. Proteins electrophoretically-separated according to their pI's on the chip can be detected by subsequent MALDI mass spectrometry. Human apolipoprotein A1 (ApoA1), a major protein constituent of high density lipoproteins (HDL), is primarily synthesized and secreted by the liver. HDL executes retrograde transport of excess cholesterol from the peripheral tissues to the liver, thus playing an important role in regulating the level of cholesterol. ApoA1 has been shown to receive various PTMs, such as proteolysis, phosphorylation and oxidation. A recent report suggested that oxidation of specific amino acid residues in ApoA1 correlates with liver cancer. We are presently interested in the relationship between the expression profiles of ApoA1 isoforms and coronary heart disease, and have started to apply our chip system to investigate the expression profiles of ApoA1 isoforms. We have so far detected two ApoA1 species with different pI's in control serum from healthy persons. These results and the analysis of serum samples from patients will be presented in the meeting.

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[Introduction] Two-dimensional gel electrophoresis is a powerful technique enabling visualization of the proteome. Recently, it is reported that fluorescence labeled two-dimensional gel electrophoresis is one of the most powerful method for comprehensive proteomic analysis. While annotated two-dimensional gel electrophoresis contain thousands of proteins, they do not represent the entire genome. Hydrophobic membrane proteins in particular are conspicuously absent from such data. There are a lot of membrane proteins with important physiological functions that is involved in signal transduction etc. Recently, the improvement of the solubilize conditions for these membrane proteins are required. Therefore, we examine the application of several non-ionic detergent for two-dimensional gel electrophoresis. [Methods] Microsomal membrane fraction was obtained by centrifugation of rat brain homogenates. Membrane fraction was solubilized with sample buffer including different detergent. Two-dimensional gel electrophoresis, isoelectric focusing (IEF) in the first dimension and SDS-PAGE in the second dimension, was performed using IEF Cell (Bio-Rad). [Results] We examined some detergents and non-detergent sulfobetaines, and compared them with CHAPS that is generally used in two-dimensional gel electrophoresis. As a result, it was suggested that some non-ionic detergents improved separation in particular high molecular weight protein region. We were able to identify some membrane proteins by peptide mass fingerprinting methods using MALDI-TOF/TOF MS. This study provides methodological tools to study particular classes of membrane proteins and should be applicable to other cellular membranes such as raft microdomain.

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Accumulation of ubiquitin-conjugated proteins in brain tissues has been documented in various neurodegenerative disorders. Identification of such ubiquitin-conjugated proteins is thought to be important to diagnose the pathogenic significance of the protein ubiquitination. Takada and his coworkers recently reported their original protocol for immunoaffinity separation of the accumulated ubiquitin-conjugated proteins solubilized with SDS. We further assessed the performance of CID-mode MS/MS-ion search on AXIMA-TOF² for identification of polyubiquitin-conjugated protein separated by 2-DE after immunoaffinity purification. The target protein-derived peptide fragments were generally very low abundant in the tryptic in-gel digests of polyubiquitinated protein separated by 2-E gel electrophoresis. Therefore, in order to identify the target protein of polyubiquitination, the highest sensitivity of MS/MS-ion search in CID-mode is required for data-dependent analysis of a unique MS signal detected by subtraction of ubiquitin-derived abundant peptides. Polyubiquitin-conjugated lysozyme was used as an authentic sample to assess the quality of MS/MS signals in this study, and we confirmed that the CID-mode MS/MS-ion search on AXIMA-TOF² has enough high performance to identify low abundant polyubiquitin-conjugated protein.

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The current explosion of emerging technologies of proteomics is expected to discover biomarkers and mechanisms of diseases. To define the difference in protein profile among experimental samples or in diseased tissues, the procedures in proteomic analysis should be considered to avoid loss of proteins with low abundance and to minimize experimental errors and contamination of keratins. In the present study, we examined and improved several procedures to reduce the protein loss during sample handling and storage. Microtubes from several companies were used to aliquot and store samples for MS analysis. We found several microtubes brought about high-level noise or high absorption of peptides. To minimize human keratin contamination we aimed to determine the process or procedure in which the keratin contamination was easy to happen during various steps of sample preparation from 2D gel electrophoresis to in gel digestion steps. We found that charge of static electricity on microtubes used for sample preparation was essential for adhesion of human keratin from air dust. Therefore, we modified the procedures to shorten the time for sample preparation by using dispensers for liquid handling and a vacuum set for discarding liquid. To keep our fingertips clean during every procedure, fingers were washed in distilled water or Milli-Q water frequently, and the vacuum set was applied to remove air dust adhered to the microtubes. By comparing the conventional procedures with our modified ones for the keratin contamination, the most critical step for avoiding keratin contamination was in-gel digestion step. By using our modified procedures, we could identify very low abundant proteins and avoid the human keratin contamination, indicating that our procedure is a practically good and easy way to minimize keratin contamination and to identify small copies proteins by mass spectrometry.

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In the research field of metabolomics, the lower and wide-spread reaches of proteomics/genomics, non-target NMR-metabolic profiling is one of easy and useful approach for the first stage screening, to assess and evaluate, to track metabolic situations in biological systems by using mixture solution samples like urine. However, limitations such as sample volume (nearly 0.5ml), viscosity, sensitivity, and others exist as far as using solution NMR hardware(s).
In this study, we tried to measure NMR spectrum using highly diluted urine with conventional NMR spectrometer/probe head and standard 5mm diameter NMR tube followed by statistical analysis(PCA). Urine samples from adult mouse and its diluted samples(x60) were subjected to water suppressed high resolution 1D NMR spectroscopy to find the quality of spectra were sufficient for doing NMR-metabolic profiling.
Several urine samples from genetically engineered newborn mice(<0.03ml) were collected and analyzed by NMR-metabolic profiling along with diluted adult urine.
Methodology, protocol, and its usefulness will be presented.

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Biomarker development with proteomics consists of the upstream discovery stages, generally indicating several or more marker candidates, and the following validation phases to confirm them using other focused methods. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been extensively accepted for a proteome-wide profiling from a complex peptide mixture. Recent advances in this methodology have enabled differential display of the multiple two-dimensional peptide profiles of the LC separation and mass measurement. We developed an algorithm to compare and statistically analyze ion intensities among the two-dimensional profiles with alignment of nonlinear fluctuation in the axis of LC elution time. This analytical platform was applied into clinical studies, including identification of the diagnosis markers of stage I primary lung adenocarcinoma tissues with relationship between expression of the proteins and prognosis after the tissue resection. These candidates were confirmed with the immunological staining of the tissues using specific antibodies, which is one of the successful choices for the clinical utility of protein biomarkers. We will also discuss a data processing of the peptide profiles to interface the peptide-based discovery with the direct protein validation.

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Modulating protein-protein interactions (PPIs) by small ligands is a challenging but attractive strategy for therapeutic intervention of various human diseases. To date, over 30 PPIs such as MDM2/TP53, BCL-X_L(BCL-2)/BAK, and IL2/IL2 receptor alpha have been intensively studied as target for small ligands. In the era of rapid accumulation of a huge number of PPI data, lack of the methodology that aims to efficiently select drug target PPIs by holistically assessing druggability of each PPI is an issue that should be immediately resolved. To address the issue, we have recently proposed an integrative in silico approach for discovering druggable PPIs by detecting interacting domains, evaluating similarity in biological function between interacting proteins with Gene Ontology terms, and finding ligand-binding pockets on protein surface. Application of our approach to a large number of human PPI data showed its effectiveness for assessing druggabilities of PPIs and selecting some promising candidates of target PPIs.

As the next step, we introduce a support vector machine (SVM)-based machine-learning method to our approach and apply it to human PPIs. In this study, we focus on human PPIs with tertiary structures of their protein complex already resolved. Known target PPIs, carefully selected from previous studies focusing on the development of PPI-inhibiting ligands, were used as training data in our SVM-based method. Twenty-six physicochemical, 16 drug/chemical, and 26 functional attributes of interacting proteins were utilized as feature vector of PPIs. The best SVM model constructed can distinguish known target PPIs from others at a high accuracy of 85% (sensitivity, 89%; specificity, 81%). We will discuss the effectiveness of our SVM-based approach and novel target PPIs predicted as potentially druggable by our method. To our knowledge, this study is the first application of a machine-learning method to the prediction of druggable PPIs.

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Amino acid sequence database is one of the essential components in current proteomics with mass spectrometry. Protein identification routine as well as posttranslational modification analysis is based on correlation between the mass spectrometry data of peptides obtained from a proteome and the entry sequences in the database. While different sequence databases are usually acquired from public resources for the correlation search, unmodified precursor sequences in the databases can be processed into more useful forms using sequence annotations. A program, Sequence Composer, enabled a collective alteration of the sequences in a database according to their biological processes and variations. User-directed sequence alteration is available with different parameters including selection of the variation type(s), marking of modified amino acid residues and addition of the other sequence(s). The correlation search against posttranslationally-processed sequences with alternative splicing variants resulted in additional significant peptide hits. Sequence Composer will be practically beneficial to the wide range from the detail analysis focusing on a single protein to medical applications including biomarker discovery studies for the clinical utility

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H-Invitational Database (H-InvDB; http://www.h-invitational.jp/) is an integrated database of human genes, transcripts and proteins (Fig. 1.). By extensive analyses of all human transcripts, we provide curated annotations of human genes and transcripts that include gene structures, alternative splicing isoforms, non-coding functional RNAs, protein functions, functional domains, sub-cellular localizations, metabolic pathways, protein 3D structure, genetic polymorphisms, relation with diseases, gene expression profiling, molecular evolutionary features, protein-protein interactions (PPIs) and gene families/groups.
H-InvDB provides human short protein dataset which amino acid length shorter than 80 a.a.. The total number of them, including those predicted from transcripts, is 7,096. We classified those human short proteins into six categories based on the similarities to known proteins and functional motifs, and currently we assigned function to 765 human short proteins (Table 1.). The coverage of those evidenced human short proteins is 2.2% (765/34,442) in the whole human proteome in H-InvDB release 5.0. The characterization of those human proteome dataset may lead to discoveries of novel human gene families.

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Alzheimer's disease (AD) is the most common form of dementia and leads to irreversible neurogenerative damage of the brain. AD affects nearly 10% of the population after 65 years of age. Although the progression of AD is slow and it takes several years from onset of cognitive decline to diagnosis, the current diagnostic tools have poor sensitivity, especially for the early stages of AD and do not allow for diagnosis until AD has lead to irreversible brain damage. Therefore, it is crucial that AD is detected as early as possible. Since it is very hard, laborious and time-consuming to gather many AD and non-AD samples, it is very desirable to develop a predictive learning method to exhibit high performance using both training samples and test samples. To address this problem, we propose semi-supervised distance metric learning using Random Forests with label propagation (SRF-LP), which incorporates labeled data for obtaining good metrics and propagates labels based on them. We applied our proposed method, SRF-LP, to cytokine antibody arrays datasets, which was produced from plasma samples for Alzheimer's classification and diagnosis. The datasets consist of 83 training set and 92 test set. Training set consists of 43 AD patients and 40 non-AD individuals, and test set consists of 42 AD patients and 50 non-AD individuals. Experimental results showed that SRF-LP outperformed standard supervised learning algorithms, i.e., RF, SVM, Adaboost and CART, and reached 93.1% accuracy at a maximum. Especially, SRF-LP largely outperformed when the number of training samples is very small. Thus, we demonstrated that labeled data should be incorporated for distance metric and learned metrics are appropriate for label propagation. Moreover, we showed that SRF-LP is able to reduce the number of training samples by about one-half to achieve the comparable accuracy (89%) of the original classifier, NSC (Nearest Shrunken Centroid).

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