Extraction of leukemia specific glycan motifs in humans by computational glycomics

Hizukuri, Yoshiyuki; Yamanishi, Yoshihiro; Nakamura, Osamu; Yagi, Fumio; Goto, Susumu; Kanehisa, Minoru

doi:10.1016/j.carres.2005.07.012

Cited by 43 publications

(47 citation statements)

References 24 publications

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“…Abnormal glycosylation results in exposure of the peptide core, as well as in exposure of the normally cryptic core Tn, sialyl-Tn and T-antigens. In leukemias, alteration in the expression of the Neu5Acα 2,3Galß1,4GlcNAc structure has been reported (Hizukuri et al 2005). The Arachis hypogaea lectin binds to T-lymphoblasts.…”

Section: Discussionmentioning

confidence: 99%

Potential Use of the Macrobrachium Rosenbergii Lectin for Diagnosis of T-Cell Acute Lymphoblastic Leukemia

Mayoral

Ruı́z-Argüelles

Pérez‐Romano

et al. 2008

Tohoku J. Exp. Med.

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T-cell acute lymphoblastic leukemia is the most common form of cancer in children. Lectins are proteins or glycoproteins from plants or animals that recognize oligossacharides on the cell surface and have been used to characterize the structural changes of oligosaccharides in leukemias. In this study, we used the lectin from the freshwater prawn Macrobrachium (M. rosenbergii), specifi c for acetyl groups in sialylated glycans, because increased sialylation of glycoproteins and glycolipids has been identifi ed in lymphoblastic leukemias. We compared the specifi city of the M. rosenbergii lectin for lymphoblastic leukemias with the specifi cities of the lectins from Triticum vulgaris, Solanum tuberosum, Arachis hipogaea, and Phytolacca americana. By morphologic and phenotype characterization with a panel of monoclonal antibodies, we identifi ed four types of leukemias from 106 leukemia patients: 11 cases of T-cell acute lymphoblastic leukemia, 61 cases of B-cell acute lymphoblastic leukemia, 24 cases of acute myeloblastic leukemia, and 10 cases of acute biphenotypic leukemia. As determined by cytofl uorometric assays, nine of the eleven cases with T-cell acute lymphoblastic leukemia (8 ± 3 years old) were specifi cally identifi ed with the lectin from M. rosenbergii. In contrast, only six cases of B-cell leukemia, one case of myeloblastic leukemia, and 2 cases of biphenotypic leukemia were identifi ed with this M. rosenbergii lectin. The other lectins tested showed no capacity to differentiate, in a signifi cant manner, any of the four types of leukemias tested. Thus, the lectin from M. rosenbergii could be considered a useful tool for the diagnosis and study of T-cell acute lymphoblastic leukemia.T-cell; Macrobrachium rosenbergii lectin; cytofl uorometry; leukemias; sialic acid; glycoproteins.Tohoku

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Section: Discussionmentioning

confidence: 99%

Potential Use of the Macrobrachium Rosenbergii Lectin for Diagnosis of T-Cell Acute Lymphoblastic Leukemia

Mayoral

Ruı́z-Argüelles

Pérez‐Romano

et al. 2008

Tohoku J. Exp. Med.

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“…For example, the 3-mer subtree kernel [15] evaluates pairs (X , Y ) such that they meet the rules dp• X = dp• Y as well as X ≡ Y .…”

Section: Determining M Xymentioning

confidence: 99%

“….}. [15] χ X Contiguous subtrees of size 3 M X,Y X ≡ Y and dp• X = dp• Y γ X and k Invariant function. i(X ) = 1, if dp• X = 0, and i(X ) = 1 − e −α(dp• X +1) , if dp• X > 0 [9,11] K Taï (X, Y ) described in Subsection 4.1 is defined as follows according to the framework.…”

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confidence: 99%

A Generalization of Haussler's Convolution Kernel — Mapping Kernel and Its Application to Tree Kernels

Shin

Kuboyama

2010

J. Comput. Sci. Technol.

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Haussler's convolution kernel provides an effective framework for engineering positive semidefinite kernels, and has a wide range of applications. On the other hand, the mapping kernel that we introduce in this paper is its natural generalization, and will enlarge the range of application significantly. Our main theorem with respect to positive semidefiniteness of the mapping kernel (1) implies Haussler's theorem as a corollary, (2) exhibits an easy-to-check necessary and sufficient condition for mapping kernels to be positive semidefinite, and (3) formalizes the mapping kernel so that significant flexibility is provided in engineering new kernels. As an evidence of the effectiveness of our results, we present a framework to engineer tree kernels. The tree is a data structure widely used in many applications, and tree kernels provide an effective method to analyze tree-type data. Thus, not only is the framework important as an example but also as a practical research tool. The description of the framework accompanies a survey of the tree kernels in the literature, where we see that 18 out of the 19 surveyed tree kernels of different types are instances of the mapping kernel, and examples of novel interesting tree kernels.

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“…The Glycan Miner Tool has been tested on a similar dataset as that used in Hizukuri et al (2005) for glycan structure related to leukemic cells. Figure 8 is a snapshot of the resulting structures using this data.…”

Section: Figmentioning

confidence: 99%

“…Finally, in the field of data mining, kernel methods for glycan marker prediction (Hizukuri et al, 2005), probabilistic models for glycan profile extraction Hashimoto et al, 2008a;Ueda et al, 2005) and frequent subtree mining of glycan structures [Hashimoto et al, 2008 have been performed. Glycan marker prediction entailed the use of kernel methods and support vector machines (SVMs) for classifying glycan structures between those that were related to cancer and those that were not.…”

Section: Introductionmentioning

confidence: 99%

The RINGS Resource for Glycome Informatics Analysis and Data Mining on the Web

Akune

Hosoda²,

Kaiya³

et al. 2010

OMICS: A Journal of Integrative Biology

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In the bioinformatics field, many computer algorithmic and data mining technologies have been developed for gene prediction, protein-protein interaction analysis, sequence analysis, and protein folding predictions, to name a few. This kind of research has branched off from the genomics field, creating the transcriptomics, proteomics, metabolomics, and glycomics research areas in the postgenomic age. In the glycomics field, given the complexity of glycan structures with their branches of monosaccharides in various conformations, new data mining and algorithmic methods have been developed in an attempt to gain a better understanding of glycans. However, these methods have not all been implemented as tools such that the glycobiology community may utilize them in their research. Thus, we have developed RINGS (Resource for INformatics of Glycomes at Soka) as a freely available Web resource for glycobiologists to analyze their data using the latest data mining and algorithmic techniques. It provides a number of tools including a 2D glycan drawing and querying interface called DrawRINGS, a Glycan Pathway Predictor (GPP) tool for dynamically computing the N-glycan biosynthesis pathway from a given glycan structure, and data mining tools Glycan Miner Tool and Profile PSTMM. These tools and other utilities provided by RINGS will be described. The URL for RINGS is http://rings.t.soka.ac.jp/.

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Extraction of leukemia specific glycan motifs in humans by computational glycomics

Cited by 43 publications

References 24 publications

Potential Use of the Macrobrachium Rosenbergii Lectin for Diagnosis of T-Cell Acute Lymphoblastic Leukemia

Potential Use of the Macrobrachium Rosenbergii Lectin for Diagnosis of T-Cell Acute Lymphoblastic Leukemia

A Generalization of Haussler's Convolution Kernel — Mapping Kernel and Its Application to Tree Kernels

The RINGS Resource for Glycome Informatics Analysis and Data Mining on the Web

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