<Emphasis Type="Italic">mab-31</Emphasis> and the TGF-β pathway act in the ray lineage to pattern <Emphasis Type="Italic">C. elegans</Emphasis>male sensory rays

Background  

C. elegans TGF-β-like Sma/Mab signaling pathway regulates both body size and sensory ray patterning. Most of the components in this pathway were initially identified by genetic screens based on the small body phenotype, and many of these mutants display sensory ray patterning defect. At the cellular level, little is known about how and where these components work although ray structural cell has been implicated as one of the targets. Based on the specific ray patterning abnormality, we aim to identify by RNAi approach additional components that function specifically in the ray lineage to elucidate the regulatory role of TGF-β signaling in ray differentiation.     

Enhanced validation of small-molecule ligands and carbohydrates in the Protein Data Bank

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ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains

MOTIVATION: Carbohydrate sugar chains, or glycans, are considered the third major class of biomolecules after DNA and proteins. They consist of branching monosaccharides, starting from a single monosaccharide. They are extremely vital to the development and functioning of multicellular organisms because they are recognized by various proteins to allow them to perform specific functions. Our motivation is to study this recognition mechanism using informatics techniques from the data available. Previously, we introduced a probabilistic sibling-dependent tree Markov model (PSTMM), which we showed could be efficiently trained on sibling-dependent tree structures and return the most likely state paths. However, it had some limitations in that the extra dependency between siblings caused overfitting problems. The retrieval of the patterns from the trained model also involved manually extracting the patterns from the most likely state paths. Thus we introduce a profilePSTMM model which avoids these problems, incorporating a novel concept of different types of state transitions to handle parent-child and sibling dependencies differently. RESULTS: Our new algorithms are more efficient and able to extract the patterns more easily. We tested the profilePSTMM model on both synthetic (controlled) data as well as glycan data from the KEGG GLYCAN database. Additionally, we tested it on glycans which are known to be recognized and bound to proteins at various binding affinities, and we show that our results correlate with results published in the literature.     

Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules

MOTIVATION: Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule. These methods are based on known binding peptide sequences. However, current available peptide databases do not have very abundant amounts of examples and are highly redundant. Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules. RESULTS: We experimented on the HLA-A family with the binding nonamers of A1 supertype (HLA-A*0101, A*2601, A*2902, A*3002), A2 supertype (A*0201, A*0202, A*0203, A*0206, A*6802), A3 supertype (A*0301, A*1101, A*3101, A*3301, A*6801) and A24 supertype (A*2301 and A*2402), whose data were collected from six publicly available peptide databases and two private sources. The results show that our approach significantly improves the prediction accuracy of peptides that bind a specific HLA molecule when we combine binding data of HLA molecules in the same supertype. Our approach can thus be used to help find new binders for MHC molecules.     

Extracting glycan motifs using a biochemicallyweighted kernel

Carbohydrates, or glycans, are one of the most abundant and structurally diverse biopolymers constitute the third major class of biomolecules, following DNA and proteins. However, the study of carbohydrate sugar chains has lagged behind compared to that of DNA and proteins, mainly due to their inherent structural complexity. However, their analysis is important because they serve various important roles in biological processes, including signaling transduction and cellular recognition. In order to glean some light into glycan function based on carbohydrate structure, kernel methods have been developed in the past, in particular to extract potential glycan biomarkers by classifying glycan structures found in different tissue samples. The recently developed weighted qgram method (LK-method) exhibits good performance on glycan structure classification while having limitations in feature selection. That is, it was unable to extract biologically meaningful features from the data. Therefore, we propose a biochemicallyweighted tree kernel (BioLK-method) which is based on a glycan similarity matrix and also incorporates biochemical information of individual q-grams in constructing the kernel matrix. We further applied our new method for the classification and recognition of motifs on publicly available glycan data. Our novel tree kernel (BioLK-method) using a Support Vector Machine (SVM) is capable of detecting biologically important motifs accurately while LK-method failed to do so. It was tested on three glycan data sets from the Consortium for Functional Glycomics (CFG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) GLYCAN and showed that the results are consistent with the literature. The newly developed BioLK-method also maintains comparable classification performance with the LK-method. Our results obtained here indicate that the incorporation of biochemical information of q-grams further shows the flexibility and capability of the novel kernel in feature extraction, which may aid in the prediction of glycan biomarkers.     

UniCarbKB: putting the pieces together for glycomics research

Despite the success of several international initiatives the glycosciences still lack a managed infrastructure that contributes to the advancement of research through the provision of comprehensive structural and experimental glycan data collections. UniCarbKB is an initiative that aims to promote the creation of an online information storage and search platform for glycomics and glycobiology research. The knowledgebase will offer a freely accessible and information-rich resource supported by querying interfaces, annotation technologies and the adoption of common standards to integrate structural, experimental and functional data. The UniCarbKB framework endeavors to support the growth of glycobioinformatics and the dissemination of knowledge through the provision of an open and unified portal to encourage the sharing of data. In order to achieve this, the framework is committed to the development of tools and procedures that support data annotation, and expanding interoperability through cross-referencing of existing databases. Database URL: http://www.unicarbkb.org.     

High throughput screening to investigate the interaction of stem cells with their extracellular microenvironment

Stem cells in vivo are housed within a functional microenvironment termed the “stem cell niche.” As the niche components can modulate stem cell behaviors like proliferation, migration and differentiation, evaluating these components would be important to determine the most optimal platform for their maintenance or differentiation. In this review, we have discussed methods and technologies that have aided in the development of high throughput screening assays for stem cell research, including enabling technologies such as the well-established multiwell/microwell plates and robotic spotting, and emerging technologies like microfluidics, micro-contact printing and lithography. We also discuss the studies that utilized high throughput screening platform to investigate stem cell response to extracellular matrix, topography, biomaterials and stiffness gradients in the stem cell niche. The combination of the aforementioned techniques could lay the foundation for new perspectives in further development of high throughput technology and stem cell research.     

Identification of genes required for neural-specific glycosylation using functional genomics

Glycosylation plays crucial regulatory roles in various biological processes such as development, immunity, and neural functions. For example, α1,3-fucosylation, the addition of a fucose moiety abundant in Drosophila neural cells, is essential for neural development, function, and behavior. However, it remains largely unknown how neural-specific α1,3-fucosylation is regulated. In the present study, we searched for genes involved in the glycosylation of a neural-specific protein using a Drosophila RNAi library. We obtained 109 genes affecting glycosylation that clustered into nine functional groups. Among them, members of the RNA regulation group were enriched by a secondary screen that identified genes specifically regulating α1,3-fucosylation. Further analyses revealed that an RNA-binding protein, second mitotic wave missing (Swm), upregulates expression of the neural-specific glycosyltransferase FucTA and facilitates its mRNA export from the nucleus. This first large-scale genetic screen for glycosylation-related genes has revealed novel regulation of fucTA mRNA in neural cells.     

Frontiers in glycomics: bioinformatics and biomarkers in disease. An NIH white paper prepared from discussions by the focus groups at a workshop on the NIH campus, Bethesda MD (September 11-13, 2006)

Key issues relating to glycomics research were discussed after the workshop entitled "Frontiers in Glycomics: Bioinformatics and Biomarkers in Disease" by two focus groups nominated by the organizers. The groups focused on two themes: (i) glycomics as the new frontier for the discovery of biomarkers of disease and (ii) requirements for the development of informatics for glycomics and glycobiology. The mandate of the focus groups was to build consensus on these issues and develop a summary of findings and recommendations for presentation to the NIH and the greater scientific community. A list of scientific priorities was developed, presented, and discussed at the workshops. Additional suggestions were solicited from workshop participants and collected using the workshop mailing list. The results are summarized in this White Paper, authored by the co-chairs of the focus groups.