Bioinformatics Approach for Functional Glycomics

Presentation in the Human Glycomics Proteomics Disease Initiative (HGPI) session

Multivalent ligands of CD22 for targeting of B cells

Multivalent ligands of CD22 for active targeting of B cells. Mary O’Reilly, Wei Hsu Chen, Gladys Completo, Ying Zeng, Satoshi Futakawa and Cory Rillahan and James C. Paulson, Departments of Chemical Physiology and Molecular Biology, The Scripps Research Institute, 10550 N. Torrey Pines Road, La Jolla, CA, 92037

The siglecs comprise 13 members of the immunoglobulin superfamily that recognize sialic acid containing glycans, and are differentially expressed on leukocytes and glial cells. The natural ligands of siglecs typically occur on the same cell (in cis) and/or on adjacent cells (in trans). Cis ligands mask the binding of multivalent synthetic sialoside ligands and are thought to regulate the activity of siglecs as modulators of cell signaling. However, synthetic ligands of sufficient avidity can compete with cis ligands, demonstrating a dynamic equilibrium of cis and trans ligand probes. We have explored the relationship between valency, affinity and geometry for achieving avidity sufficient to compete with cis ligands of CD22 (Siglec-2) in situ. A notable achievement is a hetero-bifunctional ligand approach to create multivalent ligands using antibodies as a protein scaffold. The ligand is comprised of a CD22 ligand, 9-biphenylcarboxyl-NeuAcα2-6Galβ1-4GlcNAc (BPCNeuAc), coupled to an antigen, 4-hydroxy-3-nitrophenylacetic acid (NP) (BPCNeuAc-NP). The BPCNeuAc-NP ligand is able to efficiently assemble complexes of anti-NP antibodies with CD22 on both asialo- and native B cells. Surprisingly, assembly of the tertiary complex occurs with anti-NP IgM (n=10), IgA (n=4) and IgG (n=2). The results suggest that spacing of ligands using an antibody optimizes the contribution of geometry to achieve high avidity with low valency. Other multivalent configurations also show promise for targeting B cells. In particular, liposomes bearing BPCNeuAc ligands bind avidly to B cells and are endocytosed. Thus, BPCNeuAc-lipososomes may prove effective in delivery of therapeutic agents to B cells (Supported by NIH grants GM60938, AI50143 ).
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A comparison of three heuristics to choose the variable ordering for CAD

Cylindrical algebraic decomposition (CAD) is a key tool for problems in real algebraic geometry and beyond. When using CAD there is often a choice over the variable ordering to use, with some problems infeasible in one ordering but simple in another. Here we discuss a recent experiment comparing three heuristics for making this choice on thousands of examples

Using Machine Learning to Improve Cylindrical Algebraic Decomposition

Cylindrical Algebraic Decomposition (CAD) is a key tool in computational algebraic geometry, best known as a procedure to enable Quantifier Elimination over real-closed fields. However, it has a worst case complexity doubly exponential in the size of the input, which is often encountered in practice. It has been observed that for many problems a change in algorithm settings or problem formulation can cause huge differences in runtime costs, changing problem instances from intractable to easy. A number of heuristics have been developed to help with such choices, but the complicated nature of the geometric relationships involved means these are imperfect and can sometimes make poor choices. We investigate the use of machine learning (specifically support vector machines) to make such choices instead. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we apply it in two case studies: the first to select between heuristics for choosing a CAD variable ordering; the second to identify when a CAD problem instance would benefit from Groebner Basis preconditioning. These appear to be the first such applications of machine learning to Symbolic Computation. We demonstrate in both cases that the machine learned choice outperforms human developed heuristics.This work was supported by EPSRC grant EP/J003247/1; the European Union’s Horizon 2020 research and innovation programme under grant agreement No 712689 (SC2); and the China Scholarship Council (CSC)

Identification of Siglec-9 as the receptor for MUC16 on human NK cells, B cells, and monocytes

<p>Abstract</p> <p>Background</p> <p>MUC16 is a cell surface mucin expressed at high levels by epithelial ovarian tumors. Following proteolytic cleavage, cell surface MUC16 (csMUC16) is shed in the extracellular milieu and is detected in the serum of cancer patients as the tumor marker CA125. csMUC16 acts as an adhesion molecule and facilitates peritoneal metastasis of ovarian tumors. Both sMUC16 and csMUC16 also protect cancer cells from cytotoxic responses of natural killer (NK) cells. In a previous study we demonstrated that sMUC16 binds to specific subset of NK cells. Here, we identify the csMUC16/sMUC16 binding partner expressed on immune cells.</p> <p>Results</p> <p>Analysis of immune cells from the peripheral blood and peritoneal fluid of ovarian cancer patients indicates that in addition to NK cells, sMUC16 also binds to B cells and monocytes isolated from the peripheral blood and peritoneal fluid. I-type lectin, Siglec-9, is identified as the sMUC16 receptor on these immune cells. Siglec-9 is expressed on approximately 30-40% of CD16^pos/CD56^dimNK cells, 20-30% of B cells and >95% of monocytes. sMUC16 binds to the majority of the Siglec-9^posNK cells, B cells and monocytes. sMUC16 is released from the immune cells following neuraminidase treatment. Siglec-9 transfected Jurkat cells and monocytes isolated from healthy donors bind to ovarian tumor cells via Siglec-9-csMUC16 interaction.</p> <p>Conclusions</p> <p>Recent studies indicate that csMUC16 can act as an anti-adhesive agent that blocks tumor-immune cell interactions. Our results demonstrate that similar to other mucins, csMUC16 can also facilitate cell adhesion by interacting with a suitable binding partner such as mesothelin or Siglec-9. Siglec-9 is an inhibitory receptor that attenuates T cell and NK cell function. sMUC16/csMUC16-Siglec-9 binding likely mediates inhibition of anti-tumor immune responses.</p

A pathway for mitotic chromosome formation

Mitotic chromosomes fold as compact arrays of chromatin loops. To identify the pathway of mitotic chromosome formation, we combined imaging and Hi-C analysis of synchronous DT40 cell cultures with polymer simulations. Here we show that in prophase, the interphase organization is rapidly lost in a condensin-dependent manner, and arrays of consecutive 60-kilobase (kb) loops are formed. During prometaphase, ~80-kb inner loops are nested within ~400-kb outer loops. The loop array acquires a helical arrangement with consecutive loops emanating from a central spiral staircase condensin scaffold. The size of helical turns progressively increases to ~12 megabases during prometaphase. Acute depletion of condensin I or II shows that nested loops form by differential action of the two condensins, whereas condensin II is required for helical winding

A Machine Checked Model of Idempotent MGU Axioms For Lists of Equational Constraints

We present formalized proofs verifying that the first-order unification algorithm defined over lists of satisfiable constraints generates a most general unifier (MGU), which also happens to be idempotent. All of our proofs have been formalized in the Coq theorem prover. Our proofs show that finite maps produced by the unification algorithm provide a model of the axioms characterizing idempotent MGUs of lists of constraints. The axioms that serve as the basis for our verification are derived from a standard set by extending them to lists of constraints. For us, constraints are equalities between terms in the language of simple types. Substitutions are formally modeled as finite maps using the Coq library Coq.FSets.FMapInterface. Coq's method of functional induction is the main proof technique used in proving many of the axioms.Comment: In Proceedings UNIF 2010, arXiv:1012.455

Receptor specificity of subtype H1 influenza A viruses isolated from swine and humans in the United States

The evolution of classical swine influenza viruses receptor specificity preceding the emergence of the 2009 H1N1 pandemic virus was analyzed in glycan microarrays. Classical swine influenza viruses from the α, β, and γ antigenic clusters isolated between 1945 and 2009 revealed a binding profile very similar to that of 2009 pandemic H1N1 viruses, with selectivity for α2-6-linked sialosides and very limited binding to α2-3 sialosides. Despite considerable genetic divergence, the ‘human-like’ H1N1 viruses circulating in swine retained strong binding preference for α2-6 sialylated glycans. Interspecies transmission of H1N1 influenza viruses from swine to humans or from humans to swine has not driven selection of viruses with distinct novel receptor binding specificities. Classical swine and human seasonal H1N1 influenza viruses have conserved specificity for similar α2-6-sialoside receptors in spite of long term circulation in separate hosts, suggesting that humans and swine impose analogous selection pressures on the evolution of receptor binding function

Chemically Defined Sialoside Scaffolds for Investigation of Multivalent Interactions with Sialic Acid Binding Proteins

Four glycodendrons and a glycocluster were synthesized from carbohydrate building blocks to form paucivalent (di- to tetravalent) structures of controlled scaffold architectures. Enzymatic sialylation of the functionalized cluster and dendrons, terminated in lactose residues, generated a library of paucivalent synthetic sialosides displaying sialic acids with different dispositions. These newly constructed bioactive sialic acid-based structures were differentially recognized by sialoadhesin, a mammalian macrophage sialic acid binding protein. The binding of the sialosides to sialoadhesin was evaluated by an enzyme-linked immunosorbant assay to investigate the complementarity of scaffold structure and binding to sialoadhesin. Modulating the interaction between sialoadhesin and its sialic acid ligands has important implications in immunobiology