The Complete Structure of the Core Oligosaccharide from Edwardsiella tarda EIB 202 Lipopolysaccharide

The chemical structure and genomics of the lipopolysaccharide (LPS) core oligosaccharide of pathogenic Edwardsiella tarda strain EIB 202 were studied for the first time. The complete gene assignment for all LPS core biosynthesis gene functions was acquired. The complete structure of core oligosaccharide was investigated by 1H and 13C nuclear magnetic resonance (NMR) spectroscopy, electrospray ionization mass spectrometry MSn, and matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry. The following structure of the undecasaccharide was established: The heterogeneous appearance of the core oligosaccharide structure was due to the partial lack of β-d-Galp and the replacement of α-d-GlcpNAcGly by α-d-GlcpNGly. The glycine location was identified by mass spectrometry

The influence of microRNAs and poly(A) tail length on endogenous mRNA–protein complexes

Background: All mRNAs are bound in vivo by proteins to form mRNA-protein complexes (mRNPs), but changes in the composition of mRNPs during posttranscriptional regulation remain largely unexplored. Here, we have analyzed, on a transcriptome-wide scale, how microRNA-mediated repression modulates the associations of the core mRNP components eIF4E, eIF4G, and PABP and of the decay factor DDX6 in human cells. Results: Despite the transient nature of repressed intermediates, we detect significant changes in mRNP composition, marked by dissociation of eIF4G and PABP, and by recruitment of DDX6. Furthermore, although poly(A)-tail length has been considered critical in post-transcriptional regulation, differences in steady-state tail length explain little of the variation in either PABP association or mRNP organization more generally. Instead, relative occupancy of core components correlates best with gene expression. Conclusions: These results indicate that posttranscriptional regulatory factors, such as microRNAs, influence the associations of PABP and other core factors, and do so without substantially affecting steady-state tail length.National Institutes of Health (U.S.) (Grant K99GM102319)National Institutes of Health (U.S.) (Grant T32GM007753)National Institutes of Health (U.S.) (Grant R01GM067031)National Institutes of Health (U.S.) (Grant R35GM118135)Natural Sciences and Engineering Research Council of Canada (Discovery Grant

Algebraic Relations Between Harmonic Sums and Associated Quantities

We derive the algebraic relations of alternating and non-alternating finite harmonic sums up to the sums of depth~6. All relations for the sums up to weight~6 are given in explicit form. These relations depend on the structure of the index sets of the harmonic sums only, but not on their value. They are therefore valid for all other mathematical objects which obey the same multiplication relation or can be obtained as a special case thereof, as the harmonic polylogarithms. We verify that the number of independent elements for a given index set can be determined by counting the Lyndon words which are associated to this set. The algebraic relations between the finite harmonic sums can be used to reduce the high complexity of the expressions for the Mellin moments of the Wilson coefficients and splitting functions significantly for massless field theories as QED and QCD up to three loop and higher orders in the coupling constant and are also of importance for processes depending on more scales. The ratio of the number of independent sums thus obtained to the number of all sums for a given index set is found to be $\leq 1/d$ with $d$ the depth of the sum independently of the weight. The corresponding counting relations are given in analytic form for all classes of harmonic sums to arbitrary depth and are tabulated up to depth $d=10$.Comment: 39 pages LATEX, 1 style fil

Scalable Graph Algorithms in a High-Level Language Using Primitives Inspired by Linear Algebra

This dissertation advances the state of the art for scalable high-performance graph analytics and data mining using the language of linear algebra. Many graph computations suffer poor scalability due to their irregular nature and low operational intensity. A small but powerful set of linear algebra primitives that specifically target graph and data mining applications can expose sufficient coarse-grained parallelism to scale to thousands of processors.In this dissertation we advance existing distributed memory approaches in two important ways. First, we observe that data scientists and domain experts know their analysis and mining problems well, but suffer from little HPC experience. We describe a system that presents the user with a clean API in a high-level language that scales from a laptop to a supercomputer with thousands of cores. We utilize a Domain-Specific Embedded Language with Selective Just-In-Time Specialization to ensure a negligible performance impact over the original distributed memory low-level code. The high-level language enables ease of use, rapid prototyping, and additional features such as on-the-fly filtering, runtime-defined objects, and exposure to a large set of third-party visualization packages.The second important advance is a new sparse matrix data structure and set of algorithms. We note that shared memory machines are dominant both in stand-alone form and as nodes in distributed memory clusters. This thesis offers the design of a new sparse-matrix data structure and set of parallel algorithms, a reusable implementation in shared memory, and a performance evaluation that shows significant speed and memory usage improvements over competing packages. Our method also offers features such as in-memory compression, a low-cost transpose, and chained primitives that do not materialize the entire intermediate result at any one time. We focus on a scalable, generalized, sparse matrix-matrix multiplication algorithm. This primitive is used extensively in many graph algorithms such as betweenness centrality, graph clustering, graph contraction, and subgraph extraction

Dectin-2 recognises mannosylated O-antigens of human opportunistic pathogens and augments lipopolysaccharide activation of myeloid cells

Lipopolysaccharide (LPS) consists of a relatively conserved region of lipid A and core-oligosaccharide, and a highly variable region of O-antigen polysaccharide. While lipid A is known to bind to the toll-like receptor 4 (TLR4)-myeloid differentiation factor 2 (MD2) complex, the role of the O-antigen remains unclear. Here we report a novel molecular interaction between dendritic cell-associated C-type lectin-2 (Dectin-2) and the mannosylated O-antigen found in a human opportunistic pathogen Hafnia alvei PCM 1223, which has a repeating unit of [-Man-α1,3-Man-α1,2-Man-α1,2-Man-α1,2-Man-α1,3-]. H. alvei LPS induced higher levels of TNFα and IL-10 from mouse bone marrow-derived dendritic cells (BM-DCs), when compared to Salmonella enterica O66 LPS which has a repeat of [-Gal-α1,6-Gal-α1,4-[Glc-β1,3]GalNAc-α1,3-GalNAc-β1,3-]. In a cell-based reporter assay, Dectin-2 was shown to recognise H. alvei LPS. This binding was inhibited by mannosidase treatment of H. alvei LPS and by mutations in the carbohydrate-binding domain of Dectin-2, demonstrating that H. alvei LPS is a novel glycan ligand of Dectin-2. The enhanced cytokine production by H. alvei LPS was Dectin-2 dependent, as Dectin-2 knockout BM-DCs failed to do so. This receptor crosstalk between Dectin-2 and TLR4 involved events including spleen tyrosine kinase (Syk) activation and receptor juxtaposition. Furthermore, another mannosylated LPS from Escherichia coli O9a, also bound to Dectin-2 and augmented TLR4 activation of BM-DCs. Taken together, these data indicate that mannosylated O-antigens from several gram-negative bacteria augment TLR4 responses through interaction with Dectin-2

Cytosolic superoxide dismutase activity after photodynamic therapy, intracellular distribution of Photofrin II and hypericin, and P-glycoprotein localization in human colon adenocarcinoma.

In photodynamic therapy (PDT), a tumor-selective photosensitizer is administered and then activated by exposure to a light source of applicable wavelength. Multidrug resistance (MDR) is largely caused by the efflux of therapeutics from the tumor cell by means of P-glycoprotein (P-gp), resulting in reduced efficacy of the anticancer therapy. This study deals with photodynamic therapy with Photofrin II (Ph II) and hypericin (Hyp) on sensitive and doxorubicin-resistant colon cancer cell lines. Changes in cytosolic superoxide dismutase (SOD1) activity after PDT and the intracellular accumulation of photosensitizers in sensitive and resistant colon cancer cell lines were examined. The photosensitizers' distributions indicate that Ph II could be a potential substrate for P-gp, in contrast to Hyp. We observed an increase in SOD1 activity after PDT for both photosensitizing agents. The changes in SOD1 activity show that photodynamic action generates oxidative stress in the treated cells. P-gp appears to play a role in the intracellular accumulation of Ph II. Therefore the efficacy of PDT on multidrug-resistant cells depends on the affinity of P-gp to the photosensitizer used. The weaker accumulation of photosensitizing agents enhances the antioxidant response, and this could influence the efficacy of PDT

High-performance analysis of filtered semantic graphs

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry \attributes" of various types. Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must either view the graph through a filter that passes only those individual vertices and edges of interest, or else must first materialize a subgraph or subgraphs consisting of only the vertices and edges of interest. The filtered approach is superior due to its generality, ease of use, and memory efficiency, but may carry a performance cost. In the Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, the user writes filters in a high-level language, but those filters result in relatively low performance due to the bottleneck of having to call into the Python interpreter for each edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance C++ /MPI Combinatorial BLAS engine, and show that the productivity gained by using a high-level filtering language comes without sacrificing performance. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM)