Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Disease-causing mutations improving the branch site and polypyrimidine tract: pseudoexon activation of LINE-2 and antisense Alu lacking the poly(T)-tail.

Cryptic exons or pseudoexons are typically activated by point mutations that create GT or AG dinucleotides of new 5' or 3' splice sites in introns, often in repetitive elements. Here we describe two cases of tetrahydrobiopterin deficiency caused by mutations improving the branch point sequence and polypyrimidine tracts of repeat-containing pseudoexons in the PTS gene. In the first case, we demonstrate a novel pathway of antisense Alu exonization, resulting from an intronic deletion that removed the poly(T)-tail of antisense AluSq. The deletion brought a favorable branch point sequence within proximity of the pseudoexon 3' splice site and removed an upstream AG dinucleotide required for the 3' splice site repression on normal alleles. New Alu exons can thus arise in the absence of poly(T)-tails that facilitated inclusion of most transposed elements in mRNAs by serving as polypyrimidine tracts, highlighting extraordinary flexibility of Alu repeats in shaping intron-exon structure. In the other case, a PTS pseudoexon was activated by an A>T substitution 9 nt upstream of its 3' splice site in a LINE-2 sequence, providing the first example of a disease-causing exonization of the most ancient interspersed repeat. These observations expand the spectrum of mutational mechanisms that introduce repetitive sequences in mature transcripts and illustrate the importance of intronic mutations in alternative splicing and phenotypic variability of hereditary disorders.

Conversion of membrane-bound Fas(CD95) ligand to its soluble form is associated with downregulation of its proapoptotic activity and loss of liver toxicity.

Human Fas ligand (L) (CD95L) and tumor necrosis factor (TNF)-alpha undergo metalloproteinase-mediated proteolytic processing in their extracellular domains resulting in the release of soluble trimeric ligands (soluble [s]FasL, sTNF-alpha) which, in the case of sFasL, is thought to be implicated in diseases such as hepatitis and AIDS. Here we show that the processing of sFasL occurs between Ser126 and Leu127. The apoptotic-inducing capacity of naturally processed sFasL was reduced by >1,000-fold compared with membrane-bound FasL, and injection of high doses of recombinant sFasL in mice did not induce liver failure. However, soluble FasL retained its capacity to interact with Fas, and restoration of its cytotoxic activity was achieved both in vitro and in vivo with the addition of cross-linking antibodies. Similarly, the marginal apoptotic activity of recombinant soluble TNF-related apoptosis-inducing ligand (sTRAIL), another member of the TNF ligand family, was greatly increased upon cross-linking. These results indicate that the mere trimerization of the Fas and TRAIL receptors may not be sufficient to trigger death signals. Thus, the observation that sFasL is less cytotoxic than membrane-bound FasL may explain why in certain types of cancer, systemic tissue damage is not detected, even though the levels of circulating sFasL are high.

Structural prediction of peptides bound to MHC class I.

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.