Folate-sensitive fragile site FRA10A is due to an expansion of a CGG repeat in a novel gene, FRA10AC1, encoding a nuclear protein

Fragile sites appear visually as nonstaining gaps on chromosomes that are inducible by specific cell culture conditions. Expansion of CGG/ CCG repeats has been shown to be the molecular basis of all five folate-sensitive fragile sites characterized molecularly so far, i.e., FRAXA, FRAXE, FRAXF, FRA11B, and FRA16A. In the present study we have refined the localization of the FRA10A folate-sensitive fragile site by fluorescence in situ hybridization. Sequence analysis of a BAC clone spanning FRA10A identified a single, imperfect, but polymorphic CGG repeat that is part of a CpG island in the 5'UTR of a novel gene named FRA10ACl. The number of CGG repeats varied in the population from 8 to 13. Expansions exceeding 200 repeat units were methylated in all FRA10A fragile site carriers tested. The FRA10ACl gene consists of 19 exons and is transcribed in the centromeric direction from the FRA10A repeat. The major transcript of similar to 1450 nt is ubiquitously expressed and codes for a highly conserved protein, FRA10ACl, of unknown function. Several splice variants leading to alternative 3' ends were identified (particularly in testis). These give rise to FRA10ACl proteins with altered COOH-termini. Immunofluorescence analysis of full-length, recombinant EGFP-tagged FRA10ACl protein showed that it was present exclusively in the nucleoplasm. We show that the expression of FRA10A, in parallel to the other cloned folate-sensitive fragile sites, is caused by an expansion and subsequent methylation of an unstable CGG trinucleotide repeat. Taking advantage of three cSNPs within the FRA10ACl gene we demonstrate that one allele of the gene is not transcribed in a FRA10A carrier. Our data also suggest that in the heterozygous state FRA10A is likely a benign folate-sensitive fragile site. (C) 2004 Elsevier Inc. All rights reserved.

Virtual models of the HLA class I antigen processing pathway

Antigen recognition by cytotoxic CD8 T cells is dependent upon a number of critical steps in MHC class I antigen processing including proteosomal cleavage, TAP transport into the endoplasmic reticulum, and MHC class 1 binding. Based on extensive experimental data relating to each of these steps there is now the capacity to model individual antigen processing steps with a high degree of accuracy. This paper demonstrates the potential to bring together models of individual antigen processing steps, for example proteosome cleavage, TAP transport, and MHC binding, to build highly informative models of functional pathways. In particular, we demonstrate how an artificial neural network model of TAP transport was used to mine a HLA-binding database so as to identify H LA-binding peptides transported by TAP. This integrated model of antigen processing provided the unique insight that HLA class I alleles apparently constitute two separate classes: those that are TAP-efficient for peptide loading (HLA-B27, -A3, and -A24) and those that are TAP-inefficient (HLA-A2, -B7, and -B8). Hence, using this integrated model we were able to generate novel hypotheses regarding antigen processing, and these hypotheses are now capable of being tested experimentally. This model confirms the feasibility of constructing a virtual immune system, whereby each additional step in antigen processing is incorporated into a single modular model. Accurate models of antigen processing have implications for the study of basic immunology as well as for the design of peptide-based vaccines and other immunotherapies. (C) 2004 Elsevier Inc. All rights reserved.

Encephalitogenicity of murine, but not bovine, DM20 in SJL mice is due to a single amino acid difference in the immunodominant encephalitogenic epitope

Myelin proteolipid protein (PLP) contains 2 immunodominant encephalitogenic epitopes in SJL mice, namely PLP residues 139-151 and 178-191. DM20, a minor isoform of PLP, lacks residues 116-150 and consequently contains only the single major encephalitogenic epitope 178-191. However, it has been found previously that bovine DM20 is not encephalitogenic in SJL mice. Since residue 188 within peptide 178-191 is phenylalanine (F) in murine DM20 and alanine (A) in bovine DM20, we tested the effect of this difference on the immune responses and induction of EAE. SJL mice were immunized with either highly purified murine or bovine DM20. Residues 178-191 were found to be immunodominant for each, but only murine and not bovine DM20 was encephalitogenic. A synthetic peptide corresponding to the murine 178-191 sequence (F188) was also encephalitogenic, whereas the peptide corresponding to the bovine sequence (A188) was not. Both F188 and A188 bind with high affinity to I-A(s) and both are recognized by the SJL T cell repertoire. A188-specific T cell lines reacted to both A188 and F188, but F188-specific T cell lines were not stimulated by A188. F188-specific T cell lines produced mRNA for the Th1 cytokines IL2 and IFN gamma and, in passive transfer experiments, were encephalitogenic upon stimulation with F188, but not A188. In contrast, A188-specific T cell lines produced mRNA for IL4, IL5 and IL10, in addition to IL2 and IFN gamma, and were not encephalitogenic after stimulation with either F188 or A188. Cotransfer of A188-specific T cell lines with F188-specific T cell lines resulted in protection from EAE. Thus, A188 induces a functionally different phenotype of T cells from that induced by F188. Taken together these data suggest that the failure of bovine DM20 to induce EAE may be attributable to induction of protective rather than pathogenic T cells by the immunodominant epitope.

Beach water table fluctuations due to wave run-up: Capillarity effects

High-frequency beach water table fluctuations due to wave run-up and rundown have been observed in the field [Waddell, 1976]. Such fluctuations affect the infiltration/exfiltration process across the beach face and the interstitial oxygenation process in the beach ecosystem. Accurate representation of high-frequency water table fluctuations is of importance in the modeling of (1) the interaction between seawater and groundwater, more important, the effects on swash sediment transport and (2) the biological activities in the beach ecosystem. Capillarity effects provide a mechanism for high-frequency water table fluctuations. Previous modeling approaches adopted the assumption of saturated flow only and failed to predict the propagation of high-frequency fluctuations in the aquifer. In this paper we develop a modified kinematic boundary condition (kbc) for the water table which incorporates capillarity effects. The application of this kbc in a boundary element model enables the simulation of high-frequency water table fluctuations due to wave run-up. Numerical tests were carried out for a rectangular domain with small-amplitude oscillations; the behavior of water table responses was found to be similar to that predicted by an analytical solution based on the one-dimensional Boussinesq equation. The model was also applied to simulate the water table response to wave run-up on a doping beach. The results showed similar features of water table fluctuations observed in the field. In particular, these fluctuations are standing wave-like with the amplitude becoming increasingly damped inland. We conclude that the modified kbc presented here is a reasonable approximation of capillarity effects on beach water table fluctuations. However, further model validation is necessary before the model can confidently be used to simulate high-frequency water table fluctuations due to wave run-up.

Virtual plants - integrating architectural and physiological models

Recent advances in computer technology have made it possible to create virtual plants by simulating the details of structural development of individual plants. Software has been developed that processes plant models expressed in a special purpose mini-language based on the Lindenmayer system formalism. These models can be extended from their architectural basis to capture plant physiology by integrating them with crop models, which estimate biomass production as a consequence of environmental inputs. Through this process, virtual plants will gain the ability to react to broad environmental conditions, while crop models will gain a visualisation component. This integration requires the resolution of the fundamentally different time scales underlying the approaches. Architectural models are usually based on physiological time; each time step encompasses the same amount of development in the plant, without regard to the passage of real time. In contrast, physiological models are based in real time; the amount of development in a time step is dependent on environmental conditions during the period. This paper provides a background on the plant modelling language, then describes how widely-used concepts of thermal time can be implemented to resolve these time scale differences. The process is illustrated using a case study. (C) 1997 Elsevier Science Ltd.