Glutamate transporters in platelets: EAAT1 decrease in aging and in Alzheimer's disease.

Platelets release glutamate upon activation and are an important clearance system of the amino acid from blood, through high-affinity glutamate uptake, similar to that described in brain synaptosomes. Since platelet glutamate uptake is decreased in neurodegenerative disorders, we performed a morphological and molecular characterization of platelet glutamate transporters. The three major brain glutamate transporters EAAT1, EAAT2 and EAAT3 are expressed in platelets, with similar molecular weight, although at lower density than brain. A Na(+)-dependent-high-affinity glutamate uptake was competitively inhibited by known inhibitors but not by dihydrokainic acid, suggesting platelet EAAT2 does not play a major role in glutamate uptake at physiological conditions. We observed decreased glutamate uptake V(max), without modification of transporter affinity, in aging, which could be linked to the selective decrease of EAAT1 expression and mRNA. Moreover, in AD patients we found a further EAAT1 reduction compared to age-matched controls, which could explain the decrease of platelet uptake previously described. Platelet glutamate transporters may be used as peripheral markers to investigate the role of glutamate in patients with neuropsychiatric disorders.

Genetics of Healthy Aging in Europe.The EU-Integrated Project GEHA (GEnetics of Healthy Aging)

The aim of the 5-year European Union (EU)-Integrated Project GEnetics of Healthy Aging (GEHA), constituted by 25 partners (24 from Europe plus the Beijing Genomics Institute from China), is to identify genes involved in healthy aging and longevity, which allow individuals to survive to advanced old age in good cognitive and physical function and in the absence of major age-related diseases. To achieve this aim a coherent, tightly integrated program of research that unites demographers, geriatricians, geneticists, genetic epidemiologists, molecular biologists, bioinfomaticians, and statisticians has been set up. The working plan is to: (a) collect DNA and information on the health status from an unprecedented number of long-lived 90+ sibpairs (n = 2650) and of younger ethnically matched controls (n = 2650) from 11 European countries; (b) perform a genome-wide linkage scannning in all the sibpairs (a total of 5300 individuals); this investigation will be followed by linkage disequilibrium mapping (LD mapping) of the candidate chromosomal regions; (c) study in cases (i.e., the 2650 probands of the sibpairs) and controls (2650 younger people), genomic regions (chromosome 4, D4S1564, chromosome 11, 11.p15.5) which were identified in previous studies as possible candidates to harbor longevity genes; (d) genotype all recruited subjects for apoE polymorphisms; and (e) genotype all recruited subjects for inherited as well as epigenetic variability of the mitochondrial DNA (mtDNA). The genetic analysis will be performed by 9 high-throughput platforms, within the framework of centralized databases for phenotypic, genetic, and mtDNA data. Additional advanced approaches (bioinformatics, advanced statistics, mathematical modeling, functional genomics and proteomics, molecular biology, molecular genetics) are envisaged to identify the gene variant(s) of interest. The experimental design will also allow (a) to identify gender-specific genes involved in healthy aging and longevity in women and men stratified for ethnic and geographic origin and apoE genotype; (b) to perform a longitudinal survival study to assess the impact of the identified genetic loci on 90+ people mortality; and (c) to develop mathematical and statistical models capable of combining genetic data with demographic characteristics, health status, socioeconomic factors, lifestyle habits.

Muscle-specific mutations accumulate with aging in critical human mtDNA control sites for replication.

The recently discovered aging-dependent large accumulation of point mutations in the human fibroblast mtDNA control region raised the question of their occurrence in postmitotic tissues. In the present work, analysis of biopsied or autopsied human skeletal muscle revealed the absence or only minimal presence of those mutations. By contrast, surprisingly, most of 26 individuals 53 to 92 years old, without a known history of neuromuscular disease, exhibited at mtDNA replication control sites in muscle an accumulation of two new point mutations, i.e., A189G and T408A, which were absent or marginally present in 19 individuals younger than 34 years. These two mutations were not found in fibroblasts from 22 subjects 64 to 101 years of age (T408A), or were present only in three subjects in very low amounts (A189G). Furthermore, in several older individuals exhibiting an accumulation in muscle of one or both of these mutations, they were nearly absent in other tissues, whereas the most frequent fibroblast-specific mutation (T414G) was present in skin, but not in muscle. Among eight additional individuals exhibiting partial denervation of their biopsied muscle, four subjects >80 years old had accumulated the two muscle-specific point mutations, which were, conversely, present at only very low levels in four subjects <or =40 years old. The striking tissue specificity of the muscle mtDNA mutations detected here and their mapping at critical sites for mtDNA replication strongly point to the involvement of a specific mutagenic machinery and to the functional relevance of these mutations.

Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network

An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the properties of maraging steels and composition, processing and working conditions. The input parameters of the model consist of alloy composition, processing parameters (including cold deformation degree, ageing temperature, and ageing time), and working temperature. The outputs of the ANN model include property parameters namely: ultimate tensile strength, yield strength, elongation, reduction in area, hardness, notched tensile strength, Charpy impact energy, fracture toughness, and martensitic transformation start temperature. Good performance of the ANN model is achieved. The model can be used to calculate properties of maraging steels as functions of alloy composition, processing parameters, and working condition. The combined influence of Co and Mo on the properties of maraging steels is simulated using the model. The results are in agreement with experimental data. Explanation of the calculated results from the metallurgical point of view is attempted. The model can be used as a guide for further alloy development.

Artificial Intelligence and Cognitive Science

Editorial for 17th AICS Conference