Project description:Analyses of new genomic, transcriptomic or proteomic data commonly result in trashing many unidentified data escaping the ‘canonical’ DNA-RNA-protein scheme. Testing systematic exchanges of nucleotides over long stretches produces inversed RNA pieces (here named “swinger” RNA) differing from their template DNA. These may explain some trashed data. Here analyses of genomic, transcriptomic and proteomic data of the pathogenic Tropheryma whipplei according to canonical genomic, transcriptomic and translational 'rules' resulted in trashing 58.9% of DNA, 37.7% RNA and about 85% of mass spectra (corresponding to peptides). In the trash, we found numerous DNA/RNA fragments compatible with “swinger” polymerization. Genomic sequences covered by «swinger» DNA and RNA are 3X more frequent than expected by chance and explained 12.4 and 20.8% of the rejected DNA and RNA sequences, respectively. As for peptides, several match with “swinger” RNAs, including some chimera, translated from both regular, and «swinger» transcripts, notably for ribosomal RNAs. Congruence of DNA, RNA and peptides resulting from the same swinging process suggest that systematic nucleotide exchanges increase coding potential, and may add to evolutionary diversification of bacterial populations.
Project description:Aging is the primary risk factor of neurodegenerative disorders such as Alzheimer's disease (AD). However, the molecular events occurring during brain aging are extremely complex and still largely unknown. For a better understanding of these age-associated modifications, animal models as close as possible to humans are needed. We thus analyzed the transcriptome of the temporal cortex of the primate Microcebus murinus using human oligonucleotide microarrays (Affymetrix). Gene expression profiles were assessed in the temporal cortex of 6 young adults, 10 healthy old animals and 2 old, "AD-like" animals that presented b-amyloid plaques and cortical atrophy, which are pathognomonic signs of AD in humans. Gene expression data of the 14,911 genes that were detected in at least 3 samples were analyzed. By SAM (significance analysis of microarrays), we identified 47 genes that discriminated young from healthy old and "AD-like" animals. These findings were confirmed by principal component analysis (PCA). ANOVA of the expression data from the three groups identified 695 genes (including the 47 genes previously identified by SAM and PCA) with significant changes of expression in old and "AD-like" in comparison to young animals. About one third of these genes showed similar changes of expression in healthy aging and in M-bM-^@M-^\AD-likeM-bM-^@M-^] animals, whereas more than two thirds showed opposite changes in these two groups in comparison to young animals. Hierarchical clustering analysis of the 695 markers indicated that each group had distinct expression profiles which characterized each group, especially the "AD-like" group. Functional categorization showed that most of the genes that were up-regulated in healthy old and down-regulated in "AD-like" animals belonged to metabolic pathways, particularly protein synthesis. These data suggest the existence of compensatory mechanisms during physiological brain aging that disappear in M-bM-^@M-^\AD-likeM-bM-^@M-^] animals. These results open the way to new exploration of physiological and M-bM-^@M-^\AD-likeM-bM-^@M-^] aging in primates. Microcebus murinus were divided in 3 groups: the first group included 6 young adults (4 females and 2 males), the second included 10 healthy old animals (7 females and 3 males) and the third one was composed by 2 "AD-like" old females. Since Microcebus murinus microarrays do not exist, we decided to use Affymetrix human genome chips (HG U133 plus 2), since studies have illustrated the feasibility of detecting non-human primate brain transcripts using human genome chips.