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Abstract: Type 1 diabetes (T1D) represents a serious health burden in the world, complicated by the fact that disease onset can be preceded by a long time period without evident clinical signs. It would be then of critical importance to detect the disease in its early stages. In this direction, we seek here to identify early preinflammatory markers for autoimmune diabetes, mining our previously reported transcriptome data relevant to distinct early sub-phenotypes in the NOD mouse, associated with early insulin autoantibodies (E-IAA). More specifically we focus on secreted or transmembrane protein transcripts, identifying in this category 71 differentially expressed transcripts which are regulated at the early preinflammatory stages of T1D in the pancreatic lymph nodes (PLN). Following the expression patterns of these 71 transcripts, correspondence analysis (a multivariate analysis method) reveals a clear-cut segregation of the individual samples according to the early subphenotype used. Thus the 71 transcripts coding for secreted proteins constitute a candidate-set of predictive biomarkers for the development of autoimmune damage of the β cells of the pancreas. The majority of these genes have human orthologs and accordingly they represent potential candidate biomarkers for the human disease. In addition, for predictive purposes, the analysis reveals the possibility to reduce significantly the size of the candidate-set in practice, with various genes displaying identical expression profiles. [Copyright &y& Elsevier]

Abstract: The proper detection of orthologs is crucial for evolutionary studies of genes and species. Despite large efforts to solve this problem the methodological situation appears unsettled to a large extent and the “quest for orthologs” is still an ongoing task in large-scale genome comparisons. Here, we introduce a simple operational framework for the detection of orthologs and their classification. The operational framework relies on well-established principles, optimizing their implementation for the considered purposes, and chaining components in coherent procedures: 1) We take advantage of the efficiency and simplicity of the Reciprocal Best Hit (RBH) detections, remedying (by design) the drawback concerning the limitations in terms of 1:1 detections. The procedure is based on the partitioning of Reciprocal Best Hits, with the further merging of partitions including members of the same paralogous classes (“SuperPartition of Orthologs” (SPOs)). 2) We then resort to the conservation profiles of the obtained clusters, allowing simple detection of SPOs containing duplicated members. Based on accepted evolutionary principles, such members can be further tagged as in-paralogs (co-orthologs) or out-paralogs. The method is illustrated and validated by extensive genomic analyses. The performances of the overall approach are characterized in global terms for three sets of species (Chlamydiae, Mycobacteria, Aspergilli), showing that at least 75% of the sets of orthologs contain at most one protein from a given species. The sets including more than one protein from a given species are shown to contain in-paralogs in proportions varying from 28% to 58%. The characterizations also show that the large majority of SPOs are associated with ancestral motifs, and accordingly not prone to chaining effects that might be triggered by multi-domain proteins. Further the SPO formulation is compared to other similarity based ortholog detection methods. Beyond core common results, significant differences are observed between various methods, which can be accounted for to a large extent on conceptual grounds, relative to the different merging schemes involved. Such comparisons highlight a major advantage of the SPO approach concerning the proper clustering of associated paralogs, which appear to be often dispatched spuriously into distinct orthologous classes. Finally the perspectives for future applications and elaborations of SPO-based compositional analyses are discussed. [Copyright &y& Elsevier]

Large-scale genome comparisons have shown that no gene sets are shared exclusively by both Aspergillus fumigatus and any other human pathogen sequenced to date, such as Candida or Cryptococcus species. By contrast, and in agreement with the environmental occurrence of this fungus in decaying vegetation, the enzymatic machinery required by a fungus to colonize plant substrates has been found in the A. fumigatus genome. In addition, the proteome of this fungus contains numerous efflux pumps, including >100 major facilitators that help the fungus to resist either natural aggressive molecules present in the environment or antifungal drugs in humans. Environment sensing, counteracting reactive oxidants, and retrieving essential nutriments from the environment are general metabolic traits that are associated with the growth of the saprotrophic mold A. fumigatus in an unfriendly environment such as its human host. [Copyright &y& Elsevier]

Can we infer the lifestyle of an organism from the characteristic properties of its genome? More precisely, what are the relations between easily quantifiable properties from genomic sequences, such as amino-acid compositions, and more subtle characteristics concerning for example lifestyles or evolutionary trends? Here, we seek a global picture for such properties, based on a large number (56) of complete genomes, including significant numbers of representatives from the three domains of life. We consider the amino acid compositions of the predicted proteomes, and we use correspondence analysis, as a multivariate method to extract the relevant information from the large-scale data. From these analyses we derive a series of conclusions, concerning lifestyles, as well as physico-chemical and evolutionary trends: (1) correspondence analysis of the amino acid compositions permits discrimination between the three known lifestyles (mesophily/thermophily/hyperthermophily). (2) For various organisms, amino-acid composition properties are essentially driven by GC content, and to a significantly lesser extent by growth temperatures associated with lifestyles. Roughly speaking, the respective contributions of these two components are 57 and 20%. It is notable that these proportions are essentially unchanged with respect to a previous analysis (Nature 393 (1998) 537), which involved only 15 genomes, available at the time. (3) In terms of amino acid compositional biases, two specific ‘signatures’ for thermophily (in a broad sense, including hyperthermophily) can be detected. First, thermophilic species display a relative abundance in glutamic acid (Glu), concomitantly with the depletion in glutamine. Second, in thermophilic species, the relative abundance in Glu (negative charge) is significantly correlated (Pearson correlation coefficient r=0.83 with P<0.0001), with the increase in the lumped ‘pool’ lysine+arginine (positive charges). This correlation (absent in mesophiles) could be interpreted on a physico-chemical basis, relevant to the thermostability of proteins. (4) Statistically significant differences are observed between the average lengths of the genes in the surveyed species, which follow their distribution between the three domains of life. Also a significant difference is observed between the average lengths of thermophilic (283.0±5.8) versus mesophilic (340±9.4) genes. It is thus possible that the ‘general’ shortening of the primary sequences in thermophilic proteins plays a role in thermostability. (5) Considering various combinations of conservation properties (genes conserved exclusively in eukaryotes, in archaea, in bacteria, in combinations of two domains, etc.) correspondence analysis reveals a trend towards thermophilic-hyperthermophilic profiles for the most conserved subset of genes (ancient genes). (6) When limited to the subset of species-specific genes, correspondence analysis leads to a different picture for the clustering of genomes following amino-acid compositions: for example, the ‘core’ specific part of a genome can bear lifestyle signatures different from those of the complete genome.Various results are discussed both on methodological and biological grounds. The evolutionary perspectives opened by our analyses are noted. [Copyright &y& Elsevier]