Whole-genome sequencing efforts have revolutionized the study of bifidobacterial genetics and physiology. Unfortunately, the sequence of a single genome does not provide information on bifidobacterial genetic diversity and on how genetic variability supports improved adaptation of these bacteria to the environment of the human gastrointestinal tract (GIT). Analysis of nine genomes from bifidobacterial species showed that such genomes display an open pan-genome structure. Mathematical extrapolation of the data indicates that the genome reservoir available to the bifidobacterial pan-genome consists of more than 5000 genes, many of which are uncharacterized, but which are probably important to provide adaptive abilities pertinent to the human GIT. We also define a core bifidobacterial gene set which will undoubtedly provide a new baseline from which one can examine the evolution of bifidobacteria. Phylogenetic investigation performed on a total of 506 orthologues that are common to nine complete bifidobacterial genomes allowed the construction of aBifidobacteriumsupertree which is largely concordant with the phylogenetic tree obtained using 16S rRNA genes. Moreover, this supertree provided a more robust phylogenetic resolution than the 16S rRNA gene-based analysis. This comparative study of the genusBifidobacteriumthus presents a foundation for future functional analyses of this important group of GIT bacteria.

A linear method for the search of eukaryotic nuclear tRNA genes in DNA databases is described. Based on a modified version of the general weight matrix procedure, our algorithm relies on the recognition of two intragenic control regions known as A and B boxes, a transcription termination signal, and on the evaluation of the spacing between these elements. The scanning of the eukaryotic nuclear DNA database using this search algorithm correctly identified 933 of the 940 known tRNA genes (0.74% of false negatives). Thirty new potential tRNA genes were identified, and the transcriptional activity of two of them was directly verified by in vitro transcription. The total false positive rate of the algorithm was 0.014%. Structurally unusual tRNA genes, like those coding for selenocysteine tRNAs, could also be recognized using a set of rules concerning their specific properties, and one human gene coding for such tRNA was identified. Some of the newly identified tRNA genes were found in rather uncommon genomic positions: 2 in centromeric regions and 3 within introns. Furthermore, the presence of extragenically located B boxes in tRNA genes from various organisms could be detected through a specific subroutine of the standard search program.

A well-known mechanism through which new protein-coding genes originate is by modification of pre-existing genes, e.g. by duplication or horizontal transfer. In contrast, many viruses generate protein-coding genes de novo, via the overprinting of a new reading frame onto an existing (“ancestral”) frame. This mechanism is thought to play an important role in viral pathogenicity, but has been poorly explored, perhaps because identifying the de novo frames is very challenging. Therefore, a new approach to detect them was needed. We assembled a reference set of overlapping genes for which we could reliably determine the ancestral frames, and found that their codon usage was significantly closer to that of the rest of the viral genome than the codon usage of de novo frames. Based on this observation, we designed a method that allowed the identification of de novo frames based on their codon usage with a very good specificity, but intermediate sensitivity. Using our method, we predicted that the Rex gene of deltaretroviruses has originated de novo by overprinting the Tax gene. Intriguingly, several genes in the same genomic region have also originated de novo and encode proteins that regulate the functions of Tax. Such “gene nurseries” may be common in viral genomes. Finally, our results confirm that the genomic GC content is not the only determinant of codon usage in viruses and suggest that a constraint linked to translation must influence codon usage. How does novelty originate in nature? It is commonly thought that new genes are generated mainly by modifications of existing genes (the “tinkering” model). In contrast, we have shown recently that in viruses, numerous genes are generated entirely de novo (“from scratch”). The role of these genes remains underexplored, however, because they are difficult to identify. We have therefore developed a new method to detect genes originated de novo in viral genomes, based on the observation that each viral genome has a unique “signature”, which genes originated de novo do not share. We applied this method to analyze the genes of Human T-Lymphotropic Virus 1 (HTLV1), a relative of the HIV virus and also a major human pathogen that infects about twenty million people worldwide. The life cycle of HTLV1 is finely regulated – it can stay dormant for long periods and can provoke blood cancers (leukemias) after a very long incubation. We discovered that several of the genes of HTLV1 have originated de novo. These novel genes play a key role in regulating the life cycle of HTLV1, and presumably its pathogenicity. Our investigations suggest that such “gene nurseries” may be common in viruses.

Overlapping genes represent a fascinating evolutionary puzzle, since they encode two functionally unrelated proteins from the same DNA sequence. They originate by a mechanism of overprinting, in which point mutations in an existing frame allow the expression (the "birth") of a completely new protein from a second frame. In viruses, in which overlapping genes are abundant, these new proteins often play a critical role in infection, yet they are frequently overlooked during genome annotation. This results in erroneous interpretation of mutational studies and in a significant waste of resources. Therefore, overlapping genes need to be correctly detected, especially since they are now thought to be abundant also in eukaryotes. Developing better detection methods and conducting systematic evolutionary studies require a large, reliable benchmark dataset of known cases. We thus assembled a high-quality dataset of 80 viral overlapping genes whose expression is experimentally proven. Many of them were not present in databases. We found that overall, overlapping genes differ significantly from non-overlapping genes in their nucleotide and amino acid composition. In particular, the proteins they encode are enriched in high-degeneracy amino acids and depleted in low-degeneracy ones, which may alleviate the evolutionary constraints acting on overlapping genes. Principal component analysis revealed that the vast majority of overlapping genes follow a similar composition bias, despite their heterogeneity in length and function. Six proven mammalian overlapping genes also followed this bias. We propose that this apparently near-universal composition bias may either favour the birth of overlapping genes, or/and result from selection pressure acting on them.