Pan-species transcriptomic analysis reveals a constitutive adaptation against oxidative stress for the highly virulent <i>Leptospira</i> species
Notice bibliographique
Résumé
Abstract Different environments exert selective pressures on bacterial populations, favoring individuals with particular genetic traits that are well-suited for survival in those conditions. Evolutionary mechanisms such as natural selection have, therefore, shaped bacterial populations over time selecting, in a stepwise manner, the fittest bacteria that gave rise to the modern lineages that exist today. Advances in genomic sequencing, computational analysis, and experimental techniques continue to enhance our understanding of bacterial evolution and its implications. Nevertheless, these are often limited to genomic comparisons of closely related species. In the present study, we introduce Annotator-RNAtor, a graphical user interface (GUI) method for performing pan-species transcriptomic analysis and studying intragenus evolution. The pipeline uses third-party software to infer homologous genes in various species and highlight differences in the expression of the core-genes. To illustrate the methodology and demonstrate its usefulness, we focus on the emergence of the highly virulent Leptospira subclade known as P1+, which includes the causative agents of leptospirosis. Here, we expand on the genomic study through the comparison of transcriptomes between species from P1+ and their related P1-counterparts (low-virulent pathogens). In doing so, we shed light on differentially expressed pathways and focused on describing a specific example of adaptation based on a differential expression of PerRA-controlled genes. We showed that P1+ species exhibit higher expression of the katE gene, a well-known virulence determinant in pathogenic Leptospira species correlated with greater tolerance to peroxide. Switching PerRA alleles between P1+ and P1-species demonstrated that the lower repression of katE and greater tolerance to peroxide in P1+ species was solely controlled by PerRA and partly caused by a PerRA amino-acid permutation. Overall, these results demonstrate the strategic fit of the methodology and its ability to decipher adaptive transcriptomic changes, not observable by comparative genome analysis, that may have been crucial for the emergence of these pathogens. Author summary Natural selection is one of the central mechanisms of the bacterial evolution. Speciation events and adaptation occurs such as mutations, deletions and horizontal gene transfers to enhance our understanding of evolution. Nevertheless, these are often limited to genomic comparisons between species. Here, we are developed a graphical user interface method, named Annotator-RNAtor, to perform pan-species transcriptomic analysis and studying intragenus evolution. To illustrate the methodology, we focus on the emergence of the virulent Leptospira species, causative agents of leptospirosis. We shed light on a differential regulation of several PerRA-controlled genes in P1+ Leptospira subclade (highly virulent pathogens) compared to P1- Leptospira subclade (low virulent pathogens). P1+ species exhibit higher expression of the catalase-encoding gene katE , than P1-species, correlating with a greater ability to withstand peroxide. Additionally, we demonstrate that the difference in katE expression is mediated only by PerRA and the residue 89 of the PerRA protein participates on this regulation. These findings highlight the importance to decipher adaptative transcriptomic changes to fully understand the emergence of pathogenic species.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».