Pan-species transcriptomic analysis reveals a constitutive adaptation against oxidative stress for the highly virulent <i>Leptospira</i> species
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".