Dual RNA-seq study of the dynamics of coding and non-coding RNAs expression during <i>Clostridioides difficile</i> infection in a mouse model
Bibliographic record
Abstract
Abstract Clostridioides difficile is the leading cause of healthcare associated diarrhoea in industrialized countries. Many questions remain to be answered about the mechanisms governing its interaction with the host during infection. Non-coding RNAs (ncRNAs) contribute to shaping virulence in many pathogens and modulate host responses, however, their role in C. difficile infection (CDI) has not been explored. To better understand the dynamics of ncRNAs expression contributing to C. difficile infectious cycle and host response, we used a dual RNA- seq approach in a conventional murine model. From the pathogen side, this transcriptomic analysis revealed the upregulation of virulence factors, metabolism and sporulation genes, as well as the identification of 61 ncRNAs differentially expressed during infection that correlated with the analysis of available raw RNA-seq datasets from two independent studies. From these data we identified 118 potential new transcripts in C. difficile including 106 new ncRNA genes. From the host side, we observed the induction of several pro-inflammatory pathways and, among the 185 differentially expressed ncRNAs, the overexpression of microRNAs (miRNAs) previously associated to inflammatory responses or unknown long ncRNAs and miRNAs. A particular host gene expression profile could be associated to the symptomatic infection. In accordance, the metatranscriptomic analysis revealed specific microbiota changes accompanying CDI and specific species associated with symptomatic infection in mice. This first adaptation of in vivo dual RNA-seq to C. difficile contributes to unravelling the regulatory networks involved in C. difficile infectious cycle and host response and provides valuable resources for further studies of RNA-based mechanisms during CDI. Importance Clostridioides difficile is a major cause of nosocomial infections associated with antibiotic therapy classified as an urgent antibiotic resistance threat. This pathogen interacts with host and gut microbial communities during infection, but the mechanisms of these interactions remain largely to be uncovered. Noncoding RNAs contribute to bacterial virulence and host responses, but their expression has not been explored during C. difficile infection. We took advantage of the conventional mouse model of C. difficile infection to look simultaneously to the dynamics of gene expression in pathogen, its host and gut microbiota composition providing valuable resources for future studies. We identified a number of ncRNAs that could mediate the adaptation of C. difficile inside the host and the crosstalk with the host immune response. Promising inflammation markers and potential therapeutic targets emerged from this work open new directions for RNA-based and microbiota-modulatory strategies to improve the efficiency of C. difficile infection treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".