Metatranscriptomic Profiling of Host-Microbiome Interactions in the Telencephalon and Liver of Carollia perspicillata.
Bibliographic record
Abstract
RNA-seq data provide valuable insights into both host transcriptomes and microbial transcripts from active microbiota within host tissues. The presence of microbial transcripts within specific tissues indicates the replication and transcriptional activity of microorganisms. Integrative analyses of the host transcriptome and the meta-transcriptome allow for the characterization of microbial gene expression and the interactive response of the host to the microbiome in each sample. In this study, we employed metatranscriptomics to explore microbial communities in the telencephalon and liver of Carollia perspicillata. By combining host and microbial RNA-seq data, we identified 287 microbial species in the liver and 283 in the telencephalon, revealing tissue-specific microbial diversity. Bacteria were the most abundant taxa in both tissues, followed by notable eukaryotic, archaeal, and viral populations. Using the Metatranscriptome Detector pipeline and NCBI databases, we identified species of potential epidemiological relevance and characterized the host's transcriptional response to the microbiome. Functional analyses indicated differential expression of microbial and host genes across tissues, with enriched metabolic pathways and Gene Ontology terms aligning with hepatic and neural functions. This research underscores the tissue-specific adaptation of the microbiome to host physiology, offering new insights into host-microbiome dynamics in the telencephalon and liver of this frugivorous bat.
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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 | 0.001 | 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".