A survey of Fusobacterium nucleatum genes modulated by host cell infection
Bibliographic record
Abstract
Here, we report comprehensive transcriptomic profiles from Fusobacterium nucleatum under conditions that mimic the first stages of bacterial infection in a highly differentiated adenocarcinoma epithelial cell line. Our transcriptomic in vitro adenocarcinoma approach allows us to measure the expression dynamics and regulation of bacterial virulence and response factors in real time, and is a novel strategy for clarifying the role of F. nucleatum infection in colorectal cancer (CRC) progression. Our data show that: (i) infection alters metabolic and functional pathways in F. nucleatum , allowing the bacterium to adapt to the host-imposed milieu; (ii) infection also stimulates the expression of genes required to help induce and promote a hypoxic and inflammatory microenvironment in the host; and (iii) F. nucleatum invasion occurs by a haematogenous route of infection. Our study identifies novel gene targets from F. nucleatum that are activated during invasion and which may aid in determining how this species invades and promotes disease within the human gastrointestinal tract. These invasion-specific genes may be useful as biomarkers for CRC progression in a host and could also assist in the development of new diagnostic tools and treatments (such as vaccines or small molecule drug targets), which will be able to combat infection and inflammation in the host while circumventing the potential problem of F. nucleatum tolerization.
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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 | 0.001 |
| 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".