The Fasciola hepatica genome: gene duplication and polymorphism reveals adaptation to the host environment and the capacity for rapid evolution
Bibliographic record
Abstract
BACKGROUND: The liver fluke Fasciola hepatica is a major pathogen of livestock worldwide, causing huge economic losses to agriculture, as well as 2.4 million human infections annually. RESULTS: Here we provide a draft genome for F. hepatica, which we find to be among the largest known pathogen genomes at 1.3 Gb. This size cannot be explained by genome duplication or expansion of a single repeat element, and remains a paradox given the burden it may impose on egg production necessary to transmit infection. Despite the potential for inbreeding by facultative self-fertilisation, substantial levels of polymorphism were found, which highlights the evolutionary potential for rapid adaptation to changes in host availability, climate change or to drug or vaccine interventions. Non-synonymous polymorphisms were elevated in genes shared with parasitic taxa, which may be particularly relevant for the ability of the parasite to adapt to a broad range of definitive mammalian and intermediate molluscan hosts. Large-scale transcriptional changes, particularly within expanded protease and tubulin families, were found as the parasite migrated from the gut, across the peritoneum and through the liver to mature in the bile ducts. We identify novel members of anti-oxidant and detoxification pathways and defined their differential expression through infection, which may explain the stage-specific efficacy of different anthelmintic drugs. CONCLUSIONS: The genome analysis described here provides new insights into the evolution of this important pathogen, its adaptation to the host environment and external selection pressures. This analysis also provides a platform for research into novel drugs and vaccines.
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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 | 0.001 |
| 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".