The Tannase Gene: Metaphylogenomics, Global Distribution and Presence in the Midgut Flora of the Forest Tent Caterpillar Malacosoma disstria Hübner
Bibliographic record
Abstract
Tannase enzymes hydrolyze tannins, a class of plant polyphenolics that defend against herbivory. Prior to the 1980’s, most studies were focused on the tannase gene in fungi owing to the interest surrounding their industrial value. Since then a large number of bacterial tannase genes have also been discovered. I performed phylogenetic analysis on 110 fungal and bacterial tannase reference sequences in an effort to observe the relationships between fungal and bacterial tannase. The generated maximum likelihood tree shows eight strongly supported tannase clades, with a rift among fungal tannases, which either align with proteobacterial tannase or actinobacterial tannase. Metagenomes were used to assess the biogeographical distribution of the tannase clades, revealing that they may have environmental specificity. \n \nAn unpublished observation made by the Despland lab of Concordia University suggested that forest tent caterpillars (Malacosoma disstria) from western Canada are unable to survive on tannin-rich sugar maple foliage (Acer saccharum), whereas populations in eastern Canada develop on sugar maple. In Chapter 2 I examine this observation, and explore the possibility of tannase-expressing secondary symbionts in eastern M. disstria. For the first time, the microbiome of M. disstria has been described using 16S rRNA gene sequencing and is shown to contain several genera known for expressing the tannase gene.
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 | 0.000 |
| 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".