A comparative ‘omics approach to candidate pathogenicity factor discovery in the brain-eating amoeba <i>Naegleria fowleri</i>
Bibliographic record
Abstract
Abstract Of the 40 described Naegleria species, only N. fowleri can establish infection in humans, killing almost invariably within two weeks. In the brain, the amoeba performs piece-meal ingestion, or trogocytosis, of brain material causing massive inflammation. Conversely, its close relative Naegleria gruberi , which is used as a laboratory model organism, is non-pathogenic. The exact pathogenicity factors distinguishing N. fowleri from its harmless relatives are unclear. We have here taken an -omics approach to understanding N. fowleri biology and infection at the system level. We provide the first analysis of genomic diversity between strains, finding little conservation in synteny but high conservation in protein complement. We also demonstrate that the N. fowleri genome encodes a similarly complete cellular repertoire to that found in N. gruberi . Our comparative genomic analysis, together with a transcriptomic analysis of low versus high pathogenicity N. fowleri cultured in a mouse infection model, allowed us to construct a model of cellular systems involved in pathogenicity and furthermore provides ~500 novel candidate pathogenicity factors in this currently rare but highly fatal pathogen.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".