Genomics and transcriptomics yields a system-level view of the biology of the pathogen Naegleria fowleri
Bibliographic record
Abstract
BACKGROUND: The opportunistic pathogen Naegleria fowleri establishes infection in the human brain, killing almost invariably within 2 weeks. The amoeba performs piece-meal ingestion, or trogocytosis, of brain material causing direct tissue damage and massive inflammation. The cellular basis distinguishing N. fowleri from other Naegleria species, which are all non-pathogenic, is not known. Yet, with the geographic range of N. fowleri advancing, potentially due to climate change, understanding how this pathogen invades and kills is both important and timely. RESULTS: Here, we report an -omics approach to understanding N. fowleri biology and infection at the system level. We sequenced two new strains of N. fowleri and performed a transcriptomic analysis of low- versus high-pathogenicity N. fowleri cultured in a mouse infection model. Comparative analysis provides an in-depth assessment of encoded protein complement between strains, finding high conservation. Molecular evolutionary analyses of multiple diverse cellular systems demonstrate that the N. fowleri genome encodes a similarly complete cellular repertoire to that found in free-living N. gruberi. From transcriptomics, neither stress responses nor traits conferred from lateral gene transfer are suggested as critical for pathogenicity. By contrast, cellular systems such as proteases, lysosomal machinery, and motility, together with metabolic reprogramming and novel N. fowleri proteins, are all implicated in facilitating pathogenicity within the host. Upregulation in mouse-passaged N. fowleri of genes associated with glutamate metabolism and ammonia transport suggests adaptation to available carbon sources in the central nervous system. CONCLUSIONS: In-depth analysis of Naegleria genomes and transcriptomes provides a model of cellular systems involved in opportunistic pathogenicity, uncovering new angles to understanding the biology of a 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".