Stochastic variation in surface protein expression diversifies <i>Trypanosoma cruzi</i> infection
Bibliographic record
Abstract
Trypanosoma cruzi possesses hundreds of genes associated with its pathogenesis. The extent and organization of this diverse gene repertoire, expression, and role in infection remain unclear. Using accurate long-read sequencing and chromatin conformation capture, T. cruzi chromosomes were assembled from telomere-to-telomere. The genome revealed multigene families of virulence genes accounting for ~70% of some chromosomes, organized in clusters or scattered through housekeeping genes. Quantitative proteomics identified stage-specific proteins and numerous trans-sialidases upregulated in trypomastigotes. Notably, the expression of virulence gene families changed stochastically in trypomastigotes, conferring T. cruzi fitness to cardiomyocyte infection while diversifying the invasion to multiple tissues. A T. cruzi genome-wide yeast surface display screen against Chagas disease patients' antibodies revealed virulence genes expressed during human infections. However, limited conservation in their antibody-binding sites suggests their sequence diversity and variation help parasites avert antibody recognition. The data point to a role for virulence multigene families in infection persistence.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".