Distribución de la zorra del desierto (Vulpes macrotis) en relación a sus presas y a su principal depredador, el coyote (Canis latrans)
Bibliographic record
Abstract
The lack of genetic tools to manipulate protozoan pathogens has limited the use of genome-wide approaches to identify drug or vaccine targets and understand these organisms' biology. We have developed an efficient method to construct genome-wide libraries for yeast surface display (YSD) and developed a YSD fitness screen (YSD-FS) to identify drug targets. We show the efficacy of our method by generating genome-wide libraries for <i>Trypanosoma brucei</i>, <i>Trypanosoma cruzi</i>, and <i>Giardia lamblia</i> parasites. Each library has a diversity of ∼10<sup>5</sup> to 10<sup>6</sup> clones, representing ∼6- to 30-fold of the parasite's genome. Nanopore sequencing confirmed the libraries' genome coverage with multiple clones for each parasite gene. Western blot and imaging analysis confirmed surface expression of the <i>G. lamblia</i> library proteins in yeast. Using the YSD-FS assay, we identified bonafide interactors of metronidazole, a drug used to treat protozoan and bacterial infections. We also found enrichment in nucleotide-binding domain sequences associated with yeast increased fitness to metronidazole, indicating that this drug might target multiple enzymes containing nucleotide-binding domains. The libraries are valuable biological resources for discovering drug or vaccine targets, ligand receptors, protein-protein interactions, and pathogen-host interactions. The library assembly approach can be applied to other organisms or expression systems, and the YSD-FS assay might help identify new drug targets in protozoan pathogens.
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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 | 0.001 | 0.001 |
| 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 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".