Quantitative Trait Locus (QTL) Mapping Reveals a Role for Unstudied Genes in Aspergillus Virulence
Bibliographic record
Abstract
Infections caused by the fungus Aspergillus are a major cause of morbidity and mortality in immunocompromised populations. To identify genes required for virulence that could be used as targets for novel treatments, we mapped quantitative trait loci (QTL) affecting virulence in the progeny of a cross between two strains of A. nidulans (FGSC strains A4 and A91). We genotyped 61 progeny at 739 single nucleotide polymorphisms (SNP) spread throughout the genome, and constructed a linkage map that was largely consistent with the genomic sequence, with the exception of one potential inversion of ∼527 kb on Chromosome V. The estimated genome size was 3705 cM and the average intermarker spacing was 5.0 cM. The average ratio of physical distance to genetic distance was 8.1 kb/cM, which is similar to previous estimates, and variation in recombination rate was significantly positively correlated with GC content, a pattern seen in other taxa. To map QTL affecting virulence, we measured the ability of each progeny strain to kill model hosts, larvae of the wax moth Galleria mellonella. We detected three QTL affecting in vivo virulence that were distinct from QTL affecting in vitro growth, and mapped the virulence QTL to regions containing 7-24 genes, excluding genes with no sequence variation between the parental strains and genes with only synonymous SNPs. None of the genes in our QTL target regions have been previously associated with virulence in Aspergillus, and almost half of these genes are currently annotated as "hypothetical". This study is the first to map QTL affecting the virulence of a fungal pathogen in an animal host, and our results illustrate the power of this approach to identify a short list of unknown genes for further investigation.
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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 | 0.000 | 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".