A Screen for Genes of Heme Uptake Identifies the FLC Family Required for Import of FAD into the Endoplasmic Reticulum
Bibliographic record
Abstract
Although Candida albicans and Saccharomyces cerevisiae express very similar systems of iron uptake, these species differ in their capacity to use heme as a nutritional iron source. Whereas C. albicans efficiently takes up heme, S. cerevisiae grows poorly on media containing heme as the sole source of iron. We identified a gene from C. albicans that would enhance heme uptake when expressed in S. cerevisiae. Overexpression of CaFLC1 (for flavin carrier 1) stimulated the growth of S. cerevisiae on media containing heme iron. In C. albicans, deletion of both alleles of CaFLC1 resulted in a decrease in heme uptake activity, whereas overexpression of CaFLC1 resulted in an increase in heme uptake. The S. cerevisiae genome contains three genes with homology to CaFLC1, and two of these, termed FLC1 and FLC2, also stimulated growth on heme when overexpressed in S. cerevisiae. The S. cerevisiae Flc proteins were detected in the endoplasmic reticulum and the FLC genes encoded an essential function, as strains deleted for either FLC1 or FLC2 were viable, but deletion of both FLC1 and FLC2 was synthetically lethal. FLC gene deletion resulted in pleiotropic phenotypes related to defects in cell wall integrity. High copy suppressors of this synthetic lethality included three mannosyltransferases, VAN1, KTR4, and HOC1. FLC deletion strains exhibited loss of cell wall mannose phosphates, defects in cell wall assembly, and delayed maturation of carboxypeptidase Y. Permeabilized cells lacking FLC proteins exhibited dramatic loss of FAD import activity. We propose that the FLC genes are required for import of FAD into the lumen of the endoplasmic reticulum, where it is required for disulfide bond formation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".