Editorial: Functional Genomics of Transcriptional Regulation in Pathogenic Fungi
Bibliographic record
Abstract
Approximately one hundred thousand fungal species have been described and some studies estimate that there are over five million (Blackwell, 2011).Many fungi play a positive role in various ecosystems, such as their symbiotic association with plants while others have been successfully used in the biotechnology field, as exemplified by the baker's yeast Saccharomyces cerevisiae and Komagataella phaffii (Pichia pastoris).However, a number of fungal species constitute a major threat to plants and animals, including humans (Fisher et al., 2012).In humans, fungi are responsible for 1.5 million deaths each year with Aspergillus, Candida, and Cryptococcus species being the fungal pathogens generating the majority of cases of serious fungal disease.Candida albicans is the principal cause of invasive infections with C. glabrata ranking second; other fungi play less lethal roles, such as Microsporum canis which is a common skin fungus.In this series of publications, the authors describe the role of a number of transcriptional regulators involved in controlling diverse processes in a variety of pathogenic fungi.C. albicans is a typically commensal organism that inhabits the mucosal linings of warm-blooded animals, but as stated above, it is also the major culprit in human fungal infections.C. albicans is a severe and persistent opportunistic pathogen of immunocompromised individuals.Rogriguez et al. reviewed gene networks involved in the regulation of developmental processes in C. albicans.The authors provide a detailed assessment of the factors that form the basis of transcriptional circuits involved in controlling three central developmental processes.(1) association of virulence of C. albicans with its ability to switch from yeast to hyphae (and vice-versa), (2) ability of C. albicans to switch from white to opaque forms (a process important for its parasexual cycle and (3) the formation of biofilms containing a protective extra-cellular matrix, a structure providing resistance to antifungal treatment.The authors also discuss the interconnection among these circuits.In C. albicans, the role of many transcription factors has been uncovered using transcriptomics and genome-wide location analyses.However, the majority of these studies have been performed under normoxic conditions even though hypoxia is a condition frequently encountered in the human host and is a major signal for filamentous growth.Henry et al. were interested in better characterizing the link between hypoxia and growth in a filamentous form.A genetic screen with deletion mutants identified a number of factors involved in this process, including the transcriptional regulators Ahr1 and Tye7 that were known to regulate genes involved in glycolysis and adhesion, respectively.The authors show that Ahr1 and Tye7 act as negative
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".