Transcriptional profile of the human skin pathogenic fungus Mucor irregularis in response to low oxygen
Bibliographic record
Abstract
Mucormycosis is one of the most invasive mycosis and has caused global concern in public health. Cutaneous mucormycosis caused by Mucor irregularis (formerly Rhizomucor variabilis) is an emerging disease in China. To survive in the human body, M. irregularis must overcome the hypoxic (low oxygen) host microenvironment. However, the exact molecular mechanism of its pathogenicity and adaptation to low oxygen stress environment is relatively unexplored. In this study, we used Illumina HiSeq technology (RNA-Seq) to determine and compare the transcriptome profile of M. irregularis CBS103.93 under normal growth condition and hypoxic stress. Our analyses demonstrated a series of genes involved in TCA, glyoxylate cycle, pentose phosphate pathway, and GABA shunt were down-regulated under hypoxic condition, while certain genes in the lipid/fatty acid metabolism and endocytosis were up-regulated, indicating that lipid metabolism was more active under hypoxia. Comparing the data with other important human pathogenic fungi such as Aspergillus spp., we found that the gene expression pattern and metabolism in responses to hypoxia in M. irregularis were unique and different. We proposed that these metabolic changes can represent a species-specific hypoxic adaptation in M. irregularis, and we hypothesized that M. irregularis could use the intra-lipid pool and lipid secreted in the infection region, as an extracellular nutrient source to support its hypoxic growth. Characterizing the significant differential gene expression in this species could be beneficial to uncover their role in hypoxia adaptation and fungalpathogenesis and further facilitate the development of novel targets in disease diagnosis and treatment against mucormycosis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".