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Record W2110466942 · doi:10.1128/mbio.00259-12

Comparative Genome Analysis of <i>Trichophyton rubrum</i> and Related Dermatophytes Reveals Candidate Genes Involved in Infection

2012· article· en· W2110466942 on OpenAlexaff
Diego Martinez, Brian G. Oliver, Yvonne Gräser, Jonathan M. Goldberg, Wenjun Li, Nilce Maria Martinez-Rossi, Michel Monod, Ekaterina Shelest, Richard Barton, Elizabeth Birch, Axel A. Brakhage, Zehua Chen, Sarah J. Gurr, David I. Heiman, Joseph Heitman, Idit Kosti, Antônio Rossi, Sakina Saif, Marketa Samalova, Charles W. Saunders, Terrance Shea, Richard C. Summerbell, Jun Xu, Sarah Young, Qiandong Zeng, Susan J. Birren, Christina A. Cuomo, Theodore C. White

Bibliographic record

VenuemBio · 2012
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesNational Human Genome Research InstituteBiotechnology and Biological Sciences Research CouncilNational Institutes of HealthBroad Institute
KeywordsBiologyDermatophyteTrichophyton rubrumGeneGenomeMicrobiologyMicrosporum canisGenetics

Abstract

fetched live from OpenAlex

ABSTRACT The major cause of athlete’s foot is Trichophyton rubrum , a dermatophyte or fungal pathogen of human skin. To facilitate molecular analyses of the dermatophytes, we sequenced T. rubrum and four related species, Trichophyton tonsurans , Trichophyton equinum , Microsporum canis , and Microsporum gypseum . These species differ in host range, mating, and disease progression. The dermatophyte genomes are highly colinear yet contain gene family expansions not found in other human-associated fungi. Dermatophyte genomes are enriched for gene families containing the LysM domain, which binds chitin and potentially related carbohydrates. These LysM domains differ in sequence from those in other species in regions of the peptide that could affect substrate binding. The dermatophytes also encode novel sets of fungus-specific kinases with unknown specificity, including nonfunctional pseudokinases, which may inhibit phosphorylation by competing for kinase sites within substrates, acting as allosteric effectors, or acting as scaffolds for signaling. The dermatophytes are also enriched for a large number of enzymes that synthesize secondary metabolites, including dermatophyte-specific genes that could synthesize novel compounds. Finally, dermatophytes are enriched in several classes of proteases that are necessary for fungal growth and nutrient acquisition on keratinized tissues. Despite differences in mating ability, genes involved in mating and meiosis are conserved across species, suggesting the possibility of cryptic mating in species where it has not been previously detected. These genome analyses identify gene families that are important to our understanding of how dermatophytes cause chronic infections, how they interact with epithelial cells, and how they respond to the host immune response. IMPORTANCE Athlete’s foot, jock itch, ringworm, and nail infections are common fungal infections, all caused by fungi known as dermatophytes (fungi that infect skin). This report presents the genome sequences of Trichophyton rubrum , the most frequent cause of athlete’s foot, as well as four other common dermatophytes. Dermatophyte genomes are enriched for four gene classes that may contribute to the ability of these fungi to cause disease. These include (i) proteases secreted to degrade skin; (ii) kinases, including pseudokinases, that are involved in signaling necessary for adapting to skin; (iii) secondary metabolites, compounds that act as toxins or signals in the interactions between fungus and host; and (iv) a class of proteins (LysM) that appear to bind and mask cell wall components and carbohydrates, thus avoiding the host’s immune response to the fungi. These genome sequences provide a strong foundation for future work in understanding how dermatophytes cause disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.018
GPT teacher head0.288
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations231
Published2012
Admission routes1
Has abstractyes

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