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Record W3210283185 · doi:10.3390/d13110529

Transcriptome Comparison of Secondary Metabolite Biosynthesis Genes Expressed in Cultured and Lichenized Conditions of Cladonia rangiferina

2021· article· en· W3210283185 on OpenAlexaff
Natalia Sveshnikova, Michele D. Piercey‐Normore

Bibliographic record

VenueDiversity · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSecondary metabolitePolyketideTranscriptomeGeneBiologyGene clusterLichenMetaboliteGenomeMetabolomicsBotanyBiosynthesisGeneticsBiochemistryGene expressionBioinformatics

Abstract

fetched live from OpenAlex

Lichen secondary metabolites are natural products of high medicinal and industrial value, which are produced by the fungal symbiont (mycobiont) of lichens in response to environmental changes. It has been shown that the cultured mycobiont is capable of secondary metabolite production, specifically polyketides, and polyketide production is affected by the presence or absence of the algal or cyanobacterial symbiont (photobiont). Identification of polyketide synthases encoding genes is, in turn, key for understanding the regulation of secondary metabolite synthesis. Using a previously established method of resynthesis for Cladonia rangiferina as well as the sequenced and assembled genome of that species, we compared transcriptomes of C. rangiferina cultured alone and resynthesized with the photobiont (Asterochloris glomerata) to reveal transcriptionally active genes in secondary metabolic gene clusters, as well some of the neighbouring genes, induced by the presence of the photobiont and events of lichenization. The results identify potential candidates for PKS genes in C. rangiferina, identify potential neighbouring genes in the PKS cluster, and offer insights into further research. The study provides preliminary insights into the activity of several identified biosynthetic gene clusters (BGC) as well as interactions of genes within those clusters.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.025
GPT teacher head0.237
Teacher spread0.212 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2021
Admission routes1
Has abstractyes

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