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Growth on nitrate and occurrence of nitrate reductase‐encoding genes in a phylogenetically diverse range of ectomycorrhizal fungi

2008· article· en· W2143459355 on OpenAlexaff
Cajsa M. R. Nygren, Ursula Eberhardt, Magnus Karlsson, Jeri Lynn Parrent, Björn D. Lindahl, Andy F. S. Taylor

Bibliographic record

VenueNew Phytologist · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of Guelph
FundersVetenskapsrådetSvenska Forskningsrådet Formas
KeywordsBiologyBotanyNitrate reductaseNitrateEctomycorrhizaMycorrhizaSymbiosisEcologyBacteria

Abstract

fetched live from OpenAlex

Ectomycorrhizal (ECM) fungi are often considered to be most prevalent under conditions where organic sources of N predominate. However, ECM fungi are increasingly exposed to nitrate from anthropogenic sources. Currently, the ability of ECM fungi to metabolize this nitrate is poorly understood. Here, growth was examined among 106 isolates, representing 68 species, of ECM fungi on nitrate as the sole N source. In addition, the occurrence of genes coding for the nitrate reductase enzyme (nar gene) in a broad range of ectomycorrhizal fungi was investigated. All isolates grew on nitrate, but there was a strong taxonomic signature in the biomass production, with the Russulaceae and Amanita showing the lowest growth. Thirty-five partial nar sequences were obtained from 43 tested strains comprising 31 species and 10 genera. These taxa represent three out of the four clades of the Agaricales within which ECM fungi occur. No nar sequences were recovered from the Russulaceae and Amanita, but Southern hybridization showed that the genes were present. The results demonstrate that the ability to utilize nitrate as an N source is widespread in ECM fungi, even in those fungi from boreal forests where the supply of nitrate may be very low.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.539

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 head0.033
GPT teacher head0.233
Teacher spread0.200 · 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 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

Citations107
Published2008
Admission routes1
Has abstractyes

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