Transcriptomic markers of fungal growth, respiration and carbon-use efficiency

0559317 - MBÚ 2023 RIV NL eng J - Journal Article
Hasby, F. A. - Barbi, Florian - Manzoni, S. - Lindahl, B. D.
Transcriptomic markers of fungal growth, respiration and carbon-use efficiency.
FEMS Microbiology Letters. Roč. 368, č. 15 (2021), č. článku fnab100. ISSN 0378-1097. E-ISSN 1574-6968
Institutional support: RVO:61388971
Keywords : growth * respiration * carbon-use efficiency * metatranscriptomics * gene markers * fungi
OECD category: Microbiology
Impact factor: 2.820, year: 2021
Method of publishing: Open access
https://academic.oup.com/femsle/article/368/15/fnab100/6335482?login=true

Fungal metabolic carbon acquisition and its subsequent partitioning between biomass production and respiration, i.e. the carbon-use efficiency (CUE), are central parameters in biogeochemical modeling. However, current available techniques for estimating these parameters are all associated with practical and theoretical shortcomings, making assessments unreliable. Gene expression analyses hold the prospect of phenotype prediction by indirect means, providing new opportunities to obtain information about metabolic priorities. We cultured four different fungal isolates (Chalara longipes, Laccaria bicolor, Serpula lacrymans and Trichoderma harzianum) in liquid media with contrasting nitrogen availability and measured growth rates and respiration to calculate CUE. By relating gene expression markers to measured carbon fluxes, we identified genes coding for 1,3-beta-glucan synthase and 2-oxoglutarate dehydrogenase as suitable markers for growth and respiration, respectively, capturing both intraspecific variation as well as within-strain variation dependent on growth medium. A transcript index based on these markers correlated significantly with differences in CUE between the fungal isolates. Our study paves the way for the use of these markers to assess differences in growth, respiration and CUE in natural fungal communities, using metatranscriptomic or the RT-qPCR approach.
Permanent Link: https://hdl.handle.net/11104/0332644