DOI: 10.5061/dryad.pvmcvdnkm
title: Culture performance, gene marker, and transcriptome data for fungal isolates (Chalara longipes, Laccaria bicolor, Serpula lacrymans, and Trichoderma harzianum)

description: Metatranscriptomics holds the prospect of predicting fungal phenotypes
 based on patterns of gene expressions, providing new opportunities to
 obtain information about metabolic processes without disturbance of
 natural systems, and with taxonomic resolution. Acquisition of fungal
 metabolic carbon and its subsequent partitioning between biomass
 production and respiration, i.e. the carbon-use efficiency, are central
 parameters in biogeochemical modelling. However, current available
 techniques for estimating these parameters in natural systems are all
 associated with practical and theoretical shortcomings, making assessments
 unreliable. 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 carbon-use efficiency. By relating expression of
 gene markers to measured carbon fluxes, we identified genes coding for
 1,3-β-glucan synthase and 2-oxoglutarate dehydrogenase as good markers for
 growth and respiration, respectively, capturing both intraspecific
 variation as well as within-strain variation dependent on growth medium. A
 gene expression index based on these markers correlated significantly with
 differences in carbon-use efficiency between the fungal isolates. Our
 study paves the way for use of these markers to assess differences in
 growth, respiration, and carbon-use efficiency in natural fungal
 communities, using metatranscriptomic or RT-qPCR approach.
description: <b><i>Culture
 performance</i></b> Glucose concentration in the
 medium was measured at regular intervals using a GM-100 glucose monitoring
 system (BioReactor Sciences, Lawrenceville, GA, USA). The systems were
 harvested when roughly 30% of the glucose in the medium had been consumed
 (Figure S1; Table 1). Immediately before harvest, respiration rates were
 measured using an EGM-4 portable infra-red gas analyser (PP Systems,
 Amesbury, MA, USA) with a closed sampling loop that incorporated the
 fungal culture bottles via the sterile filters. Prior to measurement, the
 systems were flushed with filtered air for 1 hour to allow dissolved
 CO<sub>2</sub> to equilibrate with the atmosphere.
 CO<sub>2</sub> accumulation was measured during 2.5 minutes
 for each system. <b><i>RNA extraction,
 sequencing, and bioinformatic analyses</i></b> Mycelium was harvested by
 filtration through Whatman™ filter paper; ø 55m, pore size 12 mm
 (ThermoFisher Scientific, Waltham, MA, USA) and immediately shock-frozen
 with liquid nitrogen, freeze dried, weighted using an ES120A analytical
 balance (Precisa Gravimetrics, Dietikon, Switzerland) and stored at -80°C
 for mRNA extraction. Total RNA was extracted from the harvested mycelium
 using the RNA mini kit (Qiagen, Hilden, Germany) and cleaned from
 remaining DNA by the DNAse I kit (Sigma-Aldrich®, St. Louis, MO, USA).
 Poly-A selection and mRNA library preparation was conducted using the
 TruSeq library preparation kit (Illumina, San Diego, CA, USA). Libraries
 were sequenced on the Illumina NovaSeq 6000 SP platform, yielding 50 bp
 paired-end sequences. Poly-A selection of the mRNA, library preparation,
 and sequencing were performed by the SNP&SEQ Technology Platform
 of SciLifeLab, Uppsala, Sweden. Raw paired-end reads were
 subjected to quality control using FastQC (Andrews <i>et
 al.</i> 2010). Sequencing adapter trimming and removal of low
 quality bases were performed in the program ‘Trimmomatic’ (Bolger, Lohse
 and Usadel 2014) using the default settings. Reference genomes and gene
 annotations of <i>T. harzianum </i>(Druzhinina <i>et
 al.</i> 2018), <i>C. longipes </i>(Barbi <i>et
 al.</i> 2020), <i>L. bicolor </i>(Martin <i>et
 al.</i> 2008), and <i>S. lacrymans</i> (Eastwood
 <i>et al.</i> 2011) were retrieved from the JGI—Mycocosm
 database (https://mycocosm.jgi.doe.gov/mycocosm/home). Filtered mRNA
 sequences were mapped against respective genomes using ‘bowtie2’ (Langmead
 and Salzberg 2012). Data was sorted, indexed, and converted to gene
 expression count tables using ‘SAMtools’ (Li <i>et al.</i>
 2009; Li 2011).  Gene expression data were normalized for gene lengths and
 sequencing effort according to the RPKM method (reads per kilo base per
 million mapped reads) (Mortazavi <i>et al.</i> 2008). To
 enable analysis of expression of enzyme-encoding genes across fungal
 isolates, data was aggregated according to Enzyme Commission (EC) numbers,
 which denote a numerical classification of enzymes (Kanehisa
 2017).
description: This dataset consists of 2 files containing information about
 culture performance (carbon uptake, growth, respiration etc.), expression
 of gene markers used (RPKM normalised), and transcriptome (RPKM normalised
 and filtered). Missing values are indicated by 'NA'.