Zewei Song

Signature Wood Modifications Reveal Decomposer Community History

Correlating plant litter decay rates with initial tissue traits (e.g. C, N contents) is common practice, but in woody litter, predictive relationships are often weak. Variability in predicting wood decomposition is partially due to territorial competition among fungal decomposers that, in turn, have a range of nutritional strategies (rot types) and consequences on residues. Given this biotic influence, researchers are increasingly using culture-independent tools in an attempt to link variability more directly to decomposer groups. Our goal was to complement these tools by using certain wood modifications as ‘signatures’ that provide more functional information about decomposer dominance than density loss. Specifically, we used dilute alkali solubility (DAS; higher for brown rot) and lignin:density loss (L:D; higher for white rot) to infer rot type (binary) and fungal nutritional mode (gradient), respectively. We first determined strength of pattern among 29 fungi of known rot type by correlating DAS and L:D with mass loss in birch and pine. Having shown robust relationships for both techniques above a density loss threshold, we then demonstrated and resolved two issues relevant to species consortia and field trials, 1) spatial patchiness creating gravimetric bias (density bias), and 2) brown rot imprints prior or subsequent to white rot replacement (legacy effects). Finally, we field-tested our methods in a New Zealand Pinus radiata plantation in a paired-plot comparison. Overall, results validate these low-cost techniques that measure the collective histories of decomposer dominance in wood. The L:D measure also showed clear potential in classifying ‘rot type’ along a spectrum rather than as a traditional binary type (brown versus white rot), as it places the nutritional strategies of wood-degrading fungi on a scale (L:D=0-5, in this case). These information-rich measures of consequence can provide insight into their biological causes, strengthening the links between traits, structure, and function during wood decomposition.

Fungal Endophytes as Priority Colonizers Initiating Wood Decomposition

This repository contains the raw sequencing data for this study. The compressed file (Fungal_ITS1_MiSeq.zip) contains 82 illumia MiSeq 25 bp Read 1 FASTQ files. These files were used to generate the fungal community structure and function data in our paper, following the pipeline in the supplement File S1. The metadata of these samples were saved in the Excel file: Sample_metadata.csv

Competition between two wood-degrading fungi with distinct influences on residues

Many wood-degrading fungi colonize specific types of forest trees, but often lack wood specificity in pure culture. This suggests that wood type affects competition among fungi and indirectly influences the soil residues generated. While assessing wood residues is an established science, linking this information to dominant fungal colonizers has proven to be difficult. In the studies presented here, we used isolate-specific quantitative PCR to quantify competitive success between two distinct fungi, Gloeophyllum trabeum and Irpex lacteus, brown and white rot fungi, respectively, colonizing three wood types (birch, pine, oak). Ergosterol (fungal biomass), fungal species-specific DNA copy numbers, mass loss, pH, carbon fractions, and alkali solubility were determined 3 and 8 weeks postinoculation from replicate wood sections. Quantitative PCR analyses indicated that I. lacteus consistently outcompeted G. trabeum, by several orders of magnitude, on all wood types. Consequently, wood residues exhibited distinct characteristics of white rot. Our results show that competitive interactions between fungal species can influence colonization success, and that this can have significant consequences on the outcomes of wood decomposition.

Effort Versus Reward: Preparing samples for fungal community characterization in high-throughput sequencing surveys of soils

Next generation fungal amplicon sequencing is being used with increasing frequency to study fungal diversity in various ecosystems; however, the influence of sample preparation on the characterization of fungal community is poorly understood. We investigated the effects of four procedural modifications to library preparation for high-throughput sequencing (HTS). The following treatments were considered: 1) the amount of soil used in DNA extraction, 2) the inclusion of additional steps (freeze/thaw cycles, sonication, or hot water bath incubation) in the extraction procedure, 3) the amount of DNA template used in PCR, and 4) the effect of sample pooling, either physically or computationally. Soils from two different ecosystems in Minnesota, USA, one prairie and one forest site, were used to assess the generality of our results. The first three treatments did not significantly influence observed fungal OTU richness or community structure at either site. Physical pooling captured more OTU richness compared to individual samples, but total OTU richness at each site was highest when individual samples were computationally combined. We conclude that standard extraction kit protocols are well optimized for fungal HTS surveys, but because sample pooling can significantly influence OTU richness estimates, it is important to carefully consider the study aims when planning sampling procedures.

Moving beyond de novo clustering in fungal community ecology

High throughput sequencing (HTS) has rapidly become the de facto tool for characterizing microbial community structure in a wide variety of habitats (Caporaso et al., 2011; Peay et al., 2016; Truong et al., 2017). Accompanying the expanding use of HTS to quantify microbial diversity is the need to delineate species, the ecological unit traditionally used to compare the richness and composition of communities across treatments, locations or habitats (Magurran, 2005). Due to the challenges in identifying microbial species using morphology or biology alone, designations are typically made by ‘binning’ DNA sequences that meet a similarity threshold into operational taxonomic units (OTUs; Blaxter et al., 2005). Currently, the most widely employed approach for defining fungal OTUs is done according to similarities among sequences within the dataset (Supporting Information Fig. S1). Commonly referred to as de novo clustering (Bik et al., 2012), this approach requires no input database as a reference, which is advantageous when characterizing communities with little a priori knowledge. Despite this benefit, the ecological insights gleaned from de novo clustering can be limited by the challenge of directly comparing OTU identity across different studies (€ Opik et al., 2014), and the coarse phylogenetic resolution of many taxonomic assignments (Halwachs et al., 2017). One alternative to de novo clustering is the closed reference approach, where OTUs are binned according to sequence similarity of those in a reference database. With this approach, both OTU clustering and taxonomic designations occur simultaneously. Although the use of closed reference clustering in fungal ecology has been scarce (Fig. S1), it has become increasingly common in the molecular characterization of arbuscular mycorrhizal (AM) fungal communities as well as in many bacterial ‘microbiome’ studies (€ Opik et al., 2014; Kelly et al., 2016). The relatively low taxonomic and phylogenetic diversity of AM fungal communities (Stajich et al., 2009; Redecker et al., 2013; Davison et al., 2015), combined with a curated database (€ Opik et al., 2010) and increasingly wide usage of the 18S rRNA gene for molecular characterization ( € Opik et al., 2014), may explain why AM fungal community ecologists (relative to other fungal ecologists) have readily embraced closed reference clustering. Notably, the closed reference clustering approach has contributed significant new ecological understanding to patterns of AM community assembly by tracking OTUs (referred to as VT, € Opik et al., 2010) across studies with both contrasting habitat types and a wide variety of spatial scales (Davison et al., 2015; Garc ıa de Le on et al., 2016). A second alternative to de novo clustering is an open reference approach, which first clusters sequences to a reference database, followed by de novo clustering of the remaining unmatched sequences. This hybrid approach can combine the advantages of the two aforementioned clustering approaches (Rideout et al., 2014; He et al., 2015), but its interpretation can be problematic if the OTU definitions between closed reference and de novo approaches differ. Although open reference clustering is the least commonly used in fungal community ecology analyses to date (Fig. S1), it has been employed in studies of both arbuscular mycorrhizal and ectomycorrhizal fungal communities (Dumbrell et al., 2010; Jarvis et al., 2015). The increasingly widespread adoption of reference-based clustering inmanymicrobial analyses raises the question: should fungal ecologists re-consider their default use of de novo clustering? In particular, it seems that reference-based clusteringmay represent an increasingly useful approach to fungal community analyses as databases such as UNITE (K~oljalg et al., 2013) grow in size and a greater diversity of fungal habitats are molecularly characterized. Recent studies have suggested that reference-based clustering can increase OTU stability and taxonomic accuracy relative to de novo clustering (He et al., 2015; Halwachs et al., 2017), although how this clustering approach influences fungal community analyses across diverse habitats is currently unclear. To assess this gap in knowledge, we compared the relative performance of de novo, closed reference, and open reference clustering approaches on a mock community, as well as samples from four ecologically distinct habitats. These habitats varied in the degree to which fungal composition was captured by the UNITE database, providing an opportunity to investigate the importance of a priori habitat characterization on clustering approach performance. Using dead wood, live wood, live leaf and forest soil samples, we quantified fungal species assignments, OTU richness and community composition from ITS1 amplicon libraries sequenced on the IlluminaMiSeq platform.We compared two de novo clustering algorithms (CD-HIT and USEARCH; Li & Godzik, 2006; Edgar, 2010), two closed reference clustering algorithms (BLAST and NINJA-OPS; Altschul et al., 1990; Al-Ghalith et al., 2016), as well as two open reference clustering scenarios (NINJA/USEARCH; BLAST/ CD-HIT) applying a 97% sequence similarity cutoff for OTU clustering aswell as taxonomy assignments (Table S1). For the open reference clustering, sequences were first clustered by a closed reference algorithm (i.e. NINJA or BLAST); the remaining sequences that failed to cluster were then clustered by a de novo clustering approach (i.e. USEARCH or CD-HIT), and the OTU tables were combined (sensu Rideout et al., 2014). The UNITE database (v.7.0) was used for reference-based clustering as well as for designating

Probing promise versus performance in longer read fungal metabarcoding

In the rapidly evolving world of methodologies to study fungi and other microorganisms, there has been growing interest in the adoption of so-called ‘third-generation’ technologies for high throughput amplicon sequencing (also known as metabarcoding). This interest is largely based on the capacity for longer sequence read lengths (> 500 bp), which have the potential to provide more accurate phylogenetic inference than current ‘second generation’ technologies (James et al., 2016; Schloss et al., 2016; Singer et al., 2016). Despite this attraction, higher error rates and the high cost per base pair have inhibited the widespread adoption of this ‘next’ in microbial metabarcoding. Given the challenges in adopting any new technology, careful benchmarking tests are needed to determine whether significantly greater insights can be gleaned, or if currently establishedmethods remain sufficient. In this issue of New Phytologist, Tedersoo et al. (2018; pp. 1370–1385) conduct the first of these benchmarking tests for fungi and other eukaryotes, directly comparing Illumina MiSeq (i.e. second generation) and PacBio datasets (i.e. third generation) generated from the same soil samples and analyzed for taxonomic richness and composition. While their collective analyses indicate that longer amplicons can be successfully generated with relatively low error rates, they also demonstrate many technical issues with PacBio-based data, which require careful attention.

Correction: Signature wood modifications reveal decomposer community history

There are errors in the third column of Table 1 that were introduced during the typesetting process. Many of the rows in the third column incorrectly list Antrodia vaillantii instead of the many different Genus species that should be listed. Please view the correct Table 1 below. The publisher apologizes for the errors. open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.