Assessment of Microbial Communities by Graph Partitioning in a Study of Soil Fungi in Two Alpine Meadows

Abstract

Understanding how microbial community structure and diversity respond to environmental conditions is one of the main challenges in environmental microbiology. However, there is often confusion between determining the phylogenetic structure of microbial communities and assessing the distribution and diversity of molecular operational taxonomic units (MOTUs) in these communities. This has led to the use of sequence analysis tools such as multiple alignments and hierarchical clustering that are not adapted to the analysis of large and diverse data sets and not always justified for characterization of MOTUs. Here, we developed an approach combining a pairwise alignment algorithm and graph partitioning by using MCL (Markov clustering) in order to generate discrete groups for nuclear large-subunit rRNA gene and internal transcript spacer 1 sequence data sets obtained from a yearly monitoring study of two spatially close but ecologically contrasting alpine soils (namely, early and late snowmelt locations). We compared MCL with a classical single-linkage method (Ccomps) and showed that MCL reduced bias such as the chaining effect. Using MCL, we characterized fungal communities in early and late snowmelt locations. We found contrasting distributions of MOTUs in the two soils, suggesting that there is a high level of habitat filtering in the assembly of alpine soil fungal communities. However, few MOTUs were specific to one location.Assessment of the diversity and composition of microbial communities is a central issue in understanding the effects of environmental conditions on microbial community assembly. With this framework, massive cloning and sequencing or pyrosequencing of universal DNA markers (e.g., rRNA genes) allows microbial communities to be characterized in a qualitative and semiquantitative way (37, 50). The composition and diversity of microbial communities based on such methods are assessed by (i) evaluating distances between DNA sequences, (ii) classifying DNA sequences in molecular taxonomic units (MOTUs), and (iii) identifying MOTUs using reference databases (i.e., barcoding approaches) (48). Although numerous studies have focused on subsequent estimation of diversity and richness (24,39,44), only a few studies have cast doubt on the alignment and sequence classification methods (25).Multiple-sequence alignment tools are fairly popular in environmental microbiology, although establishment of phylogenetic links between phylotypes is not necessarily required. DNA markers that are well suited for barcoding approaches sensu lato, as defined by Valentini et al. (48), require sufficient variation in composition and size, making analysis of subsequent data sets with multiple-sequence alignments difficult. Indeed, based on heuristics, these algorithms excessively penalize insertion-deletion events (32). Pairwise-sequence alignments obtained using exact algorithms, such as NWS (35), avoid this limitation and have already been used in bacterial (25) and fungal community studies (37, 39). However a large proportio...

Assessment of soil fungal diversity in different alpine tundra habitats by means of pyrosequencing

“…Here, MOTUs were constructed using an ab initio approach based on a pairwise alignment of pyrosequencing reads and MOTUs construction using MCL, which has been deemed more appropriate for pyrosequencing datasets of highly size polymorphic DNA fragments (Zinger et al 2009a). The approach was used to investigate the feasibility of estimating global diversity in the studied area, assessing the effect of environmental conditions on fungal communities, and gave hints of a potential fungal "rare biosphere".…”

Contrasting microbial biogeographical patterns between anthropogenic subalpine grasslands and natural alpine grasslands

“…Reads were then dereplicated, and the singletons or unique sequences displaying ambiguous bases were removed. Unique sequences were then clustered into MOTUs, as previously reported (Zinger et al, 2009). Briefly, sequences were aligned pairwise with the 'sumatra' package (http://metabarcoding.org/sumatra).…”

ABGD, Automatic Barcode Gap Discovery for primary species delimitation

“…Other methods based on Markov Chain Clustering (e.g. Zinger et al. 2009) are also used to build groups (named OTU, for Operational Taxonomic Units) but not specifically designed to delimit species, although Molecular Operational Taxonomic Units (MOTUs; Floyd et al.…”