NEW YORK (GenomeWeb) – The rarest bacterial representatives in soil microbiomes may provide pronounced clues about the structure and soil specificity of those microbial communities, according to members of an international team led by investigators at the Netherlands Institute of Ecology and the University of Manchester.
The researchers brought together dozens of bacterial sequence datasets, representing almost 2,000 soil samples from sites in 21 countries. Their machine learning analysis of the independent datasets highlighted very common bacterial taxa — those present in many or most of the soils sampled from a broad range of environments — as well as rare, community structure-related bacterial taxa that appeared to be more soil- and/or site-specific. Their results appeared online today in Nature Microbiology.
"We find that rarer taxa are more important for structuring soil communities than abundant taxa, and that these rare taxa are better predictors of community structure than environmental factors, which are often confounded across studies," senior author Franciska de Vries, an earth and environmental sciences researcher at the University of Manchester, and her colleagues wrote.
So far, the team noted, it has been less successful in bringing together high-throughput sequence data for fungi found in the soil samples.
Although large amounts of data are needed to get a grip on the diversity and complexity of soil microbial communities, the team noted, it is often difficult to bring together microbial sequence profiles from samples assessed for different studies.
"Until now, attempts to meta-analyze sequence data have been limited to assessing diversity measures or abundances of major taxa, because the merging of community data is constrained by methodological differences between sequencing studies," the authors wrote.
For their new analysis, the researchers compiled metadata from 30 prior studies that used 16S ribosomal RNA sequencing to assess microbial community members in soil. They estimated that some 8,287 bacterial taxa were present in the 1,998 samples collected for those studies.
In addition to dealing with microbiome variation related to technical factors, the team used its machine-learning method to explore soil microbial community structures, including the proportions of bacterial taxa within communities and taxon-by-taxon contributions to the overall structure of the soil microbiomes considered.
Consistent with past studies, for example, the researchers saw declining bacterial diversity in the deeper soil samples. When it came to bacterial taxa, meanwhile, their results suggest that "low-abundance taxa are disproportionately important in the non-random structure of communities, where the most important taxa are rarer than expected compared with the randomly permuted data."
Such findings hint that it may be useful to tap rare taxa sets when searching for bugs that might serve as ecological indicators or bacterial markers specifying certain soil types or sites.
"Some bacteria are common, but how many turn up in any particular soil has more to do with the details of how they were measured than any real differences among soils," co-author Christopher Knight, a senior lecturer at the University of Manchester, said in a statement. "Some are so rare that you only ever see them in a handful of soils of any sort, which doesn't say much," he explained. "But in between there are informative families of bacteria that indicate real differences among types of soil."