Relative Abundances of Species or Sequence Variants Can Be Misleading: Soil Fungal Communities as an Example

The recovery of the microbial community after plaque removal depends on periodontal health status

Plaque accumulation and microbial community changes are important causes of periodontal disease. Cleaned plaque microorganisms will reattach to form biofilms, but the recovery and outcome of plaque microbial communities in different periodontal health states remain unknown. In this study, we tracked the biofilm remodeling process in 206 dental plaque samples from 40 healthy periodontal, gingivitis and periodontitis volunteers at 6 time points before and after supragingival scaling. We found that microbial communities of different periodontal states changed asynchronously during the process, and the more severe the periodontal disease condition, the more lagged the recovery of plaque microorganisms to their original state after cleaning; this reflected a higher degree of plaque development in periodontitis samples. The plaque index and bleeding index were significantly correlated with plaque recovery, especially the recovery of bacteria such as Abiotrophia and Capnocytophaga. Meanwhile, we found that the microbial community structure of different periodontal health states was most similar at the Day 3 after plaque cleaning, and the communities gradually differentiated and developed in different directions. Abiotrophia and other bacteria might play an important role in determining the development trend of plaque biofilms. The discovery of specific time points and bacteria was of great value in clarifying the pathogenesis of periodontal disease and in seeking targets for prevention and treatment.

Composition and Dynamics of Plant- and Soil-Associated Microbial Communities in Forest and Agricultural Ecosystems

Peter Kropotkin (1842-1921) is well known as an anarchist intellectual, an amiable mass of contradictions who loved humanity and was highly regarded in academic and intellectual circles, yet also penned "fiery peans to violence" in Le Révolté, the anarchist journal he established with Elisée Reclus in the 1870s [...].

Are short-read amplicons suitable for the prediction of microbiome functional potential? A critical perspective

Taxonomic marker gene analysis allows uncovering taxonomic profiles of microbial communities at low cost, making it omnipresent in microbiome research. There is an ever-expanding set of tools to extract further biological information from this kind of data. In this perspective, we enunciate several concerns regarding the biological validity of predicting functional potential from taxonomic profiles, especially when they are generated by short-read sequencing. The taxonomic resolution of marker genes, intragenomic variability of marker genes, and the compositional nature of microbiome data are discussed. Combining actual measurements of microbiome functions with predicted functional potentials is proposed as a powerful approach to better understand microbiome functioning. In this context, the significance of predicted functional potentials for generating and testing hypotheses is highlighted. We argue that functions of microbiomes predicted from microbiome DNA read count data generated by short-read amplicon sequencing should not serve as the only basis to draw biological inferences.

High-resolution melting (HRM) curve analysis as a potential tool for the identification of earthworm species and haplotypes

Background

Earthworm communities are an important component of soil biodiversity and contribute to a number of ecosystem functions such as soil-nutrient cycling. Taxonomic identification is an essential requirement to assess earthworm biodiversity and functionality. Although morphological identification of species is labour-intensive, it is the most commonly used method due to a lack of cost-efficient alternatives. Molecular approaches to identify earthworms at species and haplotype level such as DNA barcoding are gaining popularity in science but are rarely applied in practice. In contrast to barcoding, the differentiation of PCR products based on their thermal denaturation properties using high-resolution melting (HRM) curve analysis is a fast and cost-efficient molecular closed-tube, post-PCR tool that allows identification of taxa.

Methods

We developed a HRM curve assay to identify eight earthworm species common to agricultural soils in Central Europe (Allolobophora chlorotica, Aporrectodea caliginosa, Apo. limicola, Apo. longa, Apo. rosea, Lumbricus castaneus, L. rubellus, and L. terrestris). For this, a new primer pair targeting a 158-bp long subregion of the cytochrome c oxidase I (COI) gene was designed. Our HRM assay was further tested for the differentiation of COI haplotypes using 28 individuals of the earthworm species Allo. chlorotica. Furthermore, we developed a novel extraction method for DNA from earthworm tissue that is fast and requires minimal consumables and laboratory equipment.

Results

The developed HRM curve assay allowed identifying all eight earthworm species. Performing the assay on 28 individuals of the earthworm species Allo. chlorotica enabled the distinction among different COI haplotypes. Furthermore, we successfully developed a rapid, robust, scalable, and inexpensive method for the extraction of earthworm DNA from fresh or frozen tissue.

Conclusions

HRM curve analysis of COI genes has the potential to identify earthworm species and haplotypes and could complement morphological identification, especially for juvenile or damaged individuals. Our rapid and inexpensive DNA extraction method from earthworm tissue helps to reduce the costs of molecular analyses and thereby promote their application in practice.

Abundance, Diversity, and Function of Soil Microorganisms in Temperate Alley-Cropping Agroforestry Systems: A Review

Modern temperate alley-cropping systems combine rows of trees with rows of crops (agroforestry), which allows for diverse interspecific interactions such as the complementary and competitive use of resources. The complementary use of resources between trees and crops is considered the main advantage of these multifunctional land use systems over cropland monocultures. Moreover, several studies demonstrated that agroforestry systems are environmentally more sustainable than cropland monocultures. Over two decades of research on soil microorganisms in temperate alley-cropping systems are characterized by a variety of different methodological approaches and study designs to investigate the impact of agroforestry on the soil microbiome. Here, we review the available literature on the abundance, diversity, and functionality of soil microorganisms in temperate alley-cropping systems. Further, we identify current knowledge gaps as well as important experimental factors to consider in future studies. Overall, we found that temperate alley-cropping systems increase soil microbial abundance, diversity, and functions as compared to cropland monocultures, which is expected to contribute to enhanced biological soil fertility in these systems.

‘SRS’ R Package and ‘q2-srs’ QIIME 2 Plugin: Normalization of Microbiome Data Using Scaling with Ranked Subsampling (SRS)

Several ecological data types, especially microbiome count data, are commonly sample-wise normalized before analysis to correct for sampling bias and other technical artifacts. Recently, we developed an algorithm for the normalization of ecological count data called ‘scaling with ranked subsampling (SRS)’, which surpasses the widely adopted ‘rarefying’ (random subsampling without replacement) in reproducibility and in safeguarding the original community structure. Here, we describe an implementation of the SRS algorithm in the ‘SRS’ R package and the ‘q2-srs’ QIIME 2 plugin. We also provide accessory functions for dataset exploration to guide the choice of parameters for SRS.

Early response of soil fungal communities to the conversion of monoculture cropland to a temperate agroforestry system

Background

Alley-cropping systems in the temperate zone are a type of agroforestry in which rows of fast-growing trees are alternated with rows of annual crops. With numerous environmental benefits, temperate agroforestry is considered a promising alternative to conventional agriculture and soil fungi may play a key in maintaining productivity of these systems. Agroforestry systems that are established for more than 10 years have shown to increase the fungal biomass and impact the composition of soil fungal communities. Investigations of soil fungi in younger temperate agroforestry systems are scarce and the temporal dynamic of these changes is not understood.

Methods

Our study was conducted in a young poplar-based alley cropping and adjacent monoculture cropland system in an Arenosol soil in north-west Germany. We investigated the temporal dynamics of fungal populations after the establishment of agroforestry by collecting soil samples half, one, and one and a half years after conversion of cropland to agroforestry. Samples were collected within the agroforestry tree row, at 1, 7, and 24 m distance from the tree row within the crop row, and in an adjacent conventional monoculture cropland. The biomass of soil fungi, Asco-, and Basidiomycota was determined by real-time PCR. Soil fungal community composition and diversity were obtained from amplicon sequencing.

Results

Differences in the community composition of soil fungi in the tree row and arable land were detected as early as half a year following the conversion of monoculture cropland to agroforestry. In the tree row, soil fungal communities in the plots strongly diverged with the age of the system. The presence of young trees did not affect the biomass of soil fungi.

Conclusions

The composition of soil fungal communities responded rapidly to the integration of trees into arable land through agroforestry, whereas the fungal biomass was not affected during the first one and a half years after planting the trees. Fungal communities under the trees gradually diversified. Adaptation to spatially heterogeneous belowground biomass of the trees and understory vegetation or stochastic phenomena due to limited exchange among fungal populations may account for this effect; long-term monitoring might help unravelling the cause.