Fungi are important members of soil microbial communities with a crucial role in biogeochemical processes. Although soil fungi are known to be highly diverse, little is known about factors influencing variations in their diversity and community structure among forests dominated by the same tree species but spread over different regions and under different managements. We analyzed the soil fungal diversity and community composition of managed and unmanaged European beech dominated forests located in three German regions, the Schwäbische Alb in Southwestern, the Hainich-Dün in Central and the Schorfheide Chorin in the Northeastern Germany, using internal transcribed spacer (ITS) rDNA pyrotag sequencing. Multiple sequence quality filtering followed by sequence data normalization revealed 1655 fungal operational taxonomic units. Further analysis based on 722 abundant fungal OTUs revealed the phylum Basidiomycota to be dominant (54%) and its community to comprise 71.4% of ectomycorrhizal taxa. Fungal community structure differed significantly (p≤0.001) among the three regions and was characterized by non-random fungal OTUs co-occurrence. Soil parameters, herbaceous understory vegetation, and litter cover affected fungal community structure. However, within each study region we found no difference in fungal community structure between management types. Our results also showed region specific significant correlation patterns between the dominant ectomycorrhizal fungal genera. This suggests that soil fungal communities are region-specific but nevertheless composed of functionally diverse and complementary taxa.

Background Amplicon sequencing of phylogenetic marker genes, e.g., 16S, 18S, or ITS ribosomal RNA sequences, is still the most commonly used method to determine the composition of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis in general, and most research institutes have computational infrastructures to use the bioinformatics command line tools and workflows for amplicon sequencing analysis, but requirements of bioinformatics skills often limit the efficient and up-to-date use of computational resources. Results We present dadasnake, a user-friendly, 1-command Snakemake pipeline that wraps the preprocessing of sequencing reads and the delineation of exact sequence variants by using the favorably benchmarked and widely used DADA2 algorithm with a taxonomic classification and the post-processing of the resultant tables, including hand-off in standard formats. The suitability of the provided default configurations is demonstrated using mock community data from bacteria and archaea, as well as fungi. Conclusions By use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. It is easy to install dadasnake via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake.

Climate and agricultural practice interact to influence both crop production and soil microbes in agroecosystems. Here, we carried out a unique experiment in Central Germany to simultaneously investigate the effects of climates (ambient climate vs. future climate expected in 50-70 years), agricultural practices (conventional vs. organic farming), and their interaction on arbuscular mycorrhizal fungi (AMF) inside wheat (Triticum aestivum L.) roots. AMF communities were characterized using Illumina sequencing of 18S rRNA gene amplicons. We showed that climatic conditions and agricultural practices significantly altered total AMF community composition. Conventional farming significantly affected the AMF community and caused a decline in AMF richness. Factors shaping AMF community composition and richness at family level differed greatly among Glomeraceae, Gigasporaceae and Diversisporaceae. An interactive impact of climate and agricultural practices was detected in the community composition of Diversisporaceae. Organic farming mitigated the negative effect of future climate and promoted total AMF and Gigasporaceae richness. AMF richness was significantly linked with nutrient content of wheat grains under both agricultural practices.

The increasing frequency of extreme weather events highlights the need to understand how soil microbiomes respond to such disturbances. Here, metagenomics was used to investigate the effects of future climate scenarios (+0.6 °C warming and altered precipitation) on soil microbiomes during the summers of 2014–2019. Unexpectedly, Central Europe experienced extreme heatwaves and droughts during 2018–2019, causing significant impacts on the structure, assembly, and function of soil microbiomes. Specifically, the relative abundance of Actinobacteria (bacteria), Eurotiales (fungi), and Vilmaviridae (viruses) was significantly increased in both cropland and grassland. The contribution of homogeneous selection to bacterial community assembly increased significantly from 40.0% in normal summers to 51.9% in extreme summers. Moreover, genes associated with microbial antioxidant (Ni-SOD), cell wall biosynthesis (glmSMU, murABCDEF), heat shock proteins (GroES/GroEL, Hsp40), and sporulation (spoIID, spoVK) were identified as potential contributors to drought-enriched taxa, and their expressions were confirmed by metatranscriptomics in 2022. The impact of extreme summers was further evident in the taxonomic profiles of 721 recovered metagenome-assembled genomes (MAGs). Annotation of contigs and MAGs suggested that Actinobacteria may have a competitive advantage in extreme summers due to the biosynthesis of geosmin and 2-methylisoborneol. Future climate scenarios caused a similar pattern of changes in microbial communities as extreme summers, but to a much lesser extent. Soil microbiomes in grassland showed greater resilience to climate change than those in cropland. Overall, this study provides a comprehensive framework for understanding the response of soil microbiomes to extreme summers.

Background: Amplicon sequencing of phylogenetic marker genes, e.g. 16S, 18S or ITS rRNA sequences, is still the most commonly used method to estimate the structure of microbial communities. Microbial ecologists often have expert knowledge on their biological question and data analysis in general, and most research institutes have computational infrastructures to employ the bioinformatics command line tools and workflows for amplicon sequencing analysis, but requirements of bioinformatics skills often limit the efficient and up-to-date use of computational resources. Results: dadasnake wraps pre-processing of sequencing reads, delineation of exact sequencing variants using the favorably benchmarked, widely-used the DADA2 algorithm, taxonomic classification and post-processing of the resultant tables, and hand-off in standard formats, into a userfriendly, one-command Snakemake pipeline. The suitability of the provided default configurations is demonstrated using mock-community data from bacteria and archaea, as well as fungi. Conclusions: By use of Snakemake, dadasnake makes efficient use of high-performance computing infrastructures. Easy user configuration guarantees flexibility of all steps, including the processing of data from multiple sequencing platforms. dadasnake facilitates easy installation via conda environments. dadasnake is available at https://github.com/a-h-b/dadasnake . Findings BackgroundSince the first reports 15 years ago [1], high-throughput amplicon sequencing has become the most common approach to monitor microbial diversity in environmental samples. Sequencing preparation, throughput and precision have been consistently improved, while costs have decreased. Computational methods have been refined in the recent years, especially with the shift to exact sequencing variants and better use of sequence quality data [2,3]. While amplicon sequencing can have severe limitations, such as limited and uneven taxonomic resolution [4,5], over-and underestimation of diversity [6,7], lack of quantitative value [8,9] and missing functional information, amplicon sequencing is still considered the method of choice to gain an overview of microbial diversity in a large number of samples [10,11]. Consequently, the sizes of typical amplicon sequencing datasets have grown. In addition, synthesis efforts are undertaken, requiring efficient processing pipelines for amplicon sequencing data [12]. Due to the unique, microbiome-specific characteristics of each dataset and the need to integrate the community structure data with other data types, such as abiotic or biotic parameters, users of data processing tools need to have expert knowledge on their biological question and statistics. It is therefore desirable that workflows should be as user-friendly as possible. Several widely used workflows exist e.g. qiime2 [13], mothur [14], usearch [15], lOTUs [16], with new approaches continually being developed, e.g. OCToPUS [17], PEMA [18], typically balancing learning curves, configurability and efficiency.Purpose of dadasna...