Unraveling spatiotemporal variability of arbuscular mycorrhizal fungi in a temperate grassland plot

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldmann, K., Boeddinghaus, R. S., Klemmer, S., Regan, K. M., Heintz-Buschart, A., Fischer, M., Prati, D., Piepho, H., Berner, D., Marhan, S., Kandeler, E., Buscot, F., & Wubet, T. Unraveling spatiotemporal variability of arbuscular mycorrhizal fungi in a temperate grassland plot. Environmental Microbiology, 22(3),(2020): 873-888, doi:10.1111/1462-2920.14653.Soils provide a heterogeneous environment varying in space and time; consequently, the biodiversity of soil microorganisms also differs spatially and temporally. For soil microbes tightly associated with plant roots, such as arbuscular mycorrhizal fungi (AMF), the diversity of plant partners and seasonal variability in trophic exchanges between the symbionts introduce additional heterogeneity. To clarify the impact of such heterogeneity, we investigated spatiotemporal variation in AMF diversity on a plot scale (10 × 10 m) in a grassland managed at low intensity in southwest Germany. AMF diversity was determined using 18S rDNA pyrosequencing analysis of 360 soil samples taken at six time points within a year. We observed high AMF alpha‐ and beta‐diversity across the plot and at all investigated time points. Relationships were detected between spatiotemporal variation in AMF OTU richness and plant species richness, root biomass, minimal changes in soil texture and pH. The plot was characterized by high AMF turnover rates with a positive spatiotemporal relationship for AMF beta‐diversity. However, environmental variables explained only ≈20% of the variation in AMF communities. This indicates that the observed spatiotemporal richness and community variability of AMF was largely independent of the abiotic environment, but related to plant properties and the cooccurring microbiome.We thank the managers of the three Exploratories, Kirsten Reichel‐Jung, Swen Renner, Katrin Hartwich, Sonja Gockel, Kerstin Wiesner, and Martin Gorke for their work in maintaining the plot and project infrastructure; Christiane Fischer and Simone Pfeiffer for giving support through the central office, Michael Owonibi and Andreas Ostrowski for managing the central data base, and Eduard Linsenmair, Dominik Hessenmöller, Jens Nieschulze, Ernst‐Detlef Schulze, Wolfgang W. Weisser and the late Elisabeth Kalko for their role in setting up the Biodiversity Exploratories project. The work has been funded by the DFG Priority Program 1374 ‘Infrastructure‐Biodiversity‐Exploratories’ (BU 941/22‐1, BU 941/22‐3, KA 1590/8‐2, KA 1590/8‐3). Field work permits were issued by the responsible state environmental office of Baden‐Württemberg (according to § 72 BbgNatSchG). Likewise, we kindly thank Beatrix Schnabel, Melanie Günther and Sigrid Härtling for 454 sequencing in Halle. AHB gratefully acknowledges the support of the German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig funded by the German Research Foundation (FZT 118). Authors declare no conflict of interests

The archives are half-empty : an assessment of the availability of microbial community sequencing data

As DNA sequencing has become more popular, the public genetic repositories where sequences are archived have experienced explosive growth. These repositories now hold invaluable collections of sequences, e.g., for microbial ecology, but whether these data are reusable has not been evaluated. We assessed the availability and state of 16S rRNA gene amplicon sequences archived in public genetic repositories (SRA, EBI, and DDJ). We screened 26,927 publications in 17 microbiology journals, identifying 2015 16S rRNA gene sequencing studies. Of these, 7.2% had not made their data public at the time of analysis. Among a subset of 635 studies sequencing the same gene region, 40.3% contained data which was not available or not reusable, and an additional 25.5% contained faults in data formatting or data labeling, creating obstacles for data reuse. Our study reveals gaps in data availability, identifies major contributors to data loss, and offers suggestions for improving data archiving practices

Amplicon Sequencing-Based Bipartite Network Analysis Confirms a High Degree of Specialization and Modularity for Fungi and Prokaryotes in Deadwood

Fungi and prokaryotes are dominant colonizers of wood and mediate its decomposition. Much progress has been achieved to unravel these communities and link them to specific wood properties. However, comparative studies considering both groups of organisms and assessing their relationships to wood resources are largely missing. Bipartite interaction networks provide an opportunity to investigate this colonizer-resource relationship more in detail and aim to directly compare results between different biotic groups. The main questions were as follows. Are network structures reflecting the trophic relationship between fungal and prokaryotic colonizers and their resources? If so, do they reflect the critical role of these groups, especially that of fungi, during decomposition? We used amplicon sequencing data to analyze fungal and prokaryotic interaction networks from deadwood of 13 temperate tree species at an early to middle stage of decomposition. Several diversity- and specialization-related indices were determined and the observed network structures were related to intrinsic wood traits. We hypothesized nonrandom bipartite networks for both groups and a higher degree of specialization for fungi, as they are the key players in wood decomposition. The results reveal highly modular and specialized interaction networks for both groups of organisms, demonstrating that many fungi and prokaryotes are resource-specific colonizers. However, as the level of specialization of fungi significantly surpassed that of prokaryotes, our findings reflect the strong association between fungi and their host. Our novel approach shows that the application of bipartite interaction networks is a useful tool to explore, quantify, and compare the deadwood-colonizers relationship based on sequencing data

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly used and state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete (⁠>95% pure, >90% complete) and high-quality (⁠>90% pure, >70% complete) genomes from simulated datasets from the Critical Assessment of Metagenome Interpretation initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method

Sample Preservation and Storage Significantly Impact Taxonomic and Functional Profiles in Metaproteomics Studies of the Human Gut Microbiome

With the technological advances of the last decade, it is now feasible to analyze microbiome samples, such as human stool specimens, using multi-omic techniques. Given the inherent sample complexity, there exists a need for sample methods which preserve as much information as possible about the biological system at the time of sampling. Here, we analyzed human stool samples preserved and stored using different methods, applying metagenomics as well as metaproteomics. Our results demonstrate that sample preservation and storage have a significant effect on the taxonomic composition of identified proteins. The overall identification rates, as well as the proportion of proteins from Actinobacteria were much higher when samples were flash frozen. Preservation in RNAlater overall led to fewer protein identifications and a considerable increase in the share of Bacteroidetes, as well as Proteobacteria. Additionally, a decrease in the share of metabolism-related proteins and an increase of the relative amount of proteins involved in the processing of genetic information was observed for RNAlater-stored samples. This suggests that great care should be taken in choosing methods for the preservation and storage of microbiome samples, as well as in comparing the results of analyses using different sampling and storage methods. Flash freezing and subsequent storage at −80 °C should be chosen wherever possible

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly used and state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete (⁠>95% pure, >90% complete) and high-quality (⁠>90% pure, >70% complete) genomes from simulated datasets from the Critical Assessment of Metagenome Interpretation initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method

Small RNA profiling of low biomass samples: identification and removal of contaminants

Background Sequencing-based analyses of low-biomass samples are known to be prone to misinterpretation due to the potential presence of contaminating molecules derived from laboratory reagents and environments. DNA contamination has been previously reported, yet contamination with RNA is usually considered to be very unlikely due to its inherent instability. Small RNAs (sRNAs) identified in tissues and bodily fluids, such as blood plasma, have implications for physiology and pathology, and therefore the potential to act as disease biomarkers. Thus, the possibility for RNA contaminants demands careful evaluation. Results Herein, we report on the presence of small RNA (sRNA) contaminants in widely used microRNA extraction kits and propose an approach for their depletion. We sequenced sRNAs extracted from human plasma samples and detected important levels of non-human (exogenous) sequences whose source could be traced to the microRNA extraction columns through a careful qPCR-based analysis of several laboratory reagents. Furthermore, we also detected the presence of artefactual sequences related to these contaminants in a range of published datasets, thereby arguing in particular for a re-evaluation of reports suggesting the presence of exogenous RNAs of microbial and dietary origin in blood plasma. To avoid artefacts in future experiments, we also devise several protocols for the removal of contaminant RNAs, define minimal amounts of starting material for artefact-free analyses, and confirm the reduction of contaminant levels for identification of bona fide sequences using ‘ultra-clean’ extraction kits. Conclusion This is the first report on the presence of RNA molecules as contaminants in RNA extraction kits. The described protocols should be applied in the future to avoid confounding sRNA studies. Keywords: RNA sequencing; Artefact removal; Exogenous RNA in human blood plasma; Contaminant RNA; Spin column

The multidimensionality of soil macroecology

The recent past has seen a tremendous surge in soil macroecological studies and new insights into the global drivers of one‐quarter of the biodiversity of the Earth. Building on these important developments, a recent paper in Global Ecology and Biogeography outlined promising methods and approaches to advance soil macroecology. Among other recommendations, White and colleagues introduced the concept of a spatial three‐dimensionality in soil macroecology by considering the different spheres of influence and scales, as soil organism size ranges vary from bacteria to macro‐ and megafauna. Here, we extend this concept by discussing three additional dimensions (biological, physical, and societal) that are crucial to steer soil macroecology from pattern description towards better mechanistic understanding. In our view, these are the requirements to establish it as a predictive science that can inform policy about relevant nature and management conservation actions. We highlight the need to explore temporal dynamics of soil biodiversity and functions across multiple temporal scales, integrating different facets of biodiversity (i.e., variability in body size, life‐history traits, species identities, and groups of taxa) and their relationships to multiple ecosystem functions, in addition to the feedback effects between humans and soil biodiversity. We also argue that future research needs to consider effective soil conservation policy and management in combination with higher awareness of the contributions of soil‐based nature's contributions to people. To verify causal relationships, soil macroecology should be paired with local and globally distributed experiments. The present paper expands the multidimensional perspective on soil macroecology to guide future research contents and funding. We recommend considering these multiple dimensions in projected global soil biodiversity monitoring initiatives

Organic agricultural practice enhances arbuscular mycorrhizal symbiosis in correspondence to soil warming and altered precipitation patterns

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