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      From diversity to complexity: Microbial networks in soils

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          Abstract

          Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research. Analysing microbial networks in soils opens a window to a better understanding of the complexity of microbial communities. However, this approach is unfortunately often used to draw conclusions which are far beyond the scientific evidence it can provide, which has damaged its reputation for soil microbial analysis. In this Perspectives Paper, we would like to sharpen the view for the real potential of microbial co-occurrence analysis in soils, and at the same time raise awareness regarding its limitations and the many ways how it can be misused or misinterpreted.

          Highlights

          • Networks are used to study the relationships within microbial communities.

          • Construction methods and experimental design influence conclusions.

          • Applying network analysis to soil microbial datasets holds specific challenges.

          • Soil heterogeneity and the nature of soil sequencing data has to be considered.

          • Network analysis can offer novel insights into soil microbial ecosystems.

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          Most cited references137

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as "modularity" over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.
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            Exact sequence variants should replace operational taxonomic units in marker-gene data analysis

            Recent advances have made it possible to analyze high-throughput marker-gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently such that amplicon sequence variants (ASVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced gene region. The benefits of finer resolution are immediately apparent, and arguments for ASV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits that derive from the status of ASVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how these features grant ASVs the combined advantages of closed-reference OTUs—including computational costs that scale linearly with study size, simple merging between independently processed data sets, and forward prediction—and of de novo OTUs—including accurate measurement of diversity and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that ASVs should replace OTUs as the standard unit of marker-gene analysis and reporting.
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              Microbial interactions: from networks to models.

              Metagenomics and 16S pyrosequencing have enabled the study of ecosystem structure and dynamics to great depth and accuracy. Co-occurrence and correlation patterns found in these data sets are increasingly used for the prediction of species interactions in environments ranging from the oceans to the human microbiome. In addition, parallelized co-culture assays and combinatorial labelling experiments allow high-throughput discovery of cooperative and competitive relationships between species. In this Review, we describe how these techniques are opening the way towards global ecosystem network prediction and the development of ecosystem-wide dynamic models.
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                Author and article information

                Contributors
                Journal
                Soil Biol Biochem
                Soil Biol Biochem
                Soil Biology & Biochemistry
                Elsevier
                0038-0717
                1879-3428
                1 June 2022
                June 2022
                : 169
                : 108604
                Affiliations
                [a ]Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
                [b ]Centro de Investigaciones sobre Desertificación (CIDE, CSIC-UV-GV), Carretera de Moncada-Náquera Km 4.5, 46113, Moncada, Valencia, Spain
                [c ]Doctoral School in Microbiology and Environmental Science, University of Vienna, Vienna, Austria
                Author notes
                []Corresponding author. christina.kaiser@ 123456univie.ac.at
                [∗∗ ]Corresponding author. ksenia.guseva@ 123456univie.ac.at
                Article
                S0038-0717(22)00061-X 108604
                10.1016/j.soilbio.2022.108604
                9125165
                35712047
                3db3c0b2-96d9-494c-8983-59745eade20b
                © 2022 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 August 2021
                : 8 February 2022
                : 9 February 2022
                Categories
                Perspectives Paper

                Microbiology & Virology
                microbial network analysis,co-occurrence networks,ecological networks,microbial community structure,soil microbial ecology

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