Using network analysis to explore co-occurrence patterns in soil microbial communities
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PROVIDER: S-EPMC3960547 | biostudies-literature | 2014 Mar
REPOSITORIES: biostudies-literature
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Project description:Exploring large environmental datasets generated by high-throughput DNA sequencing technologies requires new analytical approaches to move beyond the basic inventory descriptions of the composition and diversity of natural microbial communities. In order to investigate potential interactions between microbial taxa, network analysis of significant taxon co-occurrence patterns may help to decipher the structure of complex microbial communities across spatial or temporal gradients. Here, we calculated associations between microbial taxa and applied network analysis approaches to a 16S rRNA gene barcoded pyrosequencing dataset containing >160?000 bacterial and archaeal sequences from 151 soil samples from a broad range of ecosystem types. We described the topology of the resulting network and defined operational taxonomic unit categories based on abundance and occupancy (that is, habitat generalists and habitat specialists). Co-occurrence patterns were readily revealed, including general non-random association, common life history strategies at broad taxonomic levels and unexpected relationships between community members. Overall, we demonstrated the potential of exploring inter-taxa correlations to gain a more integrated understanding of microbial community structure and the ecological rules guiding community assembly.
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Project description:Co-occurrence networks allow for the identification of potential associations among species, which may be important for understanding community assembly and ecosystem functions. We employed this strategy to examine prokaryotic co-occurrence patterns in the Amazon soils and the response of these patterns to land use change to pasture, with the hypothesis that altered microbial composition due to deforestation will mirror the co-occurrence patterns across prokaryotic taxa. In this study, we calculated Spearman correlations between operational taxonomic units (OTUs) as determined by 16S rRNA gene sequencing, and only robust correlations were considered for network construction (-0.80 ≥ P ≥ 0.80, adjusted P < 0.01). The constructed network represents distinct forest and pasture components, with altered compositional and topological features. A comparative analysis between two representative modules of these contrasting ecosystems revealed novel information regarding changes to metabolic pathways related to nitrogen cycling. Our results showed that soil physicochemical properties such as temperature, C/N and H++Al3+ had a significant impact on prokaryotic communities, with alterations to network topologies. Taken together, changes in co-occurrence patterns and physicochemical properties may contribute to ecosystem processes including nitrification and denitrification, two important biogeochemical processes occurring in tropical forest systems.
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Project description:Ruminants rely on a complex rumen microbial community to convert dietary plant material to energy-yielding products. Here we developed a method to simultaneously analyze the community's bacterial and archaeal 16S rRNA genes, ciliate 18S rRNA genes and anaerobic fungal internal transcribed spacer 1 genes using 12 DNA samples derived from 11 different rumen samples from three host species (Ovis aries, Bos taurus, Cervus elephas) and multiplex 454 Titanium pyrosequencing. We show that the mixing ratio of the group-specific DNA templates before emulsion PCR is crucial to compensate for differences in amplicon length. This method, in contrast to using a non-specific universal primer pair, avoids sequencing non-targeted DNA, such as plant- or endophyte-derived rRNA genes, and allows increased or decreased levels of community structure resolution for each microbial group as needed. Communities analyzed with different primers always grouped by sample origin rather than by the primers used. However, primer choice had a greater impact on apparent archaeal community structure than on bacterial community structure, and biases for certain methanogen groups were detected. Co-occurrence analysis of microbial taxa from all three domains of life suggested strong within- and between-domain correlations between different groups of microorganisms within the rumen. The approach used to simultaneously characterize bacterial, archaeal and eukaryotic components of a microbiota should be applicable to other communities occupying diverse habitats.
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Project description:In agroecosystems, fungi not only attract attention as crop pathogens, but also play crucial roles in nutrient cycling as decomposers and arbuscular mycorrhizal mutualists. Consequently soil fungi strongly influence agroecosystem function, and are conspicuously influenced by agricultural practices. We examined the effects of four compost rates (0, 11.25, 22.5, and 45 Mg ha-1) on soil fungal community compositions and network patterns in soybean at seedling, flowering, and mature stage in a field experiment in black soil of Northeast China. Miseq sequencing was used to characterize the soil fungal community. Our results revealed that soil fungal richness was unaffected by compost addition, while soil fungal community composition was significantly influenced by compost addition across the growing season. Among the combined "top 20" fungal OTUs, 15 OTUs positively responded to compost addition, while 10 negatively responded. The abundance of predicted pathotroph was greatly decreased by the 45 Mg ha-1 compost addition. Network analysis indicated that the fungal networks in compost amended soils were more complex and harbored more positive links than the control. Fungal network harbored more positive links among saprotroph-saprotroph and saprotroph-symbiotroph in moderate level of compost amended soils than other networks. In conclusion, this study revealed that compost addition impacted positively both the soil fungal communities and network patterns within a single growing season. Thus, compost addition could be a good practice to enhance the soil fungal community and function and ultimately soil health and quality.
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Project description:The increasing amount of agricultural applications of controlled-release urea (CRU) and fulvic acids (FA) demands a better understanding of FA's effects on microbially mediated nitrogen (N) nutrient cycling. Herein, a 0-60 day laboratory experiment and a consecutive pot experiment (2016-2018) were carried out to reveal the effects of using CRU on soil microbial N-cycling processes and soil fertility, with and without the application of FA. Compared to the CRU treatment, the CRU+FA treatment boosted wheat yield by 22.1%. To reveal the mechanism of CRU+FA affecting the soil fertility, soil nutrient supply and microbial community were assessed and contrasted in this research. From 0-60 days, compared with the CRU treatment, leaching NO3--N content of CRU+FA was dramatically decreased by 12.7-84.2% in the 20 cm depth of soil column. Different fertilizers and the day of fertilization both have an impact on the soil microbiota. The most dominant bacterial phyla Actinobacteria and Proteobacteria were increased with CRU+FA treatment during 0-60 days. Network analysis revealed that microbial co-occurrence grew more intensive during the CRU+FA treatment, and the environmental change enhanced the microbial community. The CRU+FA treatment, in particular, significantly decreased the relative abundance of Sphingomonas, Lysobacter and Nitrospira associated with nitrification reactions, Nocardioides and Gaiella related to denitrification reactions. Meanwhile, the CRU+FA treatment grew the relative abundance of Ensifer, Blastococcus, and Pseudolabrys that function in N fixation, and then could reduce NH4+-N and NO3--N leaching and improve the soil nutrient supply. In conclusion, the synergistic effects of slow nutrition release of CRU and growth promoting of FA could improve the soil microbial community of N cycle, reduce the loss of nutrients, and increase the wheat yield.
| S-EPMC9502011 | biostudies-literature
Project description:Despite recent research efforts to explore the co-occurrence patterns of diverse microbes within soil microbial communities, a substantial knowledge-gap persists regarding global climate influences on soil microbiota behaviour. Comprehending co-occurrence patterns within distinct geoclimatic groups is pivotal for unravelling the ecological structure of microbial communities, that are crucial for preserving ecosystem functions and services. Our study addresses this gap by examining global climatic patterns of microbial diversity. Using data from the Earth Microbiome Project, we analyse a meta-community co-occurrence network for bacterial communities. This method unveils substantial shifts in topological features, highlighting regional and climatic trends. Arid, Polar, and Tropical zones show lower diversity but maintain denser networks, whereas Temperate and Cold zones display higher diversity alongside more modular networks. Furthermore, it identifies significant co-occurrence patterns across diverse climatic regions. Central taxa associated with different climates are pinpointed, highlighting climate's pivotal role in community structure. In conclusion, our study identifies significant correlations between microbial interactions in diverse climatic regions, contributing valuable insights into the intricate dynamics of soil microbiota.
| S-EPMC11193810 | biostudies-literature
Project description:Mainstream studies of microbial community focused on critical organisms and their physiology. Recent advances in large-scale metagenome analysis projects initiated new researches in the complex correlations between large microbial communities. Specifically, previous studies focused on the nodes (i.e. species) of the Species-Centric Networks (SCNs). However, little was understood about the change of correlation between network members (i.e. edges of the SCNs) when the network was disturbed. Here, we introduced a Correlation-Centric Network (CCN) to the microbial research based on the concept of edge networks. In CCN, each node represented a species-species correlation, and edge represented the species shared by two correlations. In this research, we investigated the CCNs and their corresponding SCNs on two large cohorts of microbiome. The results showed that CCNs not only retained the characteristics of SCNs, but also contained information that cannot be detected by SCNs. In addition, when the members of microbial communities were decreased (i.e. environmental disturbance), the CCNs fluctuated within a small range in terms of network connectivity. Therefore, by highlighting the important species correlations, CCNs could unveil new insights when studying not only the functions of target species, but also the stabilities of their residing microbial communities.
| S-EPMC7671402 | biostudies-literature
Project description:Animals that modify their physical environment by foraging in the soil can have dramatic effects on ecosystem functions and processes. We compared bacterial and fungal communities in the foraging pits created by bilbies and burrowing bettongs with undisturbed surface soils dominated by biocrusts. Bacterial communities were characterized by Actinobacteria and Alphaproteobacteria, and fungal communities by Lecanoromycetes and Archaeosporomycetes. The composition of bacterial or fungal communities was not observed to vary between loamy or sandy soils. There were no differences in richness of either bacterial or fungal operational taxonomic units (OTUs) in the soil of young or old foraging pits, or undisturbed soils. Although the bacterial assemblage did not vary among the three microsites, the composition of fungi in undisturbed soils was significantly different from that in old or young foraging pits. Network analysis indicated that a greater number of correlations between bacterial OTUs occurred in undisturbed soils and old pits, whereas a greater number of correlations between fungal OTUs occurred in undisturbed soils. Our study suggests that digging by soil-disturbing animals is likely to create successional shifts in soil microbial and fungal communities, leading to functional shifts associated with the decomposition of organic matter and the fixation of nitrogen. Given the primacy of organic matter decomposition in arid and semi-arid environments, the loss of native soil-foraging animals is likely to impair the ability of these systems to maintain key ecosystem processes such as the mineralization of nitrogen and the breakdown of organic matter, and to recover from disturbance.
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Project description:Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.
| S-EPMC5689832 | biostudies-literature
Project description:BackgroundTree mycorrhizal types (arbuscular mycorrhizal fungi and ectomycorrhizal fungi) alter nutrient use traits and leaf physicochemical properties and, thus, affect leaf litter decomposition. However, little is known about how different tree mycorrhizal species affect the microbial diversity, community composition, function, and community assembly processes that govern leaf litter-dwelling microbes during leaf litter decomposition.MethodsIn this study, we investigated the microbial diversity, community dynamics, and community assembly processes of nine temperate tree species using high-resolution molecular technique (Illumina sequencing), including broadleaved arbuscular mycorrhizal, broadleaved ectomycorrhizal, and coniferous ectomycorrhizal tree types, during leaf litter decomposition.Results and discussionThe leaves and needles of different tree mycorrhizal types significantly affected the microbial richness and community composition during leaf litter decomposition. Leaf litter mass loss was related to higher sequence reads of a few bacterial functional groups, particularly N-fixing bacteria. Furthermore, a link between bacterial and fungal community composition and hydrolytic and/or oxidative enzyme activity was found. The microbial communities in the leaf litter of different tree mycorrhizal types were governed by different proportions of determinism and stochasticity, which changed throughout litter decomposition. Specifically, determinism (mainly variable selection) controlling bacterial community composition increased over time. In contrast, stochasticity (mainly ecological drift) increasingly governed fungal community composition. Finally, the co-occurrence network analysis showed greater competition between bacteria and fungi in the early stages of litter decomposition and revealed a contrasting pattern between mycorrhizal types.ConclusionOverall, we conclude that tree mycorrhizal types influence leaf litter quality, which affects microbial richness and community composition, and thus, leaf litter decomposition.
| S-EPMC10716483 | biostudies-literature