Obtaining deeper insights into microbiome diversity using a simple method to block host and nontargets in amplicon sequencing

Abstract Profiling diverse microbiomes is revolutionizing our understanding of biological mechanisms and ecologically relevant problems, including metaorganism (host + microbiome) assembly, functions and adaptation. Amplicon sequencing of multiple conserved, phylogenetically informative loci has therefore become an instrumental tool for many researchers. Investigations in many systems are hindered, however, since essential sequencing depth can be lost by amplification of nontarget DNA from hosts or overabundant microorganisms. Here, we introduce “blocking oligos”, a low‐cost and flexible method using standard oligonucleotides to block amplification of diverse nontargets and software to aid their design. We apply them primarily in leaves, where exceptional challenges with host amplification prevail. A . thaliana ‐specific blocking oligos applied in eight different target loci reduce undesirable host amplification by up to 90%. To expand applicability, we designed universal 16S and 18S rRNA gene plant blocking oligos for targets that are conserved in diverse plant species and demonstrate that they efficiently block five plant species from five orders spanning monocots and dicots ( Bromus erectus , Plantago lanceolata , Lotus corniculatus , Amaranth sp., Arabidopsis thaliana ). These can increase alpha diversity discovery without biasing beta diversity patterns and do not compromise microbial load information inherent to plant‐derived 16S rRNA gene amplicon sequencing data. Finally, we designed and tested blocking oligos to avoid amplification of 18S rRNA genes of a sporulating oomycete pathogen, demonstrating their effectiveness in applications well beyond plants. Using these tools, we generated a survey of the A . thaliana leaf microbiome based on eight loci targeting bacterial, fungal, oomycete and other eukaryotic microorganisms and discuss complementarity of commonly used amplicon sequencing regions for describing leaf microbiota. This approach has potential to make questions in a variety of study systems more tractable by making amplicon sequencing more targeted, leading to deeper, systems‐based insights into microbial discovery. For fast and easy design for blocking oligos for any nontarget DNA in other study systems, we developed a publicly available R package

Microbial Hub Taxa Link Host and Abiotic Factors to Plant Microbiome Variation

<div><p>Plant-associated microorganisms have been shown to critically affect host physiology and performance, suggesting that evolution and ecology of plants and animals can only be understood in a holobiont (host and its associated organisms) context. Host-associated microbial community structures are affected by abiotic and host factors, and increased attention is given to the role of the microbiome in interactions such as pathogen inhibition. However, little is known about how these factors act on the microbial community, and especially what role microbe–microbe interaction dynamics play. We have begun to address this knowledge gap for phyllosphere microbiomes of plants by simultaneously studying three major groups of <i>Arabidopsis thaliana</i> symbionts (bacteria, fungi and oomycetes) using a systems biology approach. We evaluated multiple potential factors of microbial community control: we sampled various wild <i>A</i>. <i>thaliana</i> populations at different times, performed field plantings with different host genotypes, and implemented successive host colonization experiments under lab conditions where abiotic factors, host genotype, and pathogen colonization was manipulated. Our results indicate that both abiotic factors and host genotype interact to affect plant colonization by all three groups of microbes. Considering microbe–microbe interactions, however, uncovered a network of interkingdom interactions with significant contributions to community structure. As in other scale-free networks, a small number of taxa, which we call microbial “hubs,” are strongly interconnected and have a severe effect on communities. By documenting these microbe–microbe interactions, we uncover an important mechanism explaining how abiotic factors and host genotypic signatures control microbial communities. In short, they act directly on “hub” microbes, which, via microbe–microbe interactions, transmit the effects to the microbial community. We analyzed two “hub” microbes (the obligate biotrophic oomycete pathogen <i>Albugo</i> and the basidiomycete yeast fungus <i>Dioszegia</i>) more closely. <i>Albugo</i> had strong effects on epiphytic and endophytic bacterial colonization. Specifically, alpha diversity decreased and beta diversity stabilized in the presence of <i>Albugo</i> infection, whereas they otherwise varied between plants. <i>Dioszegia</i>, on the other hand, provided evidence for direct hub interaction with phyllosphere bacteria. The identification of microbial “hubs” and their importance in phyllosphere microbiome structuring has crucial implications for plant–pathogen and microbe–microbe research and opens new entry points for ecosystem management and future targeted biocontrol. The revelation that effects can cascade through communities via “hub” microbes is important to understand community structure perturbations in parallel fields including human microbiomes and bioprocesses. In particular, parallels to human microbiome “keystone” pathogens and microbes open new avenues of interdisciplinary research that promise to better our understanding of functions of host-associated microbiomes.</p></div

Computational Experiment 3: Hub microorganisms are critical determinants of the microbiome interaction network structure.

<p>A. Most high-degree bacteria (including the genus of <i>Comamonadaceae</i> designated as a hub) are first neighbors (i.e., direct and negative correlates) of the hub microbial genera <i>Albugo</i> sp. and <i>Dioszegia</i> sp., and many group into an intercorrelated cluster. First neighbors of the three “hub” microbes are shown in color and the rest of the network is shown in greyscale. The depiction is a spring-loaded visualization of the network in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002352#pbio.1002352.g002" target="_blank">Fig 2</a> where tightly correlated nodes cluster together. B. The hub microbes were partly independent, since about half of the nodes to which they correlated were unique and half were shared. They together directly reach over half (100/191) of all nodes in the network. C. Hub microbes (high degree organisms with high centrality) can be considered as reasonable keystone species, since the magnitude of their effects in the network extend over more edges than nonkeystone nodes (high abundance organisms with low degree and low centrality) but over fewer than keystone nodes (high degree organisms with low centrality). An edge was considered dependent if it was not observed in a network built using partial correlations controlling for abundance of the test microbes. Error bars show standard deviation, and significance was tested with a one-sided Welch’s <i>t</i> test where (*): <i>p</i> < 0.1, (**): <i>p</i> < 0.05 and (***): <i>p</i> < 0.01. Hub nodes: <i>Albugo</i> sp., <i>Dioszegia</i> sp. and a genus of <i>Comamonadaceae</i>. Keystone nodes: <i>Mycobacterium</i> sp., <i>Rhodoplanes</i> sp., and <i>Rhizobiales</i> (other). Nonkeystone nodes: <i>Pseudomonas</i> sp., <i>Oxalobacteriaceae</i> (other), and <i>Sphingomonas</i> sp. (S1_Data.xlsx)</p