Derek S. Lundberg

Defining the core Arabidopsis thaliana root microbiome

Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation. Colonization of the root occurs despite a sophisticated plant immune system, suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more than 600 Arabidopsis thaliana plants to test the hypotheses that the root rhizosphere and endophytic compartment microbiota of plants grown under controlled conditions in natural soils are sufficiently dependent on the host to remain consistent across different soil types and developmental stages, and sufficiently dependent on host genotype to vary between inbred Arabidopsis accessions. We describe different bacterial communities in two geochemically distinct bulk soils and in rhizosphere and endophytic compartments prepared from roots grown in these soils. The communities in each compartment are strongly influenced by soil type. Endophytic compartments from both soils feature overlapping, low-complexity communities that are markedly enriched in Actinobacteria and specific families from other phyla, notably Proteobacteria. Some bacteria vary quantitatively between plants of different developmental stage and genotype. Our rigorous definition of an endophytic compartment microbiome should facilitate controlled dissection of plant–microbe interactions derived from complex soil communities.

Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa

Immune signals shape root communities To thwart microbial pathogens aboveground, the plant Arabidopsis turns on defensive signaling using salicylic acid. In Arabidopsis plants with modified immune systems, Lebeis et al. show that bacterial communities change in response to salicylic acid signaling in the root zone as well (see the Perspective by Haney and Ausubel). Abundance of some root-colonizing bacterial families increased at the expense of others, partly as a function of whether salicylic acid was used as an immune signal or as a carbon source for microbial growth. Science, this issue p. 860; see also p. 788 Bacteria that are endosymbiotic with the plant root respond to changes in the plant’s signaling status. [Also see Perspective by Haney and Ausubel] Immune systems distinguish “self” from “nonself” to maintain homeostasis and must differentially gate access to allow colonization by potentially beneficial, nonpathogenic microbes. Plant roots grow within extremely diverse soil microbial communities but assemble a taxonomically limited root-associated microbiome. We grew isogenic Arabidopsis thaliana mutants with altered immune systems in a wild soil and also in recolonization experiments with a synthetic bacterial community. We established that biosynthesis of, and signaling dependent on, the foliar defense phytohormone salicylic acid is required to assemble a normal root microbiome. Salicylic acid modulates colonization of the root by specific bacterial families. Thus, plant immune signaling drives selection from the available microbial communities to sculpt the root microbiome.

Practical innovations for high-throughput amplicon sequencing

We describe improvements for sequencing 16S ribosomal RNA (rRNA) amplicons, a cornerstone technique in metagenomics. Through unique tagging of template molecules before PCR, amplicon sequences can be mapped to their original templates to correct amplification bias and sequencing error with software we provide. PCR clamps block amplification of contaminating sequences from a eukaryotic host, thereby substantially enriching microbial sequences without introducing bias.

The Pseudomonas syringae Type III Effector HopAM1 Enhances Virulence on Water-Stressed Plants

Pseudomonas syringae strains deliver diverse type III effector proteins into host cells, where they can act as virulence factors. Although the functions of the majority of type III effectors are unknown, several have been shown to interfere with plant basal defense mechanisms. Type III effectors also could contribute to bacterial virulence by enhancing nutrient uptake and pathogen adaptation to the environment of the host plant. We demonstrate that the type III effector HopAM1 (formerly known as AvrPpiB) enhances the virulence of a weak pathogen in plants that are grown under drought stress. This is the first report of a type III effector that aids pathogen adaptation to water availability in the host plant. Expression of HopAM1 makes transgenic Ws-0 Arabidopsis hypersensitive to abscisic acid (ABA) for stomatal closure and germination arrest. Conditional expression of HopAM1 in Arabidopsis also suppresses basal defenses. ABA responses overlap with defense responses and ABA has been shown to suppress defense against P. syringae pathogens. We propose that HopAM1 aids P. syringae virulence by manipulation of ABA responses that suppress defense responses. In addition, host ABA responses enhanced by type III delivery of HopAM1 protect developing bacterial colonies inside leaves from osmotic stress.

Host genotype and age shape the leaf and root microbiomes of a wild perennial plant

Bacteria living on and in leaves and roots influence many aspects of plant health, so the extent of a plants genetic control over its microbiota is of great interest to crop breeders and evolutionary biologists. Laboratory-based studies, because they poorly simulate true environmental heterogeneity, may misestimate or totally miss the influence of certain host genes on the microbiome. Here we report a large-scale field experiment to disentangle the effects of genotype, environment, age and year of harvest on bacterial communities associated with leaves and roots of Boechera stricta (Brassicaceae), a perennial wild mustard. Host genetic control of the microbiome is evident in leaves but not roots, and varies substantially among sites. Microbiome composition also shifts as plants age. Furthermore, a large proportion of leaf bacterial groups are shared with roots, suggesting inoculation from soil. Our results demonstrate how genotype-by-environment interactions contribute to the complexity of microbiome assembly in natural environments.

Natural soil microbes alter flowering phenology and the intensity of selection on flowering time in a wild Arabidopsis relative

Plant phenology is known to depend on many different environmental variables, but soil microbial communities have rarely been acknowledged as possible drivers of flowering time. Here, we tested separately the effects of four naturally occurring soil microbiomes and their constituent soil chemistries on flowering phenology and reproductive fitness of Boechera stricta, a wild relative of Arabidopsis. Flowering time was sensitive to both microbes and the abiotic properties of different soils; varying soil microbiota also altered patterns of selection on flowering time. Thus, soil microbes potentially contribute to phenotypic plasticity of flowering time and to differential selection observed between habitats. We also describe a method to dissect the microbiome into single axes of variation that can help identify candidate organisms whose abundance in soil correlates with flowering time. This approach is broadly applicable to search for microbial community members that alter biological characteristics of interest.

ProDeGe: a computational protocol for fully automated decontamination of genomes

Single amplified genomes and genomes assembled from metagenomes have enabled the exploration of uncultured microorganisms at an unprecedented scale. However, both these types of products are plagued by contamination. Since these genomes are now being generated in a high-throughput manner and sequences from them are propagating into public databases to drive novel scientific discoveries, rigorous quality controls and decontamination protocols are urgently needed. Here, we present ProDeGe (Protocol for fully automated Decontamination of Genomes), the first computational protocol for fully automated decontamination of draft genomes. ProDeGe classifies sequences into two classes—clean and contaminant—using a combination of homology and feature-based methodologies. On average, 84% of sequence from the non-target organism is removed from the data set (specificity) and 84% of the sequence from the target organism is retained (sensitivity). The procedure operates successfully at a rate of ~0.30 CPU core hours per megabase of sequence and can be applied to any type of genome sequence.

Learning Microbial Interaction Networks from Metagenomic Count Data.

Many microbes associate with higher eukaryotes and impact their vitality. To engineer microbiomes for host benefit, we must understand the rules of community assembly and maintenance that, in large part, demand an understanding of the direct interactions among community members. Toward this end, we have developed a Poisson-multivariate normal hierarchical model to learn direct interactions from the count-based output of standard metagenomics sequencing experiments. Our model controls for confounding predictors at the Poisson layer and captures direct taxon-taxon interactions at the multivariate normal layer using an ℓ1 penalized precision matrix. We show in a synthetic experiment that our method handily outperforms state-of-the-art methods such as SparCC and the graphical lasso (glasso). In a real in planta perturbation experiment of a nine-member bacterial community, we show our model, but not SparCC or glasso, correctly resolves a direct interaction structure among three community members that associates with Arabidopsis thaliana roots. We conclude that our method provides a structured, accurate, and distributionally reasonable way of modeling correlated count-based random variables and capturing direct interactions among them.

MT-Toolbox: improved amplicon sequencing using molecule tags

BackgroundShort oligonucleotides can be used as markers to tag and track DNA sequences. For example, barcoding techniques (i.e. Multiplex Identifiers or Indexing) use short oligonucleotides to distinguish between reads from different DNA samples pooled for high-throughput sequencing. A similar technique called molecule tagging uses the same principles but is applied to individual DNA template molecules. Each template molecule is tagged with a unique oligonucleotide prior to polymerase chain reaction. The resulting amplicon sequences can be traced back to their original templates by their oligonucleotide tag. Consensus building from sequences sharing the same tag enables inference of original template molecules thereby reducing effects of sequencing error and polymerase chain reaction bias. Several independent groups have developed similar protocols for molecule tagging; however, user-friendly software for build consensus sequences from molecule tagged reads is not readily available or is highly specific for a particular protocol.ResultsMT-Toolbox recognizes oligonucleotide tags in amplicons and infers the correct template sequence. On a set of molecule tagged test reads, MT-Toolbox generates sequences having on average 0.00047 errors per base. MT-Toolbox includes a graphical user interface, command line interface, and options for speed and accuracy maximization. It can be run in serial on a standard personal computer or in parallel on a Load Sharing Facility based cluster system. An optional plugin provides features for common 16S metagenome profiling analysis such as chimera filtering, building operational taxonomic units, contaminant removal, and taxonomy assignments.ConclusionsMT-Toolbox provides an accessible, user-friendly environment for analysis of molecule tagged reads thereby reducing technical errors and polymerase chain reaction bias. These improvements reduce noise and allow for greater precision in single amplicon sequencing experiments.

Learning microbial interaction networks from metagenomic count data

Many microbes associate with higher eukaryotes and impact their vitality. In order to engineer microbiomes for host benefit, we must understand the rules of community assembly and maintenence, which in large part, demands an understanding of the direct interactions between community members. Toward this end, we’ve developed a Poisson-multivariate normal hierarchical model to learn direct interactions from the count-based output of standard metagenomics sequencing experiments. Our model controls for confounding predictors at the Poisson layer, and captures direct taxon-taxon interactions at the multivariate normal layer using an \(\ell _1\) penalized precision matrix. We show in a synthetic experiment that our method handily outperforms state-of-the-art methods such as SparCC and the graphical lasso (glasso). In a real, in planta perturbation experiment of a nine member bacterial community, we show our model, but not SparCC or glasso, correctly resolves a direct interaction structure among three community members that associate with Arabidopsis thaliana roots. We conclude that our method provides a structured, accurate, and distributionally reasonable way of modeling correlated count based random variables and capturing direct interactions among them.