Sample naming in OTU tables

All sample names in OTU tables are in the following form: [soil type].[genotype].[sample number][fraction].[age].[experiment] [plate]. For example, M21.Col.6E.old.M1 2b should be interpreted as [soil type] = M21 = Mason Farm 2:1, [genotype] = Col = Col-0, [sample number] = 6, [fraction] = E = endophyte compartment, [age] = old, [experiment] = M1 = Mason Farm replicate 1, [plate] = 2b.

CHAPTER 3

A reduced complexity platform for a complex system

It is well established that plants assemble a distinct microbiome in and around the root (Lundberg et al., 2012; Bulgarelli et al., 2012; Schlaeppi et al., 2014) (Chapter 2), and in above ground organs (Bodenhausen et al., 2013; Horton et al., 2014; Maignien et al., 2014). At the same time, there is evidence from the Brassicaceae and Poaceae families that host phylogenetic distance correlates with microbiome composition differences across species (Schlaeppi et al., 2014; Bouffaud et al., 2014). Evidence indicates that the within-species root microbiome differences are statistically significant but small in magnitude across a variety of species: bacterial community profiles in and around the roots ofA. thaliana wild accessions in natural soil showed that only a handful taxa displayed genotype-dependent differences (Bulgarelli et al., 2012; Lundberg et al., 2012); similarly, another study found that differences between accessions were restricted to a subset of Pseudomonadaceae bacteria (Haney et al., 2015); among other species, barley rhizosphere microbial communities showed taxonomic and functional differences that might be related to domestication and explained ∼5% of the microbiome variation (Bulgarelli et al., 2015); and maize rhizospheres of 27 modern inbreds across sites exhibited small proportion of heritable variation in total bacterial diversity across

1_{The contents of this chapter has not been peer reviewed. This chapter describes the work performed to develop,}

implemente and establish the synthetic community approach in the dangl lab, as well as its application to novel questions. Besides myself (Sur Herrera Paredes), multiple people in Jeff Dangl’s group and will be recognized with authorship when some or all of this work is published. People that contributed include but are not limited to: PhD student Derek Lundberg, and undergraduate students/research technicians Meredith McDonald and Surojit Biswas. The specific contributions are as follow: SHP, DL and JD designed the experiments. SHP, DL, SB and MM performed the experiments and collected samples. SHP, DL and SB obtained the sequencing data. SHP, DL and JD analyzed the data. SHP wrote the manuscript with input from JD.

fields, and substantially more heritable variation between replicates of the inbreds within each field (Peiffer et al., 2013).

The small genotype-dependent root microbiome differences between natural accessions is in stark contrast with the differences observed in the above-ground (phyllosphere) microbiome. Field surveys of tree phyllosphere bacterial communities has revealed a stronger effect of tree species than sampling site or time (Redford and Fierer, 2009; Laforest-Lapointe et al., 2016). At the same time, a field study of A. thaliana wild accessions, showed sufficient genotype-dependent patterns to perform Genome Wide Association (GWA), and identified plant loci related to defense, cell wall integrity, trichome/cuticle synthesis and morphogenesis as relevant determinants of bacterial and fungal community assembly (Horton et al., 2014). Another large-scale field experiment in Boechera stricta (Brassicaceae) grown in multiple sites through its natural range, simultaneously profiled leaf and root bacterial communities and found a strong signature of host control on the leaf microbiome that was absent in roots (Wagner et al., 2016).

The difference in genotypic signatures between rhizosphere and phyllosphere might indicate that bacterial communities in and around the root are more dependent on microbe-microbe competition and microbial adaptation to the host-associated environment. Alternatively, it might also mean that the host selection occurs at a level that is beyond the resolution of typical microbiome profiling methods, which typically target a single marker gene and thus miss the microbial genomic context. Previous work has showed that strains of the same Pseudomonas fluorescens ribotype can differentially associate with A. thaliana accessions with consequences for plant fitness (Haney et al., 2015). Full metagenomic sequence could potentially overcome this problem by providing a full taxonomic and functional picture of the root microbiome; however, significant experimental and analytical challenges limit the utility of this approach. For instance, there is no high throughput method to physically separate bacterial and plant host DNA prior to library preparation, meaning that almost all the sequences recovered derive from the host. At the same time, metagenomic assembly

Figure 3.1: Experimental design and sample number. We tested a number of different hosts with various degrees of genotypic divergence in one media, and one host in several media in two independent experiment (left and right). Between the first and second experiment we changed to a more nutrient limiting environment to see if the more stressful conditions would reveal stronger genotypic differences. We harvested roots and neighboring soils (N) in both experiments. In the second experiment (SBS5, right) we also harvested unplanted pots (soil). We also added Johnoson media and a phosphate dropdown on this media (Johnson LowP) to determine its similarity to results on MS media. Numbers indicate number of samples that passed all quality control steps and were used for final analysis.

of complex environments is an open bioinformatics problem, with state of the art methods typically only assembling ∼10% of the data. We decided to take an approach based on microcosm reconstitution, by inoculating seedlings — growing in a calcined clay substrate — with a well-defined but complex synthetic bacterial community (SynCom), while varying either the nutritional composition of the soil, or plant host (Fig. 3.1). This approach allowed us to disentangle changes in bacterial community composition that are due to microbial adaptation to abiotic changes in the environment, and changes that are due to the action of the plant-host and are accessible to natural selection.