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. 2022 Sep 25;236(5):1838–1855. doi: 10.1111/nph.18453

Characterization of defense responses against bacterial pathogens in duckweeds lacking EDS1

Erin L Baggs 1, Meije B Tiersma 1, Brad W Abramson 2, Todd P Michael 2, Ksenia V Krasileva 1,✉
PMCID: PMC9828482  PMID: 36052715

Summary

  • ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1) mediates the induction of defense responses against pathogens in most angiosperms. However, it has recently been shown that a few species have lost EDS1. It is unknown how defense against disease unfolds and evolves in the absence of EDS1. We utilize duckweeds; a collection of aquatic species that lack EDS1, to investigate this question.

  • We established duckweed‐Pseudomonas pathosystems and used growth curves and microscopy to characterize pathogen‐induced responses. Through comparative genomics and transcriptomics, we show that the copy number of infection‐associated genes and the infection‐induced transcriptional responses of duckweeds differ from other model species.

  • Pathogen defense in duckweeds has evolved along different trajectories than in other plants, including genomic and transcriptional reprogramming. Specifically, the miAMP1 domain‐containing proteins, which are absent in Arabidopsis, showed pathogen responsive upregulation in duckweeds. Despite such divergence between Arabidopsis and duckweed species, we found conservation of upregulation of certain genes and the role of hormones in response to disease.

  • Our work highlights the importance of expanding the pool of model species to study defense responses that have evolved in the plant kingdom independent of EDS1.

Keywords: antimicrobial proteins, duckweed, EDS1, plant immunity, Pseudomonas syringae, transcriptional response

Introduction

Receptors and signaling components of the plant immune signal network likely evolved before the divergence of flowering plants 181 million years ago (Ma; Kumar et al., 2017). The two primary layers of plant innate immunity, microbe‐associated molecular pattern (MAMP)‐triggered immunity (MTI) and effector‐triggered Immunity (ETI; Alhoraibi et al., 2019), form an intricate network of cross‐amplifying pathways (Ngou et al., 2021; Tian et al., 2021; Yuan et al., 2021a). The shared evolutionary history of MTI and ETI suggests that they intricately co‐evolved and are foundational for immunity in flowering plants. ENHANCED DISEASE SUSCEPTIBILITY 1 (EDS1) is a central hub in MTI and ETI signal transduction and amplification. Mutations abolishing activity of EDS1 compromise ETI and MTI (Wagner et al., 2013; Cui et al., 2017; Pruitt et al., 2021). Surprisingly, genomic studies have revealed several angiosperms that thrive in their natural environments without EDS1, including the Lemnaceae (Baggs et al., 2020). It is unknown how the response to bacterial pathogens would proceed or evolve in the absence of a functional EDS1.

Lemnaceae diverged from other monocots at 128 Ma (Kumar et al., 2017). The common ancestor of the Lemnaceae was present in a freshwater aquatic environment and had a reduced body plan of a frond (a fused stem and leaf) and root (Acosta et al., 2021). Genera within the Lemnaceae include Spirodela, Landoltia, Lemna, Wolffia and Wolffiella, all of which primarily reproduce through asexual budding (Bog et al., 2019). The small genome and body plan is conducive to the Lemnaceae's rapid lifecycle with a doubling time of as little as 34 h (Michael et al., 2020). Lemnaceae are very easy to grow in the laboratory which has stimulated research into their use as biofuels (Su et al., 2014; Van Hoeck et al., 2015; Xu et al., 2018) and enabled the growth of genomic resources (Wang et al., 2014; Michael et al., 2017, 2020; Hoang et al., 2020; Abramson et al., 2021). Spirodela polyrhiza has a small genome of 158 Mb and has lost members of the expansin and cellulose biosynthesis families (Wang et al., 2014; Michael et al., 2017). Wolffia australiana has lost light signaling pathways and root development pathways, which is not surprising given the absence of roots in Wolffia (Michael et al., 2020). In addition to developmental pathways lost in other Lemnaceae, S. polyrhiza was previously shown to have lost EDS1 (Baggs et al., 2020), however the extent of immune pathway loss in other Lemnaceae remained unclear.

Given the absence of the EDS1 pathway in S. polyrhiza (Baggs et al., 2020), we hypothesized that duckweeds could be a model system for understanding EDS1‐independent innate immunity. Lemnaceae are globally distributed and only absent from the Arctic and Antarctica (Landolt, 1992; Crawford et al., 2006); several Lemnaceae species are invasive species (Abramson et al., 2021; CABI, 2021). The wide distribution of duckweeds means their range overlaps with several model plant pathogens: Pseudomonas syringae pv tomato DC3000, P. syringae pv syringae, Xanthomonas perforans, Xanthomonas euvesicatoria (Cai et al., 2011; Potnis et al., 2015; Gutiérrez‐Barranquero et al., 2019), and many others for which genomic and genetic resources are available. These pathogens often have devastating effects on crop plants yield and value (Martins et al., 2018).

Microbe‐associated molecular pattern‐triggered immunity immune signaling is triggered by the recognition of conserved molecular patterns through pattern recognition receptors (PRRs) and their respective co‐receptors. There are two main types of PRRs: receptor‐like kinases (RLKs) and receptor‐like proteins (RLPs), that lack an intracellular kinase domain (Lolle et al., 2020; Offor et al., 2020; Yuan et al., 2021b). Substantial differences exist between RLP and RLK signaling pathways: immune signaling through the RLP and Suppressor of BIR1 (SOBIR1) co‐receptor pathway genetically requires EDS1, Phytoalexin Deficient 4 (PAD4) and Resistance to Powdery Mildew 8 Nucleotide‐binding Leucine‐rich repeat Receptors (RNLs) and results in higher levels of ethylene and phytoalexins production, as well as Pathogenesis Related 1 gene expression (Pruitt et al., 2021; Tian et al., 2021). Pathogenesis Related (PR) genes are characterized by their rapid upregulation after pathogen infection, they include several antimicrobial genes with different modes of action (glucanase, thaumatin, chitinase, thionin and defensin; Ali et al., 2018). There are many antimicrobial peptides that are not classed as PR genes, such as proteins with the MiAMP1 domain (Marcus et al., 1997; McManus et al., 1999; Stephens et al., 2005). The MiAMP1 domain was identified as a key structural motif in the MiAMP1 protein named after its isolation from Macadamia integrifolia and its antimicrobial activity (Marcus et al., 1997; Stephens et al., 2005). Furthermore, transgenic canola expressing MiAMP1 showed enhanced resistance to the pathogen Leptosphaeria maculans (Kazan et al., 2002). The diversity and modes of action of these antimicrobials remains poorly understood.

Receptor‐like protein and RLK activation primes the cell to initiate a stronger immune response upon Nucleotide‐binding Leucine‐rich repeat Receptors (NLRs) perception of intracellular changes caused by pathogen‐derived effectors (Tian et al., 2021). Conserved domain architecture and signaling specificities of NB‐ARC domain‐containing proteins allow their classification into RNLs, coiled‐coil NLRs (CNLs), Toll/interleukin‐1 receptor NLRs (TNLs) and TIR‐NBARC‐Tetratricopeptide repeats (TNP; Nandety et al., 2013; Shao et al., 2016, 2019; Baggs et al., 2017; Johanndrees et al., 2021). Disease resistance mediated by TNLs and some CNLs is genetically dependent on RNLs and the lipase‐like proteins EDS1 and PAD4 or SAG101. The recognition of pathogen presence by an NLR typically leads to qualitative resistance where the plant shows a discrete resistant phenotype.

In the absence of NLR triggered ETI, plants are not necessarily susceptible to a pathogen. Instead, quantitative resistance may be observed where extent of resistance is more variable and, in some cases, underpinned by hundreds of loci (Corwin & Kliebenstein, 2017). Mechanisms implicated in quantitative resistance include defensins, pathogenesis‐related proteins, secondary metabolite enzymes and pathogen‐induced phytohormones accumulation (Corwin & Kliebenstein, 2017). Qualitative disease resistance follows mendelian patterns of inheritance of resistance of individuals to a given pathogen genotype. Typically, the mechanism of qualitative resistance involves MTI and ETI. In contrast, the level of quantitative resistance spans a continuous distribution and is typically governed by several small effect loci.

In this study, we investigated the phenotypic, genomic, and transcriptomic characteristics of duckweeds in response to Pseudomonas phytopathogens. We found a stepwise reduction of conserved ETI pathways within the Lemnaceae and loss of MTI immune pathway components in W. australiana. However, we observed a shared expansion of the MiAMP1 protein family across Lemnaceae. Additionally, we noticed species‐specific responses to pathogen treatments among duckweed species. We investigated the transcriptional response to pathogens of duckweed species, a system which is adapted to the absence of EDS1‐mediated immune signaling cascades. Our study highlights duckweeds as a rapid growth, high throughput, minimalist MTI‐ETI model organism. As such, it could be utilized to expedite our understanding of EDS1‐independent MTI‐ETI mechanisms of immunity as well as to dissect mechanisms of quantitative resistance.

Materials and Methods

Landoltia punctata clone 5635 DNA isolation and sequencing

Landoltia punctata clone 5635 (Lp5635, formerly DWC138) was received from the Rutgers Duckweed Stock Collective (RDSC; http://www.ruduckweed.org/). Lp5635 was collected, patted dry to remove excess water and flash frozen in liquid nitrogen. Frozen tissue was ground with a mortar and pestle in liquid nitrogen. High molecular weight (HMW) DNA was isolated using a modified Bomb protocol (Oberacker et al., 2019). DNA quality was assessed on a Bioanalyzer and HMW status was confirmed on an agarose gel. Libraries were prepared from HMW DNA using NEBnext (NEB, Beverly, MA, USA) and 2 × 150 bp paired end reads were generated on the Illumina NovaSeq (San Diego, CA, USA). Resulting raw sequence was only trimmed for adaptors, resulting > 60× coverage of the diploid L. punctata genome (350 Mb).

Landoltia punctata clone 5635 genome assembly, gene prediction and annotation

Illumina paired end reads were assembled with Spades (v.3.14.0) with the default settings (Bankevich et al., 2012). Resulting contigs were annotated using a pipeline consisting of four major steps: repeat masking, transcript assembly, gene model prediction, and functional annotation as described (Abramson et al., 2021). Repeats were identified using Edta (v.1.9.8; Ou et al., 2019) and these repeats were used for softmasking. RNA‐sequencing (RNA‐Seq) reads were aligned to the genomes using Minimap2 and assembled into transcript models using Stringtie (v.1.3.6). The soft‐masked genome and Stringtie models were then processed by Funannotate (v.1.6; https://github.com/nextgenusfs/funannotate) to produce gene models. Predicted proteins were then functionally annotated using Eggnog‐mapper (v.2; Huerta‐Cepas et al., 2017).

Pathway ortholog identification

The genomes and proteomes used included Arabidopsis thaliana TAIR 10 (Lamesch et al., 2012), Solanum lycopersicum ITAG2.3 (Tomato Genome Consortium, 2012), Oryza sativa v.7 (Ouyang et al., 2007), S. polyrhiza v.2 (Wang et al., 2014), W. australiana 8730 (Michael et al., 2020), Colocasia esculenta (L.) Schott (C. esculenta S; Yin et al., 2021) and C. esculenta Niue (C. esculenta N) (Atibalentja et al., 2019). Proteome completeness was evaluated using Busco v.5 (Simão et al., 2015; Table S1).

To date the loss of EDS1, PAD4, ADR1 and NDR1, we ran a Blastp search using the O. sativa and A. thaliana EDS1, PAD4, ADR1 and NDR1 proteins as queries against two Taro proteomes; C. esculenta (L.) Schott (C. esculenta S; Yin et al., 2021) and C. esculenta N. If no ortholog was identified, we used tBlastn to check this was not an artifact of annotation. To confirm orthologs, the Taro subject sequence was the query for Blastp to the O. sativa and A. thaliana proteomes. Upon confirming the presence of EDS1, PAD4, ADR1 and NDR1 in Taro, we utilized TimeTree.org to estimate the time during which EDS1, PAD4, ADR1 and NDR1 were lost.

To identify orthologous genes, Orthofinder (v.2.5.4; Emms & Kelly, 2019) was ran on S. polyrhiza 7498 (Wang et al., 2014), L. punctata 5635, W. australiana 8730 (Michael et al., 2020) and A. thaliana TAIR 10 (Lamesch et al., 2012). Infection responsive A. thaliana genes were identified by literature searches (Table S2). The Arabidopsis GIDs were used to extract orthogroups containing Lemnaceae homologs which were cross checked using Phytozome. Gene absence was verified using tBlastn (Camacho et al., 2009). For large gene family analysis of MiAMP1, RLK, RLP and NLRs, pfamscan (Sarris et al., 2016; Madeira et al., 2019) was used to identify proteins with domains of interest (e‐value = 10; MiAMP1, RLK, RLP and NB‐ARC domains; Table S1). Receptor‐like kinase, RLP and MiAMP1 protein sequences were aligned with Muscle v.3.8.1551 (Madeira et al., 2019) and NB‐ARC domains were aligned with hmmalign from hmmer3 (Wheeler & Eddy, 2013) to NB‐ARC1‐ARC2 (Bailey et al., 2018). Alignments were manually curated using Belvu (Barson & Griffiths, 2016) and Jalview (Waterhouse et al., 2009). Maximum likelihood phylogenies were calculated utilizing RAxML (v.8.2.9; ‐f a, ‐x 12 345, ‐p 12345, ‐# 100, ‐m PROTCATJTT).

Plant growth conditions

Duckweed fronds were propagated by transferring three mother fronds to a new well containing fresh media every 3–4 wk. Plants were grown on Schenk and Hildebrandt basal salt media (S6765‐10L, 0.8% agarose, pH 6.5; Sigma‐Aldrich) in six‐well plates and then placed in a growth chamber set to 23°C with a diurnal cycle of 16 h : 8 h, light (75 μmol) : dark.

Pathogen inoculation

Bacterial colonies were grown in Luria Broth (LB) media supplemented with appropriate antibiotics (10 μg ml−1; kanamycin (Km), 50 μg ml−1; rifampicin (Rif), 50 μg ml−1; spectinomycin (Sp)) overnight at 28°C in a shaking incubator (20.5 g). Pathogens used in this study included: P. syringae pv tomato DC3000 GFP (Matthysse et al., 1996; Mudgett & Staskawicz, 1999), P. syringae pv tomato DC3000 cma (Sreedharan et al., 2006), P. syringae pv tomato DC3000 hrcC (Mudgett & Staskawicz, 1999), P. fluorescens N2C3 (DSM 106121; Parte et al., 2020), P. syringae B7281 (Feil et al., 2005), P. syringae pv glycinea race 4 (Staskawicz et al., 1987), P. syringae pv tabaci 11528 (Institute of Medicine (US) Committee on Resource Sharing et al., 1996), P. syringae pv maculicola (Davis et al., 1991), X. euvesicatoria (Roden et al., 2004), X. gardneri (Schwartz et al., 2017), X. perforans (Bophela et al., 2019), and X. translucens (Peng et al., 2016). All strains were plated on Rif; P. syringae pv tomato DC3000 on Rif/Km and P. syringae pv tomato DC3000 cma on Rif/Km/Sp. Liquid culture was centrifuged (3000  g , 15 min). The pellet was resuspended in 10 mM magnesium chloride (MgCl2), the optical density measured at a wavelength of 600 nm (OD600) was determined, and the infiltration solution was diluted to a final standard high inoculum of OD600 = 0.1, equivalent to 1 × 108 cells of Pst DC3000. Then 500 μl of OD600 = 0.1 solution was pipetted on to three duckweed fronds per well. The plate was then returned to the incubator or placed in vacuum (0.8 PSI) for 10 min.

For growth curves, bacterial cells were resuspended at 1 × 108 colony‐forming unit (CFU) ml−1, OD600 = 0.1 in 10 mM MgCl2. The inoculum was diluted to a standard low inoculum with a final concentration of 1 × 105 CFU ml−1. Each well was inoculated with 500 μl of treatment, followed by vacuum infiltration (0.8 PSI) for 10 min. For each timepoint 1 cm2 of fronds was sampled in 150 μl MgCl2 and glass beads, then homogenized by a biospec mini‐beadbeater (2000 rpm, vital distance 3.175 cm). Serial dilutions were made and plated on selective media. Two days after plating, colonies were counted.

Microscopy

For microscopy of duckweed treated with bacteria, flood inoculation with 500 μl solutions OD600 = 0.1 were used. Whole fronds were staged in water on slides and covered with a glass coverslip. The slides were imaged on a Zeiss 710 LSM confocal microscope (Zeiss) with either the 20× (water), 63× (oil) or 100× (oil) objectives. To image bacteria on duckweed fronds, they were stained with 10 μl of 1× Syto™ BC Green Fluorescent Nucleic Acid Stain (S34855; Thermo Scientific, Waltham, MA, USA). Duckweed fronds treated with SA were imaged on an Olympus SZX12 stereo microscope.

Hormone supplementation

Coronatine (C8115‐1MG; Sigma‐Aldrich) powder was dissolved in 100% dimethyl sulfoxide (DMSO) to create a 200 μM ml−1 stock that was then diluted to 3 and 0.3 μM in double‐distilled water (ddH2O). Salicylic acid (SA) BioXtra ≥ 99.0% (S5922‐100G; Sigma‐Aldrich) was diluted in water to concentrations of 2, 0.4 and 0.2 mM. Buffer solution used was the same as the solvent for the treatment. Individual wells of 6 or 12 well plates containing three duckweed fronds were inoculated with 500 or 250 μl of phytohormone solution, respectively. Phytohormone treatments were applied just before pathogen treatment.

RNA‐Seq analysis

Inoculations were conducted as outlined earlier using standard high bacterial inoculum 1 × 108 CFU ml−1. At 30 min, 1 h, 6 h and 12 h after inoculation, fronds from the same well were transferred to tubes containing 1.5 ml RNA later and frozen in liquid nitrogen. RNA was extracted using Qiagen RNAeasy plant kit (74903; Qiagen) and samples with a RNA integrity number score of > 8.0 (Agilent Bioanalyzer 2100 performed by QB3‐UC Berkeley, Berkeley, CA, USA) were used for library preparation. Library construction and sequencing were performed by Novogene (Sacramento, CA, USA) using NEBNext Ultra II RNA library prep by Illumina (E7770; Illumina, San Diego, CA, USA) and an Illumina Novaseq 6000 S4. After sequencing, reads were demultiplexed using Illumina indices and a quality control (QC) check removed ‘N’ containing, low quality, and adapter‐related reads. Upon receiving the data from Novogene, an initial QC of reads was performed with Fastqc (Andrews, 2010). Reads were then mapped to the S. polyrhiza v.2 genome using Hisat2 (Kim et al., 2015). Read coverage tables were computed using Stringtie (Pertea et al., 2015) and differential gene expression analysis was carried out using edgeR (Dai et al., 2014). Genes were considered differentially expressed if they met the criteria ¦log2FC¦ > 1, FDR < 0.05 (FC, fold change; FDR, false discovery rate). For a detailed list of commands see: https://github.com/erin‐baggs/DuckweedRNA. Outliers were removed by visual inspection; the edgeR count tables were plotted as PlotMDS method log2FC and BCV (Figs S1–S4). Samples were removed if they alone were causing most of the variance on a dimension leading to all other samples clustered into a corner. Then, if one sample of a treatment was grouping with samples of an opposing treatment, gene expression was analyzed to check whether the sample's expression pattern may have been the result of cross contamination. If the expression was inconsistent with at least three other replicates from the treatment group it belonged to, the sample was removed. Replicates removed from S. polyrhiza analysis included B3, B20, B19, P21, B34, P114. The replicates B15, B20, P39, P70, H80, B94, P103 and H115 were removed from L. punctata analysis.

Orthofinder (Emms & Kelly, 2019) was used to identify orthogroups between A. thaliana and duckweeds. Arabidopsis thaliana gene identifiers of marker genes were then used to extract the gene identifiers of duckweed homologs. The S. polyrhiza and A. thaliana homologous relationships were then cross‐referenced by comparing to Phytozome protein homologs. Phytozome was then used to infer O. sativa homologs. The differential expression of the duckweed homologs to A. thaliana marker genes was extracted from edgeR data and plotted in R genevestigator (https://genevestigator.com; Hruz et al., 2008) was used to identify the differential expression of A. thaliana homologs to duckweed upregulated genes in two public Affymetrix Arabidopsis ATH1 genome array (AtGenExpress: ME00331 (Kemmerling et al., 2011) and GEO: GSE5520 (Thilmony et al., 2006)) a third array was used to investigate ABA response (Array Express: E‐MEXP‐2378 (Umezawa et al., 2010)). The differential expression O. sativa homologs to duckweed conserved pathogen upregulated genes was extracted from the EBI Expression atlas (Athar et al., 2019).

Results

Lemnaceae condensed many immune signaling components and expanded the MiAMP1 protein family

To understand how immune genes have diverged within the Lemnaceae, we utilized publicly available genomes of S. polyrhiza (Wang et al., 2014) and W. australiana (Michael et al., 2020). Additionally, we assembled and annotated the genome of L. punctata clone 5635 (Table S1), which allowed us to catalog the presence and absence variation across three duckweed genera (Fig. 1a; Table S3). Consistent with previous findings EDS1, PAD4, and RNLs were absent across the Lemnaceae (Lapin et al., 2019; Baggs et al., 2020; Michael et al., 2020). To estimate the timing of the EDS1 pathway loss, we used the closest related species to duckweeds with an available genome, giant Taro (C. esculenta; Yin et al., 2021). Taro has the EDS1 pathway (Fig. 1a), suggesting that the loss of these genes in Lemnaceae occurred after their divergence from Taro 104–117 Ma (Kumar et al., 2017). The identification of several MiAMP1 genes in Taro (Fig. 1b) suggests the expansion of MiAMP1 proteins can occur independently of the loss of EDS1. Without greater depth of sampling of the Araceae family and sister lineages it remains unclear if MiAMP1 expansions are independent or a single expansion event.

Fig. 1.

Fig. 1

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Copy number variation of conserved angiosperm immune signaling components. (a) Phylogenetic relationship of duckweeds to other representative angiosperms and depiction of presence of signaling components. If components were not identified by Orthofinder, reciprocal Blastp, and tBlastn, the representative symbol was not displayed in the cell. (b) The size of circles within each column are proportional to the highest copy number of that gene family in a given species, the copy number is denoted in white text. White circles with a gray outline indicate no members of that gene family were identified.

To understand the extent of the immune signaling pathways divergence across Lemnaceae, we surveyed the presence of 24 protein families that have a known role in plant disease defense and pre‐date the monocot and dicot divergence. All 24 protein families selected were present in S. polyrhiza (Tables S3, S4; Figs S5–S7). Though gene families associated with immunity were often retained in the Lemnaceae, we noticed a reduction in copy number, like NLRs (Figs 1b, S7; Table S3). Only three NB‐ARC domain‐containing proteins are present in W. australiana (Michael et al., 2020): two of contain only an NB‐ARC and the other is a TNP whose conservation is consistent with TNPs being EDS1 independent (Johanndrees et al., 2021). Wolffia australiana has retained many other immune components. While we identified no orthologs of Pathogenesis related 4 (PR4), SOBIR1 and BAK1 interacting receptor 1 (BIR1) in W. australiana, they were present in L. punctata and S. polyrhiza (Fig. 1b; Table S3). Our observations indicate that immune pathways have undergone varying degrees of gene loss across the duckweed genera.

Despite the trend towards reductionism of immune signaling components in the Lemnaceae, we identified high copy numbers of MiAMP1 domain‐containing proteins. It has previously been shown that antimicrobial proteins in general are expanded in S. polyrhiza 7498 (An et al., 2019). Phylogenies of Lemnaceae MiAMP1 proteins show lineage specific expansions and rapid birth and death (Fig. S8) suggesting that selection pressures may be favoring their diversification.

Duckweed species show variable symptoms upon Pseudomonas and Xanthomonas challenge

Since Lemnaceae species lack the EDS1 pathway, it was unclear how they would respond to bacterial pathogen infection. We challenged S. polyrhiza, L. punctata, and W. australiana to a panel of common Pseudomonas and Xanthomonas plant pathogens with a standard high bacterial inoculum (1 × 108 CFU ml−1; Fig. 2a). We observed variability among replicates in the susceptibility and resistant phenotypes (Dataset S1; 2b) which is consistent with quantitative resistance. The variability was observed when experiments were started with a single (Fig. S9) or three mother fronds (all other experiments). We identified a few virulent pathogens that produced similar symptoms across all hosts (X. gardneri, P. syringae pv tabaci) while others caused distinct symptoms on a given host species. The most common disease symptoms were chlorosis and reduced growth rate. Surprisingly, despite having fewer NLRs and lacking SOBIR1 and BIR1, the growth of W. australiana upon pathogen infection was often less stunted than that of other duckweed species.

Fig. 2.

Fig. 2

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Phenotypic response of duckweed species to bacterial pathogen treatments. (a). One replicate is shown per treatment (full experiment has been independently replicated three times, see Dataset S1; Table S5). Each well displays the most prevalent visual symptom of infection 3 wk after pathogen treatment of five frond clusters. (b) Bar graph showing the percentages of wells displaying each symptom among all replicates (Table S5). Colors of bars correspond to color legend of disease symptoms.

For further experiments, we focused on P. syringae pv tomato DC3000 (Pst DC3000) and P. syringae pv syringae B728a (Pss B728a) as there are known virulence factors, large resources of mutants, and the severity of disease symptoms caused varied across duckweed species.

Pseudomonas syringae colonizes the substomatal cavity in Spirodela polyrhiza and infection is slowed in T3SS mutants

To characterize the pathology of Pseudomonas on duckweed, we used microscopy and bacterial genetics, taking advantage of the type III secretion deficient mutant Pst DC3000 hrcC.

Microscopy of flood inoculated S. polyrhiza fronds with standard high bacterial inoculum at 5 d post inoculation (dpi) showed Pst DC3000 populations were concentrated at the node (root and frond joint) and budding pockets (where daughter fronds emerge; Fig. S10). By 7 dpi, Pst DC3000 populations were observed at the stomata and within the substomatal cavity and mesophyll (Fig. 3a; Dataset S2). We also observed Pst DC3000 populations on root tissue at 7 dpi (Fig. 3b). Pst DC3000 hrcC infection resulted in surface populations on the frond at 5 dpi (Fig. S11a). Individual bacteria were present in the mesophyll (Fig. S11b) but no clear sub‐stomatal populations. At 7 dpi like Pst DC3000 the Pst DC3000 hrcC populations were localized at the frond node (Fig. S11c) however, much smaller areas were colonized. The pattern of small surface populations and individual bacteria in the mesophyll remained the same at 5 and 7 dpi with Pst DC3000 hrcC (Fig. S11d,e). Together, our observations suggest that the visual symptoms of Pseudomonas on duckweed are a result of active bacterial infection. Therefore, the duckweed‐Pseudomonas pathosystem constitutes a valuable model for understanding disease progression and EDS1‐independent immune responses.

Fig. 3.

Fig. 3

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Pst DC3000 infection of Spirodela polyrhiza. Confocal ×100 microscopy of Spirodela polyrhiza inoculated with Pst DC3000. False colors: pink – Pst DC3000, green – plastids, gray – transmitted light. (a) Frond surface with stomata in the center of frame. (b) Root tissue. (c) Images of duckweed fronds and symptoms at 3 and 5 d post inoculation (dpi) with Pst DC3000 and Pst DC3000 hrcC. The Pst DC3000 hrcC mutant lacks the type III secretion system used for deployment of effectors to the plant. Individual fronds were photographed on a 10 μl tip to indicate size of fronds. Arrows highlight areas where symptoms are present. (d) Box plot showing the number of colony‐forming units (CFUs) of Pst DC3000 and Pst DC3000 hrcC at different time points on Spirodela polyrhiza fronds. The top horizontal line of each box represents the upper quartile followed by the median and lower quartile. The range extends between the smallest data point within the first quartile value subtract 1.5× the interquartile range and the largest data point within the third quartile value add 1.5× the interquartile range. Colored circles indicate individual data points. Mean‐t statistic with Holm adjustment used to assess statistical difference between treatments.

To advance our understanding of this pathosystem, we infiltrated S. polyrhiza plants with Pst DC3000 and Pst DC3000 hrcC with a low bacterial inoculum (1 × 105 CFU ml−1) and monitored bacterial population growth overtime (Fig. 3c,d). The first black lesions were macroscopically visible at 3 dpi with Pst DC3000 and by day 5 we observed an increase in the size, number of lesions and proportion of affected fronds (Fig. 3c). Consistent with the dense populations at the budding pocket and node observed by microscopy, macroscopic black lesions observed were initially localized at the budding pocket and node. We observed no black lesions upon inoculation with Pst DC3000 hrcC at 3 or 5 dpi. In contrast, L. punctata infiltrated with Pst DC3000 or Pss B728a showed no macroscopic lesions even 5 dpi (Fig. 4a,b). Three weeks post inoculation, 5/8 replicates of L. punctata inoculated with Pss B728a had turned white and produced fewer daughter fronds than in other treatments. However, even at 4 wk post inoculation with Pst DC3000 we observed only a slightly darker coloration and clumping. Pst DC3000 hrcC did not cause any symptoms on L. punctata throughout the 3 wk post inoculation (Fig. S12).

Fig. 4.

Fig. 4

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Pst DC3000 infection of Landoltia punctata. (a) Images of Landoltia punctata fronds and symptoms during Pst DC3000, Pst DC3000 hrcC and Pss B728a. (b) Individual fronds were photographed on a 10 μl tip to indicate size of fronds. (c) Box plot showing the number of colony‐forming units (CFUs) of Pst DC3000, Pst DC3000 hrcC and Pss B728a at different time points on Landoltia punctata fronds. The top horizontal line of each box represents the upper quartile followed by the median and lower quartile. The range extends between the smallest data point within the first quartile value subtract 1.5× the interquartile range and the largest data point within the third quartile value add 1.5× the interquartile range. Colored circles indicate individual data points. Mean‐t statistic with Holm adjustment used to assess statistical difference between treatments.

Pst DC3000 and Pst DC3000 hrcC were able to proliferate inside S. polyrhiza (Fig. 3d). Pst DC3000 multiplied to similar levels as previously described in compatible interactions with wild‐type A. thaliana (Katagiri et al., 2002; Ishiga et al., 2011; Velásquez et al., 2017). However, we observed significantly fewer colony forming units of Pst DC3000 hrcC compared to Pst DC3000. This suggests that the virulence of Pst DC3000 on S. polyrhiza relies on the presence of effectors and the type III secretion system. In contrast, consistent with the lack of disease symptoms in L. punctata neither Pst DC3000 nor Pst DC3000 hrcC showed significantly different CFU counts at 5 dpi. Instead, both bacterial numbers remained at a level similar to those of Pst DC3000 hrcC in S. polyrhiza (Fig. 4c). Interestingly, although we saw no visible lesions on L. punctata 5 d after Pss B728a inoculation, the CFU counts were significantly higher than those of Pst DC3000 after 5 d. However, the CFU counts of Pss B728a in L. punctata and Pst DC3000 in S. polyrhiza were similar. Our results show that Pseudomonas pathogens Pst DC3000 and Pss B728a can proliferate in duckweed host and bacterial multiplication is affected by unknown host factors and by the type III secretion system for S. polyrhiza–Pst DC3000 interaction.

Hormone treatment effects vary among Lemnaceae spp. and pathogen treatment

In response to biotrophy, plants upregulate the phytohormones SA (Cao et al., 1994; Clarke et al., 1998) and N‐hydroxy pipecolic acid (NHP) through pathways dependent and independent of EDS1 (Chen et al., 2018). To suppress phytohormone responses, Pst DC3000 produces the toxin coronatine (Moore et al., 1989; Mittal & Davis, 1995), a structural mimic of jasmonic acid (JA) which counteracts SA upregulation (Fonseca et al., 2009; Wasternack & Xie, 2010). Since Pst DC3000 was virulent on S. polyrhiza, we investigated the role of phytohormones and toxins in the absence of EDS1 through our duckweed pathosystem. The Pst DC3000 cma mutant is deficient in coronatine biosynthesis and causes mild symptoms on S. polyrhiza, marked by the absence of black lesions (Figs 5a, S13, S14). Addition of coronatine alone (0.3 μM) was sufficient to disfigure fronds, reduce growth, and induce the formation of turion resting bodies. The effect on growth and turion formation was stronger at higher concentrations of coronatine (3 μM). However, addition of coronatine on its own was not sufficient to recover symptoms of black lesions and white bleaching of fronds comparable to Pst DC3000 infection. The treatment of fronds with coronatine at the time of Pst DC3000 cma infection restored the black lesions to some extent, but the strain still did not induce white bleaching. Our results show that coronatines role in promoting pathogen virulence is conserved in duckweeds despite the absence of the SA promoting EDS1 pathway.

Fig. 5.

Fig. 5

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The effects of hormone treatments on Pst DC3000 and Pss B728a disease symptoms. (a) Images of disease progression 3 wk after the treatment of three Spirodela polyrhiza fronds with different concentrations and combinations of coronatine and Pst DC3000 mutants (full experiment has been independently replicated three times, see Figs S13, S14). (b) Images of disease progression 3 wk after the treatment of five S. polyrhiza fronds with different concentrations and combinations of salicylic acid (SA) and Pst DC3000 (full experiment has been independently replicated four times, see Figs S17–S19). (c) Images of disease progression 3 wk after the treatment of five Landoltia punctata fronds with different concentrations and combinations of SA and Pss B728a treatment, Pss B728a was used rather than Pst DC3000 as it gives clearer visual symptoms in L. punctata (full experiment has been independently replicated three times, see Figs S21, S22).

Given that a core function of the EDS1 pathway is to reinforce SA signaling by inducing several SA responsive genes (Jirage et al., 1999; Bonardi et al., 2011; Roberts et al., 2013; Cui et al., 2017); we were interested in how exogenous SA treatment of Lemnaceae would affect disease symptoms. Treatment of Lemnaceae with high levels of SA (2 mM) that are tolerated in Arabidopsis were phytotoxic to S. polyrhiza (Fig. S15). We therefore carried out experiments using 0.4 and 0.2 mM SA. At these lower concentrations in the absence of a pathogen, we observed dose‐dependent frond disfiguration, growth reduction and an increase in turion formation in S. polyrhiza. L. punctata only showed growth reduction with 0.4 mM SA (Fig. S16). S. polyrhiza co‐treated with SA and Pst DC3000 showed typical Pst DC3000‐induced symptoms (Figs 5b, S17–S20). However, a small protective effect of SA was observed when Pst DC3000 appeared to be less virulent (Fig. S20). We hypothesize that the variation in virulence of Pst DC3000 on S. polyrhiza affects the ability of SA to restrict pathogen growth below a critical threshold. In the L. punctata–Pss B728a pathosystem, there was a clear protective effect of SA. Priming of L. punctata with SA resulted in loss of the chlorotic symptoms characteristic of the Pss B728a treated fronds (Figs 5c, S21, S22). The effect of SA priming on duckweed pathogen interactions appears to be influenced by pathogen strain and plant genotype and complicated by strong endogenous effects of SA on duckweed physiology.

Spirodela polyrhiza and Landoltia punctata mount transcriptional responses to Pst DC3000

Next, we investigated transcriptional response to Pseudomonas infection. Despite the absence of EDS1, RNA‐Seq revealed a substantial transcriptional response as early as 30 min post infection (Figs 6a, S23; Datasets S3, S4). We first examined gene families that are differentially expressed upon pathogen treatment in other plant species: WRKY (Dong et al., 2003), MiAMP1 (Fig. 6b; Adomas & Asiegbu, 2006; Adomas et al., 2007), NB‐ARC (Richard et al., 2018), and JAZ domain‐containing genes (Ishiga et al., 2013; Fig. S24; Tables S6–S13). While we did not observe consistent upregulation log2FC > 1 of NB‐ARC and WRKY domain‐containing genes, MiAMP1 domain‐containing genes were consistently upregulated over time in both duckweed species after pathogen treatment.

Fig. 6.

Fig. 6

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Log2‐fold change (FC) of selected gene families following bacterial pathogen exposure of duckweeds. (a) Schematic of the sampling regime for RNA‐sequencing (RNA‐Seq) study. (b) Each square represents a gene with a given domain, the differential expression of the gene is shown by the color of the square. The treatment comparison is indicated by the shapes shown in (a). (c) Venn diagram of numbers of orthogroups that show differential expression (log2FC > 1, false discovery rate (FDR < 0.05) in multiple combinations of pathogen treatments and or across different species at any time point. (d) Microarray differential expression analysis of representatives from Arabidopsis thaliana Col‐0 of those orthogroups with conserved upregulation upon Pst DC3000 in Spirodela polyrhiza and Landoltia punctata.

We hypothesized that there may be some orthologs of A. thaliana bacterial‐response genes that are also upregulated in duckweed species upon bacterial infection. Among the 33 S. polyrhiza and 32 L. punctata genes considered homologous to A. thaliana bacterial‐responsive genes (Boudsocq et al., 2010; Bjornson et al., 2021; Salguero‐Linares et al., 2021; Tables S2, S14; Fig. S25), we were able to identify five S. polyrhiza and five L. punctata gene orthogroups that were significantly upregulated upon pathogen treatment (log2FC > 1, FDR < 0.05; Table S15).

Given the suppression of Pss B728a symptoms in L. punctata upon SA treatment we hypothesized the reduced symptoms seen in L. punctata compared to S. polyrhiza after Pst DC3000 treatment may be due to greater upregulation of SA pathways in L. punctata. We investigated the differential expression of duckweed homologs of A. thaliana SA biosynthetic and regulated genes (Fig. S26a; Table S16). In S. polyrhiza and L. punctata we did not observe consistent upregulation of upstream SA biosynthesis genes and regulators (ICS1, NPR1, SARD1, CBP60g) or SA catabolism (DMR6; Fig. S26b; Tables S17, S18). However, in both duckweed species we saw consistent upregulation of PR1/PR2 ‐like genes which in A. thaliana are upregulated downstream of SA production. L. punctata also upregulates WRKY40 homologs after pathogen treatment. Together this indicates that A. thaliana and duckweed pathogen transcriptional response pathways converge downstream of SA production and differences in SA signaling is unlikely to explain different disease outcomes between duckweed species.

To investigate which genes have conserved upregulation in A. thaliana and duckweeds, we created interspecific orthogroups with Orthofinder (Emms & Kelly, 2019). Then, we identified orthogroups with members that were significantly differentially expressed in both duckweeds after Pst DC3000 treatment (Fig. 6c; Table S19; Dataset S5). For orthogroups with conserved upregulation in both duckweeds, we investigated if the A. thaliana homologs were similarly upregulated upon Pst DC3000 infection, using publicly available datasets (Figs 6d, S27). Among the A. thaliana homologs, eight genes showed consistent upregulation after Pst DC3000 treatment, including a superoxide dismutase GERMIN‐LIKE PROTEIN 5. In contrast upon pathogen treatment, PATHOGENESIS RELATED 9 (PR9) was upregulated in duckweeds but not in A. thaliana. There appears to be limited overlap in the transcriptional response to Pst DC3000 between A. thaliana and duckweeds. Interestingly, half of the orthogroups upregulated in response to bacterial infection, in duckweeds and Arabidopsis were hormone‐regulated or biosynthetic genes suggesting a conserved role of hormones. As the immune pathways of monocots and dicots have diverged substantially, we looked at the differential expression of orthologs of the duckweed upregulated genes in rice across several pathogen treatments (Fig. S28; Table S20). Ten of 28 orthogroups upregulated in duckweeds were also upregulated in rice (log2FC > 1.5 across three treatments). Among these genes, five orthogroups included genes also upregulated in A. thaliana.

Finally, we identified genes upregulated and unique to duckweeds. Four orthogroups which showed conserved differential expression in both duckweed species do not have homologs in A. thaliana (Table S19) of these only two lacked homologs in rice. The two unique orthogroups were cytochrome P450 domain‐containing genes. Cytochrome P450s are present in A. thaliana and O. sativa (Nelson et al., 2004), but lack sequence conservation to those upregulated in duckweeds. MiAMP1 domain‐containing genes are not present in A. thaliana. Although MiAMP1 proteins were upregulated across timepoints in both duckweeds, orthogroups containing these proteins did not meet the criteria for conserved upregulation. Since duckweed species do not show a qualitative but a quantitative resistance, we hypothesize that such resistance mechanisms could involve MiAMP1 proteins and cytochrome P450s.

Discussion

We investigated the immune responses of Lemnaceae whose immune pathways have evolved for c. 110 million years in the absence of EDS1, a hub for signaling and crosstalk in plant immune system. Whilst the loss of EDS1 predates the divergence of Lemnaceae, only W. australiana has lost the MTI signaling components SOBIR1‐BIR1 (Gao et al., 2009; Liebrand et al., 2013; Albert et al., 2015; van der Burgh et al., 2019). Previous work illustrates RLP‐SOBIR1 dependence on EDS1 and PAD4 in A. thaliana (Pruitt et al., 2021). Despite the loss of SOBIR1, compared to other Lemnaceae, W. australiana did not have enhanced susceptibility to the bacterial pathogens tested. Given available resources, we began developing and characterizing the pathosystem of S. polyrhiza and L. punctata interactions with Pst DC3000 and Pss B728a. Transcriptional and bacterial mutant assays revealed the importance of phytohormones in their response to pathogens; however, differential expression of bacteria responsive A. thaliana orthologs was rarely observed. Furthermore, we show that there is a high copy‐number and upregulation upon pathogen infection of MiAMP1 domain‐containing proteins in Lemnaceae.

Duckweeds exist with complex bacterial communities similar to the terrestrial leaf microbiome (Acosta et al., 2020). Several duckweed‐associated bacteria have growth promoting effects (O'Brien et al., 2020). To understand disease resistance of Lemnaceae to pathogenic bacteria, we screened a range of bacterial pathogens to identify a model pathosystem. Our data shows differences in macroscopic symptoms across Lemnaceae species upon treatment with the same pathogen. Variability was present between fronds within the same treatment well, suggesting there may be environmental and population level factors affecting the outcome of infection. The variability in susceptibility across time points is consistent with quantitative resistance. The invasive nature of duckweeds and numerous healthy populations in the environment raises the questions of how environmental factors may contribute to their quantitative disease resistance. Furthermore, because duckweeds primarily reproduce clonally, have low mutation rates (Sandler et al., 2020) and effective recombination (Ho et al., 2019), there is limited opportunity for immune proteins to diversify, which is required for generating new resistance specificities. The rather unique combination of duckweeds life‐history traits of fast‐growth, clonal reproduction, reduced morphological complexity and aquatic habitat could influence mechanisms of resistance. In a freshwater habitat, the frequency of contact with MAMPs and microbes likely differs from soil and could affect the balance of the energy trade‐off between growth and defense (Huot et al., 2014).

The use of bacterial mutant Pst DC3000 hrcC revealed the type‐III secretion system can promote virulence of Pst DC30000 on S. polyrhiza, similar effects have been observed in A. thaliana (Hauck et al., 2003; Xin et al., 2016; Huot et al., 2017). Furthermore, our results suggest that virulence of Pst DC3000 on S. polyrhiza relies on manipulation of host phytohormone pathways through the production of the JA mimic coronatine, similar to in A. thaliana (Moore et al., 1989; Mittal & Davis, 1995). In contrast, L. punctata appears to have a stronger quantitative resistance response to Pst DC3000 even in the presence of coronatine. Despite the lack of conservation of the EDS1 pathway in S. polyrhiza and L. punctata, phytohormones and bacterial toxins remain key mediators of plant–bacterial infection. The reduction of MTI/ETI components in duckweeds opens questions of how duckweeds pathogens evolve; would reduction in effector repertoire be selected due to a reduced pressure for MTI/ETI suppression or would effectors further expand without ETI systems to activate.

Consistent with the variation in phenotypes upon bacterial inoculations, there was a lot of variation that was not explained by pathogen treatment between transcriptomes of duckweed biological replicates. The high variability could be a result of the asynchronous developmental stages of fronds, the quantitative nature of the resistance that we observed within the same population of duckweed or a combination of factors. The use of whole plant tissues likely diluted the signal of differential gene expression. In future studies, single cell transcriptomics might be informative for studying quantitative resistance phenotypes. Despite the variability, our transcriptomic analysis revealed that among the genes consistently differentially expressed after Pseudomonas treatment were MiAMP1‐domain‐containing proteins. We hypothesize MiAMP1‐domain proteins maybe upregulated as a nonspecific response to infection, that protects against some but not all pathogens. Such consistent upregulation invites the speculation that they could function against pathogens similarly to MiAMP1 from Macadamia (McManus et al., 1999; Stephens et al., 2005) which was shown to have anti‐microbial activity against gram‐positive bacteria and fungal pathogens (Marcus et al., 1997; Kazan et al., 2002; Stephens et al., 2005). Thus, duckweed MiAMP1 proteins warrant further investigation against some of the known fungal and oomycetes pathogens of duckweed (Fisch, 1884; Gaumann, 1928; Vanky, 1981; Rejmankova et al., 1986; Flaishman et al., 1997; Czeczuga et al., 2005; Brand et al., 2021). Other commonly differentially expressed protein families were associated with cytochrome P450s and phytohormones, indicating that specialized metabolites may have an important role in duckweeds infection response. Together, our data highlights that, despite the absence of EDS1, there are conserved areas of immune response pathways across plant species, such as phytohormones and secondary metabolism. Perhaps the reduced reliance on NLR–EDS1 pathways in duckweeds released selective constraints allowing avoidance of the typical plant–NLR vs pathogen–effector arms races and facilitating amplification of alternate immune strategies.

Duckweeds are an exciting system for research into plant immunity. They provide a reduced redundancy system in terms of both gene copy number and pathways present. In addition, their rapid lifecycle of just 34 h (Michael et al., 2020), small size and susceptibility to model pathogens make them the perfect system for high‐throughput experimentation. The genomic and genetic resources are also developing at a rapid pace (Wang et al., 2014; Michael et al., 2017, 2020; Hoang et al., 2018; An et al., 2019; Ho et al., 2019; Xu et al., 2019; Harkess et al., 2021) and a range of transformation protocols (Ko et al., 2011; J. Yang et al., 2018; G‐L. Yang et al., 2018; Liu et al., 2019; Acosta et al., 2021; Chanroj et al., 2021). Many interesting questions remain to be answered including how duckweed plants can thrive in a wide range of environments despite being highly susceptible to bacterial phytopathogens in the laboratory setting. One hypothesis is that duckweeds may be using means of disease protection that are inherently absent in the laboratory due to the way duckweeds are propagated. This may include microbiome‐mediated disease protection, small peptide defense molecules such as MiAMP1s or chemical defense strategies. A combination of genetics, molecular biology and metabolomic approaches will be needed to address these hypotheses.

Author contributions

ELB performed genomic/transcriptomic presence absence analysis, growth curves, hormone assays, microscopy and RNA‐Seq analysis. MBT and ELB performed the screen of phytopathogens across duckweed species and RNA extractions. BWA and TPM performed DNA extraction, genome assembly and annotation of L. punctata 5635 genome. ELB and KVK designed the study. ELB prepared figures and wrote the full manuscript draft; all authors have read and approved the final version of the manuscript.

Supporting information

Dataset S1 Powerpoint presentation of pathogen inoculation symptoms.

Dataset S2 Z‐stack microscopy.

Dataset S3 Differentially expressed gene tables Spirodela polyrhiza.

Dataset S4 Differentially expressed gene tables Landoltia punctata.

Dataset S5 Orthogroup assignment and overlap across pathogen treatments and species.

Click here for additional data file. (101.3KB, pdf)

Fig. S1 PlotMDS for all Spirodela polyrhiza RNA‐Seq samples before outlier removal.

Fig. S2 PlotMDS for Spirodela polyrhiza RNA‐Seq samples after outlier removal.

Fig. S3 PlotMDS for all Landoltia punctata RNA‐Seq samples before outlier removal.

Fig. S4 PlotMDS for Landoltia punctata RNA‐Seq samples after outlier removal.

Fig. S5 Phylogeny of Nucleotide‐binding Leucine‐rich repeat Receptor proteins in duckweeds.

Fig. S6 Phylogeny of receptor‐like kinase proteins in duckweeds.

Fig. S7 Phylogeny of receptor‐like protein type proteins in duckweeds.

Fig. S8 Phylogeny of MiAMP1 domain‐containing proteins in duckweed species.

Fig. S9 Symptoms of Spirodela polyrhiza populations derived from a single mother frond after infection with Pst DC3000.

Fig. S10 Microscopy of frond surface 5 d post flood inoculation with Pseudomonas syringae pv tomato DC3000.

Fig. S11 Microscopy of duckweed frond surface 5 and 7 d post flood inoculation with Pseudomonas syringae pv tomato DC3000 hrcC.

Fig. S12 Low bacterial load infection of Landoltia punctata 1 month post inoculation.

Fig. S13 Role of coronatine in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S14 Role of coronatine in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S15 Salicylic acid phytotoxicity to Spirodela polyrhiza upon buffer or Pst DC3000 treatment.

Fig. S16 Dissecting microscope images of duckweed 12 d post inoculation with salicylic acid.

Fig. S17 Role of salicylic acid in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S18 Role of salicylic acid in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S19 Role of salicylic acid in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S20 Role of salicylic acid in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S21 Role of salicylic acid in Pss B728a infection of Landoltia punctata.

Fig. S22 Role of salicylic acid in Pss B728a infection of Landoltia punctata.

Fig. S23 Bar chart of number of genes differentially expressed upon bacterial treatments.

Fig. S24 Log2 fold change of selected gene families following bacterial pathogen exposure of duckweeds.

Fig. S25 Log2 fold change of homologs of Arabidopsis thaliana bacterial responsive genes.

Fig. S26 Differential expression upon pathogen treatment of the duckweed homologs of Arabidopsis salicylic acid marker genes.

Fig. S27 Orthogroups with conserved upregulation upon pathogen treatment of duckweed species and the expression of orthologs in Arabidopsis thaliana.

Fig. S28 Heatmap displaying log2 fold change of rice homologs of duckweed conserved upregulated gene upon pathogen infection.

Click here for additional data file. (106.8MB, pdf)

Table S1 Genome assembly statistics for Landoltia punctata 5635 genome and proteome Busco scores.

Table S2 Table of common name and duckweed gene IDs homologous to Arabidopsis thaliana disease marker genes.

Table S3 Copy number of orthologs/homologs of immune associated genes.

Table S4 Table of Spirodela polyrhiza homologs to Arabidopsis immunity genes.

Table S5 Syndrome prevalence upon pathogen treatment of Lemnaceae species.

Table S6 Log2 fold change of JAZ domain‐containing Spirodela polyrhiza genes.

Table S7 Log2 fold change of WRKY domain‐containing Spirodela polyrhiza genes.

Table S8 Log2 fold change of MiAMP1 domain‐containing Spirodela polyrhiza genes.

Table S9 Log2 fold change of NB‐ARC domain‐containing Spirodela polyrhiza genes.

Table S10 Log2 fold change of JAZ domain‐containing Landoltia punctata genes.

Table S11 Log2 fold change of WRKY domain‐containing Landoltia punctata genes.

Table S12 Log2 fold change NBARC domain‐containing genes Landoltia punctata.

Table S13 Log2 fold change MiAMP1 domain‐containing genes Landoltia punctata.

Table S14 Orthologs to Arabidopsis thaliana marker genes that are significantly upregulated upon Pst treatment log2FC > 0.75 FDR < 0.05 in either Spirodela polyrhiza or Landoltia punctata.

Table S15 Homologs to Arabidopsis thaliana marker genes that are significantly upregulated upon Pst treatment log2FC > 1 FDR < 0.05.

Table S16 Table of duckweed genes homologous of Arabidopsis thaliana salicylic acid responsive genes.

Table S17 Log2 fold change of Spirodela polyrhiza genes homologous to Arabidopsis thaliana salicylic acid responsive genes.

Table S18 Log2 fold change of Landoltia punctata genes homologs to Arabidopsis thaliana salicylic acid responsive genes.

Table S19 Orthogroups upregulated in Landoltia punctata and Spirodela polyrhiza after Pst DC3000 treatment.

Table S20 Log2 fold change of rice genes homologous to pathogen upregulated duckweed genes.

Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

Click here for additional data file. (134.1KB, xlsx)

Acknowledgements

The authors would like to thank Dr Denise Schichnes and Dr Steven Ruzin for their expert advice and help with confocal microscopy. Research reported in this publication was supported in part by the National Institutes of Health (NIH) S10 program under award number 1S10RR026866‐01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Many thanks to China Lunde for propagation of duckweed during the pandemic and to Dr Wilfried Haerty and the members of the Krasileva laboratory for helpful discussions. The authors acknowledge funding from the Biotechnology and Biological Sciences Research Council (BBSRC), part of UK Research and Innovation, Core Capability Grant BB/CCG1720/1 and the work delivered via the Scientific Computing group, as well as support for the physical HPC infrastructure and data center delivered via the NBI Computing infrastructure for Science (CiS) group. This research has been supported by the Gordon and Betty Moore Foundation (8802), Innovative Genomics Institute, and the NIH Director's Award (052239‐002).

Data availability

Sequencing reads for S. polyrhiza and L. punctata RNA‐Seq can be found at the respective NCBI BioProjects PRJNA808038 and PRJNA801691. The L. punctata 5635 genome can be found on CoGe accession ID: 63585. Scripts for analysis presented in the manuscript can be found on the github repository https://github.com/erin‐baggs/DuckweedRNA. Z‐stack of Pst DC3000 infection of Spirodela polyrhiza is available from Zenodo doi: 10.5281/zenodo.5639580.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Dataset S1 Powerpoint presentation of pathogen inoculation symptoms.

Dataset S2 Z‐stack microscopy.

Dataset S3 Differentially expressed gene tables Spirodela polyrhiza.

Dataset S4 Differentially expressed gene tables Landoltia punctata.

Dataset S5 Orthogroup assignment and overlap across pathogen treatments and species.

Click here for additional data file. (101.3KB, pdf)

Fig. S1 PlotMDS for all Spirodela polyrhiza RNA‐Seq samples before outlier removal.

Fig. S2 PlotMDS for Spirodela polyrhiza RNA‐Seq samples after outlier removal.

Fig. S3 PlotMDS for all Landoltia punctata RNA‐Seq samples before outlier removal.

Fig. S4 PlotMDS for Landoltia punctata RNA‐Seq samples after outlier removal.

Fig. S5 Phylogeny of Nucleotide‐binding Leucine‐rich repeat Receptor proteins in duckweeds.

Fig. S6 Phylogeny of receptor‐like kinase proteins in duckweeds.

Fig. S7 Phylogeny of receptor‐like protein type proteins in duckweeds.

Fig. S8 Phylogeny of MiAMP1 domain‐containing proteins in duckweed species.

Fig. S9 Symptoms of Spirodela polyrhiza populations derived from a single mother frond after infection with Pst DC3000.

Fig. S10 Microscopy of frond surface 5 d post flood inoculation with Pseudomonas syringae pv tomato DC3000.

Fig. S11 Microscopy of duckweed frond surface 5 and 7 d post flood inoculation with Pseudomonas syringae pv tomato DC3000 hrcC.

Fig. S12 Low bacterial load infection of Landoltia punctata 1 month post inoculation.

Fig. S13 Role of coronatine in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S14 Role of coronatine in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S15 Salicylic acid phytotoxicity to Spirodela polyrhiza upon buffer or Pst DC3000 treatment.

Fig. S16 Dissecting microscope images of duckweed 12 d post inoculation with salicylic acid.

Fig. S17 Role of salicylic acid in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S18 Role of salicylic acid in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S19 Role of salicylic acid in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S20 Role of salicylic acid in Pst DC3000 infection of Spirodela polyrhiza.

Fig. S21 Role of salicylic acid in Pss B728a infection of Landoltia punctata.

Fig. S22 Role of salicylic acid in Pss B728a infection of Landoltia punctata.

Fig. S23 Bar chart of number of genes differentially expressed upon bacterial treatments.

Fig. S24 Log2 fold change of selected gene families following bacterial pathogen exposure of duckweeds.

Fig. S25 Log2 fold change of homologs of Arabidopsis thaliana bacterial responsive genes.

Fig. S26 Differential expression upon pathogen treatment of the duckweed homologs of Arabidopsis salicylic acid marker genes.

Fig. S27 Orthogroups with conserved upregulation upon pathogen treatment of duckweed species and the expression of orthologs in Arabidopsis thaliana.

Fig. S28 Heatmap displaying log2 fold change of rice homologs of duckweed conserved upregulated gene upon pathogen infection.

Click here for additional data file. (106.8MB, pdf)

Table S1 Genome assembly statistics for Landoltia punctata 5635 genome and proteome Busco scores.

Table S2 Table of common name and duckweed gene IDs homologous to Arabidopsis thaliana disease marker genes.

Table S3 Copy number of orthologs/homologs of immune associated genes.

Table S4 Table of Spirodela polyrhiza homologs to Arabidopsis immunity genes.

Table S5 Syndrome prevalence upon pathogen treatment of Lemnaceae species.

Table S6 Log2 fold change of JAZ domain‐containing Spirodela polyrhiza genes.

Table S7 Log2 fold change of WRKY domain‐containing Spirodela polyrhiza genes.

Table S8 Log2 fold change of MiAMP1 domain‐containing Spirodela polyrhiza genes.

Table S9 Log2 fold change of NB‐ARC domain‐containing Spirodela polyrhiza genes.

Table S10 Log2 fold change of JAZ domain‐containing Landoltia punctata genes.

Table S11 Log2 fold change of WRKY domain‐containing Landoltia punctata genes.

Table S12 Log2 fold change NBARC domain‐containing genes Landoltia punctata.

Table S13 Log2 fold change MiAMP1 domain‐containing genes Landoltia punctata.

Table S14 Orthologs to Arabidopsis thaliana marker genes that are significantly upregulated upon Pst treatment log2FC > 0.75 FDR < 0.05 in either Spirodela polyrhiza or Landoltia punctata.

Table S15 Homologs to Arabidopsis thaliana marker genes that are significantly upregulated upon Pst treatment log2FC > 1 FDR < 0.05.

Table S16 Table of duckweed genes homologous of Arabidopsis thaliana salicylic acid responsive genes.

Table S17 Log2 fold change of Spirodela polyrhiza genes homologous to Arabidopsis thaliana salicylic acid responsive genes.

Table S18 Log2 fold change of Landoltia punctata genes homologs to Arabidopsis thaliana salicylic acid responsive genes.

Table S19 Orthogroups upregulated in Landoltia punctata and Spirodela polyrhiza after Pst DC3000 treatment.

Table S20 Log2 fold change of rice genes homologous to pathogen upregulated duckweed genes.

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Click here for additional data file. (134.1KB, xlsx)

Data Availability Statement

Sequencing reads for S. polyrhiza and L. punctata RNA‐Seq can be found at the respective NCBI BioProjects PRJNA808038 and PRJNA801691. The L. punctata 5635 genome can be found on CoGe accession ID: 63585. Scripts for analysis presented in the manuscript can be found on the github repository https://github.com/erin‐baggs/DuckweedRNA. Z‐stack of Pst DC3000 infection of Spirodela polyrhiza is available from Zenodo doi: 10.5281/zenodo.5639580.

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