Reactive oxygen species in the rhizosphere orchestrate the recruitment of beneficial bacteria
Xijie Guo, Hengyi Dai, Zhiyi Jia, Ying Peng, Luotian Lu, Yaxing Su, Jianwei Li, Qinghong Li, Zeming Huang, Yucheng Wang, Fan Qi, Dayong Li, Xiaofei Lv, Yan Liang, Bin Ma
TL;DR
This study shows that reactive oxygen species in plant roots attract helpful bacteria, which in turn boost plant growth and reduce disease symptoms.
Contribution
ROS are shown to recruit beneficial bacteria, revealing a new role in plant-microbe interactions.
Findings
rbohD mutant plants have fewer beneficial bacteria in the rhizosphere compared to wild-type plants.
P. anguilliseptica is attracted to hydrogen peroxide and shows chemotactic behavior.
P. anguilliseptica reduces disease symptoms caused by pathogens in a ROS-dependent manner.
Abstract
Respiratory burst oxidase homolog D (RBOHD)-dependent reactive oxygen species (ROS) in Arabidopsis are well known to suppress pathogen colonization, but their influence on beneficial microbes remains unclear. Here, we found that the beneficial rhizobacterium Pseudomonas anguilliseptica was significantly less enriched in the rhizosphere of rbohD mutants than in that of wild-type plants. Conversely, elevated rhizosphere ROS levels, either triggered by pretreatment with pathogenic Dickeya solani bacteria or caused by mutations in ROS scavenging genes (e.g., in apx1 and cat2 mutants), promoted the rhizosphere recruitment of P. anguilliseptica. This promoting effect was abolished by catalase treatment. In situ microfluidic chemotaxis assays further revealed that P. anguilliseptica exhibits a chemotactic response to low concentrations of hydrogen peroxide ( ≤ 500 nM), accompanied by…
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Figure 14- —the National Natural Science Foundation of China
- —http://dx.doi.org/10.13039/100022963Key Research and Development Program of Zhejiang Province (Key R&D plan of Zhejiang Province)
- —http://dx.doi.org/10.13039/501100010031中 国 博 士 后 科 学 基 金| Postdoctoral Research Foundation of China (China Postdoctoral Research Foundation)
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Taxonomy
TopicsPlant-Microbe Interactions and Immunity · Plant Stress Responses and Tolerance · Plant Pathogenic Bacteria Studies
Introduction
Reactive oxygen species (ROS) are highly reactive oxygen-containing compounds, including singlet oxygen, hydroxyl radicals, superoxide anions, and hydrogen peroxide (H_2_O_2_) (Mittler et al, 2022). Among these, H_2_O_2_ exhibits moderate stability, enabling transmembrane diffusion or facilitated transport via aquaporins (Rodrigues et al, 2017; Tian et al, 2016). ROS production is a critical immune response in plants that defends them against pathogens (Jones et al, 2024; Wang et al, 2024). In Arabidopsis, ROS are predominantly generated by respiratory burst oxidase homolog D (RBOHD), a nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (Torres et al, 2002). RBOHD consists of six transmembrane domains, with a C-terminal NADPH-binding domain located in the cytoplasm (Kawahara et al, 2007; Segal, 2008). Upon activation, RBOHD transfers electrons from cytosolic NADPH across the plasma membrane to the apoplast, where oxygen is reduced to superoxide and subsequently converted to H_2_O_2_ by superoxide dismutase (Wu et al, 2022; Wu et al, 2023). ROS function as both antimicrobial agents and secondary messengers, coordinating downstream immune processes such as cell wall fortification and systemic acquired resistance (Cao et al, 2024; Fujita et al, 2020; Liao et al, 2025).
The rhizosphere microbiome is often referred to as “second genome” due to the indispensable role played by microbial communities in promoting plant growth and health (Berendsen et al, 2012). During pathogen challenge, plants activate immune responses that not only limit microbial invasion but also alter exudate composition, including polysaccharides, proteins, amino acids, organic acids, phytohormones, and phenolic compounds, which in turn facilitate the recruitment of beneficial microbes (Trivedi et al, 2020; Afridi et al, 2024). Such beneficial microorganisms provide multilayered protection against pathogens through ecological niche competition, growth modulation, and immune response priming (Van Elsas et al, 2012; Liu et al, 2025; Zamioudis and Pieterse, 2012). Previous studies have demonstrated altered microbiome composition in rbohD mutant leaves compared to wild-type plants (Pfeilmeier et al, 2021). However, whether ROS directly modulates microbiome assembly or indirectly influences it through ROS-mediated changes in plant development and exudation patterns remains unclear.
In this study, we investigate the role of rhizosphere ROS in plant–microbe interactions. Comparative analysis of rhizobacterial communities revealed significant compositional differences between wild-type and rbohD mutant plants, with the mutant rhizosphere showing altered colonization of pathogens and beneficial rhizobacteria (e.g., Dickeya solani increased and Pseudomonas anguilliseptica decreased). We further examined the chemotactic response of P. anguilliseptica to ROS and its protective effects against D. solani-induced disease symptoms. Together, these findings demonstrate that rhizosphere ROS recruit beneficial microbiota members, thereby contributing to disease suppression.
Results
The rbohD mutants exhibit altered rhizosphere microbiome assembly
To identify microbes influenced by RBOHD-derived ROS in the rhizosphere, we collected rhizosphere soil from three-week-old roots of wild-type (Col-0) and rbohD mutant plants (Fig. 1A). Quantitative PCR quantification of 16S ribosomal RNA (rRNA) gene copies showed no significant difference in bacterial abundance between wild-type and rbohD mutants (Fig. 1B). Microbial community composition was further analyzed using 16S rRNA gene sequencing. We found that α-diversity exhibited no significant variation between the wild-type plants and rbohD mutants, as measured by the Shannon and Simpson indices (Fig. 1C). However, β-diversity analysis revealed notable differences in bacterial composition (Fig. 1D). Subsequently, we identified 11,937 bacterial species by assembling and annotating metagenomic contigs (Table EV1). Among these, 347 species decreased in abundance in rbohD mutants, whereas 1554 species increased (Appendix Fig. S1A). Z-score normalization of microbial abundance data revealed pronounced taxonomic shifts between wild-type and rbohD mutant groups at both the phylum and genus levels. At the phylum level, Actinomycetota, Bacillota, Bacteroidota, Bdellovibrionota, and Verrucomicrobiota were enriched, whereas Acidobacteriota, Cyanobacteriota, Myxococcota, Planctomycetota, and Pseudomonadota were depleted in rbohD mutants (Appendix Fig. S1B). At the genus level, Bradyrhizobium, Mesorhizobium, Mycobacterium, Rhizobium, and Streptomyces were enriched, while Caulobacter, Dokdonella_A, Parafilimonas, Phenylobacterium, Rhizomicrobium, and Rhodanobacter were depleted in rbohD mutants (Fig. 1E).Figure 1RBOHD-derived ROS play a role in the assembly of the rhizosphere microbiome.(A) Representative images of three-week-old Arabidopsis wild-type (Col-0) and rbohD plants. Scale bar, 1 cm. (B) Bacterial 16S rRNA gene copies. Bacterial DNA was extracted from the rhizosphere soil, and 16S rRNA gene copy numbers were determined using quantitative PCR. (C) Alpha diversity based on Shannon and Simpson indices. The 16S sequences were denoised, and zOTUs were generated using UNOISE3. Box plots in (B, C) display the median, upper, and lower quartiles, with whiskers extending up to 1.5 times the interquartile range (n = 10 biological replicates, Student’s t-test, NS no significant difference). (D) Beta diversity was analyzed using Weighted UniFrac dissimilarity analysis (P = 1.9e-2, PERMANOVA, Adonis). Ellipses represent 100% of the data (n = 10 biological replicates). (E) Relative abundance of bacteria at the genus level, scaled by Z-score. While circle size indicates the magnitude of the Z-score, color denotes the corresponding bacterial genus level. For each genus, Z-scores were calculated across samples. A negative Z-score indicates that the abundance in a given sample is lower than the mean abundance of that genus across all samples, whereas a positive Z-score indicates a higher-than-mean abundance. (F) The differential taxa between Col-0 and rbohD at the genus level based on the LDA effect size score. Each triangle represents a unique zOTU, with its corresponding phylum depicted in various colors, and the size of each triangle corresponds to the -log_10_(P value) (P < 0.01, Wilcoxon). Source data are available online for this figure.
To further investigate these variations, we performed linear discriminant analysis (LDA) coupled with effect size analysis (LEfSe) to identify differentially enriched microbial taxa (Fig. 1F). Based on LDA scores, the ten genera were identified as genotype-specific microbial markers for Col-0 and rbohD, respectively (Appendix Fig. S1C). Species-level co-occurrence networks constructed from metagenomic profiles revealed markedly greater complexity in the rbohD rhizosphere, with 7837 nodes and 158,934 edges compared to 5386 nodes and 101,645 edges in the wild type (Appendix Fig. S1D). The rbohD network also exhibited longer average path lengths and greater modularity, suggesting increased interconnectivity and compartmentalization. In addition, microbial genes involved in methane, nitrogen, phosphorus, and sulfur cycling, as well as virulence factors, exhibited differential expression between wild-type and rbohD mutants (Appendix Fig. S2). Collectively, these results suggest that loss of RBOHD function leads to distinct compositional changes in rhizosphere microbiome assembly.
Rhizosphere ROS contribute to the enrichment of P. anguilliseptica
We subsequently analyzed differential beneficial and pathogenic microbes between wild-type and rbohD mutants to identify ROS-regulated specific microbes involved in plant growth. Metagenomic contigs were annotated using PHI-base (https://www.phi-base.org), a pathogen-host interaction database, and the Probio database (https://bidd.group/probio/homepage.htm), which catalogs published plant-beneficial bacterial species (Winnenburg et al, 2007). Compared to the wild-type, rbohD mutants exhibited a significant shift in microbial abundances, with seven beneficial species decreasing and 35 pathogenic species increasing (Appendix Fig. S3).
Increased abundance of Xanthomonas pathogens has been reported in the rbohD phyllosphere (Entila et al, 2024); however, no such increase was found in the rhizosphere. Instead, we observed enrichment of D. solani, a major causal agent of soft rot and blackleg in several crops (Matilla et al, 2023). We isolated a strain of D. solani from the rhizosphere and confirmed its identity using 16S rRNA and dnaX gene sequencing (Chen et al, 2020). Plant inoculation indicated that D. solani caused Arabidopsis soft rot symptoms (Appendix Fig. S4A–E). To examine whether increased colonization of D. solani occurred in the rhizosphere of rbohD mutants compared to the wild-type, we created a green fluorescent protein (GFP)-labeled D. solani strain by introducing a GFP-expressing plasmid that had no effect on bacterial growth (Appendix Fig. S4F). Indeed, pronounced green fluorescence was observed on the root surfaces of the rbohD mutants (Appendix Fig. S4G). Flow cytometry analysis indicated that the rbohD mutants exhibited significantly higher levels of green fluorescence than the wild-type (Appendix Fig. S4G–I). These results indicate that the mutation of RBOHD results in an increase of D. solani in the rhizosphere.
In contrast, seven beneficial bacterial species belonging to Paenibacillus, Pseudomonas, Sphingomonas, and Rahnella were reduced in rbohD mutants (Appendix Fig. S3). To further explore the potential reasons why RBOHD dysfunction caused a decline in beneficial rhizobacteria, we isolated and characterized a collection of bacterial strains from key genera such as Bacillus, Clostridium, Pseudomonas, Niallia, Acinetobacter, Pantoea, Enterobacter, and Massilia. Subsequent comparative analysis revealed that, among these isolates, only P. anguilliseptica was consistently identified as a member of the beneficial rhizobacterial community that exhibited significantly reduced abundance in the rbohD mutant (Alaa, 2018). We identified it as P. anguilliseptica through draft genome analysis (Fig. EV1A). Seedling and soil inoculation revealed that P. anguilliseptica significantly increased seedling fresh weight and rosette leaf size in wild type but not in rbohD mutants (Figs. 2A and EV1B, C).Figure 2RBOHD-derived ROS contribute to the enrichment of beneficial bacteria in the rhizosphere.(A) Pseudomonas anguilliseptica-induced enhancement of seedling fresh weight in Col-0, but not in rbohD. Box plots display the median, upper, and lower quartiles, with whiskers extending up to 1.5 times the interquartile range (n = 16 biological replicates). P values are determined (ANOVA and Tukey test). (B) Schematic diagram of the co-inoculation assay. (C) Co-inoculation assay. Ten-day-old seedlings were co-inoculated with GFP-labeled P. anguilliseptica and either D. solani or E. sesbaniae at equal cell densities (OD_600_ = 0.001). Bacterial populations were quantified by flow cytometry 3 dpi (n = 9 biological replicates). P values are determined (ANOVA and Tukey test). (D,** E**) Root colonization of GFP-labeled P. anguilliseptica in Col-0, rbohD, apx1, and cat2, with or without catalase treatment. Representative images are shown in (E) and bacterial populations were quantified by flow cytometry at 3 dpi (E). Scale bar, 100 µm. Box plots display the median, upper, and lower quartiles, with whiskers extending up to 1.5 times the interquartile range (n = 6 biological replicates). P values are determined (ANOVA and Tukey test). Source data are available online for this figure.
To test whether the reduction of P. anguilliseptica in the rbohD rhizosphere was due to competitive exclusion by pathogens, such as D. solani, we conducted a co-inoculation experiment with GFP-labeled P. anguilliseptica (Figs. 2B and EV1D). GFP-labeled P. anguilliseptica abundance in the rhizosphere was quantified by flow cytometry at 2 d post-inoculation (dpi). Ensifer sesbaniae, a beneficial nitrogen-fixing bacterium (Wang et al, 2022) whose abundance remained unchanged between wild type and rbohD mutants, served as a control (Table EV1). Our findings revealed that GFP-labeled P. anguilliseptica abundance was significantly higher in the presence of D. solani than in the presence of E. sesbaniae (Fig. 2C). These results suggest that the reduced abundance of P. anguilliseptica in rbohD mutants is not directly attributable to competition with D. solani.
To investigate whether the abundance of P. anguilliseptica in rbohD mutants correlates with rhizosphere ROS levels, we employed a custom-designed microfluidic imaging device to visualize rhizosphere ROS (Fig. EV2A). Arabidopsis seedlings were grown directly within the soil-filled chamber of the chip. Once the roots had extended and fully embedded themselves in the surrounding soil, the bottom layer of the chamber was carefully replaced with a solid agar gel filled with 2′,7′-dichlorofluorescein (H_2_DCFDA), a ROS sensitive fluorescent dye. After a 15-min incubation period and subsequent removal of the culture layer. Green fluorescence was subsequently observed at the root position, with gradient signals extending into the rhizosphere, indicating the diffusion of root-derived ROS into the rhizosphere environment (Fig. EV2B). Following inoculation with GFP-labeled P. anguilliseptica, fluorescent signals were weaker in rbohD mutants than in wild-type plants (Fig. 2D). In contrast, GFP-labeled P. anguilliseptica abundance was significantly higher in the ascorbate peroxidase 1 (apx1) and catalase 2 (cat2) mutants, which are defective in ROS scavenging and therefore accumulate higher ROS levels (Fig. 2E). Exogenous catalase treatment markedly suppressed GFP-labeled P. anguilliseptica proliferation in the rhizosphere across all genotypes (Fig. 2E), whereas the bovine serum albumin (BSA) control had no such effect. Neither catalase nor BSA affected P. anguilliseptica growth in vitro at the tested concentrations (Appendix Fig. S5). Together, these results suggest that rhizosphere ROS directly promote the enrichment of P. anguilliseptica.
Rhizosphere ROS recruit P. anguilliseptica toward the roots
The aforementioned findings prompted us to hypothesize that rhizosphere ROS directly attract P. anguilliseptica toward the roots. To test this hypothesis, bacterial chemotaxis was assessed using a microfluidic chip-based in situ chemotaxis assay (Lin et al, 2024). Various concentrations of H_2_O_2_ were injected into each well through a port. Upon loading the P. anguilliseptica bacterial suspension, H_2_O_2_ diffused from the port, creating a chemical microplume above each well, which induced chemotaxis and caused the bacteria to swim into the well. The number of bacteria in each well was then quantified by flow cytometry (Figs. 3A and EV3A). Our results indicated that bacterial numbers in wells containing 100 nM and 500 nM H_2_O_2_ were significantly higher than in the control, whereas concentrations exceeding 1 µM repelled bacterial migration (Fig. 3B). In contrast, 100 nM H_2_O_2_ inhibited Pantoea dispersa chemotaxis (Fig. EV3B). These findings suggest that 100 nM H_2_O_2_ may promote the chemotactic movement of P. anguilliseptica. Consistent with this hypothesis, we observed that 100 nM H_2_O_2_ treatment significantly enhanced P. anguilliseptica motility after 2 h (Figs. 3C and EV3C), and subsequently promoted biofilm formation upon prolonged exposure (Fig. EV3D). Furthermore, we performed transcriptomic analysis on bacteria treated with 100 nM H_2_O_2_, which revealed 358 upregulated and 314 downregulated genes at significant levels (Fig. 3D). Notably, key chemotaxis-related genes (aer2 and cheA3) and motility-associated genes (flgC, flgE, flgG, and fliQ) were highly upregulated after H_2_O_2_ treatment (Fig. 3E). The upregulation of aer2, cheA3, and flgE were subsequently confirmed by qRT-PCR (Fig. 3F). To examine whether this upregulation also occurs during plant colonization, we analyzed bacterial gene expression by isolating P. anguilliseptica from wild-type and rbohD mutant roots. Notably, bacteria colonizing rbohD roots exhibited significantly lower expression of aer2, cheA3, and flgE than those from wild-type roots (Fig. 3G). Collectively, these findings suggest that rhizosphere ROS may serve as a chemotactic cue facilitating the recruitment and colonization of P. anguilliseptica in the rhizosphere.Figure 3Pseudomonas anguilliseptica exhibits chemotaxis toward H_2_O_2_ (100 nM).(A) Schematic diagram of the in situ chemotaxis assay (ISCA). (B) Chemotaxis assay of P. anguilliseptica toward H_2_O_2._ The chemotaxis index denotes the ratio of the number of bacteria in the wells supplemented with H_2_O_2_ to that in the control well (n = 10 biological replicates). Different letters indicate significant differences among the groups. P values are determined (ANOVA and Tukey test). (C) Effect of H_2_O_2_ on P. anguilliseptica motility (n = 10 biological replicates). Different letters indicate significant differences among the groups. P values are determined (ANOVA and Tukey test). (D) Volcano plot illustrating fold changes in gene expression in P. anguilliseptica under 100 nM H_2_O_2_ versus mock treatment conditions. Each condition included three biological replicates, and each biological sample represented the average of three technical replicates. Differential expression analysis was performed using edgeR. Group comparisons were conducted using a likelihood ratio test, and differentially expressed genes were identified at a false discovery rate (FDR) <0.05 with |log_2_FC|>1. E Differential regulation of chemotaxis and motility-related genes in P. anguilliseptica following treatment with 100 nM H_2_O_2_ for 1 h. Gene expression normalized by Z-score is shown in the heatmap, while the bar plot illustrates fold changes, with yellow representing upregulation and blue indicating downregulation following H_2_O_2_ treatment (n = 3 biological replicates). (F) Effect of 100 nM H_2_O_2_ on gene expression associated with chemotaxis and motility. Gene expression was detected by qRT-PCR and normalized to that of rpoA. Data were the mean ± SD (n = 4 biological replicates). P values are determined (ANOVA and Tukey test). (G) Effect of P. anguilliseptica on the expression of chemotaxis and motility-related genes in the rhizosphere of Col-0 and rbohD. Gene expression was detected by qRT-PCR and normalized to that of rpoA. Data were the mean ± SD (n = 3 biological replicates). P values are determined (ANOVA and Tukey test). Source data are available online for this figure.
H2O2 treatment enhances the biosynthesis of 5-aminolevulinic acid in P. anguilliseptica
To elucidate the role of ROS in recruiting P. anguilliseptica to plant roots, we performed functional gene differential analysis of the transcriptomic data described above (Fig. 3D). A total of 146 metabolism-related genes were upregulated following H_2_O_2_ treatment, with enrichment observed in pathways associated with amino acid metabolism (ald, trpA, trpB, gshA, gshB, and tyrB), carbohydrate metabolism (pdhA, glnA, pckA, gabD, and aceE), energy metabolism (cysH, cysI, ppk), and cofactor/vitamin metabolism (hemB, hemH, and hemL) (Fig. 4A,B). Many of these upregulated genes are functionally linked to plant growth promotion. For instance: trpA and trpB, involved in tryptophan biosynthesis, may contribute to indole-3-acetic acid production, a phytohormone that promotes plant growth; gshA and gshB, encoding key enzymes in glutathione biosynthesis, may enhance bacterial oxidative stress tolerance and facilitate root colonization; glnA, responsible for glutamine synthesis, could enhance nitrogen availability in the rhizosphere; cysI and cysH, involved in sulfur metabolism, may influence microbial redox balance and fitness in planta; and hemL encoding glutamate-1-semialdehyde 2,1-aminomutase, an enzyme critical for the synthesis of 5-aminolevulinic acid (5-ALA) (Zhang et al, 2015), may enhance plant growth (Fig. 4C). The induction of hemL was confirmed by qRT-PCR (Fig. 4D). Consistent with ROS-dependent regulation, hemL transcript levels were significantly higher in P. anguilliseptica colonizing wild-type roots than in those colonizing rbohD mutants (Fig. 4E). Furthermore, we found that the addition of 100 nM H_2_O_2_ induced the production of 5-ALA in P. anguilliseptica (Fig. 4F), and exogenous application of 5-ALA enhanced Arabidopsis root growth and seedling fresh weight (Fig. 4G,H). Together, these findings suggest that rhizosphere ROS not only facilitate the recruitment of P. anguilliseptica but also stimulate the production of growth-promoting metabolites, thereby contributing to plant growth.Figure 4H_2_O_2_ (100 nM) promote the synthesis of 5-aminolevulinic acid (5-ALA) in Pseudomonas anguilliseptica.(A) Functional classification of differentially expressed genes in P. anguilliseptica following H_2_O_2_ treatment. Differentially expressed genes were assigned to six KEGG BRITE hierarchies: Metabolism (M), Environmental Information Processing (EIP), Genetic Information Processing (GIP), Cellular Processes (CP), Brite hierarchies (B), and Others (O). (B) Summary of enriched metabolic pathways and key genes showing significant differential expression after 1 h treatment with 100 nM H_2_O_2_. Bar plots depict fold changes of upregulated genes following H_2_O_2_ treatment. AA amino acid, CoV coenzyme, CHO carbohydrate metabolism. (n = 3 biological replicates, Wilcoxon, P ≤ 0.05). (C) Biosynthesis pathway of 5-aminolevulinic acid (5-ALA) in bacteria. (D) Effect of 100 nM H_2_O_2_ on gene expression associated with biosynthesis pathway of 5-ALA. Gene expression was detected by qRT-PCR and normalized to that of rpoA. Data were the mean ± SD (n = 3 biological replicates). P value is determined (ANOVA and Tukey test). (E) The relative expression of hemL in P. anguilliseptica within the rhizosphere of Col-0 and rbohD. Gene expression was detected by qRT-PCR and normalized to that of rpoA. Data were the mean ± SD (n = 3 biological replicates). P value is determined (ANOVA and Tukey test). (F) Levels of 5-ALA produced by P. anguilliseptica. Box plots display the median, upper, and lower quartile, with whiskers extending up to 1.5 times the interquartile range (n = 11 biological replicates). P value is determined (ANOVA and Tukey test). (G,** H**) Effect of 5-ALA on root length and seedling fresh weight. Data are presented as the mean ± SD (n = 7 biological replicates). P values are determined (ANOVA and Tukey test). Source data are available online for this figure.
Immune elicitor-induced ROS recruit P. anguilliseptica toward the roots
We next examined whether P. anguilliseptica triggers ROS production in roots and whether this response depends on RBOHD. In wild-type plants, P. anguilliseptica inoculation induced a modest yet statistically significant ROS accumulation ( ~ 20% increase) at 1 hpi, as visualized by 3,3ʹ-diaminobenzidine (DAB) staining (Fig. 5A–C). In contrast, rbohD mutants showed no detectable ROS induction under the same conditions (Fig. 5A–C). Notably, the magnitude of ROS accumulation was substantially weaker than the robust responses typically elicited by pathogens or immune elicitors (>2-fold increase) (Entila et al, 2024; Torres et al, 2006). This prompted us to test whether immune elicitor-induced ROS could enhance P. anguilliseptica colonization in the rhizosphere. To address this, we pretreated the roots with the representative pathogen-associated molecular pattern flagellin peptide (flg22) and the representative damage-associated molecular pattern, plant elicitor peptide 1 (pep1) (Li et al, 2021), and then quantified GFP-labeled P. anguilliseptica in the rhizosphere. In wild-type plants, both flg22 and Pep1 treatments significantly increased P. anguilliseptica colonization in the rhizosphere by ~36.6 and 64.2%, respectively. While this increase was less pronounced in rbohD mutants, it remained observable, likely due to the redundant role of RBOHF (Fig. 5D,E). Importantly, the addition of catalase strongly abolished the flg22- and pep1-induced enrichment of P. anguilliseptica (Fig. 5D,E). Together, these findings suggest that immune elicitor-triggered ROS may recruit P. anguilliseptica to the rhizosphere.Figure 5. Pretreatment with immune elicitors recruits Pseudomonas anguilliseptica toward the roots.(A–C) ROS levels in the roots. Five-day-old seedlings were inoculated with GFP-labeled P. anguilliseptica (OD_600_ = 0.05). (A) Roots stained with 3,3’-diaminobenzidine (DAB). (B) Relative staining intensity quantified using ImageJ software. (C) H_2_O_2_ levels assayed by the ferrous-xylenol orange method. Scale bar, 50 µm. Data were means ± SD (n = 6 biological replicates in (B); 4 biological replicates in (C). P values are determined (ANOVA and Tukey test). (D,** E**) Flg22 and Pep1 pretreatment promotes rhizosphere colonization of GFP-labeled P. anguilliseptica, while ROS scavenging by catalase reduces this effect. Ten-day-old seedlings were inoculated with GFP-labeled P. anguilliseptica (OD_600_ = 0.001). Representative images are shown in (D) and the bacterial population was quantified by flow cytometry at 3 dpi in (E). Scale bar, 100 µm. Box plots display the median, upper, and lower quartiles, with whiskers extending up to 1.5 times the interquartile range (n = 8 biological replicates). P values are determined (ANOVA and Tukey test). Source data are available online for this figure.
Pathogen-induced ROS recruit P. anguilliseptica to mitigate disease severity
The levels of P. anguilliseptica in the rhizosphere following co-inoculation with D. solani were higher than those following co-inoculation with E. sesbennia (Fig. 2C), suggesting that D. solani-induced ROS production might contribute to recruiting P. anguilliseptica to the rhizosphere. To test this hypothesis, we quantified ROS levels in both roots and the rhizosphere following D. solani inoculation. A two- to three-fold increase in ROS was observed in wild-type roots after D. solani infection, but not in rbohD mutants (Fig. 6A–C). To assess H_2_O_2_ levels in the rhizosphere, we grew Arabidopsis seedlings in a previously developed Root Chip (Dai et al, 2022) (Fig. EV4A), in which rhizophere H_2_O_2_ was collected and measured. Although H_2_O_2_ concentrations were lower in the rhizosphere than in the roots, a three-fold induction was still observed after D. solani inoculation in the wild-type but not in rbohD plants (Fig. 6D). Notably, rhizosphere ROS levels triggered by D. solani were comparable to those in apx1 and cat2 mutants (Fig. EV4B). Furthermore, P. anguilliseptica exhibited significantly higher oxidative stress tolerance than D. solani in vitro (Appendix Fig. S6). Together, these results suggest that D. solani-induced ROS may also recruit P. anguilliseptica to the rhizosphere.Figure 6. Pathogen-induced ROS recruit Pseudomonas anguilliseptica to mitigate disease severity.(A–C) ROS levels within the roots following D. solani inoculation. Five-day-old seedlings were inoculated with D. solani (OD_600_ = 0.025). ROS were detected using DAB staining (A, B) and the ferrous-xylenol orange method (C). Scale bar, 100 µm. Data were presented as the mean ± SD (n = 6 biological replicates in (B), 4 biological replicates in (C). P values are determined (ANOVA and Tukey test). (D) ROS levels in the rhizosphere. Arabidopsis roots were grown within the Root Chip, following which the solution was harvested and the concentration of H_2_O_2_ was quantified using the Ampliflu Red method. Data were presented as the mean ± SD (n = 8 biological replicates). P values are determined (ANOVA and Tukey test). (E) Root colonization competition between P. anguilliseptica and D. solani. Ten-day-old Arabidopsis seedlings were inoculated with D. solani (OD_600_ = 0.001), either alone or co-inoculated with P. anguilliseptica at an equal cell density (OD_600_ = 0.001) in the presence or absence of exogenous catalase. Bacterial populations were quantified by flow cytometry at 3 dpi. Colors indicate bacterial identity: blue, P. anguilliseptica; red, D. solani. Numbers show the relative percentage (n = 6 biological replicates). (F) Disease assay induced by D. solani. Ten-day-old Arabidopsis seedlings were inoculated with D. solani (OD_600_ = 0.001), either alone or co-inoculated with P. anguilliseptica in the presence or absence of exogenously applied catalase. Disease progression was monitored daily, and seedling mortality was scored based on visible leaf wilting (n = 24 biological replicates). P values are determined (Wilcoxon).
To test whether this ROS-mediated recruitment of P. anguilliseptica reduces D. solani-induced disease symptoms, we conducted co-inoculation experiments using seedlings. Consistent with our metagenomic sequencing data (Appendix Fig. S3), rbohD mutants harbored lower levels of P. anguilliseptica but higher levels of D. solani compared with wild-type plants, whereas catalase treatment diminished the genotype-dependent difference in P. anguilliseptica abundance (Fig. 6E). Crucially, P. anguilliseptica co-inoculation significantly increased seedling survival through an ROS-dependent mechanism, as assessed by the Wilcoxon test (Fig. 6F). Soil-based co-inoculation experiments further showed that P. anguilliseptica alleviated D. solani-induced disease symptoms in wild-type plants but not in rbohD mutants (Fig. EV5A,B). Consistently, lower abundance of P. anguilliseptica was observed in the rhizosphere of rbohD mutants (Fig. EV5C). Collectively, these findings suggest that D. solani-induced ROS recruit beneficial P. anguilliseptica, which partially alleviates disease severity caused by D. solani.
Discussion
Although ROS are generally considered oxidative stressors for microbes, their effects vary among specific microorganisms (Li et al, 2021; Song et al, 2021; Sahu et al, 2022). In this study, we found that ROS could induce P. anguilliseptica chemotaxis and promote the biosynthesis of growth-promoting compounds. Furthermore, pathogen pretreatment enhanced P. anguilliseptica abundance in the rhizosphere. These findings support a model in which plants produce ROS in root tissues as a defense response against pathogens, thereby suppressing pathogen colonization. Concurrently, root-derived ROS diffuse into the rhizosphere, where they recruit the beneficial bacterium P. anguilliseptica toward the roots, ultimately promoting plant growth and enhancing plant resistance (Fig. 7). These insights pave the way for future research on the complex regulatory mechanisms underlying plant–microbe interactions and offer potential strategies for improving plant health through microbiome management.Figure 7. Schematic model illustrating that ROS recruits beneficial bacterium Pseudomonas anguilliseptica toward Arabidopsis roots.The rhizosphere is inhabited by various microorganisms. When the roots sense pathogens, ROS are produced in the roots to defend against pathogen colonization (1). Meanwhile, ROS diffuse into the rhizosphere, where they act as chemoattractants that attract the beneficial bacterium P. anguilliseptica toward the roots to mitigate disease severity (2). In addition, these ROS signals stimulate P. anguilliseptica to synthesize 5-aminolevulinic acid (5-ALA), which promotes plant growth (3).
ROS play important roles in plant defenses against pathogens (Gao et al, 2021; Wang et al, 2022). They are not only involved in direct antimicrobial activity, but also serve as crucial messengers that modulate immune responses, ensuring that plants can effectively recognize and counteract pathogens (Mittler et al, 2022; Wang et al, 2024). However, the roles of ROS in specific pathogens vary. For most pathogens, ROS are widely accepted to enhance plant cell wall strength through the cross-linking of cell wall components, thereby establishing a physical barrier against pathogen invasion (Fujita et al, 2020; Paauw et al, 2023). When pathogens infect through stomata, ROS trigger stomatal closure to restrict their entry (Kadota et al, 2014; Li et al, 2014). In addition, ROS serve as long-distance signals that initiate and propagate systemically acquired resistance, thereby triggering robust and widespread immune responses (Cao et al, 2024). Recently, novel roles for ROS have been reported; for instance, ROS inhibit the type II secretion system of Xanthomonas, effectively converting it into a beneficial microorganism (Pfeilmeier et al, 2024; Entila et al, 2024). Furthermore, ROS can induce auxin production in Bacillus velezensis, promoting its colonization in the host rhizosphere (Tzipilevich et al, 2021). Additionally, Pseudomonas aeruginosa exhibits chemotactic behavior toward antimicrobial compounds, including H_2_O_2_ (Oliveira et al, 2022). In this study, we found that H_2_O_2_ could promote the recruitment of P. anguilliseptica. Given the diversity of microorganisms, the role of ROS in plant–microbe interactions warrants further investigation.
Recent studies have highlighted the role of RBOHD-mediated ROS in maintaining homeostasis of the phyllosphere microbiota (Pfeilmeier et al, 2021). Consistent with this, we observed a significant difference in β-diversity within the root microbiota between the wild type and rbohD mutants. These results further support the notion that RBOHD-mediated ROS production influences different microbial taxa, with significant alterations in the relative abundance of OTUs across various phyla. RBOHD is a member of the NADPH oxidase family, a group of enzymes that are highly conserved across various mammalian species (Sommer and Bäckhed, 2015), suggesting that the role of ROS in maintaining the microbiota might be conserved. Indeed, reduced ROS levels have been found to correlate with decreased gut microbiota diversity in mice (Herfindal et al, 2022). Furthermore, the reduction of NOX1-derived ROS indirectly decreases the abundance of Lactobacilli by removing their competitive advantage in the intestinal microenvironment (Matziouridou et al, 2018). Therefore, the regulation of microbial community structure, composition, and function by ROS appears to be conserved across multicellular organisms, highlighting their evolutionary significance.
In addition to their direct role in recruiting beneficial microorganisms, ROS may exert a profound influence on the metabolic pathways of plants and their associated microbiomes (Li et al, 2023; Sahu et al, 2022). This dynamic interplay could ultimately shape a unique and highly specialized microbial community tailored to a plant’s evolving needs and environmental interactions (Deng et al, 2024). ROS can directly modulate central metabolic pathways like glycolysis and phenylpropanoid biosynthesis, which may selectively enrich beneficial microbes that enhance nutrient acquisition and prime defense responses (Pinheiro et al, 2010; Yaqoob et al, 2022). Additionally, different types of ROS may play different roles in specific microbes. Singlet oxygen can selectively kill gram-positive microbes, whereas superoxide anions exhibit higher toxicity to microbes than H_2_O_2_ (Wu et al, 2022). Here, we found that H_2_O_2_ induces P. anguilliseptica chemotaxis and the biosynthesis of growth-promoting compounds. However, we cannot rule out the possibility that H_2_O_2_-regulated metabolites or other types of ROS directly or indirectly influence the activity of P. anguilliseptica. Therefore, the molecular mechanism of ROS production underlying the enrichment of P. anguilliseptica in the rhizosphere requires further investigation.
In summary, our study reveals a previously overlooked role for rhizosphere ROS in orchestrating the selective recruitment of beneficial microbiota. This work not only enhances our mechanistic understanding of plant–microbe interactions but also highlights the potential of using ROS-mediated microbial recruitment to engineer beneficial microbiomes. Ultimately, this strategy could enable the development of sustainable approaches to disease management and crop improvement, offering new opportunities to integrate plant immune regulation with ecological microbiome manipulation in agricultural systems.
Methods
Reagents and tools tableReagent/resourceReference or sourceIdentifier or catalog number Experimental models Arabidopsis: wild-type, Columbia (Col-0)ABRCArabidopsis: rbohD (AT5G47910)ABRCCS9555 Arabidopsis: apx1-2 ABRCSalk_000249C Arabidopsis: cat2-1 ABRCSalk_091880 D. solani This paperN/A P. dispersa This paperN/A P. anguilliseptica This paperN/AE. coli DH5αThis paperN/A E. sesbaniae This paperN/A Recombinant DNA pBRG-GFPThis paperN/A Antibodies
Oligonucleotides and other sequence-based reagents PCR primersThis studyTable EV2 Chemicals, enzymes and other reagents Flg22GenScript BiotechN/APep1GenScript BiotechN/ASYBR®Green Realtime PCR Master MixVazymeCat# Q311-02RNAiso PlusTaKaRaCat# 9109HiScript® Ⅱ Q RT SuperMix for qPCR (+gDNA wiper)VazymeCat# R223-01CatalaseAladdinCat#C163049-5gH_2_O_2_Sigma-AldrichCat# 88597-100ML-FTIANgel Midi Purification KitTIANGENCat# DP209-02TIANprep Mini Plasmid KitTIANGENCat# DP103-02FastDNA™ SPIN KitMP BiomedicalsCat# 6560200 Software ImageJGitHub https://github.com/imagej/ImageJ R https://www.r-project.org/ USEARCH v11GitHub https://github.com/rcedgar/usearch_old_binaries/ MEGAHITGitHub https://github.com/voutcn/megahit eggNOG-mapperGitHub https://github.com/eggnogdb/eggnog-mapper DIAMONDGitHub https://github.com/bbuchfink/diamond
Other BD FACS Melodya NovaSeq X Plus PE150Illumina
Plant materials and growth conditions
Arabidopsis mutants rbohD (CS9555), apx1-2 (Salk_000249C), and cat2-1 (Salk_091880) were obtained from the Arabidopsis Biological Resource Center (Ohio State University, USA) and used as previously described (Hong et al, 2023; Qi et al, 2023). Seeds were sterilized with 10% sodium hypochlorite and grown on 1/2 Murashige and Skoog (MS) agar medium containing 0.5% sucrose or in soil (Sun Gro Horticulture: Hawita professional = 1:1). Plants were cultivated in a growth chamber at 23 °C, 75% relative humidity, and a 12 h photoperiod.
For the seedling growth assay after bacterial inoculation, seedlings were transferred to sucrose-free agar medium. Inoculate the bacterial suspension at the root tip. For the seedling growth assay involving the addition of 5-ALA, seedlings were transferred to liquid 1/2 MS medium supplemented with 5-ALA. Root length and fresh weight were measured 5 d after incubation.
16S rRNA gene amplicon and metagenomic sequencing
Microbiome sequencing was performed as previously described with modifications (Bulgarelli et al, 2013). Briefly, rhizosphere soil was harvested from three-week-old Arabidopsis roots. Five plants were pooled into a single biological replicate. After gentle shaking to remove loosely adhering soil, the roots and tightly adhering soil aggregates were collected. Subsequently, phosphate-buffered saline (PBS) solution was added, and the mixture was vortexed to fully wash off rhizosphere microorganisms. DNA was extracted using a FastDNA™ SPIN Kit according to the manufacturer’s instructions (MP Biomedicals, Solon, OH, USA) (Bulgarelli et al, 2012). Bacterial 16S rRNA genes were amplified using primers 515 F and 907 R, which target the V4-V5 regions. Amplified PCR products were sequenced using an Illumina HiSeq PE250 sequencing platform (Illumina, San Diego, CA, USA). Sequences were denoised into zOTUs using USEARCH v11 with a sequence similarity threshold of 0.97. Taxonomic annotations were performed using the Ribosomal Database Project training set v16 with the SINTAX algorithm (Cole et al, 2014) at a confidence threshold of 0.8.
Metagenomic shotgun sequencing was performed using a NovaSeq X Plus PE150 platform (Illumina, San Diego, CA, USA) at an average sequencing depth of 20 GB per sample. For functional annotation and analysis of metagenomic data, contigs were first assembled from clean reads using MEGAHIT (version 1.2.9), followed by alignment with the Kyoto Encyclopedia of Genes and Genomes database using eggNOG-mapper for functional annotation. Functional pathways were further annotated using multiple specialized databases, including NcycDB (Tu et al, 2019), PcycDB (Zeng et al, 2022), ScycDB (Yu et al, 2021), McycDB (Qian et al, 2022), and VFDB (Dong et al, 2024). Annotation results were processed using the BLASTX option of DIAMOND v2.1.8 (Buchfink et al, 2021) with the following thresholds: amino acid sequence identity ≥60%, query length coverage ≥70%, and e-value ≤10^−5^.
Effect of H2O2 on bacterial growth, motility, and biofilm formation
D. solani and P. dispersa were cultured in Luria-Bertani medium. P. anguilliseptica was cultured on Reasoner’s 2 A agar medium. Ensifer sesbaniae was cultivated on yeast mannitol agar. To evaluate the effect of H_2_O_2_ on bacterial growth, bacteria were cultured to the logarithmic phase. Resuspend bacteria in 10 mM MgCl_2_ to an optical density (OD_600_) of 1.0. Prepare agar containing varying concentrations of H_2_O_2_. A 5 µL aliquot was added to each treatment medium. Colony counts were recorded 2 d after incubation in the dark at 28 °C.
To assess the effect of H_2_O_2_ on bacterial motility, bacterial suspensions were treated with H_2_O_2_ and incubated for 6 h. Cells were then collected by centrifugation, resuspended in 10 mM MgCl_2_, and 5 µL aliquots were spotted onto 0.3% agar plates. Incubate plates at 28 °C for 18 h. Measure colony diameter as an indicator of bacterial motility. To assess the impact of H_2_O_2_ on biofilm formation, bacteria were prepared as described above. Add corresponding concentrations of H_2_O_2_ to wells of a 96-well plate containing bacterial cultures. Incubate at 28 °C for 4 d. Quantify biofilm as previously described (Stepanović et al, 2000; Bhowmik, 2025).
Generation of GFP-labeled bacterial strains
The GFP-labeled D. solani and GFP-labeled P. anguilliseptica strains were generated by introducing a modified plasmid (pBRG-GFP) (Zhu et al, 2022), carrying a kanamycin resistance gene and GFP fluorescent marker. Introduce the pBRG-GFP plasmid into competent cells D. solani and P. anguilliseptica using an electroporator with the following settings: V = 2.2 kV·cm^−1^, C = 25 mF, R = 400 Ω.
Bacterial inoculation
All bacterial strains were grown to logarithmic phase and washed once with 10 mM MgCl_2_. For inoculation, cells were resuspended in 10 mM MgCl_2_ to the desired concentrations (OD_600_ = 0.001 for agar-based assays; OD_600_ = 0.25 or 0.5 for soil drenching experiments). For agar plate-based assays, Arabidopsis seedlings were grown vertically on 1/2 MS medium containing 1% sucrose for 9 d. Seedlings with comparable root length were then transferred to sucrose-free 1/2 MS medium. For rhizosphere colonization assays, 1 μL of bacterial suspension (OD_600_ = 0.001) was applied just below the root tip. GFP fluorescence was imaged 3 d after inoculation, following nine rinses with sterile water. For quantification, roots from four seedlings were pooled as one biological replicate, washed nine times with sterile water, vortexed for 35 s, and subjected to flow cytometry. For catalase treatments, catalase was applied at 1500 U/g, and its concentration was quantified using a BCA protein assay kit. BSA at the same protein concentration was used as a control.
For soil-based inoculation assays, seedlings were transplanted into soil and treated with P. anguilliseptica by soil drenching at one-, two-, and three-week post-transplantation (OD_600_ = 0.5, 15 mL per plant, applied around but not directly on the root). Plants were phenotyped at 4 weeks for growth promotion. To quantify P. anguilliseptica levels in the rhizosphere, the solution containing bacteria surrounding plant roots were serially diluted and plated to calculate c.f.u. levels. For co-inoculation, GFP-labeled P. anguilliseptica and D. solani were mixed at equal cell densities and were applied at the same total volume (OD_600_ = 0.5, 15 mL per plant). Disease symptoms were observed and recorded over time.
RNA isolation and reverse transcription quantitative PCR (RT-qPCR)
Total RNA was extracted using an RNeasy kit according to the manufacturer’s instructions (Tiangen Biotech, Beijing, China). cDNA was synthesized from 1 μg of isolated RNA using HiScript II reverse transcriptase (Vazyme Biotech, Nanjing, China). RT-qPCR was performed using SYBR Green Master Mix (Vazyme Biotech). The relative gene expression levels were calculated using the 2^−ΔΔCt^ method with 16S rRNA as an internal control (Paulin et al, 2009). All primers used for RT-qPCR are listed in Table EV2. To extract RNA from P. anguilliseptica in the root rhizosphere, bacteria adhering to the root surface were gently scraped off and immediately processed for RNA extraction.
Bacterial RNA-Seq
Bacteria were cultured to logarithmic phase, and H_2_O_2_ was then added to a final concentration of 100 nM. Bacterial cells were collected by centrifugation after 1 h of incubation. cDNA libraries were constructed using a NovaSeq platform (Illumina, San Diego, CA, USA). The reference genome index was established in FASTA format, and filtered reads were aligned to the reference genome using Bowtie2. Gene read counts were calculated with HTSeq 0.6.1p2 (https://htseq.readthedocs.io/en/latest/) and was used as raw expression values. Differential expression analysis was performed using DESeq with the following criteria: log_2_ fold-change >1 and differentially expressed genes were identified at a false discovery rate (FDR) <0.05.
Detection of H2O2 levels in the roots and rhizosphere
To detect H_2_O_2_ levels in the roots, five-day-old Arabidopsis seedlings were transferred to sucrose-free 1/2 MS liquid medium and incubated overnight. Following treatment with D. solani (OD_600_ = 0.025) and P. anguilliseptica (OD_600_ = 0.05), the roots were washed three times with 10 mM MgCl_2_. For H_2_O_2_ detection using DAB staining, the roots were immersed in 0.03% DAB in the dark for 8 min. Images were acquired using a stereomicroscope (Eclipse Ni-U; Nikon, Tokyo, Japan). The intensity of staining in each image was quantified using ImageJ software (https://imagej.nih.gov/ij/). For H_2_O_2_ detection via the ferrous-xylenol orange method, approximately 0.05 g of roots were ground in liquid nitrogen. After centrifugation at 14,000×g at 4 °C, the supernatant was quantified as previously described (Wang et al, 2023). To detect H_2_O_2_ levels in the rhizosphere, Arabidopsis seeds were germinated in a 200 μL pipette tip inserted into the access hole of the Root Chip (Massalha et al, 2017). When the Arabidopsis root system penetrated approximately 1 cm into the hole, the rhizosphere fluid was collected. H_2_O_2_ was quantified using the Ampliflu Red method (Dai et al, 2024).
Chemotaxis assay
To measure the chemotactic response of microorganisms to H_2_O_2_, a microfluidics-based in situ chemotaxis assay was employed (Hallstrøm et al, 2022). Different concentrations of H_2_O_2_ were loaded into the wells of the microfluidic chip via micro-ports, allowing outward diffusion of H_2_O_2_ to generate a concentration gradient. Subsequently, the microfluidic chip was placed in 240 mL of bacterial suspension and incubate at 25 °C for 1 h. The solution in each well was harvested using a 1-mL syringe equipped with a 27-G needle. Bacterial levels were analyzed using a flow cytometer (BD FACS Melody with a 100-µm nozzle, USA). The forward scatter, side scatter, and GFP were recorded for each sample. Microbial populations were characterized based on side scatter and GFP fluorescence. The chemotaxis index (Ic) was determined as the ratio of cells in a specific treatment to those in the PBS control. Ic >1 indicates attraction, whereas Ic <1 indicates repulsion.
Quantification of 5-ALA
The levels of 5-ALA secreted by bacteria were determined as previously described, with slight modifications (Li et al, 2014). Briefly, 400 µL of bacterial suspension was mixed with 200 µL sodium acetate buffer (pH 4.6) and 100 µL acetylacetone. The mixture was boiled for 15 min, and subsequently cooled to room temperature, followed by the addition of 700 µL Ehrlich’s reagent under light-protected conditions. After 20 min of color development, absorbance was measured at 554 nm.
Statistical analysis
Microbial community and functional analyses were conducted using the R software (version 4.2.3; R Core Team, Vienna, Austria). Community and functional gene matrices were normalized using the R package DESeq2 (Love et al, 2014). Principal coordinate analysis of the Weighted UniFrac (Hamady et al, 2010) distance was used to visualize microbial community variation between samples. The R package edgeR (Robinson et al, 2010) was used to identify differences in zOTU and functional genes between the two groups, with a false discovery rate less than 5%. LEfSe analysis was performed using the R package microbiomeMarker (Cao et al, 2022). Experimental data analysis and visualization were performed in R (version 4.2.3). No statistical tests were performed to determine sample sizes. Experiments were randomized, with plants shuffled weekly within the growth chambers throughout the study.
Graphics Figs. 2B, 3A, 7 and EV4A were created with BioRender.com.
Supplementary information
Table EV1 Table EV2 Appendix Peer Review File Source data Fig. 1 Source data Fig. 2 Source data Fig. 3 Source data Fig. 4 Source data Fig. 5 Figure EV1 Source Data Figure EV2 Source Data Figure EV3 Source Data Figure EV4 Source Data Figure EV5 Source Data Expanded View Figures