Reduced methane emissions in transgenic rice genotypes are associated with altered rhizosphere microbial hydrogen cycling

Domain: neuroscience
Published: 2026-01-26
Editorial outcome: minor_revision

Abstract
Rice paddies significantly contribute to atmospheric methane (CH4). Here, we show that two independent rice genotypes overexpressing genes for PLANT PEPTIDES CONTAINING SULFATED TYROSINE (PSY) reduce cumulative CH4 emissions by 38% (PSY1) and 58% (PSY2) over 70 days of growth compared with controls. Genome-resolved metatranscriptomic data from PSY rhizosphere soils reveal lower ratios of gene activities for (mostly hydrogenotrophic) CH4 production versus consumption, decreased activity of H2-producing genes, and increased activity of bacterial H2 oxidation pathways. Metabolic modeling using metagenomic and metabolomic data predicts elevated H2 oxidation and suppressed H2 production in the PSY rhizosphere. Assembled genomes of rhizosphere H2-oxidizing bacteria are enriched in genes utilizing gluconeogenic acids compared with H2-producing counterparts, and their activities are likely stimulated by elevated levels of gluconeogenic acids, primarily amino acids, in PSY root exudates. Overall, our study indicates that decreased CH4 emissions are due to a lower amount of H2 available for hydrogenotrophic methanogenesis and provides a powerful strategy to mitigate CH4 emissions from increasingly widespread rice cultivation.

Introduction
Methane (CH4) is a greenhouse gas with a 27–30 times greater warming effect than carbon dioxide (CO2) over a 100-year period. Atmospheric CH4 has recently increased to a record-high concentration of 1933 ppb (May, 2025)1. One of the most important CH4 sources is the waterlogged, anaerobic soils of rice paddies, which support the growth of methanogenic archaea that catalyze the final step in anaerobic decomposition of organic matter, generating CH4 2,3. Although produced CH4 can be partially oxidized in situ through aerobic and/or anaerobic methane oxidation4,5, the imbalance between CH4 production and oxidation leads to rice paddies contributing 7-17% of global CH4 emissions6,7. Emissions are predicted to further increase due to the expansion and intensification of rice cultivation needed to feed the fast-growing human population8.

Numerous approaches have been explored to mitigate CH4 emissions from rice paddies, primarily focusing on farming practices such as managing organic matter, water, and nitrogen9. Genetic optimization of rice plants, for example, allocating less photosynthate to roots, is an alternative CH4 mitigation strategy. Transgenic introduction of a single transcription factor gene, barley SUSIBA2, into rice plants confers more nutrients to aboveground biomass and less to roots, thereby reducing CH4 formation10. Similarly, a loss-of-function allele of rice gs3 (a gene on chromosome 3 controlling grain size) allocates more carbon to the grain and less to the roots and causes lower production of CH4 11.

Another approach to reduce CH4 emissions is to alter the rice root phenotype. Through programmed cell death, rice roots develop aerenchyma, which facilitates gas diffusion and enables efficient oxygen supply from the aerial parts of the plant to the submerged roots and surrounding soil. Simultaneously, plants deposit suberin and lignin to form complete apoplastic barriers in the outer root layer to reduce radial oxygen loss12,13. It has been reported that rice genotypes with a higher capacity for internal oxygen diffusion grow larger root systems in waterlogged soils and are associated with lower CH4 emissions due to their stronger ability to oxygenate the soil14,15. Previously, we and others have shown that small tyrosine-sulfated peptides, such as the PLANT PEPTIDES CONTAINING SULFATED TYROSINE (PSY) family, play central roles in coordinating root development. Acting as extracellular signals, these peptide hormones synchronize cellular activities across tissues by binding to leucine-rich repeat receptor-like kinases (LRR-RLKs), thereby triggering downstream signaling cascades16. A critical post-translational modification, sulfation of a tyrosine residue by TYROSYLPROTEIN SULFOTRANSFERASE (TPST), is essential for receptor recognition and signaling efficiency17–20. In Arabidopsis thaliana, overexpression of PSY genes enhances root growth by promoting cell elongation and increasing the size of mature cortical cells21–23. These peptides exert their effects through three known LRR-RLK receptors, PSYR1, PSYR2, and PSYR3, recently identified as direct mediators of PSY signaling22,24. Notably, PSY1 signaling in Arabidopsis activates genes involved in secondary metabolism that regulate the extent of cell elongation and transition to maturation. Some of these metabolites are subsequently secreted into the rhizosphere as part of the root exudate profile21,23,25.

Given that methanogenic archaea inhabit the rice root rhizosphere and that substrates for CH4 production (e.g., H2/CO2 and acetate) ultimately derive from root exudates26, we hypothesized that modulation of the rice root phenotype would affect microbiome activity and CH4 cycling. To this end, we generated rice plants with altered seminal roots by overexpressing the rice PSY genes (OsPSY1 and OsPSY2) and cultivated them in a greenhouse, along with the control Kitaake genotype, in soils from rice paddies. Plant physiological traits, root exudate chemistry, CH4 emissions, rhizosphere microbial community composition, and in situ gene activities were compared over 70 days of rice growth.

Results
Generation of rice plants overexpressing rice PSY1 and PSY2 genes
To identify putative rice PSY genes, we surveyed the Nipponbare and Kitaake genomes using bioinformatic approaches as previously described25,27, and identified nine candidate PSY genes (Supplementary Fig. 1a). Sequence alignment of the mature peptide domain revealed high conservation between Arabidopsis (AtPSYs) and rice (OsPSYs) peptides (Supplementary Fig. 1b), suggesting that OsPSY peptides may regulate rice root growth similarly to AtPSY peptides21–23,25,28. Supporting this hypothesis, we demonstrated that Kitaake seedlings, grown on solid media or hydroponically, developed longer seminal roots when treated with synthetic OsPSY1 (Fig. 1a, b). Publicly available transcriptomic datasets29,30 reveal that OsPSY1, also known as CHALKY GRAIN5 (OsCG5)31, is predominantly expressed in spikelets, with lower expression levels in other rice tissues, including roots (Supplementary Fig. 1c–e). We hypothesize that the root growth effect observed after synthetic peptide treatment may result from the presence of leucine-rich repeat receptor-like kinases (LRR-RLKs) in root tissues that recognize OsPSY1. It has been recently shown in Arabidopsis, that PSY peptides directly bind to LRR-RLKs from subfamily XI to trigger a signaling cascade that regulates root development22. The conservation of LRR-RLKs across plant species suggests that functionally similar receptors likely exist in rice and may mediate comparable developmental responses. In contrast to OsPSY1, other members of this gene family, such as OsPSY2, displayed more ubiquitous expression across different rice organs (Supplementary Fig. 1d). Specifically, OsPSY2 expression in rice roots increased progressively in the differentiation zone (DZ), a pattern previously noted for AtPSY1 promoter activity23 (Supplementary Fig. 1e).

To further investigate the roles of OsPSY1 and OsPSY2 in rice development, we independently overexpressed each gene in the Kitaake genetic background. We generated >50 independently transformed lines using the constitutive UBIQUITIN (Ubi) promoter to drive the expression of OsPSY1 and OsPSY2 (Ubi::OsPSY1 and Ubi::OsPSY2). We self-pollinated the resulting transgenic plants and conducted analyses on the progeny derived from four independently transgenic lines, including Ubi::OsPSY1#7, Ubi::OsPSY1#9 (hereafter referred to as PSY1-9), Ubi::OsPSY1#17 (PSY1-17), Ubi::OsPSY1#32, Ubi::OsPSY2#8 (PSY2-8), Ubi::OsPSY2#19, Ubi::OsPSY2#21 and Ubi::OsPSY2#34 (PSY2-34).

Expression analysis of root tissues showed that progeny derived from the Ubi::OsPSY1 and Ubi::OsPSY2 transgenic lines accumulated significantly higher levels of OsPSY1 or OsPSY2 transcripts compared with Kitaake (Fig. 1c, d). Consistent with the synthetic peptide treatment analysis, rice plants expressing higher levels of OsPSY1 or OsPSY2 exhibited longer seminal roots (Fig. 1e, f) and also carried the transgene (Supplementary Fig. 1f, g).

Given that in Arabidopsis, AtPSY1 regulates root growth by controlling the extent to which cells elongate before reaching their final differentiated size22,23, we measured the length of mature cortical cells in the DZ of PSY1-9 and PSY1-17 genotypes. Consistent with the phenotypes observed in Arabidopsis, we found that the roots of rice genotypes accumulating higher levels of OsPSY1 exhibited longer mature cortical cells (Fig. 1g, h). Additionally, the amount of lignin in PSY1-17 and PSY2-34 plants was significantly lower compared with Kitaake (Fig. 1i). A similar phenotype was observed in the roots of Arabidopsis plants with higher levels of AtPSY1, where lignin deposition in the tracheary elements of the protoxylem occurred further away from the root tip compared with the wild type23. Collectively, these results suggest that PSY peptides from different species may regulate root development through similar molecular mechanisms.

Finally, we assessed aerenchyma formation in a selected subset of the rice PSY1 (PSY1-9 and PSY1-17) and PSY2 (PSY2-8 and PSY2-34) genotypes. We found that in all the transgenic lines tested, the percentage of aerenchyma formation was significantly higher compared with Kitaake (Fig. 1j–l). However, no differences in aerenchyma were observed between the PSY1 and PSY2 lines (Fig. 1j–l). Given the observed phenotypic modifications in roots of rice seedlings, particularly the longer seminal roots and enhanced aerenchyma formation, we hypothesized that PSY genotypes may impact the rhizosphere microbial community and associated CH4 cycling.

Growth of PSY rice genotypes in agronomic soils mitigated CH4 emissions compared with controls
To assess the effect of PSY overexpression on CH4 emissions, we cultivated three independent transgenic rice lines—PSY1-9, PSY1-17, and PSY2-34—along with the control Kitaake plants in a greenhouse using soils collected from a rice paddy in Davis, CA. We also included bulk soil controls that were not planted. Throughout the experiments, all rice lines developed similarly to Kitaake, with heading observed at 55 days after being transferred to the greenhouse (Fig. 2a). By 70 days post-transplant, both PSY1 and PSY2 genotypes were slightly shorter than Kitaake (Fig. 2b and Supplementary Fig. 2a), but they produced significantly more tillers (Fig. 2c and Supplementary Fig. 2b), resulting in comparable dry weights of stems and leaves per plant at the end of the plant life cycle (Fig. 2d). PSY1 genotypes showed partially filled panicles, fewer seeds, and lower thousand-grain weight (TGW) per plant, compared with Kitaake (Fig. 2e–g and Supplementary Fig. 2c). These phenotypes could be attributed to abnormal panicle development, as we found that the tips of the panicles in PSY1 genotypes often contained smaller and unfilled seeds, a phenotype not seen in PSY2 plants (Fig. 2h). Consistently, no significant differences in seed production occurred in the PSY2 genotypes compared with Kitaake (Fig. 2e–h).

We also investigated whether the longer seminal root phenotype observed in young seedlings persisted in older PSY plants. We found that PSY1-17 plants developed longer roots 30 days after transplanting, although their root dry biomass was significantly lower than that of Kitaake plants (Fig. 2i and Supplementary Fig. 2d, e). Similar differences in root dry biomass were observed at the end of the plant life cycle (Fig. 2j). Additionally, lignin content in the roots of adult plants revealed that, consistent with findings in young seedlings, PSY1-17 plants accumulated less lignin than Kitaake (Fig. 2k). Although PSY2 plants also had reduced lignin content, their root biomass remained comparable to that of Kitaake (Fig. 2j, k).

Despite these phenotypic differences, no significant differences in soil CH4 emissions were observed between the PSY1-9, PSY1-17, and PSY2-34 genotypes (Fig. 2l). For these analyses, CH4 emissions were measured in situ using a Picarro gas analyzer, and soil samples were collected for microbiological analyses throughout the rice growth period (i.e., 15, 30, 55, and 70 days after transplanting). Negligible CH4 emissions were observed from the bulk soil controls, while differentiated emissions were recorded from pots with PSY and Kitaake rice plants over the 10-week growth period (Fig. 2l and Supplementary Fig. 3a). All PSY rice genotypes emitted substantially less CH4 compared with Kitaake, with reductions of up to ~30% observed after heading (55 and 70 days; Fig. 2l and Supplementary Fig. 3a). Notably, cumulative CH4 emissions throughout the growth period for PSY1 and PSY2 genotypes showed an average decrease of ~38% and ~58% relative to Kitaake, respectively (Supplementary Fig. 3b). Given the crucial role of microorganisms in the production and consumption of CH4, we hypothesized that distinct microbial communities might accumulate in the rhizospheres of the different rice genotypes, potentially affecting the rice CH4 emissions. Because both PSY1 and PSY2 genotypes displayed comparable reductions in CH4 emissions, we focused on PSY1 genotypes for further study.

Rice PSY genotypes affected microbial activity but not microbial community structure
To explore the difference in root microbiomes, we extracted and sequenced total DNA and RNA from 72 soil samples (3 replicates for each of the bulk soils and 5 replicates for each planted with the PSY1 rice genotypes and Kitaake control, at each collection time point) (Supplementary Data 1). We used assembled ribosomal protein S3 (rpS3) sequences to perform an initial census of microbial diversity, including organism type and abundance32, in 16 representative samples obtained by combining replicates. In total, 4,087 rpS3 sequences were identified and grouped into 1,656 non-redundant, approximately species-level clusters. Phylogenetic analysis based on rpS3 sequences classified all detected microorganisms into at least 4 archaeal and 14 bacterial phyla (Fig. 3a). By mapping metagenomic and metatranscriptomic reads to rpS3 genes, the most abundant and active organisms were Proteobacteria, Chloroflexi, Acidobacteria, Bacteroidetes, Gemmatimonadetes, and Actinobacteria (Fig. 3b, c), consistent with previous soil surveys33–35. Between 1 and 5% of sequences were assigned to the “Candidate Phyla Radiation” (CPR) bacteria and DPANN archaea (Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota and Nanohaloarchaeota), both of which were recently reported in paddy soils36,37.

Non-metric multidimensional scaling analyses (NMDS) show clear differences in community structure and microbial activity between soils collected from the rhizosphere and unplanted bulk soil (Fig. 3d, e), indicating a strong effect from the rice plants38. For the planted groups, cultivation time had a large and statistically significant effect on the community structure (dissimilarity score of up to 0.539), whereas rice genotype did not (Fig. 3d). Notably, however, microbial activity was significantly influenced by both time and genotype with comparable contributions (dissimilarity scores of 0.265 and 0.195, respectively; Fig. 3e).

Lower activity of CH4-producing genes in microbiomes associated with PSY1 rice genotypes
CH4 fluxes from rice paddy soils reflect the combined impact of the activities of methanogens and methanotrophs. A search for methyl-coenzyme M reductases (McrA) that participate in methanogenesis and anaerobic CH4 oxidation identified multiple homologs assigned to Methanobacterium, Methanomassiliicoccus, Methanoregula, Methanocella, and Methanosarcina (Fig. 4a). None of the recovered McrA sequences are similar to those found in anaerobic CH4-oxidizing archaea, suggesting that anaerobic methanotrophs were either absent or too rare to be detected. Many particulate methane monooxygenases (PmoB) genes that enable aerobic CH4 oxidation were identified and taxonomically assigned to several bacterial genera (Fig. 4b). The soluble form of methane monooxygenase was not detected. These genes are typically only expressed under copper-limiting conditions39 (e.g., <0.1μM versus 0.4μM in our fertilized water; Supplementary Data 2).

In general, the transcriptional activity of mcrA increased with cultivation time in the three rice genotypes. For example, the activity of Methanocella mcrA in the Kitaake genotype increased by approximately one order of magnitude in the last growth stage (70 days, Fig. 4c). The transcriptional activity of pmoB also increased with cultivation time, and we observed more active terminal oxidases in microbes associated with the PSY1 genotypes late in the experiment (Supplementary Fig. 4), suggestive of higher O2 concentrations. However, the ratio of mcrA to pmoB remained stable over the whole growth period, except for Kitaake (Fig. 4c). Given the importance of the balance between activities that produce and consume CH4, we calculated the fold changes of mcrA/pmoB ratios for each group relative to that at the corresponding first sampling point (i.e., 15 days after transplanting), and observed notable increases in Kitaake but negligible changes in PSY1 genotypes (Fig. 4c). This result indicates that, over the rice growth period, mcrA activity was promoted much more than pmoB in Kitaake, leading to a higher ratio of CH4 production to consumption, as compared to the PSY1 genotypes.

PSY1 rice genotypes supported higher activities of microbial H2-consuming genes but lower activities of H2-producing genes
Expressed mcrA transcripts were predominantly from Methanocella, Methanoregula, and Methanobacterium (>90%; Fig. 4c). Archaea of these genera all use H2, instead of acetate or methylated compounds, to produce CH4 40. Thus, we identified genes involved in H2 production and consumption and tracked their expression levels as a function of genotype and time. Two types of hydrogenases that catalyze the reversible conversion between H2 and protons/electrons41, namely [NiFe] and [FeFe] hydrogenases, were identified and divided into four and three subtypes, respectively, using protein phylogeny (Fig. 5a, b). The most active clade was [NiFe] Group 1 hydrogenases that oxidize H2 (Fig. 5c). The transcriptional activities of this group were generally higher in the two PSY1 genotypes than in the Kitaake control, particularly in the first and last growth stages where the differences were significant (15 and 70 days; Fig. 5d). The [NiFe] Group 4 and [FeFe] Group A1 (further classified by HydDB42) are predicted to produce H2 43. Transcript quantification shows [NiFe] Group 4 hydrogenases were more active than [FeFe] Group A1 hydrogenases, and the combined activity of these two H2-producing hydrogenases was significantly higher in Kitaake compared with the two PSY1 genotypes, especially in the second and last growth stages (30 and 70 days; Fig. 5d). Based on the transcriptomic profile analysis, we hypothesize that the microbiomes of the PSY1 genotypes might produce less and consume more H2 than those in Kitaake. As a result, the pool of hydrogen available for CH4 production in soils from the PSY genotypes would be expected to be lower than that of the Kitaake control.

H2 may also be produced as a byproduct of N2 fixation (one molecule of H2 per N2 fixed). The nitrogenase gene (nifH) was used as a marker of N2 fixation activity and associated H2 production44,45. The transcriptional activity of nifH generally increased with cultivation time and was linked to Delta-, Gamma-, and Alphaproteobacteria (Supplementary Fig. 5). Like H2-producing hydrogenases, nifH activity was significantly higher in Kitaake than in the PSY1 genotypes, especially in the last two growth stages (55 and 70 days; Fig. 5d).

Altered PSY1 root exudates modulate activities of H2-metabolizing microorganisms
To explore the influence of rice phenotypes on rhizosphere microbiome activity, we performed metabolomic analysis on root tissues and exudates from axenic rice seedling roots to identify compounds secreted by the PSY1 genotypes. Metabolites were extracted using methanol and profiled on an HPLC-MS platform. Using gradient-based hydrophilic interaction liquid chromatography (HILIC) in positive-ion mode, we identified a total of 3612 features across all exudate samples, of which 2047 were shared between the Kitaake and PSY1 genotypes (Supplementary Fig. 6a). Principal component analysis (PCA) showed a clear separation between PSY1 and Kitaake exudate samples (Supplementary Fig. 6b). Among the shared features, 168 (8.3%) were significantly differentially abundant in PSY1 exudates (|log2FC|>0.5, p<0.05), including 146 novel and 15 depleted features (Supplementary Fig. 6c). A subset of these differentially abundant features was putatively identified and classified using Global Natural Products Social Molecular Networking (GNPS) (Supplementary Data 3–5). Most of the differentially abundant metabolites in PSY1 root exudates samples belong to the same compound classes, including ‘amino acids’, ‘alkaloids’, and ‘shikimates and phenylpropanoids’ (Supplementary Fig. 6d). Similarly, in root tissues, we identified 2,237 shared features, with PCA indicating an even stronger genotypic separation (Supplementary Fig. 6e, f). Of these, 272 (12%) were differentially abundant in PSY1 tissues, featuring 208 new and 80 depleted metabolite signals (Supplementary Fig. 6g), again reflecting shifts in similar compound classes (Supplementary Fig. 6h). Overall, more features increased than decreased in PSY1 samples. Similar results were obtained from HILIC in negative ionization mode and reverse phase (C18) in both positive and negative ionization modes and are detailed in Supplementary Data 3–5, and Supplementary Figs. 7–9. Altogether, our analysis indicated significant changes in the abundance of compounds in the PSY1 root exudates compared with Kitaake. Approximately 90% of the metabolites enriched in the PSY1 metabolome are predicted to be involved in microbial gluconeogenic pathways (Supplementary Data 6). We also noted the presence of two putative flavonoids, isorhamnetin 3-rutinoside and malvidin-3-glucoside, that differentially accumulated in exudate samples from the PSY1 genotypes but were absent from Kitaake, a feature observed in previous studies in Arabidopsis23.

To assess whether these metabolic differences reflected changes in the total amount of carbon released, we measured the concentration of non-purgeable organic carbon (NPOC) in the root exudate samples. No significant differences in total carbon content were detected between Kitaake and PSY genotypes, indicating that PSY overexpression primarily alters the composition, instead of the overall quantity, of secreted metabolites (Supplementary Fig. 10).

To determine whether the altered exudate composition of PSY genotypes can directly influence microbial activity, we conducted soil incubation assays using paddy soil from the same rice field used in our greenhouse experiments. Exudates collected from PSY1- and PSY2-overexpressing rice seedlings were added as amendments, with exudates from Kitaake and sterile water as controls. CH4 emissions were then monitored over time. No CH4 flux was detected in water-treated soils, confirming that exudates were necessary to stimulate methanogenesis. Soils treated with Kitaake exudates exhibited consistently higher CH4 emissions than those treated with PSY1 or PSY2 exudates, mirroring trends observed in the greenhouse-grown plant experiments (Supplementary Fig. 11).

Next, we analysed all the microbial genomes reconstructed from the rhizosphere (n=296) and found 17 had transcriptional activities that were significantly upregulated in soils of rice PSY genotypes relative to the Kitaake control (log2 Fold Change ≥1, p≤0.05; Supplementary Fig. 12). The genomes of five of the 17 encode H2-consuming hydrogenases, including those affiliated with Pyrinomonadaceae, Burkholderiaceae, Haliangiaceae, Rhodocyclaceae, and Desulfurispiraceae, but none contain H2-producing enzymes (Supplementary Data 7). This result suggests that H2-consuming bacteria may respond to the PSY rice genotype-derived exudates differently than organisms that produce H2. We therefore surveyed the entire H2-consuming (n=55) and H2-producing (n=36) populations and calculated glycolytic sugar versus gluconeogenic acid preference values (SAP) using the method described previously46. We found that the H2-consuming organisms have much higher preferences for gluconeogenic acids than the H2-producing organisms, and their genomes have higher GC contents, likely reflecting this metabolic preference46 (Fig. 6a, b; Supplementary Data 7). Consequently, H2-consuming organisms had higher transcriptional activities in association with the PSY1 genotypes, whereas their H2-producing counterparts were more active in the Kitaake rhizosphere (Fig. 6c).

To further investigate how rice physiological traits affect microbial H2 cycling, we built metabolic models using representative, high-quality genomes (completeness >90%, contamination <5%). We then predicted H2 fluxes in response to variations in PSY1-secreted exudate compounds and O2 concentrations inferred from aerenchyma densities of the different rice genotypes (Supplementary Data 8). The only identified compounds under-secreted by PSY1 compared with Kitaake were two glycerolipids, putatively 1-oleoyl-2,3-di-linoleoyl-sn-glycerol and 1,2-di-hexadecanoyl-3-(9Z,12Z,15Z-octadecatrienoyl)-sn-glycerol, and one dipeptide containing leucine (Supplementary Data 3). However, none of the modeled H2-related organisms can metabolize these compounds. As a result, the PSY1 under-secreted metabolites are predicted to have no effect on H2 fluxes. In contrast, when over-secreted compounds increase from 0.01 to 0.05-fold, all three modeled H2-producing bacteria from different phyla (Bacteroidia, Ignavibacteriota, and Moranbacteria) show the same trend, with maximal produced H2 decreasing (Fig. 7a–c). Beyond this range, H2 production is predicted to plateau. Microbial growth increased with increasing concentration of over-secreted compounds and plateaued when the H2 production plateaued, except for Bacteroidia_37, which continually increased its biomass over the whole simulation (Supplementary Fig. 13a–c). The results suggest that the modeled H2-producing bacteria can use over-secreted PSY1 compounds to support growth, but not via pathways that generate H2. Increased O2 availability has little or no effect on H2 production (Fig. 7a–c).

The H2-consuming organisms from Acidobacteriota, Proteobacteria, and Deinococcota are modeled to oxidize more H2 (and accordingly yield more biomass) in the presence of higher O2 concentrations that are associated with higher aerenchyma formation in PSY1 rice genotypes (Fig. 7d–f and Supplementary Fig. 13d–f). The PSY1 over-secreted exudate compounds have a negligible influence on H2 oxidation, yet still contribute substantially to the accumulation of biomass of H2-consuming organisms (Supplementary Fig. 13d, e). These modeling results support the metagenomics-based hypothesis that PSY1 rice genotypes enhance the growth and activity of H2-consuming organisms due to their distinct aerenchyma characteristics and exudate profiles.

Methods
Plant materials and growth conditions
Oryza sativa ssp. Japonica cultivar Kitaake was used throughout this study as the wild-type (wt) background. Rice seeds were dehulled, surface-sterilized in 20% bleach for 30 minutes, and thoroughly washed with autoclaved water. The seeds were grown in 1× Murashige and Skoog (1× MS) salt mixture with 1% sucrose (pH 5.65), either as a solid medium containing 0.3% Gellex (Gellan Gum, Caisson Laboratories) or hydroponically in flasks with 100 mL of the same media without Gellex. For solid medium conditions, seeds were sown on plates, sealed with Micropore tape for gas exchange, and placed vertically for 7 days. For hydroponic conditions, seeds were first germinated on plates with water for 2 days in darkness, then transferred to flasks for another 5 days. The incubators used for rice germination and growth were set with a 14-h-light/10-h-dark photoperiod at 28°C/24°C.

For synthetic peptide treatments in hydroponic conditions, peptide (or water for untreated plants) was added to the MS media before transferring the plants. The length and concentration of the peptide treatment are specified in the figure legends. The synthetic OsPSY1 peptide was obtained from Pacific Immunology (Ramona, CA, USA), is tyrosine sulfated, and was diluted in ddH2O to a final concentration of 1 mM. For each experiment, MS media was freshly prepared and cooled for 1 hour in a 55–60°C water bath after autoclaving before adding chemicals.

For primary root elongation, seedlings grown hydroponically or in solid media were photographed 7 days after germination, and the primary root length was measured with Fiji Is Just ImageJ71.

Sequence alignment and peptide conservation analysis
To assess the conservation of the PSY peptides across species, we extracted the mature peptide sequences from Arabidopsis thaliana and Oryza sativa PSY family members. The peptide sequences were defined as the 13-amino acid mature domain, including the conserved aspartate-tyrosine (DY) motif, following the strategy described previously25. A sequence logo was generated using WebLogo web interface (http://weblogo.threeplusone.com)72, which provides a graphical representation of the multiple sequence alignment, where each position’s stack height indicates sequence conservation, and symbol heights within the stack reflect the relative frequency of individual amino acids at that position.

Plasmid construction and rice transformation
DNA constructs were created with the Gateway cloning technology73. The genomic OsPSY1 and OsPSY2 sequences were amplified using the primers described in Supplementary Data 9. These sequences were then recombined with pENTRTM/D-TOPO (Invitrogen, Cat# 45-0218) to yield pTOPO_OsPSY1 and pTOPO_OsPSY2. The latter vectors were used in a Gateway LR cloning (Gateway® LR ClonaseTM II Plus Enzyme Mix, Invitrogen; Cat#: 12538-120) with pC1300-Ubi-Nos to yield Ubi::OsPSY1 and Ubi::OsPSY2 constructs. The generated vectors were transferred to Agrobacterium tumefaciens strain EHA105 and then introduced into the rice lines via Agrobacterium-mediated transformation. Rice transformation was performed following previously published protocols74. Post-transformant screening was based on hygromycin resistance and PCR determination of T-DNA insertion. Out of 52 positive lines for Ubi::OsPSY1 and 53 for Ubi::OsPSY2, four homozygous lines were selected for characterization. Eventually, three homozygous lines, Ubi::OsPSY1#9 (PSY1-9), Ubi::OsPSY1#17 (PSY1-17), and Ubi::OsPSY2#34 (PSY2-34), were selected for detailed studies.

Gene expression analysis by quantitative PCR (qPCR)
Total RNA (1 mg) was extracted from the roots of seven-day-old rice seedlings grown hydroponically using TRIzol reagent (Invitrogen) and treated with the TURBO DNA-free kit (Ambion) to remove residual genomic DNA. cDNA was synthesized using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). The cycle threshold (Ct) value was measured on a Bio-Rad CFX96 Real-Time System coupled to a C1000 Thermal Cycler (Bio-Rad) using the iTaq Universal SYBR Green Supermix (Bio-Rad). Normalized relative quantities (NRQs) were obtained using the qBase method75, with LOC_Os08g18110 (putative alpha-soluble NSF attachment protein) and LOC_Os11g26910 (putative SKP1-like protein 1B) as reference genes for normalization across samples76. NRQ values were normalized to the mean value obtained in wild-type plants. NRQ Melting curve analyses at the end of the process and “no template controls” were performed to ensure product-specific amplification without primer–dimer artifacts. Primer sequences are given in Supplementary Data 9. Three biological replicates were analyzed.

Cellular analysis using LSCM
Laser scanning confocal microscopy (LSCM) was performed throughout the study using a Plan Apochromat ×63/1.40 oil and ×20/0.75 CS2 lens on a Leica TCS SP8 microscope.

Cell wall staining, seedling fixation and staining were performed using an adapted Clearsee protocol77. Briefly, the primary root of 7-day-old seedlings was sectioned into three short segments with a length of 0.5 cm starting from the root tip. The root segments were fixed for 1 h at room temperature in 10% neutral buffered formalin in PBS, using six-well plates, then washed five times for 1 min with PBS 1×. For root transverse section analysis, the 1 cm root fragments collected 4 cm from the root tip were embedded in 4% agarose and sectioned using a Leica automated vibrating blade microtome (VT1200S). Once fixed, root fragments and transverse sections from those fragments were cleared in Clearsee solution for at least 24 hours under mild shaking. Fixed and cleared samples were incubated overnight in a Clearsee solution supplemented with 0.1% Calcofluor White. After 12 hours, the staining solution was removed, and samples were rinsed once in fresh Clearsee solution, then washed twice for at least 120 minutes in a renewed Clearsee solution with gentle shaking. Roots were carefully placed on a microscope slide with ClearSee and covered with a coverslip. Excitation and detection windows were set as follows: Calcofluor white for excitation at 405 nm, and detection between 415 and 570 nm. Central longitudinal section images were acquired from root complete fragments. Mature cortical cell length was assessed in 10 consecutive cells in the second cortical layer. Transverse section images were acquired to quantify the percentage aerenchyma formation, expressed as a percentage of the total cortical cell area.

Soil collection and growth conditions in the greenhouse experiments
All data presented here were generated from two greenhouse studies carried out at the University of California—Davis in the fall of 2022 and the spring-summer of 2024. Soil from a rice field in Davis (38.543224 degrees North and -121.810536 West) was collected in July 2022 and in July and December 2023 using a front-end loader to gather down to a depth of ~6–10 inches. The soil at the field site belongs to the Capay soil series, a fine, smectitic, thermic Typic Haploxerert. Soil samples were analyzed at the UC Davis Analytical Lab for chemical content (Supplementary Data 10). Soil pH was 6.59 on average. For both experiments, all soils were transported back to the greenhouse, crushed to a 2 mm particle size, thoroughly homogenized, and stored until planting. Then, the soil was transferred into new 5.5×5.5 inch pots (2000 g per pot), which were then placed into five 23 gallon tubs per genotype (18 pots each). Prior to seedling transplantation, enough water was added to the tubs to submerge the soils.

Seeds were germinated in solid MS media, and root growth was followed as previously described for 7 days. Axenic seedlings were then transplanted into the watered pots (one seedling per pot) in the greenhouse, where they were irrigated with deionized water every other day to keep the soil submerged. Fertilized water [N, 77 ppm (parts per million); P, 20 ppm; K, 75 ppm; Ca, 27 ppm; Mg, 17 ppm; S, 68 ppm; Fe, 1.5 ppm; Cu, 0.02 ppm; Mn, 0.5 ppm; Mo, 0.01 ppm; Zn, 0.1 ppm; B, 0.5 ppm] replaced the distilled water in the tubs after microbiome samples were collected on day 15, 30, 55 and 70 after transferring to the pots. Each tub contained only one plant genotype to avoid root exudate compositional changes. We also kept a separate tub with unplanted pots and bulk soil controls. Tubs were distributed in the greenhouse benches following a completely randomized block design. All weeds were manually removed from the pots when identified.

Plant developmental stage and height were measured on the microbiome samples collection days. Roots and shoots were collected 100 days after transferring plants to pots, and dry biomass was measured. Seed number and one thousand seed weight were also calculated for each plant.

Cell wall residue preparation
Lyophilized rice roots used for cell wall residue preparation method were adapted from Fonseca Garcia et al.78. Samples for lignin analysis were collected from 7-day-old rice seedlings or from root systems of 90-day-old plants grown in agricultural soil. Samples from older root systems were collected in the same root-shoot junction location as the one used for rhizosphere sample collection.

The fresh root biomass was lyophilized for 3 days, and then ball-milled biomass was extracted sequentially by sonication (15 min) with 80% ethanol (three times), acetone (one time), chloroform–methanol (1:1, v/v, one time) and acetone (one time).

TGAlignin
Aliquots of 15 mg of cell wall residue were weighed into Eppendorf tubes and mixed with 1 ml of 3N HCl and 0.1 ml thioglycolic acid (adapted after Suzuki et al.79). Samples were incubated at 80°C for 4 hours and repeatedly mixed. Samples were rapidly cooled on ice and centrifuged for 10 min at 15,000×g. The supernatant was discarded. Pellets were washed three times with distilled water (1 ml). Thereafter, pellets were incubated with 1 ml of 1N NaOH for 16 hours on a shaker at room temperature. The suspension was centrifuged for 10 min at 15,000×g. The supernatant was carefully transferred into a 2-ml Eppendorf tube. The resulting supernatant was combined with the first alkaline supernatant and mixed with 0.2 ml concentrated HCl. Samples were incubated for 4 hours at 4°C to precipitate the lignothioglycolate derivates. The samples were centrifuged, the supernatant discarded, and the pellet solubilized in 1 ml of 1N NaOH. Absorbance of the resulting solution was measured at 280 nm. Results were expressed as an absorbance per mg cell wall.

Measurement of methane emissions
Gas flux measurements of CH4 were made in real-time in a transparent dynamic flow chamber using Picarro G2508 Gas Analyzer. In the fall 2022 experiment, fluxes at a total of four time points (15 d, 30 d, 55 d, 70 d) were measured across the growing season for 5 biological replicates corresponding to Kitaake, PSY1-17, PSY1-9, and bulk soil sample types. In the spring-summer 2024 experiment, CH4 fluxes at a total of 12-time points were measured across the growing season for five biological replicates corresponding to Kitaake, PSY1-17, PSY2-34, and bulk soil sample types. Each replicate constituted a single rice plant except for bulk soil samples, which represented a negative control. Outlier detection was performed using the median absolute deviation (MAD) approach. Measurements deviating by more than three MADs from the median within a given time point and genotype were classified as outliers and excluded. This resulted in a reduced number of biological replicates for some time points. Because plants were destructively sampled for microbiome analyses at specific time points, repeated measurements over time corresponded to the same tub rather than the same individual plant. Accordingly, area under the curve (AUC) was calculated for each tub by integrating methane flux values across time. At the start of every measurement, a gas-tight chamber was placed over a rice plant with minimal disturbance. The chamber was left on for at least 5 minutes for flux analysis. Gas fluxes were calculated by fitting linear regression lines across the measurement time window.

The conversion to mass fluxes using the Ideal Gas Law equation:

F = (P / R × T) × (ΔC / Δt) × (V / A) × 10^-6

where F is the CH4 flux (mg m−2 h−1), P is the pressure, V is the volume, and A is the area of the sampling chamber. R is the ideal gas constant, and ΔCH4/Δt is the slope of the gas concentration change over time fitted by a linear equation. T is the temperature in the sampling chamber (K).

Microbiome sample collection
Rhizosphere samples were collected 15, 30, 55, and 70 days after rice was transplanted into pots. Briefly, we removed rice roots from pots and manually shook them to remove excess soil from the roots, leaving only ~1 mm of soil attached. These roots were transferred to 50-mL Falcon tubes that contained 15 mL of Powerbead Solution (Qiagen), stirred vigorously with sterile forceps to obtain closely attached soil, and frozen immediately in liquid nitrogen after removing roots. Samples were stored in a −80°C freezer before nucleic acid extraction.

Extraction and sequencing of total DNA and RNA
Approximately 2.0 g of rhizosphere soil was used for co-extraction of DNA and RNA by combining the Qiagen RNeasy PowerSoil Total RNA Kit and RNeasy PowerSoil DNA Elution Kit. Isolated nucleic acids were qualified and quantified using Nanodrop (Thermo Fisher) and Qubit (Invitrogen). For DNA samples, libraries were prepared using a Roche KAPA Hyper Prep Kit and sequenced on Illumina NovaSeq 6000 PE150 platform in Maryland Genomics. For RNA samples, ribosomal RNA (rRNA) was first depleted using the NEBNext rRNA Depletion Kit, then libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit and sequenced as above. In total, 72 DNA samples and 72 RNA samples (three replicates for each of the bulk soils and five replicates for each planted with the PSY1 rice genotypes and Kitaake control, at each collection time point) were sequenced, generating 974.8 Gbp and 920.7 Gbp of metagenomic and metatranscriptomic raw read data. Details for each sample can be found in Supplementary Data 1.

Metaomics de novo assembly and binning
Raw 150-bp paired reads from metagenomic sequencing were processed using BBDuk contained in the BBTools suite (https://jgi.doe.gov/data-and-tools/software-tools/bbtools/). Biological replicates from the same samples were merged using BBMerge. Samples were individually assembled using metaSPAdes (v3.15.5) with default parameters80. Scaffolds longer than 1 kb were binned by a combination of MetaBAT2 (v2.15)81, CONCOCT (v1.1.0)82, MaxBin2 (v2.2.7)83, and VAMB (v3.0.2)84, using differential abundance calculated by pairwise cross-mapping against all samples by BBMap. Resulting bins from the same samples were integrated and optimized using DASTool (v1.1.2)85. Optimized bins derived from all samples were dereplicated at 98% whole-genome average nucleotide identity (ANI) using dRep (v3.4.0)86, generating a set of approximately subspecies-level genomes. Quality of representative genomes was assessed using CheckM2 (v1.0.1)87, and taxonomy was assigned by GTDB-Tk (v2.3.0)88 based on the reference database version r21489 and further converted to NCBI taxon. Only genomes with predicted completeness ≥50% and contamination ≤5% were used for downstream analyses (n=296).

For metatranscriptomics, raw 150-bp paired reads were pre-treated using BBDuk, and rRNA was filtered by SortMeRNA (v4.3.6)90. Clean reads primarily containing mRNA were individually assembled using rnaSPAdes (v3.15.5)91 to generate transcripts.

Gene prediction and annotation
Open reading frames (ORFs) in assembled scaffolds and transcripts were predicted by Prodigal (v2.6.3)92. Protein sequences from all samples were combined together and dereplicated at 95% identity along with 85% coverage to generate a non-redundant gene set, using MMseqs293,94. Predicted proteins were annotated against the KEGG database95, UniProt96, and against the CAZy database98 using the online server following the manual instruction99. Protein domains were searched against Pfam (release 36.0)100 using Astra (https://github.com/jwestrob/astra).

For the identification and clustering of rpS3 genes, the PF00189 HMM was first used to search against predicted proteins based on pre-built noise cutoffs using HMMER (v3.3)101. Corresponding nucleotide sequences of protein hits were pulled out and clustered at 99% ident102 using VSEARCH (v2.13.3)103. Representative sequences from each cluster were blasted against the Genbank nr database to get preliminary taxonomic information104. Precise taxonomy was further retrieved by phylogeny construction that involved alignment of sequences using MAFFT (v7.453)105, trimming of alignments using trimal (v1.4.rev15)106, and generation of phylogenetic trees using IQ-TREE v1.6.12 with best-fit models automatically determined107. Trees were decorated on the iTOL server for better visualization108.

For genes closely related to CH4 cycling, the PF02249 and PF02745 HMMs were used to recruit McrA-like sequences; the TIGR03079 was used to recruit PmoB-like sequences; and the PF02964 was used to recruit the gamma subunit of methane monooxygenase hydroxylase. Further identification and classification were performed based on phylogeny as described above.

For genes involved in H2 production and consumption, three curated HMMs were used to target sequences homologous to [NiFe], [FeFe], and [Fe] hydrogenases, respectively42. Groups and subtypes of identified hydrogenases were distinguished based on protein phylogeny. [FeFe] Group A1 hydrogenases were further classified using the online tool HydDB, which integrates genetic context for accurate classification42. For genes related to nitrate reduction, proteins that contain the molybdopterin-binding domain or molybdopterin oxidoreductase domain were phylogenetically classified into distinct groups, including NarG and NapA exclusive groups. NifH proteins were identified using the TIGR01287 HMM that targets nitrogenase iron protein, included in all three nitrogenase forms (i.e., Mo-Fe, V-Fe, and Fe-only). Reductive DsrA was identified by DiSCo (v1.0.0)109. Taxonomy of genes was identical to that of gene-located scaffolds, which was assigned if 50% of the co-existing proteins belonged to the same taxon.

Abundances of assembled genomes and genes
Metagenomic abundances of genomes and genes were calculated using CoverM (v0.6.1)110. Given genomes dereplicated at 98% ANI, a minimum read identity during mapping was set at 98% together with a minimum aligned percent of 75% in the “genome” mode. For non-redundant genes at a 95% identity level, corresponding scaffolds were mapped by reads with a minimum identity of 95% and an alignment percentage of 75% using the “contig” mode. Abundances were considered to be zero if the covered fraction by any read was <50%. Normalized abundances were calculated by dividing coverage by total read length and multiplying by a factor of 1E11 for visualization.

Overall metatranscriptomic abundances of genomes were calculated using CoverM with the same cutoff parameters as above, followed by a normalization in which aligned read counts for each genome were divided by total read size and genome length, and multiplied by a factor of 1E15. To calculate the abundances of genes, corresponding scaffolds were first mapped by metatranscriptomic reads using Bowtie2 with “--end-to-end” “--sensitive” mode111. Exactly aligned reads for genes inside scaffolds were counted using featureCounts (v2.0.6)112. Final abundances, that said transcriptional activities, were normalized by dividing read counts by gene length and multiplying a factor of 1E11 for visualization.

Enrichment of genes/pathways in upregulated genomes
Based on calculated overall transcriptional abundances of genomes, DESeq2113 was used to identify significant differences in genome activities between the Kitaake and PSY1 genotypes, and between rice planted and unplanted groups. We consider genomes as “upregulated genomes” if: 1) they had significantly higher overall activities (log2FoldChange ≥1, p≤0.05) in PSY1 rice genotypes compared with the Kitaake in ≥1 rice growth stage; 2) they never showed significantly higher overall activities in the Kitaake than PSY1 genotypes; and 3) they were more active in rice planted groups than in bulk soils. Finally, 17 of 296 genomes were identified as up-regulated genomes in PSY1 genotypes.

KEGG Orthology (KO) identifiers were assigned to predicted proteins in all genomes, using kofamscan (v1.3.0; https://github.com/takaram/kofam_scan) with KOfam set r2023-10-02. Only hits above score thresholds and with the highest score were selected for individual proteins. Next, we performed Fisher’s exact test to evaluate the over/under enrichment of identified KOs in up-regulated genomes (n=17) relative to remaining genomes (n=279). A total of 3509 KOs that appeared in ≥10 genomes were tested, and the resulting p values were adjusted using Benjamini–Hochberg method114. KOs with FDR ≤ 0.05 were considered significantly enriched (odds ratio > 1) or depleted (odds ratio < 1) in up-regulated genomes.

Given KEGG metabolic pathways comprised of multiple KOs, we quantified pathways by summing up the abundances of all genes that are associated with related KOs. Differential abundances were calculated by subtracting abundances in the PSY1 genotypes from abundances in the Kitaake, shown as PSY1-17 and PSY1-9, respectively.

Calculations of genome-wide SAP values
We followed the previous paper to predict SAP values from genomes46. Briefly, we first calculated the relative abundances (number of genes normalized by the total number of genes in genomes) of sugar and acid genes that are implicated in the corresponding degradation pathways, based on the list in the above reference. The generalized linear model was then used to predict SAP values for each genome, as:

SAP = tanh(60.76 × S − 20.21 × A)

where S and A indicate relative abundances of sugar and acid genes, respectively. Statistical difference in average SAP values between genome groups was calculated using the Wilcoxon rank sum test.

Rice roots and exudate collection
Samples for metabolomic analysis were collected from 7-day-old rice seedlings grown vertically on 1× MS solid media plates. Prior to collection, seedlings were removed from the plates and thoroughly washed with deionized water to remove any residual media from the roots. For root tissue samples, the entire root system of 20 seedlings was ground using a mortar and pestle, with a subsample transferred to a 2 mL tube (Eppendorf #022363352). Tissue fresh weight was recorded with pre-weighted tubes, and samples were stored at −80°C until further processing. For root exudate samples, 15 seedlings were placed in 50 mL flasks containing ultra-pure water and held in place by sponges so that only the root tips were submerged. The flasks were incubated with shaking for 2 hours before the exudates were collected in 50 mL tubes (Fisher Scientific #14-959-49A). Exudates were initially stored at −20°C for a few hours, then transferred to −80°C until further use.

Liquid chromatography sample preparation
To extract metabolites from 300 to 400 mg root tissue and agar control media, samples were first frozen, lyophilized dry (FreeZone 2.5 Plus; Labconco), then powderized by bead-beating (Mini-Beadbeater 96, BioSpec) for 5 seconds, 2×, using a 3.2 mm stainless steel bead. Next, 500 μL of 100% methanol was added to each powderized root sample and 1.5 mL to each powderized agar sample, briefly vortexed, then sonicated in an ice water bath for 10 minutes. Samples were centrifuged (5 min, 11,000×g) to pellet debris, supernatant removed, and transferred to a 2 mL Eppendorf, dried in a SpeedVac (SPD111V; Thermo Fisher Scientific). To extract metabolites from exudates collected in 40 mL water, samples were first frozen then lyophilized dry. Next, 3 mL of 100% methanol was added to each sample, briefly vortexed, then sonicated in an iced water bath for 10 minutes. Samples were centrifuged (5 min, 7000×g) to pellet debris, supernatant removed and transferred to a 5 mL Eppendorf, then dried in a SpeedVac. Next, 1 mL of 100% methanol was added to each dried extract, briefly vortexed, then sonicated in an ice water bath for 10 minutes. Samples were centrifuged again, then supernatant removed and transferred to a 2 mL Eppendorf and dried in a SpeedVac.

All dried extracts were stored frozen at −80°C. The same extraction procedures were followed using empty 50 mL and 2 mL tubes to serve as extraction controls, and a 50 mL tube containing 50 mL water to serve as an exudate collection control (3 replicates each).

In preparation for LC-MS, samples were resuspended in 100% methanol containing a mix of isotopically labeled internal standards (https://doi.org/10.17504/protocols.io.kxygxydwkl8j/v1). Resuspension volume was varied for each root extract to normalize by amount of extracted biomass (1500 mg/mL), while all other samples were resuspended in 150 μL.

LC-MS/MS metabolomics analysis
Samples were run using both normal and reverse-phase chromatography. Each of these was performed using an Agilent 1290 LC stack, with MS and MS/MS data collected using an Exploris 120 Orbitrap MS (Thermo Fisher Scientific, San Jose, CA) for normal phase, and Q Exactive HF Orbitrap for reverse phase. Mass spectrometer parameters and gradients are provided in this protocols.io (https://doi.org/10.17504/protocols.io.kxygxydwkl8j/v1) for normal phase, and this protocols.io (https://doi.org/10.17504/protocols.io.ewov19r27lr2/v1) for reverse phase. For both normal phase and reverse phase, full MS1 spectra were collected at 60k resolution in both positive and negative ionization mode, with MS/MS fragmentation data acquired using stepped then averaged 10, 20, and 40 eV collision energies at 15k resolution. For normal phase, the MS1 m/z range for data collection was 70-1050, while for reverse phase, both m/z 80–1200 and m/z 1000–3000 were used in separate runs to cover mass ranges for metabolites and peptides. Samples consisted of three biological replicates each and extraction controls, with sample injection order randomized by replicate and an injection blank of 100% methanol run between each sample, with the blank replaced by an injection of internal standard mix every third sample, as well as QC mix every 15 samples. The raw data from the metabolite LC-MS runs are provided.

For untargeted data analysis, features with 0.8 min < RT < 18.5 min (HILIC column) or features with 0.5 min < RT < 9.5 min (C18 column), and maximum peak height fold-change between sample and extraction control > 3 were selected. We performed a Feature-Based Molecular Networking (FBMN)115 workflow using MZmine-3.7.2116 and a Global Natural Products Social Molecular Networking (GNPS2; http://gnps2.org)117. The MZmine workflow was used to generate a list of features obtained from extracted ion chromatograms containing chromatographic peaks within a narrow m/z range and filtered to remove isotopes. For each feature, the most intense fragmentation spectrum was uploaded to GNPS for putative identification by comparison with mass spectra deposited in the database. Compound classes were attributed to identified compounds using NPClassifier118. For targeted analysis, compound identification was performed by comparing detected m/z, retention time (RT) and fragmentation pattern to standards run in-house using the same LC-MS methods and assigning a confidence level based on these criteria. For each identified compound, the highest level of confidence (Exceeds Level 1)119 was achieved when the measured m/z agreed with the expected theoretical m/z with less than 5 ppm error, detected RT of the feature was within 0.5 minutes of theoretical, and the fragmentation pattern matched that of the standard. When MSMS was not collected, some compounds were identified based on only mz and RT (Level 1); mis-matching MSMS would invalidate an identification.

NPOC measurement
Frozen root exudates collected as described previously were thawed at 4°C and then diluted to 8 mL using Milli-Q water. Samples were analyzed on a Shimadzu TOC-L CPH total organic carbon analyzer equipped with TNM-L and ASI-L modules using UHP synthetic air mix as carrier gas. Calibration curves for non-purgeable organic carbon were prepared using potassium hydrogen phthalate. Standards and samples were analyzed under the same settings; blanks and control samples were included for quality control purposes. Sample measurements were setup with 1 wash step, followed by automated acid addition at 1.5% 1M HCl with a 2.5-minute sparging period at 80 mL/min to remove inorganic carbon and purgeable carbon. Acidified, sparged samples containing non-purgeable organic carbon were then injected at 50 μL for analysis using a max 4.5 min integration window for each carbon measurement; this was performed with 3–5 technical replicate injections with the max cv set at 2%. Measurements were corrected for dilution factors and then reported as ppm C (mg of C per L of exudate).

Soil incubations with rice root exudates
To assess the direct impact of rice root exudates on CH4 emissions, microcosm incubations were performed in wide-mouth glass mason jars (volume: 500 mL) fitted with gas-tight rubber septa. Each microcosm was prepared by mixing 10 g of air-dried agronomic soil, collected from the same rice basin used in the greenhouse experiments, with 10 mL of deionized water. The microcosms were incubated at 28°C in the dark for 48 hours to allow microbial respiration to stabilize. Root exudates from Kitaake, PSY1-, and PSY2-overexpressing rice seedlings were collected as previously described, lyophilized, and reconstituted in 1 mL of Milli-Q water. Each microcosm was flushed with nitrogen gas for 1 minute to displace ambient oxygen, and then exudates were gently added to the soil, taking care to avoid physical disturbance. The jars were sealed immediately, and headspace gas samples were collected at 0, 5, 10, 20, 30, 60, 120, 180, 240, and 300 minutes post-addition. CH4 concentrations in the headspace were quantified using a cavity ring-down spectrometer (Picarro G2201, Picarro Inc.), providing a direct measurement of microbial CH4 emissions in response to rice root exudate chemistry. CH4 concentrations in ppm were converted to nanograms of CH4 using the ideal gas law, assuming a constant headspace volume of 500 mL throughout the experiment, with final values expressed in mg CH4 per g of dried soil.

Construction and analysis of genome-scale metabolic models
We selected microbial genomes for metabolic modeling based on the following criteria: (1) genomes contain H2-producing or H2-consuming hydrogenases; (2) genomes are predicted to have completeness >90% and contamination <5%; (3) genomes are affiliated with different phyla. Three H2-producing genomes from Bacteroidia, Ignavibacteriota, and Moranbacteria, and three H2-consuming genomes from Acidobacteriota, Proteobacteria, and Deinococcota were finally selected and annotated using the App “Annotate Genome/Assembly with RASTtk” (v1.073) in KBase120. The resulting annotated genomes were used as inputs for the App “MS2 - Build Prokaryotic Metabolic Models with OMEGGA” (v.2.0.0) to build gap-filled metabolic models using media comprised of basic nutrients and PSY1 over-secreted compounds (Supplementary Data 8). Basic nutrients included glucose and glutamate as primary carbon sources, which were present and had no significant difference in concentrations across rice genotypes. Also included were standard essential factors for microbial growth (e.g., Fe2+). PSY1 over-secreted compounds were measured at significantly higher concentrations in root exudates from PSY1 rice genotypes compared with the Kitaake.

Dynamic flux balance analysis (dFBA) was performed for each microbial model individually, assuming well-mixed batch growth conditions, using the Computation Of Microbial Ecosystems in Time and Space (COMETS) platform (v2.12.2)121. We set the initial biomass at 1×10−6 g and used default simulation parameters. To compare results across varying conditions, we calculated the maximal potential of substrate turnover and biomass yield when microbial growth stopped and substrates no longer changed, for each condition. As glucose and glutamate in basic nutrients were likely the most abundant carbohydrate and amino acid secreted by rice roots122,123, concentrations of PSY1 over-secreted compounds were varied from 0.01–1-fold as that of basic nutrients. O2 is transported to the rhizosphere through rice aerenchyma, and its concentrations were set at 1–10 fold that of basic nutrients. H2 concentrations were set at 0 when testing H2 production potential and at 10-fold when testing H2 consumption potential. For visualization purposes, we calculated the consumed H2 amounts by subtracting the final values from the initial.

Statistical analysis
NMDS analyses for the rpS3 gene were performed and plotted using “vegan” and “ggplot2” packages in R (v4.2.2)124. Differences and significance between groups were assessed by analysis of similarities and permutational multivariate analysis of variance tests. Outliers were detected and removed from replicates using the MAD test125. Differential expressions of genomes between samples were calculated by DESeq2113.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of Manuscript NCOMMS-24-78529\nTitle: Reduced methane emissions in transgenic rice genotypes are associated with altered rhizosphere microbial hydrogen\ncycling.\nBy: Shi et al.\nThe manuscript demonstrates the potential functions of PSY in controlling CH4 emissions that arise from rice cultivation.\nThe authors present data that links PSY to CH4 mitigation through metagenomics, metatranscriptomic and metabolomics\nanalysis. Considering that rice cultivation is a sector of crop production that contributes approximately 10% to the global\nCH4 budget, mitigation practices are one strategy that may have great potential to mitigate the climate crisis.\nThe manuscript provides valuable insights into the factors that influence methane emissions from rice cultivations. Still,\nsome parts of the manuscript need further clarification before it is ready for publication. Please see my comments below.\nGeneral comments:\n1. Most of the conclusions concerning microbial activities come from different omics techniques. Preferentially, those should\nbe complemented with functional experiments that demonstrate the ability of the suggested bacteria to, e.g. produce or\nconsume hydrogen.\n2. The authors present data from Greenhouse experiments. The data presented demonstrate that PSY can influence CH4\nemissions. However, it is not certain that PSY will do so under field conditions since different soil and temperature\nconditions influence methane emissions from rice cultivations. Preferentially, the authors should complement the methane\nemission measurements under Greenhouse conditions with field trial measurements to prove that the reduction in methane\nemissions is a general phenomenon.\nSpecific comments:\n3. Line 104-106. I don’t think the phylogenetic tree displayed in Supplemental Figure 1 b illustrates the claim that the authors\nmake, and I lack a description of how the phylogeny was done. Is only the conserved domain included in the analysis? As\nfar as I can tell, this analysis is not mentioned in the materials and methods section. If the authors want to compare the\nconserved domain, maybe this could be better shown with a sequence alignment.\n4. Line 116-117. The authors hypothesize that LRR-RLKs may interact with the applied synthetic OsPSY peptides in root\ntissues and that this could influence root growth. Please provide an explanation for why.\n5. Line 143-143. The authors detect lower lignin content in PSY1-17 lines compared with Kitaake but not in the other\ntransgenic lines. Please provide an explanation for this discrepancy between the phenotypes of the transgenic lines.\n6. Line 166-188. The structure of the text is difficult to follow since the results for EXP2 are presented before EXP1. I think\nthe authors should restructure the text.\n7. Line 212-219. The style of writing in this section resembles a Materials and Methods section. Please reformulate.\n8. Line 220. The authors base their taxonomy on assembled rpS3 sequences. It has recently been shown that metagenome\nassemblies frequently fail to detect ribosomal sequences (Mise, K., Iwasaki, W. Unexpected absence of ribosomal protein\ngenes from metagenome-assembled genomes (ISME COMMUN. 2, 118 (2022). https://doi.org/10.1038/s43705-022-00204-\n6). The authors should revise their taxonomic analysis accordingly and include more sequences as a base for their\ntaxonomic classification.\n9. Line 232-234. The text in the manuscript does not match the text in the legend of Figure 3d:\nText in the manuscript:…cultivation time has a large and statistically significant effect on the community structure\n(dissimilarity score of up to 0.539) but rice genotype does not (Fig. 3d)\nText in the Figure legend:…for community structure (d), R=0.539, P=0.001 between rice genotypes\n(highlighted by ovals), and R=0.0729, P=0.238 between cultivation time;\n10. Line 287-290. This is a strong statement since the authors base their conclusion on transcriptome data, not functional\nmicrobiome studies. The authors should acknowledge this fact.\n11. Line 301. Root secretion is an end product of root and soil microbial activity. The authors use axenic rice roots in their\nmetabolic analyses, which may differ from real-life rhizospheric conditions. The authors should acknowledge this in the text.\n12. Line 304-310. I think the authors could help the reader here by rearranging either the text in the manuscript or the figures.\nThe authors write “root tissues or exudates” in the text and refer to Fig S5. a and e. However, Fig S5 a shows data for root\nexudates, and Fig S5 e shows data for root tissues. The remaining text in this paragraph follows a similar pattern. This\nmakes the text unnecessarily difficult to follow.\n13. Line 355-378. The metabolic models need further clarifications:\na. The authors use PSY over secreted compounds in their models. How do the models look if the corresponding over-\nsecreted compounds from the wild-type comparator were used?\nb. Also, does the total amount of secreted compounds differ between the genotypes or is only the composition. If there is an\nincreased total amount of compounds released, this will likely have a large effect on methane emissions.\nc. The autors identify 17 microbial genomes with increased transcriptional activities in soils of PSY genotypes of which five\nhad H2-consuming hydrogenases, please provide information on the taxonomy of these microbial genomes.\nd. The authors then build the models on three of these microbial genomes. How were these three genomes selected? Which\ngenomes of the 17 were used, and what were the species? All this information is needed to evaluate the models.\ne. Line 375-378. The final sentence should be clarified since it conveys conflicting results.\nf. Finally, if the species used in the models can be cultivated, it should also be possible to verify the models experimentally.\n14. Line 398-404. The authors state that methane cannot be derived from acetate as low activity of known aceticlastic\nmethanogens was found, and they suggest that acetate might not even be produced or only be present at low levels.\nAcetate, together with H2, is the final products from degradation of all organic compounds in an anaerobic environment, and\nthus, it seems very unlikely that no acetate is produced. Moreover, the authors do not mention the possibility of the\nalternative route for methane production from acetate, that is syntrophic acetate oxidation (SAO). This route for methane\nproduction has been found to be present in rice soil and is likely to be one pathway for hydrogen production feeding\nhydrogenotrophic methanogens. The text has to be revised accordingly. Moreover, the activity of this group would also be\nrelevant to explore in the transcriptomic data, e g the WL pathway, possibly combined with the glycine cleavage system\nrecently shown important for SAO. This pathway is also relevant for H2 oxidizing acetogens, possibly representing an\nimportant H2 oxidizing bacterial group, in addition to sulfate, iron and reducing bacteria.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "Overall comments\n1. The manuscript is well-structured and provides a comprehensive analysis demonstrating the methane emission mitigation\neffect of PSY in rice. However, the metagenomic and metabolomic data interpretations should provide more concrete\nevidence of causality rather than correlation. For example, while PSY-expressed plants show altered exudates, how do we\nconfirm that these exudates are indeed directly responsible for microbial shifts?\n2. The microbial community analysis (Figure 3) could benefit from clearer labeling—perhaps grouping similar microbial taxa\ntogether visually.\n3. The authors propose that PSY exudates selectively enhance H₂-oxidizing microbes while suppressing H₂-producing\nones. This is intriguing, but it is unclear whether:\n-This effect is directly caused by specific exudate compounds or just root-associated environmental changes.\n-The microbial shifts persist beyond controlled conditions.\n-The observed metabolic effects (e.g., gluconeogenic acid enrichment) are due to PSY expression or indirect plant growth\neffects.\n4. What are the agronomic trade-offs of PSY-expressing rice? The paper mentions shorter stature, reduced grain weight, and\nother agronomic traits. How might these factors impact yield? Similarly, Iqbal et al. (2021) demonstrated that while methane\nemissions were significantly reduced, there was a trade-off with rice yield. Both OsLSD (Iqbal et al.) and OsPSY influence\nroot biomass and aerenchyma development, which typically leads to reduced rice growth..\nIqbal, M. F., Liu, S., Zhu, J., Zhao, L., Qi, T., Liang, J., ... & Fan, X. (2021). Limited aerenchyma reduces oxygen diffusion and\nmethane emission in paddy. Journal of Environmental Management, 279, 111583.\n5. Is it differently expressed PSY on other parts? (panicle, leaf etc)\n6. PSY decrease lignin contents, please provide correlation with lignin and methane emission.\n7. How do the haplotypes of the PSY gene appear in natural variants, and what are their potential effects?\nDetail comments\n-p. 16 Line 1: It is not appropriate to compare the loss-of-function GS3 allele with PSY overexpression, as GS3 is a natural\nvariant, whereas PSY is an overexpressed mutant. Therefore, the methane mitigation effect should be compared with other\ngene-edited varieties or genes.\n-(p. 5 Line : 90 Introduction) Please provide sufficient information about PSY gene and role of PSY1 and PSY2\n-(p. 23 Line : 654 Methods) Please provide how many bp read to analyze metagenomics analysis (rpS3)"}, "author_response": "We thank the reviewer for their careful reading of the manuscript and\nappreciation of the significance of this work. To improve the manuscript, we have revised\nthe text, figures, and tables as requested, and below provides point-by-point responses\nto each comment. Reviewers’ comments are in normal text, followed by our response in\ndark blue. Dark blue font is also used to provide the revised manuscript text.\nGeneral comments:\n1. Most of the conclusions concerning microbial activities come from different omics\ntechniques. Preferentially, those should be complemented with functional experiments\nthat demonstrate the ability of the suggested bacteria to, e.g. produce or consume\nhydrogen.\nResponse: We appreciate the reviewer’s constructive suggestions. To further support\nour findings, we performed additional experiments where we incubated agronomic soil\nwith root exudates collected from different rice genotypes and monitored CH emissions\n4\nover time.\nWhile directly measuring hydrogen dynamics, as the reviewer suggested, would be an\nideal way to validate our model, the rapid onset of CH production and consumption upon\n4\nexudate addition indicates that hydrogen turnover occurs on a timescale that is likely too\nfast for reliable detection under our experimental conditions. This is in agreement with\nprevious observations that hydrogen is rapidly consumed by metabolic processes and\n1\nmaintained at very low steady-state partial pressures (e.g., DOI: 10.1007/BF02341425).\nDespite this limitation, our results clearly demonstrate a direct and reproducible effect of\nexudates on microbial activity in the soil, independent of other plant phenotypes. These\nfindings provide additional support for the model proposed in the manuscript and the\nconclusions drawn from our omics data.\nManuscript now reads as (lines 356-366):\nTo determine whether the altered exudate composition of PSY-genotypes can directly\ninfluence microbial activity, we conducted soil incubation assays using paddy soil from\nthe same rice field used in our greenhouse experiments. Exudates collected from PSY1-\nand PSY2-overexpressing rice seedlings were added as amendments, with exudates\nfrom Kitaake and sterile water as controls. CH emissions were then monitored over time.\n4\nto stimulate methanogenesis. Soils treated with Kitaake exudates exhibited consistently\nhigher CH emissions than those treated with PSY1 or PSY2 exudates, mirroring trends\n4\nobserved in the greenhouse-grown plant experiments (Supplementary Fig. 11).\nSupplementary Figure 11. Soil incubation with exudates from PSY-overexpressing\nand control rice genotypes. Agronomic paddy soil was incubated under anaerobic\nconditions with root exudates collected from PSY1- and PSY2-overexpressing seedlings\nor from wild-type Kitaake. A sterile water control was also included. Cumulative CH\n4\nemissions were measured over time. CH emissions were normalized by grams of dry\n4\nweight soil, as NPOC was comparable between different exudate samples.\nSoil incubations with rice root exudates\nTo assess the direct impact of rice root exudates on CH emissions, microcosm\n4\nincubations were performed in wide-mouth glass mason jars (volume: 500 mL) fitted with\ngas-tight rubber septa. Each microcosm was prepared by mixing 10 g of air-dried\nagronomic soil — collected from the same rice basin used in the greenhouse experiments\n— with 10 mL of deionized water. The microcosms were incubated at 28 °C in the dark\nfor 48 hours to allow microbial respiration to stabilize. Root exudates from Kitaake, PSY1-,\nand PSY2-overexpressing rice seedlings were collected as previously described,\nlyophilized, and reconstituted in 1 mL of Milli-Q water. Each microcosm was flushed with\nnitrogen gas for 1 minute to displace ambient oxygen, and then exudates were gently\nadded to the soil, taking care to avoid physical disturbance. The jars were sealed\nimmediately, and headspace gas samples were collected at 0, 5, 10, 20, 30, 60, 120, 180,\n240, and 300 minutes post-addition. CH concentrations in the headspace were quantified\n4\nusing a cavity ring-down spectrometer (Picarro G2201, Picarro Inc.), providing a direct\nmeasurement of microbial CH emissions in response to rice root exudate chemistry.CH\n4 4\nconcentrations in ppm were converted to nanograms of CH₄ using the ideal gas law,\nassuming a constant headspace volume of 500 mL throughout the experiment, with final\nvalues expressed in mg CH₄ per g of dried soil.\n2. The authors present data from Greenhouse experiments. The data presented\ndemonstrate that PSY can influence CH4 emissions. However, it is not certain that PSY\nwill do so under field conditions since different soil and temperature conditions influence\nmethane emissions from rice cultivations. Preferentially, the authors should complement\nthe methane emission measurements under Greenhouse conditions with field trial\nmeasurements to prove that the reduction in methane emissions is a general\nphenomenon.\nResponse: We thank the reviewer for highlighting the importance of validating our\nfindings under field conditions. We fully agree that such confirmation is essential to\nestablish the broader relevance of our greenhouse-based results. However, conducting\na field study — particularly one that incorporates the same level of multi-omics\ncharacterization presented here — would require substantial time and adherence to\nregulatory requirements associated with the cultivation of transgenic plants. In our case,\na field trial would take a minimum of 18 months to complete, due to the seasonal nature\nof planting, the need for multi-time point sampling, and post-harvest analyses. We have\nadded a statement to the Discussion to acknowledge this limitation and to describe our\nplans for upcoming field-based validation studies.\nWhile our greenhouse-based experiments using agronomic soil collected from a rice\nbasin provide clear evidence that rice genotypes overexpressing PSY genes reduce CH\n4\nemissions, controlled environments do not fully replicate the complexity of field conditions,\nwhere factors such as temperature fluctuations and seasonal dynamics may play major\nroles. We consider our study a critical first step in uncovering mechanistic links between\nspecific plant genotypes, root exudate chemistry, and CH cycling. To validate and extend\n4\nthese findings to agronomically relevant conditions, field trials incorporating multi-omics\nprofiling — similar in scope to our current approach — are necessary. Such future\nresearch will be essential to determine the generalizability and robustness of PSY-\nmediated reduced CH emissions across various soil types and growing conditions.\n3. Line 104-106. I don’t think the phylogenetic tree displayed in Supplemental Figure 1 b\nillustrates the claim that the authors make, and I lack a description of how the phylogeny\nwas done. Is only the conserved domain included in the analysis? As far as I can tell, this\nanalysis is not mentioned in the materials and methods section. If the authors want to\ncompare the conserved domain, maybe this could be better shown with a sequence\nalignment.\nResponse: We thank the reviewer for pointing out the lack of methodological detail\nrelated to the phylogenetic analysis and for the helpful suggestion to revise this figure.\nWe agree that the original version of Supplementary Fig. 1b may not have been the most\nappropriate representation to support our claim regarding sequence conservation of the\nmature PSY peptide across plant species.\nIn response, we have replaced the phylogenetic tree with a multiple sequence alignment\nand a sequence logo representation of the 13-amino acid mature PSY peptide domain\nfrom both Arabidopsis and rice. This revised visualization more clearly illustrates the high\ndegree of conservation in the active peptide sequence, particularly around the conserved\nDY motif, which supports our hypothesis of functional conservation across species.\nTo reflect these changes, we have revised the figure and the main text, and added a\ndetailed description of the sequence alignment and logo generation to the Materials and\nMethods section.\nManuscript now reads as (lines 118-124):\nTo identify putative rice PSY genes, we surveyed the Nipponbare and Kitaake genomes\nusing bioinformatic approaches as previously described25,27, and identified nine candidate\nrevealed high conservation between Arabidopsis (AtPSYs) and rice (OsPSYs) peptides\n(Supplementary Fig. 1b), suggesting that OsPSY peptides may regulate rice root growth\nsimilarly to AtPSY peptides21–23,25,28.\nMaterials and Methods section now reads as (lines 575-585):\nSequence alignment and peptide conservation analysis\nTo assess the conservation of the PSY peptides across species, we extracted the mature\npeptide sequences from Arabidopsis thaliana and Oryza sativa PSY family members. The\npeptide sequences were defined as the 13-amino acid mature domain, including the\nconserved aspartate-tyrosine (DY) motif, following the strategy described previously25. A\nsequence logo was generated using WebLogo web interface\n(http://weblogo.threeplusone.com)72, which provides a graphical representation of the\nmultiple sequence alignment, where each position’s stack height indicates sequence\nconservation, and symbol heights within the stack reflect the relative frequency of\nindividual amino acids at that position.\nSupplementary Figure 1. (b) Sequence logo generated using aligned 13-amino acid\nmature peptide sequences, including the conserved DY motif of the mature PSY peptide\ndomain across Arabidopsis thaliana and Oryza sativa. Amino acids are color-coded\nbased on chemistry.\n4. Line 116-117. The authors hypothesize that LRR-RLKs may interact with the applied\nsynthetic OsPSY peptides in root tissues and that this could influence root growth. Please\nprovide an explanation for why.\nResponse: We agree that additional clarification is needed to support our hypothesis that\nleucine-rich repeat receptor-like kinases (LRR-RLKs) in rice roots may mediate the\nresponse to synthetic OsPSY peptides. This hypothesis is based on recent findings in\nArabidopsis, where three different LRR-RLKs from subfamily XI were shown to function\nas direct ligand-binding receptors for PSY peptides and to regulate developmental\nprocesses such as root growth (Ogawa-Ohnishi et al., 2022, 10.1126/science.abq5735).\ntext to clarify this point.\nManuscript now reads as (lines 129-136):\nWe hypothesize that the root growth effect observed after synthetic peptide treatment\nmay result from the presence of leucine-rich repeat receptor-like kinases (LRR-RLKs) in\nroot tissues that recognize OsPSY1. It has been recently shown in Arabidopsis, that PSY\npeptides directly bind to LRR-RLKs from subfamily XI to trigger a signaling cascade that\nregulates root development22. The conservation of LRR-RLKs across plant species\nsuggests that functionally similar receptors likely exist in rice and may mediate\ncomparable developmental responses.\n5. Line 143-143. The authors detect lower lignin content in PSY1-17 lines compared with\nKitaake but not in the other transgenic lines. Please provide an explanation for this\ndiscrepancy between the phenotypes of the transgenic lines.\nResponse: At the time of the initial submission, lignin content had only been measured\nin PSY1-17 seedlings, which showed a significant reduction compared to Kitaake. For\nthis revision, we have extended the analysis to include PSY2-34 lines. These new results\ndemonstrate that the roots from seedlings that overexpress OsPSY2 also exhibit\nsignificantly reduced lignin levels compared to Kitaake. This finding is consistent with our\nobservations in root systems from adult rice plants, where both PSY1-17 and PSY2-34\nlines show reduced lignin accumulation (Fig. 2j). We have updated Figure 1i to include\nthe new PSY2-34 data and revised the manuscript text accordingly to reflect these results:\nManuscript now reads as (lines 162-164):\nAdditionally, the amount of lignin in PSY1-17 and PSY2-34 plants was significantly lower\ncompared with Kitaake (Fig. 1i).\nFigure 1. (i) Lignin content (4 replicates of 15 complete seedling root systems).\n6\n6. Line 166-188. The structure of the text is difficult to follow since the results for EXP2\nare presented before EXP1. I think the authors should restructure the text.\nResponse: We thank the reviewer for this helpful comment. We have reorganized the\ntext based on this suggestion and created a new figure that includes all the transgenic\nlines.\nManuscript now reads as (lines 180-224):\nTo assess the effect of PSY overexpression on CH emissions, we cultivated three\n4\nindependent transgenic rice lines — PSY1-9, PSY1-17, and PSY2-34 — along with the\ncontrol Kitaake plants in a greenhouse using soils collected from a rice paddy in Davis,\nCA. We also included bulk soil controls that were not planted. Throughout the\nexperiments, all rice lines developed similarly to Kitaake, with heading observed at 55\ndays after being transferred to the greenhouse (Fig. 2a). By 70 days post-transplant, both\nPSY1 and PSY2 genotypes were slightly shorter than Kitaake (Fig. 2b and\nSupplementary Fig. 2a), but they produced significantly more tillers (Fig. 2c and\nSupplementary Fig. 2b), resulting in comparable dry weights of stems and leaves per\nplant at the end of the plant life cycle (Fig. 2d). PSY1 genotypes showed partially filled\npanicles, fewer seeds, and lower thousand-grain weight (TGW) per plant, compared with\nKitaake (Fig. 2e-g and Supplementary Fig. 2c). These phenotypes could be attributed to\nabnormal panicle development, as we found that the tips of the panicles in PSY1\ngenotypes often contained smaller and unfilled seeds, a phenotype not seen in PSY2\nplants (Fig. 2h). Consistently, no significant differences in seed production occurred in the\nPSY2 genotypes compared with Kitaake (Fig. 2e-h).\nWe also investigated whether the longer seminal root phenotype observed in young\nseedlings persisted in older PSY plants. We found that PSY1-17 plants developed longer\nroots 30 days after transplanting, although their root dry biomass was significantly lower\nthan that of Kitaake plants (Fig. 2k and Supplementary Fig. 2d-e). Similar differences in\nroot dry biomass were observed at the end of the plant life cycle (Fig. 2i). Additionally,\nlignin content in the roots of adult plants revealed that, consistent with findings in young\nseedlings, PSY1-17 plants accumulated less lignin than Kitaake (Fig. 2j). Although PSY2\nplants also had reduced lignin content, their root biomass remained comparable to that\nof Kitaake (Fig. 2i-j).\nDespite these phenotypic differences, no significant differences in soil CH emissions\n4\nwere observed between the PSY1-9, PSY1-17, and PSY2-34 genotypes (Fig. 2l). For\nthese analyses, CH emissions were measured in situ using a Picarro gas analyzer, and\n4\nsoil samples were collected for microbiological analyses throughout the rice growth period\n(i.e., 15, 30, 55, and 70 days after transplanting). Negligible CH emissions were observed\n4\nfrom the bulk soil controls, while differentiated emissions were recorded from pots with\ngenotypes emitted substantially less CH compared with Kitaake, with reductions of up to\n4\n~30% observed after heading (55 and 70 days; Fig. 2l). Notably, cumulative CH\n4\nemissions throughout the growth period for PSY1 and PSY2 genotypes showed an\naverage decrease of ~38% and ~58% relative to Kitaake, respectively (Supplementary\nFig. 3). Given the crucial role of microorganisms in the production and consumption of\nCH , we hypothesized that distinct microbial communities might accumulate in the\n4\nrhizospheres of the different rice genotypes, potentially affecting the rice CH emissions.\n4\nemissions, we focused on PSY1 genotypes for further study.\nNew Figure 2I and Supplementary Figure 3.\nFig. 2 Kitaake plants with elevated expression of OsPSY1 or OsPSY2 exhibited\nreduced CH emissions. (l) Methane emission rates from different rice genotypes and\n4\nbulk soil controls are normalized to the mean value obtained in Kitaake plants.\nSupplementary Fig. 3 Methane emission rates over days after transplanting different\nrice genotypes. (b) Cumulative methane flux was calculated as the area under the curve\n8\nin (a) expressed in arbitrary units (A.U.). Different letters indicate significant differences,\nas determined by one-way ANOVA followed by Tukey's multiple comparison test (P <\n0.05).\n7. Line 212-219. The style of writing in this section resembles a Materials and Methods\nsection. Please reformulate.\nResponse: We have moved some information to the Methods section and rephrased the\nsentences.\nManuscript now reads as (lines 227-234):\nTo explore the difference in root microbiomes, we extracted and sequenced total DNA\nand RNA from 72 soil samples (3 replicates for each of the bulk soils and 5 replicates for\neach planted with the PSY1 rice genotypes and Kitaake control, at each collection time\npoint) (Supplementary Table 1). We used assembled ribosomal protein S3 (rpS3)\nsequences to perform an initial census of microbial diversity, including organism type and\nabundance32, in the 16 representative samples obtained by combining replicates. In total,\n4,087 rpS3 sequences were identified and grouped into 1,656 non-redundant,\napproximately species-level clusters.\n8. Line 220. The authors base their taxonomy on assembled rpS3 sequences. It has\nrecently been shown that metagenome assemblies frequently fail to detect ribosomal\nsequences (Mise, K., Iwasaki, W. Unexpected absence of ribosomal protein genes from\nmetagenome-assembled genomes (ISME COMMUN. 2, 118 (2022).\nhttps://doi.org/10.1038/s43705-022-00204-6). The authors should revise their taxonomic\nanalysis accordingly and include more sequences as a base 0 for their taxonomic\nclassification.\nResponse: The cited paper reports that ribosomal protein genes (including rpS3) can be\nundetected in metagenome-assembled genomes (not assemblies, as the reviewer notes),\ndue to the dependence on scaffold tetranucleotide frequencies of metagenomic binning.\nWe tested this expectation by profiling the metagenome-derived bins from our study for\nsingle-copy genes in two categories: (i) single-copy genes that are not ribosomal proteins,\ne.g., tRNA synthetases, and (ii) ribosomal protein genes. We asked whether or not a bin\ncontained the selected marker genes (yes or no). Using bins ranging from near-complete\nto half-complete (approximately) as well as using bins that were at least ~78% complete,\nthe detection of genes in these categories was not significantly different. More relevant to\nthe concern as it relates to this paper, we note that our study analyzed all the rpS3\nsequences in scaffolds to profile the community structure (i.e., we did not rely on binned\nrpS3 genes only), which should not have been affected by the above algorithmic flaw.\ngenes that could be recovered is > 5 times more than the number of genomes that could\nbe binned in complex environmental samples, including soils (please see Fig. 3c in:\nOlm et al., Consistent metagenome-derived metrics verify and delineate bacterial species\nboundaries. mSystems 5.1 (2020). 10.1128/msystems.00731-19).\n9. Line 232-234. The text in the manuscript does not match the text in the legend of Figure\n3d:\nText in the manuscript:…cultivation time has a large and statistically significant effect on\nthe community structure (dissimilarity score of up to 0.539) but rice genotype does not\n(Fig. 3d)\nText in the Figure legend:…for community structure (d), R=0.539, P=0.001 between rice\ngenotypes (highlighted by ovals), and R=0.0729, P=0.238 between cultivation time;\nResponse: We apologize for the omission. The legend of Fig. 3d has been corrected.\nThe figure legend now reads as:\nFor community structure (d), R=0.539, P=0.001 between cultivation time (highlighted by\novals), and R=0.0729, P=0.238 between rice genotypes.\n10. Line 287-290. This is a strong statement since the authors base their conclusion on\ntranscriptome data, not functional microbiome studies. The authors should acknowledge\nthis fact.\nResponse: We agree that our conclusion regarding hydrogen dynamics is based on\ntranscriptomic data, but we do not know exactly what a “functional microbiome study”\nrefers to. We have toned down the statement and clearly presented it as a hypothesis.\nManuscript now reads as (lines 302-306):\nBased on the transcriptomic profile analysis, we hypothesize that the microbiomes in the\nPSY1 genotypes may produce less and consume more H₂ than those in Kitaake. As a\nresult, the pool of hydrogen available for CH₄ production in soils from the PSY genotypes\nwould be expected to be lower than that in the Kitaake control.\n11. Line 301. Root secretion is an end product of root and soil microbial activity. The\nauthors use axenic rice roots in their metabolic analyses, which may differ from real-life\nrhizospheric conditions. The authors should acknowledge this in the text.\nroot exudates, as this approach offers several experimental advantages: precise control\nover the chemical environment, sufficient recovery of fresh exudates, reduced mineral\nsorption artifacts, and minimal microbial transformation of metabolites (Zhalnina et al.,\n2018, 10.1038/s41564-018-0129-3). We acknowledge, however, that hydroponically\nderived exudation profiles may differ from those in soil-grown systems, where root–\nmicrobe interactions and soil matrix can alter both exudation rate and composition.\nTherefore, while the axenic method effectively isolates plant-derived metabolites, it fails\nto capture the dynamic interplay between plant roots and the soil microbiota. We have\nupdated the Discussion to clarify this trade-off and to highlight the importance of\ncombining hydroponic and soil-based exudate collection throughout plant development\nto uncover further ecological relevance.\nManuscript now reads as (lines 526-537):\nIt has been demonstrated that exudation rate and composition vary throughout plant\ndevelopment68. In this study, we collected exudates from seedlings grown under axenic\nhydroponic conditions. This method offers several advantages: precise control over the\nchemical milieu, adequate recovery of fresh exudates, avoidance of mineral sorption\nbiases, and minimization of microbial degradation69. However, hydroponic growth can\nalter exudate profiles compared with soil systems, often increasing total carbon release\nbut reducing metabolite diversity due to the absence of rhizosphere feedbacks70. Given\nthese methodological trade-offs, future studies could integrate both axenic hydroponic\nand soil-based exudate sampling across multiple developmental stages to gain a more\ncomprehensive and ecologically relevant understanding of how PSY influences plant–\nmicrobe interactions and biogeochemical cycles.\n12. Line 304-310. I think the authors could help the reader here by rearranging either the\ntext in the manuscript or the figures. The authors write “root tissues or exudates” in the\ntext and refer to Fig S5. a and e. However, Fig S5 a shows data for root exudates, and\nFig S5 e shows data for root tissues. The remaining text in this paragraph follows a similar\npattern. This makes the text unnecessarily difficult to follow.\nResponse: We agree that the current description could be clearer, especially in\ndistinguishing results from root tissues versus exudates. To improve clarity, we have\nreorganized the text, aligning each description with the corresponding panels in\nSupplementary Figures 6-9 (originally Supplementary Figures 5-8). Additionally, we have\nupdated the figure legends.\nManuscript now reads as (lines 316-349):\nperformed metabolomic analysis on root tissues and exudates from axenic rice seedling\nroots to identify compounds secreted by the PSY1 genotypes. Metabolites were extracted\nusing methanol and profiled on an HPLC-MS platform (Supplementary Dataset 1). Using\ngradient-based hydrophilic interaction liquid chromatography (HILIC) in positive-ion\nmode, we identified a total of 3,612 features across all exudate samples, of which 2,047\nwere shared between the Kitaake and PSY1 genotypes (Supplementary Fig. 6a).\nPrincipal component analysis (PCA) showed a clear separation between PSY1 and\nKitaake exudate samples (Supplementary Fig. 6b and Supplementary Dataset 2). Among\nthe shared features, 168 (8.3%) were significantly differentially abundant in PSY1\nexudates (|log₂FC| > 0.5, p < 0.05), including 146 novel and 15 depleted features\n(Supplementary Fig. 6c). A subset of these differentially abundant features was putatively\nidentified and classified using Global Natural Products Social Molecular Networking\n(GNPS) (Supplementary Tables 3-5). Most of the differentially abundant metabolites in\nPSY1 root exudate samples belong to the same compound classes, including ‘amino\nacids’, ‘alkaloids’, and ‘shikimates and phenylpropanoids’ (Supplementary Fig. 6d).\nSimilarly, in root tissues, we identified 2,237 shared features, with PCA indicating an even\nstronger genotypic separation (Supplementary Fig. 6e-f). Of these, 272 (12%) were\ndifferentially abundant in PSY1 tissues, featuring 208 new and 80 depleted metabolite\nsignals (Supplementary Fig. 6g), again reflecting shifts in similar compound classes\n(Supplementary Fig. 6h). Overall, more features increased than decreased in PSY1\nsamples. Similar results were obtained from HILIC in negative ionization mode and\nreverse-phase (C18) in both positive and negative ionization modes and are detailed in\nSupplementary Dataset 2, Supplementary Tables 3-5, and Supplementary Figs. 7-9.\nAltogether, our analysis indicated significant changes in the abundance of compounds in\nthe PSY1 root exudates compared with Kitaake. Approximately 90% of the metabolites\nenriched in the PSY1 metabolome are predicted to be involved in microbial gluconeogenic\npathways (Supplementary Table 6). We also noted the presence of two putative\nflavonoids, isorhamnetin 3-rutinoside and malvidin-3-glucoside, that differentially\naccumulated in exudate samples from the PSY1 genotypes but were absent from Kitaake,\na feature observed in previous studies in Arabidopsis23.\nLegends of Supplementary Figures 6–9 read as:\nSupplementary Figure 6. Metabolomic profiling of root exudates and tissues using\nHILIC (positive ionization mode). (a) Venn diagram of features detected in axenic root\nexudates from Kitaake and PSY1 seedlings. (b) Principal Component Analysis (PCA) of\nexudate features. (c) Volcano plot of differentially abundant metabolites in exudates\n(yellow = increased, blue = decreased; thresholds: |log₂ FC| > 0.5, p < 0.05). (d) Chemical\nclassification of differentially abundant exudate metabolites, showing counts of\nincreased/new vs. decreased/depleted compounds. (e–h) Analogous analyses for root\n12\ntissue samples: (e) Venn diagram, (f) PCA, (g) volcano plot, and (h) chemical\nclassification. Numbers indicate features unique to or shared between genotypes and\nhow many were significantly up- or down-regulated.\nSupplementary Figure 7. Metabolomic profiling of root exudates and tissues using\nHILIC (negative ionization mode). (a) Venn diagram of features detected in axenic root\nexudates from Kitaake and PSY1 seedlings. (b) Principal Component Analysis (PCA) of\nexudate features. (c) Volcano plot of differentially abundant metabolites in exudates\n(yellow = increased, blue = decreased; thresholds: |log₂ FC| > 0.5, p < 0.05). (d) Chemical\nclassification of differentially abundant exudate metabolites, showing counts of\nincreased/new vs. decreased/depleted compounds. (e–h) Analogous analyses for root\ntissue samples: (e) Venn diagram, (f) PCA, (g) volcano plot, and (h) chemical\nclassification. Numbers indicate features unique to or shared between genotypes and\nhow many were significantly up- or down-regulated.\nSupplementary Figure 8. Metabolomic profiling of root exudates and tissues using\nC18 (positive ionization mode). (a) Venn diagram of features detected in axenic root\nexudates from Kitaake and PSY1 seedlings. (b) Principal Component Analysis (PCA) of\nexudate features. (c) Volcano plot of differentially abundant metabolites in exudates\n(yellow = increased, blue = decreased; thresholds: |log₂ FC| > 0.5, p < 0.05). (d) Chemical\nclassification of differentially abundant exudate metabolites, showing counts of\nincreased/new vs. decreased/depleted compounds. (e–h) Analogous analyses for root\ntissue samples: (e) Venn diagram, (f) PCA, (g) volcano plot, and (h) chemical\nclassification. Numbers indicate features unique to or shared between genotypes and\nhow many were significantly up- or down-regulated.\nSupplementary Figure 9. Metabolomic profiling of root exudates and tissues using\nC18 (negative ionization mode). (a) Venn diagram of features detected in axenic root\nexudates from Kitaake and PSY1 seedlings. (b) Principal Component Analysis (PCA) of\nexudate features. (c) Volcano plot of differentially abundant metabolites in exudates\n(yellow = increased, blue = decreased; thresholds: |log₂ FC| > 0.5, p < 0.05). (d) Chemical\nclassification of differentially abundant exudate metabolites, showing counts of\nincreased/new vs. decreased/depleted compounds. (e–h) Analogous analyses for root\ntissue samples: (e) Venn diagram, (f) PCA, (g) volcano plot, and (h) chemical\nclassification. Numbers indicate features unique to or shared between genotypes and\nhow many were significantly up- or down-regulated.\n13. Line 355-378. The metabolic models need further clarifications:\na. The authors use PSY over secreted compounds in their models. How do the models\nlook if the corresponding over-secreted compounds from the wild-type comparator were\nused?\nover-secreted by the wild-type Kitaake are two glycerolipids, TG(18:1/18:2/18:2) and\nTG(16:0/16:0/18:3), and one dipeptide containing leucine. We input the representative of\nglycerolipids, triglyceride (KEGG: C00422), and the leucyl-leucine (KEGG: C11332) into\nour metabolic models and found that none of the organisms could metabolize these\ncompounds. Consequently, the Kitaake over-secreted metabolites did not affect the\npredicted H flux. We have revised the text accordingly.\n…predicted H fluxes in response to variations in PSY1-secreted exudate compounds\n2\nand O concentrations inferred from aerenchyma densities of the different rice genotypes\n2\n(Supplementary Table 8). The only identified compounds under-secreted by PSY1\ncompared with Kitaake were two glycerolipids, putatively 1-oleoyl-2,3-di-linoleoyl-sn-\nglycerol and 1,2-di-hexadecanoyl-3-(9Z,12Z,15Z-octadecatrienoyl)-sn-glycerol, and one\ndipeptide containing leucine (Supplementary Table 3). However, none of the modeled H -\n2\nrelated organisms can metabolize these compounds. As a result, the PSY1 under-\nsecreted metabolites are predicted to have no effect on H fluxes. In contrast, when over-\n2\nsecreted compounds increase from 0.01 to 0.05 fold, all three modeled H -producing\n2\nbacteria from different phyla (Bacteroidia, Ignavibacteriota, and Moranbacteria) show the\nsame trend, with maximal produced H decreasing (Fig. 7a-c).\n2\nb. Also, does the total amount of secreted compounds differ between the genotypes or is\nonly the composition. If there is an increased total amount of compounds released, this\nwill likely have a large effect on methane emissions.\nResponse: To determine whether the total amount of secreted compounds differed\nbetween genotypes, we quantified the concentration of non-purgeable organic carbon\n(NPOC) in the exudate samples used for metabolomic analysis. The results showed no\nsignificant differences in total carbon content among PSY genotypes and Kitaake\nexudates, indicating that the primary variation lies in compound composition rather than\ntotal exudation rate. We have added this clarification to the end of the metabolomics\nsection in the revised manuscript.\nManuscript now reads as (lines 350-355):\nTo assess whether these metabolic differences reflected changes in the total amount of\ncarbon released, we measured the concentration of non-purgeable organic carbon\n(NPOC) in the root exudate samples. No significant differences in total carbon content\nwere detected between Kitaake and PSY genotypes, indicating that PSY overexpression\nprimarily alters the composition, instead of the overall quantity, of secreted metabolites\n(Supplementary Fig. 10).\npurgeable organic carbon (NPOC, mg L⁻¹) was measured in exudates collected from 8-\nday-old rice seedlings of Kitaake, PSY1, and PSY2 genotypes, along with a water-only\ncontrol. Bars represent the mean ± SEM from eight biological replicates per genotype.\nDifferent letters indicate significant differences, as determined by one-way ANOVA\nfollowed by Tukey's multiple comparison test (P < 0.05).\nMaterials and Methods now read as (lines 939-954):\nNon-purgeable organic carbon (NPOC) measurement\nFrozen root exudates collected as described previously were thawed at 4°C and then\ndiluted to 8 mL using Milli-Q water. Samples were analyzed on a Shimadzu TOC-L CPH\ntotal organic carbon analyzer equipped with TNM-L and ASI-L modules using UHP\nsynthetic air mix as carrier gas. Calibration curves for non-purgeable organic carbon were\nprepared using potassium hydrogen phthalate. Standards and samples were analyzed\nunder the same settings; blanks and control samples were included for quality control\npurposes. Sample measurements were set up with 1 wash step, followed by automated\nacid addition at 1.5% 1M HCl with a 2.5 minute sparging period at 80 mL/min to remove\ninorganic carbon and purgeable carbon. Acidified, sparged samples containing non-\npurgeable organic carbon were then injected at 50 uL for analysis using a max 4.5 min\nintegration window for each carbon measurement; this was performed with 3-5 technical\nreplicate injections with the max cv set at 2%. Measurements were corrected for dilution\nfactors and then reported as ppm C (mg of C per L of exudate).\n15\nc. The autors identify 17 microbial genomes with increased transcriptional activities in\nsoils of PSY genotypes of which five had H2-consuming hydrogenases, please provide\ninformation on the taxonomy of these microbial genomes.\nResponse: This information was initially included in the Supplementary Table 7 only. To\nmake it clear, we have now added it to the main text.\nManuscript now reads as (lines 370-374):\nThe genomes of five of the 17 encode H -consuming hydrogenases, including those\n2\naffiliated with Pyrinomonadaceae, Burkholderiaceae, Haliangiaceae, Rhodocyclaceae,\nand Dissulfurispiraceae, but none contain H -producing enzymes (Supplementary Table\n2\n7).\nd. The authors then build the models on three of these microbial genomes. How were\nthese three genomes selected? Which genomes of the 17 were used, and what were the\nspecies? All this information is needed to evaluate the models.\nResponse: First, we used metabolic modeling to investigate how rice physiological\nfeatures, particularly the over-secreted compounds, affect H -related organisms and the\n2\nassociated H -cycling process. Thus, only the genomes that have H -producing or H -\n2 2 2\nconsuming hydrogenases were selected. Second, metabolic modeling requires high-\nquality genomes. Here, we filtered those of predicted completeness > 90% and\ncontamination < 5%. Finally, we chose genomes from different phyla to better represent\nthe H -related population. Based on these criteria, H -producing genomes affiliated with\n2 2\nBacteroidia, Ignavibacteriota, Moranbacteria, and H -consuming genomes from\nwere ultimately selected for metabolic modeling. We have added this and the taxonomic\ninformation of the selected genomes to the manuscript.\nManuscript now reads as (lines 407-411):\nThe H -consuming organisms from Acidobacteriota, Proteobacteria, and Deinococcota\n2\nare modeled to oxidize more H (and accordingly yield more biomass) in the presence of\n2\nhigher O concentrations that are associated with higher aerenchyma formation in PSY1\n2\nrice genotypes (Fig. 7d-f and Supplementary Fig. 13d-f).\nIn Figure 7 legend:\nGenome name abbreviations: Bacteroidia_37, REG_22_10_11_KiT_metabat2_37 in\nSupplementary Table 7; Ignavibacteriota_4780, REG_22_11_18_KiT_metabat2_4780;\nMoranbacteria_75, REG_22_11_18_KiT_metabat2_75; Acidobacteriota_95,\nREG_22_09_28_Psy117_metabat2_95; Proteobacteria_149,\nREG_22_09_28_KiT_vamb_10534. All detailed information of these genomes is\npresent in Supplementary Table 7.\nIn Methods (lines 976-983):\nWe selected microbial genomes for metabolic modeling based on the following criteria:\n(1) genomes contain H -producing or H -consuming hydrogenases; (2) genomes are\n2 2\npredicted to have completeness > 90% and contamination < 5%; (3) genomes are\naffiliated with different phyla. Three H -producing genomes from Bacteroidia,\nusing the App “Annotate Genome/Assembly with RASTtk” (v1.073) in KBase120.\nIn Data availability (lines 1018-1019):\nMicrobial metabolic models have been deposited in figshare\n(https://doi.org/10.6084/m9.figshare.29115692).\ne. Line 375-378. The final sentence should be clarified since it conveys conflicting results.\nResponse: The PSY rice genotypes have more aerenchyma and a distinct exudate\nprofile compared with the Kitaake control. Our modeling suggests that the resulting higher\nO concentrations and PSY1 exudates promote H oxidation and biomass yield of H -\n2 2 2\nconsuming organisms. We have rephrased the sentence for clarity.\nManuscript now reads as (lines 407-416):\nThe H -consuming organisms from Acidobacteriota, Proteobacteria, and Deinococcota\n2\nare modeled to oxidize more H (and accordingly yield more biomass) in the presence of\n2\nhigher O concentrations that are associated with higher aerenchyma formation in PSY1\n2\nrice genotypes (Fig. 7d-f and Supplementary Fig. 13d-f). The PSY1 over-secreted\nexudate compounds have a negligible influence on H oxidation, yet still contribute\n2\nsubstantially to accumulation of biomass of H -consuming organisms (Supplementary\nPSY1 rice genotypes enhance the growth and activity of H -consuming organisms due to\n2\ntheir distinct aerenchyma characteristics and exudate profiles.\nf. Finally, if the species used in the models can be cultivated, it should also be possible\nto verify the models experimentally.\nlimitation in the Discussion and would like to try isolation and cultivation in future\nexperiments.\nManuscript now reads as (lines 518-525):\nOverall, our metabolic modeling predicts that the exudate profiles of PSY1 rice genotypes\nmay inhibit the activities of H -producing microorganisms and stimulate those of the H -\n2 2\nconsuming counterparts. This result suggests that in the PSY1 rhizosphere, the bacterial\nmetabolic activities further diminish the pool of H available for methanogenesis thus\n2\nmitigating overall CH emissions (Fig. 8). Future studies could isolate and co-cultivate the\n4\norganisms used in the models to validate the inferred microbial responses to root-derived\nenvironmental changes.\n14. Line 398-404. The authors state that methane cannot be derived from acetate as low\nactivity of known aceticlastic methanogens was found, and they suggest that acetate\nmight not even be produced or only be present at low levels. Acetate, together with H2,\nis the final products from degradation of all organic compounds in an anaerobic\nenvironment, and thus, it seems very unlikely that no acetate is produced. Moreover, the\nauthors do not mention the possibility of the alternative route for methane production from\nacetate, that is syntrophic acetate oxidation (SAO). This route for methane production\nhas been found to be present in rice soil and is likely to be one pathway for hydrogen\nproduction feeding hydrogenotrophic methanogens. The text has to be revised\naccordingly. Moreover, the activity of this group would also be relevant to explore in the\ntranscriptomic data, e g the WL pathway, possibly combined with the glycine cleavage\nsystem recently shown important for SAO. This pathway is also relevant for H2 oxidizing\nacetogens, possibly representing an important H2 oxidizing bacterial group, in addition to\nsulfate, iron and reducing.\nResponse: We agree with the reviewer that there must be acetate production. Based on\nthe literature, the substrate affinity of acetate of methanogens appears to be much higher\nthan that of hydrogen (0.2-1.2 mM acetate versus 2.8-10 Pa H ). Meanwhile, other co-\n2\nexisting organisms may outcompete methanogens for the in situ produced acetate, e.g.,\nfor the reduction of nitrate and sulfate. Thus, we hypothesize that the hydrogenotrophic\npathway was selected as the dominant methanogenesis pathway under such\nenvironmental conditions, which is consistent with previous observations (e.g.,\n10.1038/s41558-023-01872-5, 10.3389/fmicb.2012.00004).\nWe also thank the reviewer for mentioning the possibility of syntrophic acetate oxidation\n(SAO) for methane production. SAO bacteria use the reverse Wood-Ljungdahl pathway\n(sometimes combined with the glycine cleavage system) and hydrogenases to oxidize\nacetate to CO and H , the substrates for hydrogenotrophic methanogenesis. Thus, we\n2 2\n18\ninvestigated the key enzymes in our 36 H -producing microbial genomes. However, none\n2\nof them encode complete Wood-Ljungdahl pathways, although some genomes are\npredicted to be > 90% complete (by CheckM2), precluding their ability to perform SAO.\nThis is not unexpected because most of our H -producing genomes are from the phyla\n(Supplementary Table 7). On the contrary, most of the reported SAO bacteria that co-\nexist with methanogens belong to the phylum Firmicutes (GTDB taxon: Bacillota).\nBacteria of this phylum were rarely detected in our study (< 1%; Fig. 3). Of the three\norganisms detected, one affiliates with the family Symbiobacteriaceae and two with the\nclass Negativicutes (Supplementary Table 7), and none encode the Wood-Ljungdahl\npathway.\nWe also analyzed our 55 H -consuming microbial genomes and found no H -oxidizing\n2 2\nhomoacetogenesis pathways. This is not surprising because none of these genomes are\nfrom the class Clostridia of Firmicutes, which includes most of the discovered\nhomoacetogens.\nOverall, we acknowledge the possibility of syntrophic acetate oxidation for methane\nproduction and the potential of homoacetogenesis for H competition, but they appeared\n2\nto make a minor contribution. We have added the above information and discussion in\nthe related text.\nManuscript now reads as (lines 432-455):\nPrevious rice field studies using isotope-labeled substrates revealed that the presence of\nelectron acceptors such as sulfate and nitrate channeled acetate towards CO , and that\n4 2 2\nH is likely explained by acetate concentrations that are insufficient for methane\n2\nproduction. Acetoclastic methanogens (e.g., Methanosarcina) require 0.2-1.2 mM acetate\nto activate CH formation53,54 whereas hydrogenotrophic methanogens (e.g.,\n2\nto the rapid in situ utilization by co-existing organisms that outcompete methanogens for\nthe acetate. One possible route for acetate consumption is syntrophic acetate oxidation\n(SAO) that uses the reverse Wood-Ljungdahl (WL) pathway (sometimes combined with\nthe glycine cleavage system) and hydrogenases to produce CO and H , the substrates\n2 2\nfor hydrogenotrophic methanogenesis57–59. However, none of our hydrogenase-harboring\nmicrobial genomes encode complete WL pathways, although some unclassified\nFirmicutes members that may include SAO bacteria were detected at low abundance (<\n1%; Fig. 3). An alternative way for acetate consumption is the reduction of nitrate and\nsulfate, observed in previous studies51,52. This activity is indicated by the expression of\nnitrate and sulfate reductases in our system (Supplementary Figs. 14 and 15). The\n19\nsubstrates (i.e., ~2.0 mM of both nitrate and sulfate) were periodically added to water\nused to fertilize the rice plants during growth (Supplementary Table 2). Given the absence\nof WL pathways in hydrogenase-containing microorganisms, the homoacetogenesis\nprocess that relies on H oxidation to produce acetate appears to be negligible in our\n2\nstudy.\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review and\nto provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse: We appreciate the reviewer and the journal for the huge support for early-\ncareer researchers.\nReviewer #3 (Remarks to the Author):\nOverall comments\n1. The manuscript is well-structured and provides a comprehensive analysis\ndemonstrating the methane emission mitigation effect of PSY in rice.\nResponse: We thank the reviewer for their time and overall positive comment on the\nmanuscript.\nHowever, the metagenomic and metabolomic data interpretations should provide more\nconcrete evidence of causality rather than correlation. For example, while PSY-expressed\nplants show altered exudates, how do we confirm that these exudates are indeed directly\nresponsible for microbial shifts?\nResponse: We thank the reviewer for highlighting the importance of distinguishing\ncausality from correlation in linking PSY-altered exudates to microbial activity. To address\nthis, we conducted additional soil incubation experiments using rice root exudates. These\nexudates were added to paddy soil collected from the same field site used in our\ngreenhouse experiments, and then CH emissions were measured over time. We found\n4\nthat soil treated with Kitaake exudates consistently exhibited higher CH emissions than\n4\nsoil treated with PSY1 or PSY2 exudates, recapitulating the patterns we observed in\nplanta. Importantly, soil incubated with water alone did not emit measurable CH ,\n4\nconfirming that microbial activity was driven by the chemical composition of the exudates.\nThis response is consistent with prior studies showing that root exudates directly stimulate\nCH production in paddy soils, for example, Aulakh et al., 2001 (https://doi.org/10.1055/s-\n4\n2001-12905).\nThese new results provide functional evidence that PSY-driven changes in exudate\nchemistry directly influence microbial activity in the rhizosphere, independent of other\nplant phenotypes. We have added these results to the revised manuscript. Please also\nsee our response to Comment 1 from Reviewer #1 (above).\n21\n2. The microbial community analysis (Figure 3) could benefit from clearer labeling—\nperhaps grouping similar microbial taxa together visually.\nResponse: We thank the reviewer for the suggestion. The rpS3 protein sequences in Fig.\n3a were clustered at 99% identity before the alignment and the phylogeny reconstruction.\nWe have now modified the figure by collapsing the clades that are from the same phylum\nfor clearer labeling. We copied the updated Fig. 3 below for your convenience.\nFig. 3 Microbial community structure and activity.\n3. The authors propose that PSY exudates selectively enhance H₂-oxidizing microbes\nwhile suppressing H₂-producing ones. This is intriguing, but it is unclear whether:\n-This effect is directly caused by specific exudate compounds or just root-associated\nenvironmental changes.\nResponse: We thank the reviewer for this thoughtful question. To begin addressing\nwhether the microbial shifts we observed are directly caused by PSY-modulated exudate\ncomposition or by broader root-associated environmental changes, we conducted\nadditional soil incubation experiments using exudates collected from PSY-overexpressing\ngenotypes and Kitaake seedlings, as described above. By applying these exudates to\nagronomic soil under controlled conditions, we found that soils treated with Kitaake-\nderived exudates consistently emitted significantly more CH than those treated with\n4\nexudates from PSY-overexpressing plants. These results closely mirror the trends\nobserved in our greenhouse experiments, providing evidence that root exudate\ncomposition alone can influence microbial activity related to methane cycling.\nWhile our current study isolates the effect of exudates, we cannot disentangle whether it\nwas caused by a specific compound or by a combination of multiple compounds. We also\n22\ncannot fully preclude the possibility that root-associated environmental changes may\ncontribute to microbial shifts (e.g., by altering local redox conditions). Due to the scale\nand complexity of these experiments, these additional layers of mechanistic insight will\nbe pursued as part of a follow-up project. We have discussed this in the revised\nmanuscript.\nManuscript now read as (lines 501-507):\nWhile our soil exudate incubation experiments clearly show the effect of rice root\nexudates on microbial shifts and associated CH emissions, it is unclear whether this is\n4\ncaused by a specific exudate compound or by a combination of multiple compounds. It is\nalso possible that root-associated environmental changes (e.g., altered redox conditions)\nmay affect the rhizosphere microbiome. Future research is warranted to investigate these\npossibilities.\n-it is unclear whether the microbial shifts persist beyond controlled conditions.\nResponse: We acknowledge that validation under field settings would be essential to\nconfirm the generalizability of our greenhouse findings. However, conducting a field trial\nutilizing transgenic rice genotypes, as well as comprehensive transcriptome, metabolome,\nand microbiome profiling, would require extensive time and regulatory approval. We\nanticipate this effort would span at least 18 months, due to planting schedules, seasonal\nsampling, and post-harvest analyses. We have added a statement to the Discussion to\nacknowledge this limitation and to describe our plans for upcoming field-based validation\nstudies. Please see the response to Comment 2 from Reviewer #1 in the preceding.\n-it is unclear whether the observed metabolic effects (e.g., gluconeogenic acid\nenrichment) are due to PSY expression or indirect plant growth effects.\nResponse: We appreciate the reviewer’s insight regarding whether the observed\nchanges in exudate chemistry are a direct result of PSY signaling cascade or indirectly\ndriven by alterations to root structure, including longer seminal roots, increased\naerenchyma, and reduced lignin. While elucidating the mechanistic pathway underlying\nthe changes in exudate chemistry would be a significant contribution, it would require\nadditional experiments, and we believe these experiments fall beyond the scope of this\nmanuscript.\nIt is worth noting that recent work has demonstrated that developmental changes in\nendodermal integrity, such as delayed Casparian strip formation, can lead to amino acid\nleakage and altered spatial colonization by the root microbiota (Tsai et al., 2025,\n10.1126/science.adu4235). In our system, PSY-overexpressing lines exhibit reduced\nlignin deposition, and it would be interesting to test whether this correlates with a delay in\nto the observed enrichment of gluconeogenic compounds in the rhizosphere.\n4. What are the agronomic trade-offs of PSY-expressing rice? The paper mentions\nshorter stature, reduced grain weight, and other agronomic traits. How might these factors\nimpact yield? Similarly, Iqbal et al. (2021) demonstrated that while methane emissions\nwere significantly reduced, there was a trade-off with rice yield. Both OsLSD (Iqbal et al.)\nand OsPSY influence root biomass and aerenchyma development, which typically leads\nto reduced rice growth..\nResponse: As shown in Figure 2, PSY1 genotypes exhibit reduced plant height and\naltered panicle development, which are associated with a decrease in thousand‑grain\nweight (TGW). In contrast, PSY2 lines maintain both yield and shoot biomass comparable\nto Kitaake. Notably, although PSY2 genotypes do not display yield penalties, they exhibit\nroot phenotypes similar to PSY1 lines, including longer seminal roots, reduced lignin\ndeposition, and increased aerenchyma formation. These observations suggest that while\nPSY1 and PSY2 belong to the same peptide family, their differential phenotypes may\nresult from distinct affinities to specific LRR‑RLK receptors, leading to divergent\ndevelopmental outcomes.\nAerenchyma formation has been linked to changes in methane emissions. The OsLSD\nstudy referenced by the reviewer describes a rice mutant with decreased aerenchyma\nand reduced CH production, but also reported yield penalties. In contrast, our results\n4\nshow that PSY2 lines exhibit increased aerenchyma and reduced CH emissions without\n4\ncompromising grain yield. This indicates that, in our system, methane mitigation can be\ndecoupled from agronomic penalties.\n5. Is it differently expressed PSY on other parts? (panicle, leaf etc)\nResponse: Recent comprehensive analyses in Arabidopsis thaliana using reporter lines\nand transcriptome databases have revealed that PSY-family peptides and their cognate\nreceptors exhibit distinct, organ-specific expression profiles (Ogawa-Ohnishi et al., 2022,\n10.1126/science.abq5735). Although similar detailed expression profiling has yet to be\nperformed in rice, we have leveraged available public transcriptomic data (Supplementary\nFig. 1) to begin characterizing OsPSY expression patterns across various tissues.\nAdditionally, the transgenic lines used in this study express OsPSY1 and OsPSY2 under\na constitutive promoter, ensuring broad expression across roots, shoots, and panicles.\nThis broad overexpression supports the potential for PSY peptides to interact with LRR-\nRLK receptors across multiple plant organs.\n6. PSY decrease lignin contents, please provide correlation with lignin and methane\nemission.\nroots through deposition in the exodermis, which restricts radial oxygen loss (ROL), a trait\nvital for waterlogging tolerance that also controls the oxygen reaching the rhizosphere\n(Peralta Ogorek et al., 2021 & 2023, 10.1111/nph.17474, 10.1071/FP23133). Although\nthis link remains hypothetical, the observed decrease in lignin content in PSY-\noverexpressing lines suggests a weakened root ROL barrier, which may enhance oxygen\ndiffusion into the rhizosphere. This increased oxygen availability likely promotes aerobic\nmethane oxidation, offering a plausible explanation for the reduced CH emissions we\n4\nrecorded.\n7. How do the haplotypes of the PSY gene appear in natural variants, and what are their\npotential effects?\nResponse: While investigating haplotype variation and natural diversity in the 10,000\nsequenced rice lines is a valuable direction for future research, it falls outside the scope\nof the present study.\nDetail comments\n-p. 16 Line 1: It is not appropriate to compare the loss-of-function GS3 allele with PSY\noverexpression, as GS3 is a natural variant, whereas PSY is an overexpressed mutant.\nTherefore, the methane mitigation effect should be compared with other gene-edited\nvarieties or genes.\nResponse: We agree that comparison with the loss-of-function GS3 allele may not be\nthe most suitable comparison, given that we are using a transgenic approach. We have\nremoved that phrase from the text to avoid confusion.\n-(p. 5 Line : 90 Introduction) Please provide sufficient information about PSY gene and\nrole of PSY1 and PSY2\nResponse: We thank the reviewer for highlighting the need to include more background\ninformation on PSY peptides and their specific roles. In response, we have expanded the\nrelevant sections of the manuscript to provide additional context.\nManuscript now reads as (line 90-106):\nPreviously, we and others have shown that small tyrosine-sulfated peptides, such as the\nPLANT PEPTIDES CONTAINING SULFATED TYROSINE (PSY) family, play central\nroles in coordinating root development. Acting as extracellular signals, these peptide\nhormones synchronize cellular activities across tissues by binding to leucine-rich repeat\nreceptor-like kinases (LRR-RLKs), thereby triggering downstream signaling cascades16.\nA critical post-translational modification, sulfation of a tyrosine residue by\nrecognition and signaling efficiency17–20. In Arabidopsis thaliana, overexpression of PSY\ngenes enhances root growth by promoting cell elongation and increasing the size of\nmature cortical cells21–23. These peptides exert their effects through three known LRR-\nRLK receptors — PSYR1, PSYR2, and PSYR3 — recently identified as direct mediators\nof PSY signaling22,24. Notably, PSY1 signaling in Arabidopsis activates genes involved in\nsecondary metabolism that regulate the extent of cell elongation and transition to\nmaturation. Some of these metabolites are subsequently secreted into the rhizosphere\nas part of the root exudate profile21,23,25.\n-(p. 23 Line : 654 Methods) Please provide how many bp read to analyze metagenomics\nanalysis (rpS3)\nResponse: We used 150-bp paired reads for both metagenomic and metatranscriptomic\nsequencing. The rpS3 analysis was based on assembled scaffolds (not reads).\nWe have rephrased the sentences, which now read as (lines 744-745 and line 760):\nRaw 150-bp paired reads from metagenomic sequencing were processed using BBDuk…\nFor metatranscriptomics, raw 150-bp paired reads were pre-treated using BBDuk…"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Review of Manuscript NCOMMS-24.785229\nI thank the authors for their updated and improved manuscript. The authors have provided tangible answers to most of my\ncomments and concerns regarding the original submission. I only have a few additional comments:\n1. I note that the authors have chosen not to perform field trials with their transgenic plants. While such experiments would\nbe informative, acknowledging this limitation suffices, given the quality of the data now presented in the manuscript.\n2. The authors do not understand my reference to “functional microbiome studies” (comment 10 in the first round of revision).\nFunctional microbiome studies would include cultivation of microbiome isolates and studies of H2 production and CH4\nemissions under controlled conditions. As noted by the authors in lines 524-524, such experiments could be used to further\ntest and substantiate the models presented in Figure 7. In this context, it is also important to acknowledge the limitations of\ntranscriptome studies and that they are not the same as functional studies. However, I think the text edits the authors have\nnow made clarify the limitations of their study sufficiently.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We thank the reviewer for recognizing the quality of the data presented in the\nmanuscript. As mentioned in the previous round, this limitation has been acknowledged\nand discussed in the manuscript (lines 533-543).\nResponse: As noted by the reviewers, the limitations of the study have been clearly\nclarified in the manuscript. We thank the reviewer for all the constructive comments.\nResponse: We appreciate the reviewer and the journal for the huge support for early-\ncareer researchers."}]

Decision letter