Do seed microorganisms mediate nitrate or glucose effects on germination of an agricultural weed?

A poster for the International Seed Science Conference 2025, Perth, Australia

event
poster
Please see the contents of the poster I am presenting below.
Authors
Affiliations

Jonathan Binder

Royal Holloway, University of London, Egham, UK

Kazumi Nakabayashi

Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Japan

Tom Holloway

Syngenta, Bracknell, UK

Ben Oyserman

Syngenta, Stein, Switzerland

Gerhard Leubner-Metzger

Royal Holloway, University of London, Egham, UK

Published

September 15, 2025

Introduction

Nitrate (\(\mathrm{NO_3^-}\)) and glucose are present in soil and consumed by microorganisms. These chemicals also act as seed germination signaling molecules (Price et al., 2003; Alboresi et al., 2005). Understanding how seed-associated microorganisms mediate glucose and nitrate exposure or how glucose and nitrate levels shift microbial community structure and function could improve prediction of dormancy break, germination, or decay of weed seeds. This experiment explored the relationships between microorganisms and germination of Alopecurus myosuroides (an economically-important weed) under various nitrate and glucose levels.

Figure 1: A. myosuroides seeds with fungal growth observed during a germination assay.

Methods

European seed populations up to 5 years old were obtained from Syngenta.

Dose-response germination assays were conducted in triplicate with nitrate (as \(\mathrm{KNO_3}\)) and glucose in Petri dishes with approximately 25 seeds at 20 °C and constant light in an environmental chamber. Models were fit with four-parameter log-logistic functions.

DNA was isolated from dry seeds using the DNeasy PowerSoil Pro kit (Qiagen) and shotgun metagenomic sequencing was conducted on a subset of the populations in triplicate with a MinION Mk1b (Oxford Nanopore Technologies). Nitrogen (N) cycling genes were identified using the NCycDB database (Tu et al., 2019) and pathogen microbiomes were classified using a eukaryotic pathogen database (EuPathDB-Clean) (Lu and Salzberg, 2018).

Results

Seed populations showed varying sensitivities to nitrate and glucose (Fig. 2).

Figure 2: Germination (\(G_{max}\)) respones of 10 A. myosuroides populations to varying levels of nitrate (\(\mathrm{NO_3^-}\)) and glucose (\(\mathrm{C_6H_{12}O_6}\)).

N cycling-related gene expression of seed-associated microorganisms showed relatively similarity (Fig. 3) and there were no statistical differences in gene composition between populations (PERMANOVA, p=0.3). Pathogen metagenomes showed separation (Fig. 4) and metagenome compositions were different (PERMANOVA, p<0.001).

Figure 3: Non-metric multidimensional scaling (NMDS) plot (Bray-Curtis distances) of N cyle genes in microbial metagenomes of 7 A. myosuroides populations.
Figure 4: NMDS plot (Bray-Curtis distances) of eukaryotic pathogen microbial metagenomes of 7 A. myosuroides populations.

Discussion and Conclusion

Lack of differences in microbial N cycle gene compositions between populations suggests that seed microorganisms may not play an important role in seed responses to nitrate. While eukaryotic pathogen microbiomes were different between populations, additional analysis will be completed in an attempt to connect germination dynamics with microbial composition. Because \(\mathrm{C_6H_{12}O_6}\) is a signalling molecule, use of non-signal carbon sources that may provide more fuel to pathogenic seed microorganisms may be better in elucidating seed microorganism effects. Taxonomic and differential abundance analyses will be used to further investigate these research question.

This experiment focused on seed-associated microorganisms in Petri dishes and ignored soil and soil microbial communities which may have a large effect on nutrient concentrations experienced by the seed.

Funding provided by:

References

Alboresi, A., C. Gestin, M.-T. Leydecker, M. Bedu, C. Meyer, et al. 2005. Nitrate, a signal relieving seed dormancy in Arabidopsis. Plant, Cell & Environment 28(4): 500–512. doi: 10.1111/j.1365-3040.2005.01292.x.
Lu, J., and S.L. Salzberg. 2018. Removing contaminants from databases of draft genomes. PLoS Computational Biology 14(6): e1006277. doi: 10.1371/journal.pcbi.1006277.
Price, J., T.-C. Li, S.G. Kang, J.K. Na, and J.-C. Jang. 2003. Mechanisms of glucose signaling during germination of Arabidopsis. Plant Physiology 132(3): 1424. doi: 10.1104/pp.103.020347.
Tu, Q., L. Lin, L. Cheng, Y. Deng, and Z. He. 2019. NCycDB: a curated integrative database for fast and accurate metagenomic profiling of nitrogen cycling genes (J. Wren, editor). Bioinformatics 35(6): 1040–1048. doi: 10.1093/bioinformatics/bty741.