Glycerol-3-phosphate mediates rhizobia-induced systemic signaling in soybean

M. B. Shine, Qing ming Gao, R. V. Chowda-Reddy, Asheesh K. Singh, Pradeep Kachroo, Aardra Kachroo

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Glycerol-3-phosphate (G3P) is a well-known mobile regulator of systemic acquired resistance (SAR), which provides broad spectrum systemic immunity in response to localized foliar pathogenic infections. We show that G3P-derived foliar immunity is also activated in response to genetically-regulated incompatible interactions with nitrogen-fixing bacteria. Using gene knock-down we show that G3P is essential for strain-specific exclusion of non-desirable root-nodulating bacteria and the associated foliar pathogen immunity in soybean. Grafting studies show that while recognition of rhizobium incompatibility is root driven, bacterial exclusion requires G3P biosynthesis in the shoot. Biochemical analyses support shoot-to-root transport of G3P during incompatible rhizobia interaction. We describe a root-shoot-root signaling mechanism which simultaneously enables the plant to exclude non-desirable nitrogen-fixing rhizobia in the root and pathogenic microbes in the shoot.

Original languageEnglish
Article number5303
JournalNature Communications
Issue number1
StatePublished - Dec 1 2019

Bibliographical note

Funding Information:
We thank Dr. Hongyang Zhu (L76-1988/L82-257 soybean seeds and USDA122/ 257 strains), Dr. Carl Bradley (amplifying soybean seed stock), Dr. Emily Pfeuffer (statistical analyses), Dr. Anne-Frances Miller (ESR analysis) and Amy Crume (management of plant growth facilities) for their generous help. This work was supported by NSF (IOS #1457121), USDA National Institute of Food and Agriculture (Hatch project 1014539), and Kentucky Soybean Promotion Board grants to A.K. and P.K. and faculty start-up funds by Iowa State University to A.K.S.

Funding Information:
Transcriptome analysis. The total RNA integrity or quality was checked (Agilent 2100 Bioanalyser, Agilent, USA) and raw RNA-Seq data was obtained through 100 cycle HISeq high-output mode sequence method per lane. Sequence quality was assessed using FastQC (, v0.10.1) for all samples. The paired end reads were then mapped against the STAR indexed reference genome61 of Glycine max (a2.v1) downloaded from Phytozome 1162. Splice aware mapping algorithm STAR63 (v2.5.2a) was used for map RNA-seq reads to the reference genome using default parameters. For assigning sequence reads to the genomics features, feature counts from the Subread64 (v1.4.6) package was used. Only primary alignments, ignoring multi-mapped and chimeric reads were used for generating counts. Counts from all the samples were converted to a desirable format using AWK command and differential gene expression analyses (DGE) was carried out by EdgeR65 (v3.14.0) using negative binomial and generalized linear models. The DGE is expressed as Log2FC and are considered significant if the false discovery rate (FDR) was <0.05. Information for the differentially expressed genes were paired from the official gene annotations obtained from Phytozome to help identify relevant trends. A total of 3 such comparisons were performed comparing control with USDA122, control with USDA257, or USDA122 with USDA257. The authors acknowledge the support of the Genome Informatics Facility, Office of Biotechnology at Iowa State University, for RNA-seq analysis.

Publisher Copyright:
© 2019, The Author(s).

ASJC Scopus subject areas

  • Chemistry (all)
  • Biochemistry, Genetics and Molecular Biology (all)
  • Physics and Astronomy (all)


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