Transcriptome analysis of rice root tips reveals auxin, gibberellin, and ethylene signalling underlying nutritropism.
Kiyoshi YamazakiYoshihiro OhmoriHirokazu TakahashiAtsushi ToyodaYutaka SatoMikio NakazonoToru FujiwaraPublished in: Plant & cell physiology (2024)
Nutritropism is a positive tropism towards nutrients in plant roots. An NH4+ gradient is a nutritropic stimulus in rice (Oryza sativa L.). When rice roots are exposed to an NH4+ gradient generated around nutrient sources, root tips bend towards and coil around the sources. The molecular mechanisms are largely unknown. Here, we analysed the transcriptomes of the inside and outside of bending root tips exhibiting nutritropism to reveal nutritropic signal transduction. Tissues facing the nutrient sources (inside) and away (outside) were separately collected by laser microdissection. Principal component analysis revealed distinct transcriptome patterns between the two tissues. Annotations of 153 differentially expressed genes implied that auxin, gibberellin, and ethylene signalling were activated differentially between the sides of the root tips under nutritropism. Exogenous application of transport and/or biosynthesis inhibitors of these phytohormones largely inhibited the nutritropism. Thus, signalling and de novo biosynthesis of the three phytohormones is necessary for nutritropism. Expression patterns of IAA genes implied that auxins accumulated more in the inside tissues, meaning that ammonium stimulus is transduced to auxin signalling in nutritropism as same as gravity stimulus in gravitropism. SAUR and expansin genes, which are known to control cell wall modification and to promote cell elongation in shoot gravitropism, were highly expressed in the inside tissues rather than the outside tissues, and our transcriptome data are unexplainable for differential elongation in root nutritropism.
Keyphrases
- cell wall
- single cell
- genome wide
- gene expression
- rna seq
- drinking water
- dna methylation
- poor prognosis
- genome wide identification
- room temperature
- bioinformatics analysis
- stem cells
- high resolution
- big data
- heavy metals
- transcription factor
- deep learning
- ionic liquid
- data analysis
- high speed
- risk assessment
- bone marrow