Biology subjects

Nakazono, M.

Publications and source records attributed to Nakazono, M..

4 recordsLinked to original sources

bioRxiv2023

Single-cell transcriptomic analysis of pea shoot development and cell-type-specific responses to boron deficiency

Understanding how nutrient stress impacts plant growth is fundamentally important to the development of approaches to improve crop production under nutrient limitation. Here we applied single-cell RNA sequencing to shoot apices of Pisum sativum grown under boron (B) deficiency. We identified up to fifteen cell clusters based on the clustering of gene expression profiles and verified cell identity with cell-type-specific marker gene expression. Different cell types responded differently to B deficiency. Specifically, the expression of photosynthetic genes in mesophyll cells (MCs) was down-regulated by B deficiency, consistent with impaired photosynthetic rate. Furthermore, the down-regulation of stomatal development genes in guard cells (GCs), including homologs of MUTE and TOO MANY MOUTHS, correlated with a decrease in stomatal density under B deficiency. We also constructed the developmental trajectory of the shoot apical meristem (SAM) cells and a transcription factor (TF) interaction network. The developmental progression of SAM to MC was characterized by up-regulation of genes encoding histones and chromatin assembly and remodeling proteins including homologs of FASCIATA1 (FAS1) and SWITCH DEFECTIVE/SUCROSE NON-FERMENTABLE (SWI/SNF) complex. However, B deficiency suppressed their expression, which helps to explain impaired SAM development under B deficiency. These results represent a major advance over bulk-tissue RNA-seq analysis in which cell-type-specific responses are lost and hence important physiological responses to B deficiency are missed. The reported approach and resources have potential applications well beyond P. sativum species and could be applied to various legumes to improve their adaptability to multiple nutrient or abiotic stresses.

plant biology↗

bioRxiv2023

Evaluating drought tolerance stability in soybean by theresponse of irrigation change captured from time-seriesmultispectral data

This study investigated a method to evaluate the drought tolerance stability of a genotype in a single environmental trial by capturing the plant response to irrigation changes. Genotypes that exhibit stable phenotypes under various drought stress conditions are required for stable crop production. However, considerable time and money are required to evaluate the environmental stability of a genotype through multiple environmental trials. As an index of drought tolerance stability, we calculated the coefficient of variation (CV) of shoot fresh weight of 178 soybean (Glycine max (L.) Merr.) accessions in a total of nine types of drought treatments, including changing irrigation treatments (every five or ten days) over 3-year trials. To capture the plant responses to irrigation changes, time-series multispectral (MS) data were collected, including the timings of the irrigation/non-irrigation switch in the changing irrigation treatments. We built a random regression model (RRM) for each of the nine treatments using the time-series MS data. We built a genomic prediction model (MTRRM model) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In two out of the three years, the prediction accuracy of MTRRM models built in the changing irrigation treatment was higher than that in the continuous drought treatment in the same year. When the CV was predicted using the MTRRM model across years in the changing irrigation treatment, the prediction accuracy was 61% higher than that of the simple genomic prediction model. These results suggest that drought tolerance stability can be evaluated in a single environmental trial, which may reduce the time and cost of selecting genotypes with high drought tolerance stability.

genetics↗

bioRxiv2022

Identification of basic helix-loop-helix transcription factors that activate betulinic acid biosynthesis by RNA-sequencing of hydroponically cultured Lotus japonicus

Although triterpenes are ubiquitous in plant kingdom, their biosynthetic regulatory mechanisms are limitedly understood. Here, we found that hydroponic culture dramatically activated betulinic acid (BA) biosynthesis in the model Fabaceae Lotus japonicus, and investigated its transcriptional regulation. Fabaceae plants develop secondary aerenchyma (SA) on the surface of hypocotyls and roots during flooding for root air diffusion. Hydroponic culture induced SA in L. japonicus and simultaneously increased the accumulation of BA and the transcript levels of its biosynthetic genes. RNA-sequencing of soil-grown and hydroponically cultured plant tissues, including SA collected by laser microdissection, revealed that several transcription factor genes were co-upregulated with BA biosynthetic genes. Overexpression of LjbHLH32 and LjbHLH50 in L. japonicus transgenic hairy roots upregulated the expression of BA biosynthesis genes, resulting in enhanced BA accumulation. However, transient luciferase reporter assays in Arabidopsis mesophyll cell protoplasts showed that LjbHLH32 transactivated promoters of biosynthetic genes in the soyasaponin pathway but not the BA pathway, like its homolog GubHLH3, a soyasaponin biosynthesis regulator in Glycyrrhiza uralensis. This suggested the evolutionary origin and complex regulatory mechanisms of BA biosynthesis in Fabaceae. This study sheds light on the unrevealed biosynthetic regulatory mechanisms of triterpenes in Fabaceae plants. HighlightHydroponic culture enhanced betulinic acid synthesis in Lotus japonicus. RNA-sequencing and functional characterization experiments suggest that LjbHLH32 and LjbHLH50 are the transcription factors activating betulinic acid biosynthesis.

plant biology↗

bioRxiv2021

Time-series Multi-spectral Imaging in Soybean for Improving Biomass and Genomic Prediction Accuracy

Multi-spectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of above-ground biomass (AGB) and determined which developmental stages should be used for accurate prediction in soybean. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early to late growth stages. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multi-trait genomic prediction. The accuracy of the prediction model increased starting at an early stage of growth (31 days after sowing). To predict phenotypic values of AGB, we employed multi-kernel genomic prediction. Consequently, the prediction accuracy of phenotypic values reached a maximum at a relatively early growth stage (38 days after sowing). Hence, the optimal timing for MS imaging may depend on the irrigation levels.

plant biology↗