The transcriptional dynamics during de novo shoot organogenesis of Ma bamboo (Dendrocalamus latiflorus Munro): implication of the contributions of the abiotic stress response in this process

Abstract

Summary De novo shoot organogenesis is an important biotechnological tool for fundamental studies in plant. However, it is difficult in most bamboo species, and the genetic control of this highly dynamic and complicated regeneration process remains unclear. In this study, based on an in‐depth analysis at the cellular level, the shoot organogenesis from calli of Ma bamboo (Dendrocalamus latiflorus Munro) was divided into five stages. Subsequently, single‐molecule long‐read isoform sequencing of tissue samples pooled from all five stages was performed to generate a full‐length transcript landscape. A total of 83 971 transcripts, including 73 209 high‐quality full‐length transcripts, were captured, which served as an annotation reference for the subsequent RNA sequencing analysis. Time‐course transcriptome analysis of samples at the abovementioned five stages was conducted to investigate the global gene expression atlas showing genome‐wide expression of transcripts during the course of bamboo shoot organogenesis. K‐means clustering analysis and stage‐specific transcript identification revealed important dynamically expressed transcription regulators that function in bamboo shoot organogenesis. The majority of abiotic stress‐responsive genes altered their expression levels during this process, and further experiments demonstrated that exogenous application of moderate but not severe abiotic stress increased the shoot regeneration efficiency. In summary, our study provides an overview of the genetic flow dynamics during bamboo shoot organogenesis. Full‐length cDNA sequences generated in this study can serve as a valuable resource for fundamental and applied research in bamboo in the future.

An Efficient Genetic Transformation and CRISPR/Cas9-Based Genome Editing System for Moso Bamboo (Phyllostachys edulis)

“…An optimized combination of cytokinins and auxins was selected to improve the shoot differentiation rate. Recently, transcriptome analysis showed that the expression levels of the majority of ABA-related genes were substantially altered during shoot induction in Ma bamboo, and the exogenous application of an optimized ABA concentration improved the shoot regeneration efficiency (Tu et al, 2021). Our results showed that the shoot differentiation frequency was significantly increased with ABA pretreatment in subculture stage.…”

Multifaceted roles of transcription factors during plant embryogenesis

“…Morphogenesis-related processes such as light-controlled seedling morphology, the formation of floral traits, fruit morphology, as well as thermomorphogenesis, where the morphology changes under high temperatures, all have TFs’ involvement ( Sasaki, 2018 ; Shi et al., 2018 ; Han et al., 2019 ; Zhang et al., 2020a ; Chopy et al., 2023 ). TFs also modulate organogenesis such as leaf, shoot and root development, or even nodule development in the symbiotic relationship between legume and rhizobium ( Sluis and Hake, 2015 ; Tu et al., 2021 ; Chakraborty et al., 2022 ; Liu et al., 2022 ). Gene transcriptional regulation, facilitated by TFs, is essential in controlling numerous biological processes in plants, including signal transduction, stress and defense responses, as well as carbohydrate metabolism ( Seo and Choi, 2015 ; Hoang et al., 2017 ; Shahzad et al., 2021 ; Strader et al., 2022 ; Zou and Sun, 2023 ).…”

Finding the LMA needle in the wheat proteome haystack

“…Group 14 with a variance of 35% contained 93 biomarker peptides spanning a distance of 0.12 to 0.94, including Cluster_AAA whose distance was 0.79 (Supplementary Table S4). K-means clustering was well adopted by the proteomics community to group gene products of similar profiles, notably in plants such as bamboo (105), nightshade (106), or grape (107), but to our knowledge not in wheat. In developing corn grains, coordinated protein expression associated with different functional categories was revealed by a k-means clustering analysis (108).…”