Biology subjects

Kamitani, M.

Publications and source records attributed to Kamitani, M..

6 recordsLinked to original sources

bioRxiv2021

Fillable and Unfillable Gaps in Rice Transcriptome under Field and Controlled Environments

The differences between plants grown in field and controlled environments have long been recognised; however, few studies have addressed the underlying molecular mechanisms. Here, we show fillable and unfillable gaps in the transcriptomes of rice grown in field and controlled environments by utilising SmartGC, a high-performance growth chamber that reproduces the fluctuating irradiance, temperature, and humidity of field environments. Rice transcriptome dynamics in SmartGC mimicked those in the field, particularly during the morning and evening; those in conventional growth chamber conditions did not. Further analysis revealed that fluctuation of irradiance affects transcriptome dynamics in the morning and evening, while fluctuation of temperature only affects transcriptome dynamics in the morning. We found upregulation of genes related to biotic and abiotic stress, whose expression was affected by environmental factors that cannot be mimicked by SmartGC. Our results accelerate the understanding of plant responses to field environments for both field and laboratory studies.

plant biology↗

bioRxiv2021

Artificial mimicry of seasonal transcriptome dynamics in Arabidopsis thaliana reveals short- and long-term responses to environmental conditions

Plants must respond to various seasonally changing environmental stimuli. In a previous study, seasonally oscillating genes were identified by a massive time-series transcriptome analysis for the wild population of Arabidopsis halleri ssp. gemmifera, a sister species to Arabidopsis thaliana. It was not clear how environmental stimuli shaped the seasonal expression pattern of these seasonally oscillating genes. In this study we show that responses in different time-scales contributed to the formation of seasonal expression patterns for several genes. To analyze the seasonally oscillating genes, we established an experimental system to mimic seasonal expression trends using A. thaliana and a "smart growth chamber mini," a hand-made low-cost small chamber. Arabidopsis thaliana plants were cultured under conditions that mimicked the average monthly temperatures and daylengths under different day-scale incubation. In total, the seasonal trends of 1627 seasonally oscillating genes were mimicked, and they showed varying temporal responses (constant, transient, and incremental) to environmental stimuli. Our results suggest that plants perceive and integrate information regarding environmental stimuli in the field by combining seasonally oscillating genes with different temporal responsiveness.

plant biology↗

bioRxiv2020

DeLTa-Seq: direct-lysate targeted RNA-Seq from crude tissue lysate.

Using current mRNA quantification methods such as RT-qPCR and RNA-Seq, it is very difficult to examine thousands of tissue samples due to cost and labor of RNA extraction and quantification steps. Here, we developed Direct-RT buffer in which homogenization of tissue samples and direct-lysate reverse transcription can be conducted without RNA purification. We showed that appreciate concentration of DTT prevented RNA degradation but not RT in the lysates of several plants tissues, yeast, and zebrafish larvae. Using the buffer, direct reverse transcription on the lysates could produce comparable amount of cDNA with that synthesized from purified RNA. Furthermore, we established DeLTa-Seq (Direct-Lysate reverse transcription and Targeted RNA-Seq) method. DeLTa-Seq is a cost-effective, high-throughput and highly-precise quantification method for the expressions of hundreds of genes. It enables us to conduct large-scale studies using thousands of samples such as chemical screening, field experiments and studies focusing on individual variability.

molecular biology↗

bioRxiv2020

Removal of repressive histone marks creates epigenetic memory of recurring heat in Arabidopsis

Acclimation to high temperature increases plants tolerance of subsequent lethal high temperatures1-3. Although epigenetic regulation of plant gene expression is well studied, how plants maintain a memory of environmental changes over time remains unclear. Here, we show that JUMONJI (JMJ) proteins4-8, demethylases involved in histone H3 lysine 27 trimethylation (H3K27me3), are necessary for Arabidopsis thaliana heat acclimation. Acclimation induces sustained H3K27me3 demethylation at key HEAT SHOCK PROTEIN (HSP) loci by JMJs, poising the HSP genes for subsequent activation. Upon sensing heat after a 3-day interval, JMJs directly reactivate HSP genes. Finally, jmj mutants fail to maintain heat memory under fluctuating field temperature conditions. Our findings of an epigenetic memory mechanism involving histone demethylases may have implications for environmental adaptation of field plants.

plant biology↗

bioRxiv2018

High-throughput RNA-Seq analysis on plant response under fluctuating temperature conditions

RNA-Seq is a whole-transcriptome analysis method used to research biological mechanisms and functions; its use in large-scale experiments is limited by costs and labour. In this study, we established a high-throughput and cost effective RNA-Seq library preparation method that did not require mRNA enrichment. The method adds unique index sequences to samples during reverse transcription (RT) that is conducted at a higher temperature ([≥]62{degrees}C) to suppress RT of A-rich sequences in rRNA, and then pools all samples into a single tube. Both single-read and paired end sequencing of libraries is enabled. We found that the pooled RT products contained large amounts of RNA, mainly rRNA, and caused over-estimations of the quantity of DNA, resulting in unstable tagmentation results. Degradation of RNA before tagmentation was necessary for the stable preparation of libraries. We named this protocol low-cost and easy RNA-Seq (Lasy-Seq), and used it to investigate temperature responses in Arabidopsis thaliana. We analysed how sub-ambient temperatures (10-30{degrees}C) affected the plant transcriptomes, using time-courses of RNA-Seq from plants grown in randomly fluctuating temperature conditions. Our results suggest that there are diverse mechanisms behind plant temperature responses at different time scales.

plant biology↗

bioRxiv2017

Gauss-power mixing distributions comprehensively describe stochastic variations in RNA-seq data

MotivationGene expression levels exhibit stochastic variations among genetically identical organisms under the same environmental conditions. In many recent transcriptome analyses based on RNA sequencing (RNA-seq), variations in gene expression levels among replicates were assumed to follow a negative binomial distribution although the physiological basis of this assumption remain unclear.\n\nResultsIn this study, RNA-seq data were obtained from Arabidopsis thaliana under eight conditions (21-27 replicates), and the characteristics of gene-dependent distribution profiles of gene expression levels were analyzed. For A. thaliana and Saccharomyces cerevisiae, the distribution profiles could be described by a Gauss-power mixing distribution derived from a simple model of a stochastic transcriptional network containing a feedback loop. The distribution profiles of gene expression levels were roughly classified as Gaussian, power law-like containing a long tail, and mixed. The fitting function predicted that gene expression levels with long-tailed distributions would be strongly influenced by feedback regulation. Thus, the features of gene expression levels are correlated with their functions, with the levels of essential genes tending to follow a Gaussian distribution and those of genes encoding nucleic acid-binding proteins and transcription factors exhibiting long-tailed distributions.\n\nAvailabilityFastq files of RNA-seq experiments were deposited into the DNA Data Bank of Japan Sequence Read Archive as accession no. DRA005887. Quantified expression data are available in supplementary information.\n\nContactawa@hiroshima-u.ac.jp\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗