Fillable and unfillable gaps in plant transcriptome under field and controlled environments

Lasy-Seq: A High-Throughput 3' RNA-Seq Method for Large-Scale Transcriptome Profiling in Rice

Lasy-Seq (low-cost and easy RNA-seq) is a high-throughput library preparation method for 3' RNA-seq. Usage of oligo(dT) primers with unique index sequences allows simultaneous reverse transcription (RT) reaction and sample indexing. Therefore, each post-RT step, which was performed on a sample-by-sample basis in a conventional method, can be performed on a large number of samples together in a single tube. With the Lasy-Seq, the sequencing-ready library pool can be prepared from a few to several hundred samples in a few days.

DeLTa-Seq: High-Throughput Targeted RNA-Seq of Rice Leaves Without RNA Purification

DeLTa-seq is a high-throughput RNA-seq library preparation method that enables quantification of the expression of hundreds of arbitrarily selected genes without RNA purification. This method involves direct reverse transcription using rice leaf lysate and targeted RNA-seq library preparation. DeLTa-seq enables the precise quantification of gene expression with a small number of sequencing reads. This chapter provides detailed information on the design of gene-specific primers, sampling of rice leaves, preparation of lysates, direct-lysate reverse transcription, targeted RNA-seq library preparation, and bioinformatic analysis of DeLTa-seq data.

Development of an infiltration-based RNA preservation method for cryogen-free storage of leaves for gene expression analyses in field-grown plants

BACKGROUND

Gene expression is a fundamental process for plants to express their phenotype, and its analysis is the basis of molecular studies. However, the instability of RNA often poses an obstacle to analyzing plants grown in fields or remote locations where the availability of liquid nitrogen or dry ice is limited. To deepen our understanding of plant phenotypes and tolerance to field-specific stresses, it is crucial to develop methodologies to maintain plant RNA intact and safely transfer it for downstream analyses such as qPCR and RNA-seq.

RESULTS

In this study, the author developed a novel tissue preservation method that involved the infiltration of RNA preservation solution into the leaf apoplast using a syringe and subsequent storage at 4 °C. RNA-seq using samples stored for 5 d and principal component analyses showed that rice leaves treated with the infiltration method maintained the original transcriptome pattern better than those treated with the traditional method when the leaves were simply immersed in the solution. Additionally, it was also found that extracted RNA can be transported with minimum risk of degradation when it is bound to the membrane of RNA extraction kits. The developed infiltration method was applied to rice plants grown in a local farmer's field in northern Madagascar to analyze the expression of nutrient-responsive genes, suggesting nutrient imbalances in some of the fields examined.

CONCLUSIONS

This study showed that the developed infiltration method was effective in preserving the transcriptome status of rice and sorghum leaves when liquid nitrogen or a deep freezer is not available. The developed method was useful for diagnosing plants in the field based on the expression of nutrient-responsive marker genes. Moreover, the method used to protect RNA samples from degradation during transportation offers the possibility to use them for RNA-seq. This novel technique could pave the way for revealing the molecular basis of plant phenotypes by accelerating gene expression analyses using plant samples that are unique in the field.

Transcriptome-based prediction for polygenic traits in rice using different gene subsets

BACKGROUND

Transcriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation. The selection of large-effect genes based on a priori biological knowledge is beneficial for predicting oligogenic traits; however, such a simple gene selection method is not applicable to polygenic traits because causal genes or large-effect loci are often unknown. Here, we used several gene-level features and tested whether it was possible to select a gene subset that resulted in better predictive ability than using all genes for predicting a polygenic trait.

RESULTS

Using the phenotypic values of shoot and root traits and transcript abundances in leaves and roots of 57 rice accessions, we evaluated the predictive abilities of the transcriptome-based prediction models. Leaf transcripts predicted shoot phenotypes, such as plant height, more accurately than root transcripts, whereas root transcripts predicted root phenotypes, such as crown root length, more accurately than leaf transcripts. Furthermore, we used the following three features to train the prediction model: (1) tissue specificity of the transcripts, (2) ontology annotations, and (3) co-expression modules for selecting gene subsets. Although models trained by a gene subset often resulted in lower predictive abilities than the model trained by all genes, some gene subsets showed improved predictive ability. For example, using genes expressed in roots but not in leaves, the predictive ability for crown root diameter was improved by more than 10% (R2 = 0.59 when using all genes; R2 = 0.66, using 1,554 root-specifically expressed genes). Similarly, genes annotated as "gibberellic acid sensitivity" showed higher predictive ability than using all genes for root dry weight.

CONCLUSIONS

Our results highlight both the possibility and difficulty of selecting an appropriate gene subset to predict polygenic traits from transcript abundance, given the current biological knowledge and information. Further integration of multiple sources of information, as well as improvements in gene characterization, may enable the selection of an optimal gene set for the prediction of polygenic phenotypes.

Circadian and environmental signal integration in a natural population of Arabidopsis

Plants sense and respond to environmental cues during 24 h fluctuations in their environment. This requires the integration of internal cues such as circadian timing with environmental cues such as light and temperature to elicit cellular responses through signal transduction. However, the integration and transduction of circadian and environmental signals by plants growing in natural environments remains poorly understood. To gain insights into 24 h dynamics of environmental signaling in nature, we performed a field study of signal transduction from the nucleus to chloroplasts in a natural population of Arabidopsis halleri. Using several modeling approaches to interpret the data, we identified that the circadian clock and temperature are key regulators of this pathway under natural conditions. We identified potential time-delay steps between pathway components, and diel fluctuations in the response of the pathway to temperature cues that are reminiscent of the process of circadian gating. We found that our modeling framework can be extended to other signaling pathways that undergo diel oscillations and respond to environmental cues. This approach of combining studies of gene expression in the field with modeling allowed us to identify the dynamic integration and transduction of environmental cues, in plant cells, under naturally fluctuating diel cycles.

Beyond heat waves: Unlocking epigenetic heat stress memory in Arabidopsis

Plants remember their exposure to environmental changes and respond more effectively the next time they encounter a similar change by flexibly altering gene expression. Epigenetic mechanisms play a crucial role in establishing such memory of environmental changes and fine-tuning gene expression. With the recent advancements in biochemistry and sequencing technologies, it has become possible to characterize the dynamics of epigenetic changes on scales ranging from short term (minutes) to long term (generations). Here, our main focus is on describing the current understanding of the temporal regulation of histone modifications and chromatin changes during exposure to short-term recurring high temperatures and reevaluating them in the context of natural environments. Investigations of the dynamics of histone modifications and chromatin structural changes in Arabidopsis after repeated exposure to heat at short intervals have revealed the detailed molecular mechanisms of short-term heat stress memory, which include histone modification enzymes, chromatin remodelers, and key transcription factors. In addition, we summarize the spatial regulation of heat responses. Based on the natural temperature patterns during summer, we discuss how plants cope with recurring heat stress occurring at various time intervals by utilizing 2 distinct types of heat stress memory mechanisms. We also explore future research directions to provide a more precise understanding of the epigenetic regulation of heat stress memory.

A low-coverage 3' RNA-seq to detect homeolog expression in polyploid wheat

Although allopolyploid species are common among natural and crop species, it is not easy to distinguish duplicated genes, known as homeologs, during their genomic analysis. Yet, cost-efficient RNA sequencing (RNA-seq) is to be developed for large-scale transcriptomic studies such as time-series analysis and genome-wide association studies in allopolyploids. In this study, we employed a 3' RNA-seq utilizing 3' untranslated regions (UTRs) containing frequent mutations among homeologous genes, compared to coding sequence. Among the 3' RNA-seq protocols, we examined a low-cost method Lasy-Seq using an allohexaploid bread wheat, Triticum aestivum. HISAT2 showed the best performance for 3' RNA-seq with the least mapping errors and quick computational time. The number of detected homeologs was further improved by extending 1 kb of the 3' UTR annotation. Differentially expressed genes in response to mild cold treatment detected by the 3' RNA-seq were verified with high-coverage conventional RNA-seq, although the latter detected more differentially expressed genes. Finally, downsampling showed that even a 2 million sequencing depth can still detect more than half of expressed homeologs identifiable by the conventional 32 million reads. These data demonstrate that this low-cost 3' RNA-seq facilitates large-scale transcriptomic studies of allohexaploid wheat and indicate the potential application to other allopolyploid species.

Field plus lab experiments help identify freezing tolerance and associated genes in subtropical evergreen broadleaf trees: A case study of Camellia oleifera

The molecular mechanisms of freezing tolerance are unresolved in the perennial trees that can survive under much lower freezing temperatures than annual herbs. Since natural conditions involve many factors and temperature usually cannot be controlled, field experiments alone cannot directly identify the effects of freezing stress. Lab experiments are insufficient for trees to complete cold acclimation and cannot reflect natural freezing-stress responses. In this study, a new method was proposed using field plus lab experiments to identify freezing tolerance and associated genes in subtropical evergreen broadleaf trees using Camellia oleifera as a case. Cultivated C. oleifera is the dominant woody oil crop in China. Wild C. oleifera at the high-elevation site in Lu Mountain could survive below -30°C, providing a valuable genetic resource for the breeding of freezing tolerance. In the field experiment, air temperature was monitored from autumn to winter on wild C. oleifera at the high-elevation site in Lu Mountain. Leave samples were taken from wild C. oleifera before cold acclimation, during cold acclimation and under freezing temperature. Leaf transcriptome analyses indicated that the gene functions and expression patterns were very different during cold acclimation and under freezing temperature. In the lab experiments, leaves samples from wild C. oleifera after cold acclimation were placed under -10°C in climate chambers. A cultivated C. oleifera variety "Ganwu 1" was used as a control. According to relative conductivity changes of leaves, wild C. oleifera showed more freezing-tolerant than cultivated C. oleifera. Leaf transcriptome analyses showed that the gene expression patterns were very different between wild and cultivated C. oleifera in the lab experiment. Combing transcriptome results in both of the field and lab experiments, the common genes associated with freezing-stress responses were identified. Key genes of the flg22, Ca²⁺ and gibberellin signal transduction pathways and the lignin biosynthesis pathway may be involved in the freezing-stress responses. Most of the genes had the highest expression levels under freezing temperature in the field experiment and showed higher expression in wild C. oleifera with stronger freezing tolerance in the lab experiment. Our study may help identify freezing tolerance and underlying molecular mechanisms in trees.

Time of Day Analysis over a Field Grown Developmental Time Course in Rice

Plants integrate time of day (TOD) information over an entire season to ensure optimal growth, flowering time, and grain fill. However, most TOD expression studies have focused on a limited number of combinations of daylength and temperature under laboratory conditions. Here, an Oryza sativa (rice) expression study that followed TOD expression in the field over an entire growing season was re-analyzed. Similar to Arabidopsis thaliana, almost all rice genes have a TOD-specific expression over the developmental time course. As has been suggested in other grasses, thermocycles were a stronger cue for TOD expression than the photocycles over the growing season. All the core circadian clock genes display consistent TOD expression over the season with the interesting exception that the two grass paralogs of EARLY FLOWERING 3 (ELF3) display a distinct phasing based on the interaction between thermo- and photo-cycles. The dataset also revealed how specific pathways are modulated to distinct TOD over the season consistent with the changing biology. The data presented here provide a resource for researchers to study how TOD expression changes under natural conditions over a developmental time course, which will guide approaches to engineer more resilient and prolific crops.