Plants are primary producers and thus build the foundation of most of Earth’s ecosystems. Green plants utilize solar energy to fix inorganic carbon into organic carbohydrates providing basic food sources for animals and humans alike. The most important side product of photosynthesis is oxygen – by consumption of carbon dioxide and production of the majority of the world’s oxygen, green plants (and phytoplankton) provide the foundation of life on Earth. In addition to their vital role as source of oxygen and food, plants are used as important raw materials, providing energy sources (e.g., biofuels), fabric for clothing, building material, and serve as a major source of pharmaceutically active compounds. Due to their crucial role for the survival and well-being of animals and humans, plant science has become increasingly more important.
Advances in agricultural research and plant science are crucial for maintaining an overall stable ecosystem.
Major applications in the multidisciplinary field of plant science focus on:
- enhancing the productivity of cultured plants / crops through investigation of regulatory mechanisms,
- plant development and organ/tissue formation,
- plant breeding and seed selection to increase resilience,
- improving crop health through the use of sustainable pesticides,
- understanding plant-microbe interactions (plant-symbiont and plant-pathogen interactions),
- accelerating adaptations to changing environmental conditions, such as high temperatures, draught, saline stress, and others.
Next-Generation Sequencing in Plant Sciences
Nowadays, Next-Generation Sequencing (NGS) approaches are heavily used in plant science and have replaced classical methods, such as arrays for several applications. NGS is a hypothesis-free methodology and allows discoveries of new markers or traits. In contrast to classical methods such as qPCR or microarrays which are used to assay known markers for absence or presence in a plant sample. In addition, NGS offers high-throughput, cost-effective analysis of thousands of samples in shorter time and opened new possibilities for comparative genomics.
The first complete genome sequence of a flowering plant, Arabidopsis thaliana, was published more than 20 years ago and has significantly aided the understanding of regulatory mechanisms, the adaptation to changing conditions, and the development of plants (The Arabidopsis Genome Initiative, 2000). Even though global genome analyses and DNA sequencing are widely used in plant research for decades, plant genomes still present a major challenge to scientists. Not only are plant genomes highly variable in size and range up to ~160 giga base pairs (Gbp), approximately 50 times larger than the human genome (Fernández et al., 2024), they are also characterized by an unprecedented structural complexity, variations in the content of transposable elements, polyploidy, and large stretches of repeats. As a result, annotations are mostly available for domesticated crops, contain flaws, or are lacking for the vast majority green plants – providing a substantial challenge to plant researchers. Read more about challenges for plant transcriptomics studies and how to surpass them in one of our upcoming blogs.
Quantitative trait loci connect genetic information to phenotypes
A quantitative trait locus (QTL) is a region of the genome which is associated with a specific phenotype (trait) that varies within a plant population, for example the sweetness or color of fruit, or the nutritious content of grains, beans, or seeds. In classical molecular biology, QTLs are identified by detecting which molecular markers correlate with the phenotype of interest. High-throughput methods, such as NGS allow to assess the prevalence of quantitative traits by linking genome information with expression data and can help to investigate QTLs which influence expression of further genes. The identification of expression quantitative trait loci (eQTLs) is fundamental to understand how genetic variants, such as single nucleotide polymorphisms (SNPs), influence gene expression and thereby plant metabolism or morphology. eQTLs can act in cis (i.e., at the same locus) or in trans (i.e., at a distant locus, e.g., on another chromosome). To associate specific eQTLs to the observed phenotype, genome information has to be integrated with expression analysis by RNA-Seq for each sample within a plant population. Through statistical analysis the corresponding variants correlating with the desired phenotype can be identified. Subsequently, these traits can be selectively propagated to introduce the desired traits in the progenies and breed more resistant crops, increase sweetness of fruits or nutritious content. Gene expression profiling by RNA-Seq is the method of choice to investigate eQLTs in plants.
The Importance of RNA-Seq for Plant Research
Understanding the transcriptome is essential for linking genome information and QTLs to functions in plant metabolism and ultimately desired phenotypes (Tu et al., 2022). In addition, RNA regulation plays a pivotal role in a wide range of biological processes: from growth and stress responses, plant-microbe interactions, but also during plant and fruit development. RNA-Seq is used to assess which genes are expressed within a plant or tissue, under which condition expression is activated or deactivated, and what the quantitative level of gene expression is at a given time. All these aspects are studied by researchers to shed light on complex mechanisms such as stress responses to pathogens or environmental stressors. RNA-Seq therefore allows insights in critical aspects connected to crop survival, e.g., assessing cultured crops compared to their wild relatives, during exposure to environmental stress (e.g., heat) or upon pathogen infections. Integrative approaches using multiple “omics” techniques, for example combining genomics and transcriptomics, or generating gene co-expression networks from plants exposed to different stressors help researchers to gain key insights to functional nodes, decipher regulatory pathways, and disease mechanisms (Lee and Yeom, 2023, Kwon et al., 2024).
Plant Transcriptomics Outlook: Applications of RNA-Seq
Our next blog will summarize several applications for RNA-Seq in plant and crop sciences and highlight selected publications illustrating the use of expression profiling, small RNA discoveries, and whole transcriptome studies to assess critical adaptations to environmental stressors, improve crop fitness, survival and yield, and investigate plant development.
References
Fernández, P., Amice, R., Bruy, D., Christenhusz, M. J. M., Leitch, I. J., Leitch, A. L., Pokorny, L., Hidalgo, O., and Pellicer J. A 160 Gbp fork fern genome shatters size record for eukaryotes. iScience 27, 109889 (2024). doi: 10.1016/j.isci.2024.109889
Kwon, J.S., Shilpha, J., Lee, J., and Yeom, S.I. Beyond NGS data sharing for plant ecological resilience and improvement of agronomic traits. Sci Data 11, 466 (2024). doi: 10.1038/s41597-024-03305-0
Lee, J., Yeom, S.I. Global co-expression network for key factor selection on environmental stress RNA-seq dataset in Capsicum annuum. Sci Data 10, 692 (2023). doi: 10.1038/s41597-023-02592-3
The Arabidopsis Genome Initiative. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796–815 (2000). doi: 10.1038/35048692
Tu, M., Zeng, J., Zhang, J., Fan, G., and Song, G. Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics. Front. Plant Sci. 13 (2022). doi: 10.3389/fpls.2022.1038109
Written by Dr. Yvonne Goepel
