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2023 Volume 2
Article Contents
Abstract
Introduction
Perennial grasses as natural allies of soil health
Perennial grasses as natural allies of soil biodiversity
Perennial grasses in climate change mitigation
Perennial grasses in combating invasive plants
Conclusions
Author contributions
Data availability
Acknowledgments
Conflict of interest
Supplementary information
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References
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ARTICLE   Open Access    

Identification of long-lived and stable mRNAs in the aged seeds of wheat

  • 1.

    State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China

  • 2.

    Ministry of Education Key Laboratory for Crop Genetic Resources and Improvement in Southwest China, Sichuan Agricultural University, Chengdu, Sichuan 611130, China

  • 3.

    IFEVA - Departamento de Producción Vegetal - Facultad de Agronomía, Universidad de Buenos Aires, Argentina (C1111AAI), Buenos Aires, Argentina

More Information
  • Received: 28 October 2022
    Accepted: 28 July 2023
    Published online: 07 October 2023
    Seed Biology  2,  Article number: 14 (2023)  |  Cite this article
  • Seed germination relies on preserving mRNA integrity in dry seeds. However, the quality of mRNA in aged wheat seeds is not well understood. Here, we investigated 20 wheat varieties for seed longevity using controlled deterioration treatment (CDT) and identified that Chinese Spring seeds exhibit moderate longevity. We observed correlations between seed viability and RNA integrity in the aleurone and embryo cells after aging-treatment. Nanopore sequencing of whole seeds from natural aging treatment (NAT) and CDT for 25 d identified 3,083 full-length transcripts. We performed RNA-seq transcriptome profiling to classify the tissue origin of these transcripts under eight aging treatments, revealing the presence of 2,064 overlapping long-lived mRNAs (LLRs) in the seed embryo and 2,130 in the aleurone layers. These LLRs corresponded to genes with detectable transcription levels and at least one full-length transcript in their coding sequence. Notably, degradation percentages of these mRNAs varied among seeds of different wheat varieties with similar ages. We predicted 21 most stable LLRs with high GC% content and short coding sequence length, among which only one LLR was seed-specifically expressed and belonged to the late-embryogenesis-abundant (LEA) protein family. RT-PCR confirmed the expression of the seven LLR fragments in the aleurone layer and embryo of Chinese Spring seeds. We found three of the most stable LLRs (LLR13, LLR15, and LLR20) identified in Chinese Spring were more stable in high longevity varieties than in short longevity varieties after aging, indicating their potential roles in seed longevity and germination.

  • Introduction

    Perennial grasses [e.g., switchgrass (Panicum virgatum), big bluestem (Andropogon gerardii), indiangrass (Sorghastrum nutans), little bluestem (Schizachyrium scoparium), Maasai love grass (Eragrostis superba), and bush ryegrass (Enteropogon macrostachyus)] are plant species that live for more than two years with deep root systems and the capacity to grow in a variety of climates[1−5]. Although often overlooked, perennial grasses serve an important role in ecosystems, particularly in maintaining soil health and biodiversity, climate change mitigation, and combating alien invasive plants (AIPs)[1,4]. Thus, they are simply natural allies for soil biodiversity conservation, invasive plant management, and climate change mitigation[6,7]. The deep root systems of perennial grasses help soil structure by improving aeration, increasing water infiltration, and lowering soil erosion[1,5]. Also, their extensive root network supports the stability of the soil, making it less susceptible to degradation and encouraging a healthier ecology overall[1,8]. Further, they play a key role in the nutrient cycle by maximizing nutrient utilization and minimizing leaching[9,10]. In addition, perennial grasses contribute organic matter to the soil through biomass, which decomposes over time and enriches the soil with critical nutrients[11−13]. This process improves soil fertility, increasing productivity for other plant species, and agricultural activities[9,12].

    IAPs, also known as non–native or exotic species, are plants introduced to an ecosystem where they do not naturally occur[14−16] and pose a severe ecological, economic, and social impacts[17,18]. Unlike native species, IAPs often lack natural enemies and diseases in their new environments, allowing them to proliferate unrestrictedly[19,20]. Their invasions lead to the displacement of native flora as they outcompete native species for resources i.e., light, water, and nutrients[21,22]. As a result, causing a reduction in biodiversity and the alteration of ecosystem functions, often forming dense monocultures that hinder the growth of other plants and disrupt habitats for native wildlife[23,24]. Moreover, IAPs can alter soil chemistry and hydrology thereby negatively impacting soil biodiversity[6,7,15,25]. IAPs can further impact human health by increasing allergens and providing a habitat for disease vectors[15]. Efforts to manage IAPs typically involve early detection, prevention, and rapid response, such as biological control, mechanical removal, and herbicide treatment[19,25,26]. Although the role of perennial grasses in combating IAPs has been seldom investigated, available studies show that effective management requires integrated eco-friendly management incorporating competitive native perennial grasses to suppress IAPs[6,8,15,27].

    Furthermore, perennial grasses are ecologically significant because they enhance species diversity and soil biodiversity i.e., living forms found in soil, which includes microorganisms (bacteria and fungi), mesofauna (nematodes and mites), and macrofauna, i.e., earthworms and insects[28−32]. This diversity is critical to ecosystem function and plays an important role in nutrient cycling, soil structure maintenance, and plant growth promotion[29,30]. They contribute to nutrient-cycling activities by breaking down organic materials into simpler compounds that perennial grasses and other plants can consume, decomposing dead plants and animals, and releasing nutrients back into the soil, thus increasing soil fertility[32−34]. Further, perennial grasses also promote plant-soil symbiotic relationships such as mycorrhizal associations and rhizobium symbioses, which improves soil health and plant growth[29]. These benefits are enhanced by perennial grasses' root exudates, which support both soil microbial diversity and activity, resulting in a more dynamic and resilient soil environment[1]. However, extreme weather events, such as floods and droughts, as well as IAPs can cause soil organism loss and structural damage, thereby impeding the roles of soil organisms[35−37]. Further, increased temperatures can disrupt microbial activity and nitrogen cycling mechanisms, impacting soil health, and productivity[37,38]. Addressing these challenges needs long-term integrated management approaches that maintain natural ecosystems and increase soil biodiversity, as well as IAP control and climate change mitigation. For instance, promoting the use and maintaining the diversity of perennial grasses in rangelands and agricultural habitats[1,39,40].

    Climate change which is the average change in the earth's temperature and precipitation patterns can also disrupt the delicate balance of soil biodiversity[37,41]. It is driven primarily by human activities i.e., burning fossil fuels, deforestation, and industrial processes which lead to an unprecedented rise in greenhouse gases, such as carbon dioxide and methane in the atmosphere[37,42]. Often the earth's surface temperature increases concomitantly with these greenhouse gasses[41]. Increased temperatures contribute to sea-level rise, more frequent and intense heatwaves, wildfires, and droughts affecting biodiversity, water supply, and human health. Changes in precipitation patterns also lead to extreme weather events i.e., hurricanes, floods, and heavy rainfall, disrupting ecosystems and human societies[37]. It also negatively impacts biodiversity, as species must adapt, migrate, or face extinction due to altered habitats and shifting climate zones[36]. Addressing climate change requires global cooperation and robust policies aimed at reducing greenhouse gas emissions which include the use of eco-friendly approach, for instance, keeping the environment intact with native plants i.e., perennials grasses[43]. Perennial grasses (e.g., turfgrass) are considered potential for mitigating the effects of climate change because they have a high carbon sequestration capacity, storing carbon in both soil and aboveground biomass[44−46]. They can contribute to reducing greenhouse gas levels by absorbing and storing carbon dioxide from the atmosphere in their roots and tissues, thus helping to mitigate climate change[44]. Furthermore, their capacity to minimize greenhouse gas emissions through reduced tillage and increased nitrogen use efficiency makes them an important component of habitat restoration to mitigate climate change impacts[43].

    Consequently, native perennial grasses have been recommended by various previous studies to be used for habitat restoration, including rangelands, because of their physiological and morphological traits, which have shown great potential to improve soil health and biodiversity, mitigate climate change, and combat IAPs[1,5,8,27,40,47]. By their competitive and morphological traits, several perennial native grass species found in African rangelands (e.g., African foxtail grass (Cenchrus ciliaris), horsetail grass (Chloris roxburghiana), rhodes grass (Chloris gayana), E. superba, and E. macrostachyus) and P. virgatum, S. nutans, S. scoparium, and A. gerardii in North America have been tested and recommended for ecological restoration[1−5].

    Perennial grasses as natural allies of soil health

    Preceding studies have demonstrated that perennial grasses have the potential to improve soil health and structure in rangelands and protected habitats[1,48−50]. Unlike annual plants, which have shallow root systems, perennial grasses can penetrate deep into the soil, sometimes reaching depths of several meters as they have deep and extensive root systems[1,7,40]. These deep roots create channels that enhance soil aeration, allowing for better oxygen flow and water infiltration, thereby preventing soil compaction[49]. Perennial grasses contribute to soil stability by binding soil particles together, thereby preventing erosion (Fig. 1), which is important in ecosystems or habitats prone to heavy rainfall or wind[48,49]. This stabilization effect reduces the loss of topsoil, which contains the highest concentration of organic matter and nutrients essential for plant growth[44]. Moreover, perennial grasses have been reported to be efficient in nutrient cycling, a critical process for maintaining soil fertility[49]. For instance, their deep roots access nutrients in deeper soil layers, which might be unavailable to shallow-rooted plants[49,50]. These nutrients are then brought to the surface and incorporated into the plant biomass. When the grasses die back or shed leaves, these nutrients are returned to the soil surface as organic matter, making them accessible to other plants[32,49,51]

    Figure 1.  Diagram illustrating the multifaceted benefits of perennial grasses and their interconnected roles in promoting soil health, biodiversity, IAPs control, climate change mitigation, water retention, erosion control, and habitat provision. The arrows illustrate the complex interactions and synergies among these components, emphasizing the comprehensive ecological contributions of perennial grasses. The central position of perennial grasses highlights their pivotal role in these areas. This visual representation emphasizes how perennial grasses contribute to and enhance various aspects of ecosystem health and stability.
    DownLoad: Full-Size Img PowerPoint

    Furthermore, perennial grasses enhance soil health and structure (Fig. 1), improving the soil's ability to retain water and withstand extreme weather events i.e., heavy rainfall and floods[44,49]. Their extensive root networks stabilize the soil, reducing erosion and runoff (Fig. 1), which are critical for maintaining soil fertility and agricultural productivity under variable climatic conditions[51]. The continuous growth and decay cycle of perennial grasses contributes to the slow but steady release of nutrients[52]. This slow release is beneficial for maintaining a stable nutrient supply, as opposed to the rapid nutrient depletion often seen in soils dominated by annual crops[50]. This process also helps in reducing nutrient leaching, where nutrients are washed away from the soil profile, particularly nitrogen, which is critical for plant growth[49]. Perennial grasses help to reduce N2O emissions; excess nutrients can lead to increased N2O emissions[10,11,53]. They also contribute significantly to the soil organic matter, which is a key component of soil health[52]. Organic matter consists of decomposed plant and animal residues, which improve soil structure, water retention, and nutrient availability[50,52]. The biomass produced by perennial grasses, both above and below ground, adds a substantial amount of organic material to the soil[52]. As the plant material decomposes, it forms humus, a stable form of organic matter that enhances soil structure by increasing its capacity to hold water and nutrients[52,54]. This is particularly important in dry regions e.g. in Africa, where water retention can be a limiting factor for crop growth[49]. The organic matter also provides a habitat and food source for a diverse array of soil organisms, including bacteria, fungi, and earthworms, which further contribute to soil fertility through their biological activities[43,52,54].

    Perennial grasses as natural allies of soil biodiversity

    Perennial grasses play a crucial role in enhancing soil biodiversity (abundance and diversity) and activities within the soil[31,32,51,54]. They provide critical habitats for soil fauna i.e., earthworms, nematodes, and arthropods (Fig. 1)[32,54]. Their complex root systems create a stable environment that supports a wide range of soil organisms[55]. Also, the root systems of perennial grasses exude a variety of organic compounds, including sugars, amino acids, and organic acids, which serve as food sources for soil biodiversity[54]. This continuous supply of root exudates and a stable environment fosters a diverse macro and microbial community, which is essential for maintaining soil health[31,43,54]. For instance, it was reported by Smith et al.[54] that in areas with abundant perennial grasses, a high soil macrofaunal biodiversity (i.e., Lumbricidae, Isopoda, and Staphylinidae) was observed. They further asserted that these grasses were beneficial to soil macrofauna as they increased the abundance and species diversity of staphylinid beetles, woodlice, and earthworms. In addition, Mathieu et al.[56] reported the influence of spatial patterns of perennial grasses on the abundance and diversity of soil macrofauna in Amazonian pastures. These findings suggest that well-managed perennial grasses are vital in enhancing soil macro and microbes in ecosystems[54−56].

    These soil organisms perform various functions, including decomposing organic matter, fixing atmospheric nitrogen, and suppressing soil-borne diseases[29,30,32]. A diverse soil macro and microbial community can enhance nutrient cycling, making nutrients more available to plants[30,56]. Enhanced microbial diversity by perennial grasses contributes to the suppression of pathogens through competition and the production of antimicrobial compounds, thus promoting plant health[32]. They also help in maintaining soil structure, fertility, and overall ecosystem function[32]. For instance, earthworms, often referred to as 'ecosystem engineers', augment soil structure by creating burrows that improve aeration and water infiltration in perennial grass communities[31,51]. Their activity also helps mix organic matter into the soil, promoting nutrient cycling[31,32]. Nematodes and arthropods which feed on perennial grass species contribute to the decomposition process, breaking down organic matter and releasing nutrients that are vital for plant growth[31,54]. The presence of a diverse soil fauna community is indicative of a healthy soil ecosystem, which is more resilient to environmental stresses and disturbances[31].

    Furthermore, perennial grasses are considered as being instrumental in promoting plant-soil symbiotic relationships[43,54], which are crucial for plant health and soil fertility. One of the most well-known symbiotic relationships is between plants and mycorrhizal fungi[29,33]. These fungi colonize plant roots and extend their hyphae into the soil, increasing the root surface area and enhancing the plant's ability to absorb water and nutrients, particularly phosphorus. The relationship between perennial grasses and mycorrhizal fungi is mutually beneficial. The fungi receive carbohydrates produced by the plant through photosynthesis, while the plant gains improved access to soil nutrients and increased resistance to soil-borne pathogens[30]. This symbiotic relationship is particularly important in nutrient-poor soils, where mycorrhizal associations can significantly enhance plant growth and survival. Additionally, perennial grasses promote other beneficial plant-soil interactions, such as those involving nitrogen-fixing bacteria. These bacteria form nodules on the roots of certain perennial grasses, converting atmospheric nitrogen into a form that plants can use[29,30]. This process is essential for maintaining soil fertility, especially in ecosystems where nitrogen is a limiting nutrient.

    Perennial grasses in climate change mitigation

    Perennial grasses are increasingly recognized for their role in climate change mitigation (Fig. 1)[43,44,57]. They can sequester carbon, reduce greenhouse gas emissions, and adaptation to climate variability[58,59]. Their deep root systems and grass-like characteristics make them highly effective in capturing and storing carbon[44]. These roots can penetrate deep into the soil and store carbon for extended periods[59]. Because of this, perennial grasses show potential to enhance the resilience of ecosystems to changing climatic conditions[44]. The roots of perennial grasses are more extensive and persistent compared to annual crops, allowing for greater carbon storage both in the root biomass and the soil[45,46,60]. This process of carbon sequestration involves capturing atmospheric carbon dioxide (CO2) through photosynthesis and storing it in perennial grass tissues (e.g., turfgrasses) and soil organic matter[44−46]. Preceding studies have further shown that perennial grasses can sequester substantial amounts of carbon, contributing to the reduction of atmospheric CO2 levels[45,61]. In addition to carbon sequestration, perennial grasses can reduce greenhouse gas emissions through various mechanisms[43]. One of the primary ways is by reducing the need for frequent soil tillage, which is common in annual cropping systems. Tillage disrupts soil structure, releases stored carbon as CO2, and increases soil erosion[58,61]. Thus, with their long lifespan, perennial grasses can reduce the need for tillage, thereby minimizing CO2 emissions from soil disturbance[43,58].

    Moreover, perennial grasses can improve nitrogen use efficiency, reducing the need for synthetic fertilizers that are a major source of nitrous oxide (N2O) emissions—a potent greenhouse gas[53,62]. Their deep root systems enable them to access nutrients from deeper soil layers, reducing nutrient leaching and the subsequent emissions of N2O[53]. By optimizing nutrient use, perennial grasses contribute to lower greenhouse gas emissions associated with agricultural practices[63]. Also, perennial grasses are crucial for adapting to climate variability[44]. Their deep root systems allow them to access water from deeper soil layers, making them more resilient to drought conditions compared to annual crops[44]. This water use efficiency helps maintain plant growth and productivity even during periods of water scarcity, which are expected to become more frequent with climate change[49]. In general, perennial grasses support soil biodiversity conservation through habitat provision, climate change mitigation, and promoting ecosystem resilience[58]. Besides, these grasses are crucial for ecosystem stability and productivity, particularly in the face of climate change, and ensure the continued provision of ecosystem services (Fig. 1).

    Perennial grasses in combating invasive plants

    Previous studies have shown that IAPs pose significant threats to ecosystems worldwide by displacing native species, altering habitats, and disrupting ecosystem functions and services[15,20,23,64]. Among the integrated management techniques to combat IAPs involves the use of competitive native plants (Fig. 1) such as perennial grasses[6,7,40]. These grasses, which live for more than two years with robust root systems, growth, and resilience to varying environmental conditions, offer several advantages in controlling IAPs[1,48]. Their competitive growth patterns and ability to restore and maintain native plant communities, and establish, and thrive in diverse habitats make them formidable competitors against invasive plants[1]. One of the primary ways perennial grasses combat IAPs is through competition for resources[48]. Their extensive root systems allow them to efficiently absorb water and nutrients, outcompeting IAPs that typically have shallower roots. This competitive edge limits the resources available to IAPs, inhibiting their growth and spread. For instance, species like P. virgatum and big A. gerardii are known for their deep roots, which can reach depths of up to 10 feet (3 m), providing them with a significant advantage over many IAPs[8,48]. They can also outcompete IAPs through their competitive growth patterns including quick establishment and forming dense canopies that shade out AIPs[1,8]. For example, native perennial grasses like S. nutans and S. scoparium have been shown to effectively compete with invasive species i.e., spotted knapweed (Centaurea stoebe) by limiting light availability and space for growth[8,48].

    Moreover, using their extensive root systems that stabilize the soil, perennial grasses can prevent erosion and invasions of IAPs[44]. Invasive plants i.e., carrot weed (Parthenium hysterophorus), cheatgrass (Bromus tectorum), and kudzu (Pueraria montana) can rapidly colonize disturbed soils, leading to severe erosion problems[20,65,66]. However, perennial grasses i.e., P. virgatum and big A. gerardii have been found to reduce erosion and creating an unfavorable environment for IAPs to establish owing to their deep fibrous root systems that hold the soil in place. Perennial grasses can also modify the microenvironment in ways that make it less conducive for IAPs[1,27,66]. They produce dense root mats that strengthen the organic matter content and soil structure, improving the fertility and health of the soil. The diversity and growth of native plant species is aided by improved soil conditions, which further promote biodiversity and inhibit IAPs by strengthening ecosystem resilience[48].

    Additionally, the use of perennial grasses in restoration has shown promising results in reclaiming areas overrun by IAPs and maintaining native plant communities that are disrupted by IAPs[8,66]. By planting a mix of native perennial grasses, land managers can restore ecological balance and prevent the re-establishment of IAPs[26]. These grasses provide long-term ground cover and habitat for wildlife, contributing to the overall health and stability of the ecosystem[1,8,54]. By reintroducing native perennial grasses into areas (e.g., rangelands and protected habitats) dominated by IAPs, ecosystems, and their biodiversity can be restored to their earlier conditions[27,39,67]. For instance, the use of native perennial grasses has been successful in restoring prairie ecosystems that were previously overrun by IAPs i.e., leafy spurge (Euphorbia esula) and purple loosestrife (Lythrum salicaria)[68]. Another important example of using perennial grasses to mitigate IAPs is the restoration of tallgrass prairies in the Midwest United States[8,66]. These prairies were historically dominated by native perennial grasses i.e., S. nutans and S. scoparium, however IAPs i.e., smooth brome (Bromus inermis) and reed canarygrass (Phalaris arundinacea) displaced them, leading to biodiversity loss and altered ecosystem functions[8,66,68]. Studies show that following the restoration of these invaded habitats using perennial grasses, native grasses successfully reestablished and reduced IAPs and promoting native biodiversity[66,67]. In addition, another notable example is the use of perennial grasses to restore riparian areas which were heavily invaded and impacted by IAPs i.e., giant reed (Arundo donax) and saltcedar (Tamarix spp.)[67,69]. Planting native perennial grasses like western wheatgrass (Pascopyrum smithii) and creeping wildrye (Elymus triticoides) in these areas helped to stabilize the soil, reduce erosion, and suppress IAPs, leading to improved riparian habitat quality and ecosystem resilience[18,66,67,69].

    Therefore, competitive suppressive perennial grasses are a crucial tool in the fight against IAPs and other weeds. Their competitive abilities, contributions to soil health, and role in ecosystem restoration makes them invaluable in managing and alleviating the impacts of IAPs. As research continues, the potential for perennial grasses to be integrated into broader IAP strategies remain significant, promising a more sustainable and ecologically sound approach to preserving native biodiversity.

    Conclusions

    Perennial grasses are pivotal in enhancing soil biodiversity, mitigating climate change, and combating IAPs. Their deep root systems stabilize soils, support diverse soil faunal communities, and improve water retention. Besides, they are important grasses in sequestering carbon, reducing greenhouse gas emissions, suppressing IAPs, and supporting the reestablishment of native plant communities. Integrating perennial grasses into protected areas and rangelands management practices could offer a sustainable solution to pressing environmental challenges including invasions of IAPs. Stakeholders i.e., farmers, conservationists, ecologists, and land managers are advised to use perennial grass systems in their restoration practices, crop rotations, and pasturelands to enhance soil health and resilience. They are further commended to use perennial grasses for erosion control and to improve soil structure and fertility. Policymakers could develop and support policies that incentivize the use of perennial grasses in agricultural and restoration projects. Researchers, they are advised to conduct studies to quantify the long-term benefits of perennial grasses on soil biodiversity and climate change mitigation. Additionally, they can develop country or region-specific guidelines for the effective use of perennial grasses in different ecosystems. Hence, by integrating perennial grasses into our environmental stewardship strategies, we can ensure a thriving, balanced ecosystem capable of withstanding the impacts of climate change and IAPs.

    Author contributions

    The author confirms sole responsibility for the following: review conception and design, and manuscript preparation.

    Data availability

    Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

    Acknowledgments

    The author thanks all the colleagues who reviewed and proofread this article. This work was not supported by any funding agency.

  • The author declares that there is no conflict of interest.

  • Supplemental Fig. S1 Tissue purity and correlation between biological replicates in Illumina RNA-seq.
    Supplemental Fig. S2 The water content and RNA integrity number (RIN) of different varieties.
    Supplemental Fig. S3 Identification of stored mRNA in aged wheat seeds by Illumina RNA-seq.
    Supplemental Fig. S4 Gene Ontology (GO) enrichment of overlapping long-lived mRNAs (LLRs).
    Supplemental Fig. S5 The sequencing depth from 5' to 3' of the most stable long-lived mRNAs (LLRs) and easily degraded mRNAs.
    Supplemental Fig. S6 Sequence analysis of the most stable long-lived mRNAs (LLRs).
    Supplemental Fig. S7 The correlation between longevity and the most stable long-lived mRNAs (LLRs).
    Supplemental Table S1 Wheat accessions used in this study.
    Supplemental Table S2 Quality of Nanopore-Seq data obtained in this study for NAT_0Y (fresh seeds) and CDT_25D seeds.
    Supplemental Table S3 Full-length long-lived mRNAs (LLRs) identified by Nanopore-seq data.
    Supplemental Table S4 Quality of Illumina RNA-seq data.
    Supplemental Table S5 Identification of stored mRNAs in embryo and aleurone layer using Illumina RNA-seq.
    Supplemental Table S6 Transcripts Per Million (TPM) analysis of full-length long-lived mRNAs (LLRs) in embryo and aleurone layers using Illumina RNA-seq.
    Supplemental Table S7 long-lived mRNAs (LLRs) with different fold changes (FC) in the embryos and aleurone layers compared with NAT_0Y.
    Supplemental Table S8 Gene Ontology (GO) enrichment analysis of overlapping long-lived mRNAs (LLRs) with relatively decreased and significantly increased transcript levels in embryos and aleurone layers.
    Supplemental Table S9 Motifs in the promoter of the most stable long-lived mRNAs.
    Supplemental Table S10 Expression profiles of the most stable long-lived mRNAs in different tissues and treatments.
    Supplemental Table S11 List of genes and primers used for Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Real-Time Quantitative Polymerase Chain Reaction (qPCR) experiments.
  • [1]

    Marcos-Filho J. 2015. Seed vigor testing: an overview of the past, present and future perspective. Scientia Agricola 72:363−74

    doi: 10.1590/0103-9016-2015-0007

    CrossRef   Google Scholar

    [2]

    Oge L, Bourdais G, Bove J, Collet B, Godin B, et al. 2008. Protein repair L-isoaspartyl methyltransferase1 is involved in both seed longevity and germination vigor in Arabidopsis. The Plant Cell 20:3022−37

    doi: 10.1105/tpc.108.058479

    CrossRef   Google Scholar

    [3]

    Rajjou L, Lovigny Y, Groot SPC, Belghazi M, Job C, et al. 2008. Proteome-wide characterization of seed aging in Arabidopsis: a comparison between artificial and natural aging protocols. Plant Physiology 148:620−41

    doi: 10.1104/pp.108.123141

    CrossRef   Google Scholar

    [4]

    Groot SPC, Surki AA, de Vos RCH, Kodde J. 2012. Seed storage at elevated partial pressure of oxygen, a fast method for analysing seed ageing under dry conditions. Annals of Botany 110:1149−59

    doi: 10.1093/aob/mcs198

    CrossRef   Google Scholar

    [5]

    Walters C, Wheeler LM, Grotenhuis JM. 2005. Longevity of seeds stored in a genebank: species characteristics. Seed Science Research 15:1−20

    doi: 10.1079/SSR2004195

    CrossRef   Google Scholar

    [6]

    Spanò C, Buselli R, Ruffini Castiglione M, Bottega S, Grilli I. 2007. RNases and nucleases in embryos and endosperms from naturally aged wheat seeds stored in different conditions. Journal of Plant Physiology 164:487−95

    doi: 10.1016/j.jplph.2006.03.015

    CrossRef   Google Scholar

    [7]

    Lehner A, Bailly C, Flechel B, Poels P, Côme D, et al. 2006. Changes in wheat seed germination ability, soluble carbohydrate and antioxidant enzyme activities in the embryo during the desiccation phase of maturation. Journal of Cereal Science 43:175−82

    doi: 10.1016/j.jcs.2005.07.005

    CrossRef   Google Scholar

    [8]

    Ballesteros D, Walters C. 2019. Solid-state biology and seed longevity: a mechanical analysis of glasses in pea and soybean embryonic axes. Frontiers in Plant Science 10:920

    doi: 10.3389/fpls.2019.00920

    CrossRef   Google Scholar

    [9]

    Rajjou L, Duval M, Gallardo K, Catusse J, Bally J, et al. 2012. Seed germination and vigor. Annual Review of Plant Biology 63:507−33

    doi: 10.1146/annurev-arplant-042811-105550

    CrossRef   Google Scholar

    [10]

    Landjeva S, Lohwasser U, Börner A. 2010. Genetic mapping within the wheat D genome reveals QTL for germination, seed vigour and longevity, and early seedling growth. Euphytica 171:129−43

    doi: 10.1007/s10681-009-0016-3

    CrossRef   Google Scholar

    [11]

    Patil KG, Karjule A, Patel D, Sasidharan N. 2019. Effect of accelerated ageing on viability and longevity of wheat (Triticum aestivum) seed. The Indian Journal of Agricultural Sciences 89:920−28

    doi: 10.56093/ijas.v89i6.90760

    CrossRef   Google Scholar

    [12]

    Willi J, Küpfer P, Evéquoz D, Fernandez G, Katz A, et al. 2018. Oxidative stress damages rRNA inside the ribosome and differentially affects the catalytic center. Nucleic Acids Research 46:1945−57

    doi: 10.1093/nar/gkx1308

    CrossRef   Google Scholar

    [13]

    Saighani K, Kondo D, Sano N, Murata K, Yamada T, et al. 2021. Correlation between seed longevity and RNA integrity in the embryos of rice seeds. Plant Biotechnology 38:277−83

    doi: 10.5511/plantbiotechnology.21.0422a

    CrossRef   Google Scholar

    [14]

    Rushton PJ, Bray CM. 1987. Stored and de novo synthesised polyadenylated RNA and loss of vigour and viability in wheat seed. Plant Science 51:51−59

    doi: 10.1016/0168-9452(87)90220-2

    CrossRef   Google Scholar

    [15]

    Fleming MB, Hill LM, Walters C. 2019. The kinetics of ageing in dry-stored seeds: a comparison of viability loss and RNA degradation in unique legacy seed collections. Annals of Botany 123:1133−46

    doi: 10.1093/aob/mcy217

    CrossRef   Google Scholar

    [16]

    Rajjou L, Gallardo K, Debeaujon I, Vandekerckhove J, Job C, et al. 2004. The effect of α-amanitin on the Arabidopsis seed proteome highlights the distinct roles of stored and neosynthesized mRNAs during germination. Plant Physiology 134:1598−613

    doi: 10.1104/pp.103.036293

    CrossRef   Google Scholar

    [17]

    Nakabayashi K, Okamoto M, Koshiba T, Kamiya Y, Nambara E. 2005. Genome-wide profiling of stored mRNA in Arabidopsis thaliana seed germination: epigenetic and genetic regulation of transcription in seed. The Plant Journal 41:697−709

    doi: 10.1111/j.1365-313X.2005.02337.x

    CrossRef   Google Scholar

    [18]

    Kimura M, Nambara E. 2010. Stored and neosynthesized mRNA in Arabidopsis seeds: effects of cycloheximide and controlled deterioration treatment on the resumption of transcription during imbibition. Plant Molecular Biology 73:119−29

    doi: 10.1007/s11103-010-9603-x

    CrossRef   Google Scholar

    [19]

    Fleming MB, Richards CM, Walters C. 2017. Decline in RNA integrity of dry-stored soybean seeds correlates with loss of germination potential. Journal of Experimental Botany 68:2219−30

    doi: 10.1093/jxb/erx100

    CrossRef   Google Scholar

    [20]

    Zhao L, Wang H, Fu YB. 2020. Analysis of stored mRNA degradation in acceleratedly aged seeds of wheat and canola in comparison to Arabidopsis. Plants 9:1707

    doi: 10.3390/plants9121707

    CrossRef   Google Scholar

    [21]

    Sano N, Ono H, Murata K, Yamada T, Hirasawa T, et al. 2015. Accumulation of long-lived mRNAs associated with germination in embryos during seed development of rice. Journal of Experimental Botany 66:4035−46

    doi: 10.1093/jxb/erv209

    CrossRef   Google Scholar

    [22]

    Sano N, Takebayashi Y, To A, Mhiri C, Rajjou L, et al. 2019. Shotgun proteomic analysis highlights the roles of long-lived mRNAs and de novo transcribed mRNAs in rice seeds upon imbibition. Plant and Cell Physiology 60:2584−96

    doi: 10.1093/pcp/pcz152

    CrossRef   Google Scholar

    [23]

    Bai B, van der Horst S, Cordewener JHG, America TAHP, Hanson J, et al. 2020. Seed-Stored mRNAs that Are Specifically Associated to Monosomes Are Translationally Regulated during Germination. Plant Physiology 182:378−92

    doi: 10.1104/pp.19.00644

    CrossRef   Google Scholar

    [24]

    Agacka-Mołdoch M, Arif MAR, Lohwasser U, Doroszewska T, Qualset CO, et al. 2016. The inheritance of wheat grain longevity: a comparison between induced and natural ageing. Journal of Applied Genetics 57:477−81

    doi: 10.1007/s13353-016-0348-3

    CrossRef   Google Scholar

    [25]

    Gianella M, Balestrazzi A, Ravasio A, Mondoni A, Börner A, et al. 2022. Comparative seed longevity under genebank storage and artificial ageing: a case study in heteromorphic wheat wild relatives. Plant Biology 24(5):836−45

    doi: 10.1111/plb.13421

    CrossRef   Google Scholar

    [26]

    Petruzzelli L, Carella G. 1983. The effect of ageing conditions on loss of viability in wheat (T. durum). Journal of Experimental Botany 34:221−25

    doi: 10.1093/jxb/34.2.221

    CrossRef   Google Scholar

    [27]

    Wang W, Xu D, Sui Y, Ding X, Song X. 2022. A multiomic study uncovers a bZIP23-PER1A-mediated detoxification pathway to enhance seed vigor in rice. PNAS 119(9):e2026355119

    doi: 10.1073/pnas.2026355119

    CrossRef   Google Scholar

    [28]

    Zhao L, Wang S, Fu YB, Wang H. 2019. Arabidopsis seed stored mRNAs are degraded constantly over aging time, as revealed by new quantification methods. Frontiers in Plant Science 10:1764

    doi: 10.3389/fpls.2019.01764

    CrossRef   Google Scholar

    [29]

    Hassan MJ, Geng W, Zeng W, Raza MA, Khan I, et al. 2021. Diethyl aminoethyl hexanoate priming ameliorates seed germination via involvement in hormonal changes, osmotic adjustment, and dehydrins accumulation in white clover under drought stress. Frontiers in plant science 12:709187

    doi: 10.3389/fpls.2021.709187

    CrossRef   Google Scholar

    [30]

    Jerkovic A, Kriegel AM., Bradner JR, Atwell BJ, Roberts TH, et a. 2010. Strategic distribution of protective proteins within bran layers of wheat protects the nutrient-rich endosperm. Plant Physiology 152(3):1459−70

    doi: 10.1104/pp.109.149864

    CrossRef   Google Scholar

    [31]

    Jones RL, Jacobsen JV. 1991. Regulation of synthesis and transport of secreted proteins in cereal aleurone. International Review of Cytology 126:49−88

    doi: 10.1016/s0074-7696(08)60682-8

    CrossRef   Google Scholar

    [32]

    Bewley JD, Bradford KJ, Hilhorst HWM, Nonogaki H. 2013. Seeds: Physiology of Development, Germination and Dormancy. 3rd Edition. New York: Springer. pp. 203. https://doi.org/10.1007/978-1-4614-4693-4
    [33]

    Fath A, Bethke PC, Jones RL. 2001. Enzymes that scavenge reactive oxygen species are down-regulated prior to gibberellic acid-induced programmed cell death in barley aleurone. Plant Physiology 126:156−66

    doi: 10.1104/pp.126.1.156

    CrossRef   Google Scholar

    [34]

    Johnson RR, Dyer WE. 2000. Degradation of endosperm mRNAs during dry afterripening of cereal grains. Seed Science Research 10:233−41

    doi: 10.1017/S096025850000026X

    CrossRef   Google Scholar

    [35]

    Puchta M, Boczkowska M, Groszyk J. 2020. Low RIN value for RNA-Seq library construction from long-term stored seeds: A case study of barley seeds. Genes 11(10):1190

    doi: 10.3390/genes11101190

    CrossRef   Google Scholar

    [36]

    Fleming MB, Patterson EL, Reeves PA, Richards CM, Gaines TA, et al. 2018. Exploring the fate of mRNA in aging seeds: protection, destruction, or slow decay? Journal of Experimental Botany 69:4309−21

    doi: 10.1093/jxb/ery215

    CrossRef   Google Scholar

    [37]

    Righetti K, Vu JL, Pelletier S, Vu BL, Glaab E, et al. 2015. Inference of longevity-related genes from a robust coexpression network of seed maturation identifies regulators linking seed storability to biotic defense-related pathways. The Plant Cell 27:2692−708

    doi: 10.1105/tpc.15.00632

    CrossRef   Google Scholar

    [38]

    International Seed Testing Association. 1976. International rules for seed testing. Seed Science and Technology 4:3−49

    Google Scholar

    [39]

    de Souza RR, Moraes MP, Paraginski JA, Moreira TF, Bittencourt KC, et al. 2022. Effects of Trichoderma asperellum on germination indexes and seedling parameters of lettuce cultivars. Current Microbiology 79:5

    doi: 10.1007/s00284-021-02713-4

    CrossRef   Google Scholar

    [40]

    Wang B, Wang S, Tang Y, Jiang L, He W, et al. 2022. Transcriptome-wide characterization of seed aging in rice: Identification of specific long-lived mRNAs for seed longevity. Frontiers in plant science 13:857390

    doi: 10.3389/fpls.2022.857390

    CrossRef   Google Scholar

    [41]

    Zhu YY, Machleder EM, Chenchik A, Li R, Siebert PD. 2001. Reverse transcriptase template switching: A SMART™ approach for full-length cDNA library construction. Biotechniques 30:892−97

    doi: 10.2144/01304pf02

    CrossRef   Google Scholar

    [42]

    Zhao S, Ye Z, Stanton R. 2020. Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols. RNA 26:903−9

    doi: 10.1261/rna.074922.120

    CrossRef   Google Scholar

    [43]

    Sun H, Liu Y, Ma J, Wang Y, Song H, et al. 2021. Transcriptome analysis provides strategies for postharvest lotus seeds preservation. Postharvest Biology and Technology 179:111583

    doi: 10.1016/j.postharvbio.2021.111583

    CrossRef   Google Scholar

    [44]

    Reiman M, Laan M, Rull K, Sõber S. 2017. Effects of RNA integrity on transcript quantification by total RNA sequencing of clinically collected human placental samples. The FASEB Journal 31:3298−308

    doi: 10.1096/fj.201601031RR

    CrossRef   Google Scholar

    [45]

    Ma S, Wang M, Wu J, Guo W, Chen Y, et al. 2021. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. Molecular Plant 14:1965−68

    doi: 10.1016/j.molp.2021.10.006

    CrossRef   Google Scholar

    [46]

    Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, et al. 2020. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Molecular Plant 13:1194−202

    doi: 10.1016/j.molp.2020.06.009

    CrossRef   Google Scholar

    [47]

    Okamoto K, Kitano H, Akazawa T. 1980. Biosynthesis and excretion of hydrolases in germinating cereal seeds. Plant and Cell Physiology 21:201−4

    doi: 10.1093/oxfordjournals.pcp.a075983

    CrossRef   Google Scholar

    [48]

    Ahmed Z, Yang H, Fu YB. 2016. The Associative Changes in Scutellum Nuclear Content and Morphology with Viability Loss of Naturally Aged and Accelerated Aging Wheat (Triticum aestivum) Seeds. Frontiers in Plant Science 7:1474

    doi: 10.3389/fpls.2016.01474

    CrossRef   Google Scholar

    [49]

    Floris C, Anguillesi MC. 1974. Ageing of isolated embryos and endosperms of durum wheat: an analysis of chromosome damage. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 22:133−38

    doi: 10.1016/0027-5107(74)90093-1

    CrossRef   Google Scholar

    [50]

    Chauhan D, Deswal D, Dahiya O, Punia R. 2011. Change in storage enzymes activities in natural and accelerated aged seed of wheat (Triticum aestivum). Indian Journal of Agricultural Sciences 81:1037−40

    Google Scholar

    [51]

    Floris C. 1970. Ageing in Triticum durum seeds: behaviour of embryos and endosperms from aged seeds as revealed by the embryo-transplantation technique. Journal of Experimental Botany 21:462−68

    doi: 10.1093/jxb/21.2.462

    CrossRef   Google Scholar

    [52]

    Livesley MA, Bray CM. 1993. Heat shock and recovery in aged wheat aleurone layers. Seed Science Research 3:179−86

    doi: 10.1017/S0960258500001768

    CrossRef   Google Scholar

    [53]

    Deruyffelaere C, Bouchez I, Morin H, Guillot A, Miquel M, et al. 2015. Ubiquitin-Mediated Proteasomal Degradation of Oleosins is Involved in Oil Body Mobilization During Post-Germinative Seedling Growth in Arabidopsis. Plant and Cell Physiology 56:1374−87

    doi: 10.1093/pcp/pcv056

    CrossRef   Google Scholar

    [54]

    Lee SE, Yoon IS, Hwang YS. 2022. Abscisic acid activation of oleosin gene HvOle3 expression prevents the coalescence of protein storage vacuoles in barley aleurone cells. Journal of Experimental Botany 73:817−34

    doi: 10.1093/jxb/erab471

    CrossRef   Google Scholar

    [55]

    Ritchie S, Swanson SJ, Gilroy S. 2000. Physiology of the aleurone layer and starchy endosperm during grain development and early seedling growth: new insights from cell and molecular biology. Seed Science Research 10:193−212

    doi: 10.1017/S0960258500000234

    CrossRef   Google Scholar

    [56]

    Nagel M, Börner A. 2010. The longevity of crop seeds stored under ambient conditions. Seed Science Research 20:1−12

    doi: 10.1017/S0960258509990213

    CrossRef   Google Scholar

    [57]

    Boca S, Koestler F, Ksas B, Chevalier A, Leymarie J, et al. 2014. Arabidopsis lipocalins AtCHL and AtTIL have distinct but overlapping functions essential for lipid protection and seed longevity. Plant, Cell & Environment 37:368−81

    doi: 10.1111/pce.12159

    CrossRef   Google Scholar

    [58]

    Wiebach J, Nagel M, Börner A, Altmann T, Riewe D. 2020. Age-dependent loss of seed viability is associated with increased lipid oxidation and hydrolysis. Plant, Cell & Environment 43:303−14

    doi: 10.1111/pce.13651

    CrossRef   Google Scholar

    [59]

    González-Thuillier I, Salt L, Chope G, Penson S, Skeggs P, et al. 2015. Distribution of lipids in the grain of wheat (cv. Hereward) determined by lipidomic analysis of milling and pearling fractions. Journal of Agricultural and Food Chemistry 63:10705−16

    doi: 10.1021/acs.jafc.5b05289

    CrossRef   Google Scholar

    [60]

    Dell'Aquila A, De Leo P, Caldiroli E, Zocchi G. 1978. Damages at translational level in aged wheat embryos. Plant Science Letters 12:217−26

    doi: 10.1016/0304-4211(78)90071-8

    CrossRef   Google Scholar

    [61]

    Grilli I, Bacci E, Lombardi T, Spano C, Floris C. 1995. Natural ageing: Poly (A) polymerase in germinating embryos of Triticum durum wheat. Annals of Botany 76:15−21

    doi: 10.1006/anbo.1995.1073

    CrossRef   Google Scholar

    [62]

    Rehman Arif MA, Börner A. 2020. An SNP based GWAS analysis of seed longevity in wheat. Cereal Research Communications 48:149−56

    doi: 10.1007/s42976-020-00025-0

    CrossRef   Google Scholar

    [63]

    Mathieu O, Yukawa Y, Prieto JL, Vaillant I, Sugiura M, et al. 2003. Identification and characterization of transcription factor IIIA and ribosomal protein L5 from Arabidopsis thaliana. Nucleic Acids Research 31:2424−33

    doi: 10.1093/nar/gkg335

    CrossRef   Google Scholar

    [64]

    Arif MAR, Nagel M, Lohwasser U, Börner A. 2017. Genetic architecture of seed longevity in bread wheat (Triticum aestivum L.). Journal of Biosciences 42:81−89

    doi: 10.1007/s12038-016-9661-6

    CrossRef   Google Scholar

    [65]

    Tsugama D, Liu S, Takano T. 2012. A bZIP protein, VIP1, is a regulator of osmosensory signaling in Arabidopsis. Plant Physiology 159:144−55

    doi: 10.1104/pp.112.197020

    CrossRef   Google Scholar

    [66]

    Campos F, Cuevas-Velazquez C, Fares MA, Reyes JL, Covarrubias AA. 2013. Group 1 LEA proteins, an ancestral plant protein group, are also present in other eukaryotes, and in the archeae and bacteria domains. Molecular Genetics and Genomics 288:503−17

    doi: 10.1007/s00438-013-0768-2

    CrossRef   Google Scholar

    [67]

    Chatelain E, Hundertmark M, Leprince O, Gall SL, Satour P, et al. 2012. Temporal profiling of the heat-stable proteome during late maturation of Medicago truncatula seeds identifies a restricted subset of late embryogenesis abundant proteins associated with longevity. Plant, Cell & Environment 35:1440−55

    doi: 10.1111/j.1365-3040.2012.02501.x

    CrossRef   Google Scholar

    [68]

    Wu X, Liu H, Wang W, Chen S, Hu X, et al. 2011. Proteomic analysis of seed viability in maize. Acta Physiologiae Plantarum 33:181−91

    doi: 10.1007/s11738-010-0536-4

    CrossRef   Google Scholar

    [69]

    Hundertmark M, Buitink J, Leprince O, Hincha DK. 2011. The reduction of seed-specific dehydrins reduces seed longevity in Arabidopsis thaliana. Seed Science Research 21:165−73

    doi: 10.1017/S0960258511000079

    CrossRef   Google Scholar

    [70]

    Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, et al. 2004. UniProt: the universal protein knowledgebase. Nucleic Acids Research 32:D115−D119

    doi: 10.1093/nar/gkh131

    CrossRef   Google Scholar

    [71]

    Guillaumot D, Guillon S, Déplanque T, Vanhee C, Gumy C, et al. 2009. The Arabidopsis TSPO-related protein is a stress and abscisic acid-regulated, endoplasmic reticulum-Golgi-localized membrane protein. The Plant Journal 60:242−56

    doi: 10.1111/j.1365-313X.2009.03950.x

    CrossRef   Google Scholar

    [72]

    Vanhee C, Zapotoczny G, Masquelier D, Ghislain M, Batoko H. 2011. The Arabidopsis multistress regulator TSPO is a heme binding membrane protein and a potential scavenger of porphyrins via an autophagy-dependent degradation mechanism. The Plant Cell 23:785−805

    doi: 10.1105/tpc.110.081570

    CrossRef   Google Scholar

    [73]

    Kaur H, Petla BP, Majee M. 2016. Small heat shock proteins: roles in development, desiccation tolerance and seed longevity. In Heat shock proteins and plants, eds. Asea A, Kaur P, Calderwood S. vol 10. Switzerland: Springer, Cham. pp. 3−18. https://doi.org/10.1007/978-3-319-46340-7_1
    [74]

    Chen H, Chu P, Zhou Y, Ding Y, Li Y, et al. 2016. Ectopic expression of NnPER1, a Nelumbo nucifera 1-cysteine peroxiredoxin antioxidant, enhances seed longevity and stress tolerance in Arabidopsis. The Plant Journal 88:608−19

    doi: 10.1111/tpj.13286

    CrossRef   Google Scholar

    [75]

    Chen X, Börner A, Xin X, Nagel M, He J, et al. 2021. Comparative proteomics at the critical node of vigor loss in wheat seeds differing in storability. Frontiers in Plant Science 12:707184

    doi: 10.3389/fpls.2021.707184

    CrossRef   Google Scholar

    [76]

    Mühlemann O, Lykke-Andersen J. 2010. How and where are nonsense mRNAs degraded in mammalian cells? RNA Biology 7:28−32

    doi: 10.4161/rna.7.1.10578

    CrossRef   Google Scholar

  • Cite this article

    Liang W, Dong H, Guo X, Rodríguez V, Cheng M, et al. 2023. Identification of long-lived and stable mRNAs in the aged seeds of wheat. Seed Biology 2:14 doi: 10.48130/SeedBio-2023-0014
    Liang W, Dong H, Guo X, Rodríguez V, Cheng M, et al. 2023. Identification of long-lived and stable mRNAs in the aged seeds of wheat. Seed Biology 2:14 doi: 10.48130/SeedBio-2023-0014
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Identification of long-lived and stable mRNAs in the aged seeds of wheat

  • 1.

    State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China

  • 2.

    Ministry of Education Key Laboratory for Crop Genetic Resources and Improvement in Southwest China, Sichuan Agricultural University, Chengdu, Sichuan 611130, China

  • 3.

    IFEVA - Departamento de Producción Vegetal - Facultad de Agronomía, Universidad de Buenos Aires, Argentina (C1111AAI), Buenos Aires, Argentina

  • Received: 28 October 2022
  • Accepted: 28 July 2023
  • Published online: 07 October 2023
Seed Biology  2,  Article number: 14  (2023)  |  Cite this article

Abstract: Seed germination relies on preserving mRNA integrity in dry seeds. However, the quality of mRNA in aged wheat seeds is not well understood. Here, we investigated 20 wheat varieties for seed longevity using controlled deterioration treatment (CDT) and identified that Chinese Spring seeds exhibit moderate longevity. We observed correlations between seed viability and RNA integrity in the aleurone and embryo cells after aging-treatment. Nanopore sequencing of whole seeds from natural aging treatment (NAT) and CDT for 25 d identified 3,083 full-length transcripts. We performed RNA-seq transcriptome profiling to classify the tissue origin of these transcripts under eight aging treatments, revealing the presence of 2,064 overlapping long-lived mRNAs (LLRs) in the seed embryo and 2,130 in the aleurone layers. These LLRs corresponded to genes with detectable transcription levels and at least one full-length transcript in their coding sequence. Notably, degradation percentages of these mRNAs varied among seeds of different wheat varieties with similar ages. We predicted 21 most stable LLRs with high GC% content and short coding sequence length, among which only one LLR was seed-specifically expressed and belonged to the late-embryogenesis-abundant (LEA) protein family. RT-PCR confirmed the expression of the seven LLR fragments in the aleurone layer and embryo of Chinese Spring seeds. We found three of the most stable LLRs (LLR13, LLR15, and LLR20) identified in Chinese Spring were more stable in high longevity varieties than in short longevity varieties after aging, indicating their potential roles in seed longevity and germination.

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    Introduction

    • The reduction in seed vigor with aging significantly impacts crop production[1−3]. Seed longevity is determined by a complex interaction between genetic, environmental, and stochastic factors[4,5]. Environmental factors, such as moisture content, relative humidity, oxygen pressure, and storage temperature, can affect seed longevity. Mature orthodox seeds are desiccation tolerant and can endure extreme conditions such as high[6] and low temperatures[7]. Even in a low hydration state, seeds age at a readily manipulated rate, making them an appropriate model for studying longevity and aging[8,9]. Moreover, seed longevity varies widely even among isogenic individuals[5,10,11].

      RNA integrity number (RIN) is a reliable marker for studying seed aging, as RNA is highly susceptible to oxidative damage[12−15]. In dry seeds, various stored mRNAs are essential for protein synthesis during the early stages of germination[13,16−18]. About 12,000-18,000 mRNAs with different functions are present in dry seed, and they have been connected to seed viability at different aging stages and seed resurrection after rehydration[19,20]. Rice seeds have been shown to contain 529 stored LLRs associated with germination capacity after inhibiting new mRNA synthesis during rehydration[21,22]. Notably, seed-stored mRNAs associated with monosomes undergo translational regulation during germination[23]. These LLRs and de novo transcribed mRNAs during rehydration are crucial to the germination process, with the former providing energy during the initial stages of germination and the latter accelerating energy production thereafter[21]. A shared locus with a positive allele was found, but the parental origin of the allele differed. Correlation analysis did not reveal any relationship between induced aging treatments and long-term storage[24−26]. However, most mRNAs showed a similar pattern of deterioration during both NAT and CDT[27]. The change in stored mRNA levels during seed aging showed that CDT seeds aged similarly to NAT seeds, but the degradation of stored mRNAs in CDT seeds occurred in a shorter time frame (years)[28].

      Bran from bread wheat (Triticum aestivum 'Babbler') contains multiple outer layers of dead maternal tissues that cover living aleurone cells[29,30]. These outer layers with dead cells act as a protective barrier against degradation, while the aleurone layer mobilizes organic substrates from the endosperm during post-germinative growth. Thus, damage to stored RNA in the aleurone cells is expected to impact post-germination and early seedling development[31−33].

      Previous studies analyzing transcriptome changes during seed storage have focused on the maturity stage after the drying phase[34]. Illumina sequencing has been used to detect stored mRNAs in dry, mature seeds, but this technology requires interruption of mRNAs before sequencing to ensure accuracy, while the length of mRNA in aging seeds (low RIN samples) can still exceed 8,000 bp[35]. As a result, short-read sequencing makes it impossible to determine whether the mRNAs were full-length in the aging seeds. However, the Oxford Nanopore platform, a third-generation technology, can directly sequence DNA/RNA with over 10 Kbps, enabling the detection of mRNA in aged seeds[36].

      Our study focuses on the presence and integrity of mRNAs in aged wheat seeds. We evaluated the longevity of a diverse collection of wheat germplasms. We evaluated the germination percentage (GP) of the wheat cultivar Chinese Spring. Using MinION Nanopore sequencing, we identified full-length LLRs in wheat seeds, and next-generation sequencing (NGS) technology was further applied to investigate their expression patterns across different tissues. We identified the most stable LLRs in the embryo and the aleurone layer, including a seed-specific LLR which belongs to the LEA protein family. We further surveyed the stability of seven LLRs in Chinese Spring seeds and 18 other wheat varieties using RT-PCR and PCR amplification. We found three of the most stable LLRs identified in Chinese Spring were more stable in high longevity varieties than in short longevity varieties after aging, indicating their potential roles in seed longevity and germination. Overall, our study provides valuable insights into the mechanisms of seed longevity and may contribute to developing more effective seed storage and preservation strategies.

    Materials and methods

      Plant materials and growth conditions

    • Twenty wheat (Triticum aestivum L.) varieties were subjected to CDT for 12 d for the purpose of germination assays and 25 d for PCR amplification (Supplemental Table S1). Chinese Spring seeds under NAT were harvested in 2013 (Guanghan, Sichuan, China; Natural Aging Treatment, NAT for 8 years), 2016 (Chongzhou, Sichuan, China; NAT for 5 years), 2018 (Chongzhou, Sichuan, China; NAT for 3 years), and 2020 (Chongzhou, Sichuan, China; NAT for 1 year). Chinese Spring seeds for CDT initiation were harvested in 2018 (Chongzhou, Sichuan, China) and then subjected to CDT for 5, 15, and 25 d in 2021.

      Fresh Chinese Spring seeds were harvested in 2021 (45 d after flowering; NAT for 0 years) from plants grown in 15 cm pots in a thermostatic growth chamber with a controlled temperature of 20/12 °C (day/night) and a 16/8 h photoperiod. Then, the seeds were naturally air-dried as a control. All seeds were stored in a dry glass jar at −80 °C.

      Aging treatment and seed germination assays

    • The initial moisture content can influence the longevity of seeds[5]. Thus, seed moisture content was determined using near-infrared transmittance (NIT; Foss-Tecator 1241, Foss, Högänas, Sweden). For CDT, 100 g of wheat seeds were wrapped in nylon bags (three replicates per accession) and subjected to a 43 °C temperature and 76% relative humidity (RH) in a climate chamber[11,37]. For NAT, the seeds were dried to a consistent moisture content and stored at room temperature for 1, 3, 5, and 8 years.

      Seeds were germinated on germination paper and incubated in the dark at 20 °C. Each germination assay starts with 50 seeds with three replicates for each accession. After 7 d of imbibition, seeds were scored as germinated when the radicle emerged from the seed coat[38]. Ni is the number of germinated seeds on Day i, and the estimated germination indices were as follows[39]: (Germination percentage) GP: N7/50. After subjecting seed from each of the 20 different varieties to identical aging treatments, the ranking of seed longevity was determined by comparing the changes in germination percentage (ΔGP%) between non-aged seeds (NAT_0Y) and aged seeds (CDT_12D). ΔGP% was calculated as:

      [(NAT_0Y_GP − CDT_12D_GP)/NAT_0Y_GP] × 100%.

      The half inhibitory time is defined as the number of days required for the aging of seeds to reach a ΔGP% of 50%[40].

      RNA extraction and RIN value assay

    • Twenty whole wheat seeds were mixed directly without tissue segmentation for RNA isolation, which was then used for Nanopore sequencing. As for Illumina RNA sequencing, each embryo and aleurone layer sample was separated from 10 and 20 dry seeds by hand-cutting, respectively. After the tissue was cut, it was placed directly under the microscope and slices frozen for observation (Supplemental Fig. S1a, S1b). For samples that were used for RNA extraction, we rapidly separated the tissues on dry ice. We immediately transferred them into liquid nitrogen for storage at −80 °C to prevent RNA degradation and ensure accurate downstream analysis. Real-Time Quantitative Polymerase Chain Reaction (qPCR) was employed to validate the expression of tissue-specific genes and provide the isolated tissue's purity (Supplemental Fig. S1c). RNA extraction followed the Nanopore and Next-generation sequencing (NGS) protocol. Total RNA was extracted with the Befitt kit (Invitrogen, California, USA). The quantity and quality of the extracted RNA were determined using a Nanodrop 2000 spectrophotometer (Thermo Scientific, USA) and verified using an Agilent 2100 bioanalyzer (Agilent Technologies, USA). Then RNA was stored at −80 °C for later use. The RNA Integrity Number (RIN) was calculated for RNA extracted from Chinese Spring, Aikang 58, and Zhengmai 366 seeds before and after aging.

      Nanopore sequencing and identification of full-length LLRs

    • The poly(A) mRNAs Magnetic Isolation Module of VAHTS mRNA Capture Beads (Vazyme, Nanjing, China) was used to enrich mRNAs according to the manufacturer's protocol. Approximately 37.5 µg of total RNA was used for each sample. The final poly-A+ RNA concentration was measured using a Quantus Fluorometer (Promega Corporation, Madison, WI, USA) and checked by an Agilent 2100 bioanalyzer (Agilent Technologies, USA).

      Synthesis of cDNA for sequencing was performed by following the strand-switching protocol from Oxford Nanopore Technologies. With the protocol, an incomplete cDNA sequence should arise from an incomplete or fragmented template[41]. According to Oxford Nanopore protocols, libraries were barcoded, pooled, and prepared for sequencing. Briefly, each library pool consists of two samples, CK and CDT_25D, and was sequenced on a MinION SpotON Flow Cell MK I (R9.4) (Oxford Nanopore Technologies, Oxford, UK). Sequencing data were obtained using Albacore 0.8.4 (Oxford Nanopore Technologies). Reads were de-multiplexed based on the barcode using porkchop 0.2.0 (https://github.com/rrwick/Porechop, released 3/27/2017) with default settings. Blast (https://ftp.ncbi.nlm.nih.gov/blast/) was used to compare aligned reads and reference transcript lengths (iwgsc_refseqv1.1_ annotation_200916_HCLC_cds.fa) to identify transcripts with at least one sequence alignment in all samples. The integrity of the read was normalized between 0 and 100%, and an identity ≥ 90% was considered[36]. The sequence with the highest completeness was selected as the representative of the transcript, and this value represents the best transcript performance after decay.

      NGS and identification of stored mRNAs

    • The NGS was performed on equal molar amounts of the RNA libraries using the Illumina HiSeq-2000 and Hi-Seq Ten platforms by Berry Genomics Co., Ltd. Three sets of RNA-seq data were replicated and combined for analysis. The fluorescence image files were converted into short reads through base calling and stored in FASTQ format. The data processing followed the instructions provided by Berry Genomics in Beijing (China).

      Chinese Spring seeds were divided into aleurone layers and embryos after undergoing NAT (0, 1, 3, 5, and 8 years) and CDT (5, 15, and 25 d) and then sent for sequencing, respectively. Quality control was based on the Q30 (> 80%), GC content (50%~60%), and sequence duplication levels of the clean data. Principal component analysis (PCA) and a correlation heatmap were performed on all samples to demonstrate the reproducibility and usability of the data. Transcripts per million (TPM) calculations were performed by Kallisto[42] and compared to the reference transcriptome. The overlapping genes from CDT and NAT samples with a detectable transcripts level (TPM ≥ 1) were identified as candidates for LLRs. The average TPM values of the three replicates were calculated as the transcript level of genes in each sample (except A25_Embryo_1).

      After TPM normalization, genes with a detectable transcripts level (TPM ≥ 1) or not detectable (TPM < 1) were identified. The genes that were significant differences in transcript levels (log2 fold change (FC) ≥ 1 or log2FC ≤ −1, FDR < 0.05) were analyzed by the EdgR package (version 3.18.1) of the Trimmed Mean of M-values (TMM) algorithm[43]. After the data are normalized to equalize expression level distributions between samples, the stable transcripts attain elevated read counts in degraded samples[44]. For each most stable LLR, read depth was plotted against the base pair position to establish the distribution of sequence lengths, which was used to analyze the degradation of the transcripts. The data for other tissues (anther, leaf, pistils, root, shell, and stem) and for the developing embryo (2−38 d) and developing endosperm (8−32 d) were obtained from WheatGmap (www.wheatgmap.org) and Wheatomics[45].

      Sequence analysis of the most stable LLRs

    • The wheat gene promoter, 5' untranslated regions, 3' untranslated regions, coding sequence, and protein sequences were extracted from the IWGSC Genome (GFF, GTF) in NCBI (www.ncbi.nlm.nih.gov) and TBtools (GXF sequences extract function)[46]. The sequence 2000 bp upstream of the start codon was used for cis-acting element analysis by Plantcare (https://bioinformatics.psb.ugent.be/webtools/plantcare/html/). MEME was used to identify the enriched motifs in the coding sequence and protein sequences (https://meme-suite.org/meme/). Briefly, the classical mode was selected for motif discovery. Sequences were uploaded into the primary sequence box. The motif length was set to 6–9 bp. The average GC% content and coding sequence length of the most stable LLRs were analyzed by Notepad++, and the frequencies of the background genes (high-confidence genes) were also calculated.

      Functional analysis of LLRs

    • For Gene Ontology (GO) enrichment analysis, TGT (https://wheat.cau.edu.cn/TGT/) was used to assess the LLRs with log2FC < 0 and LLRs with log2FC ≥ 1 and FDR < 0.05 set, respectively. The function of proteins was retrieved using UniProt (www.UniProt.org) and WheatOmics databases[45].

      qPCR and RT-PCR analyses

    • The primers of genes used for assessing tissue separation purity and screening for stored mRNAs through qPCR and RT-PCR can be found in Supplemental Table S2. The total RNA of the wheat root, stem, leaf, embryo, and aleurone layer was reverse-transcribed into cDNA using HiScript® III RT SuperMix (Vazyme). One microgram of RNA was incubated with a 4× gDNA wiper mix at 42 °C for 2 min to remove genomic DNA. Then, 5× HiScript III qRT SuperMix was added to the reaction and incubated at 37 °C for 15 min, followed by 85 °C for 5 s to synthesize cDNA. The cDNA mixture was diluted 1:4 with sterile H2O. The qPCR experiment was carried out using the ChamQ Universal SYBR qPCR Master mix (Vazyme) as the reaction reagent. The reaction mixture, comprising 12.5 μl of the master mix, 0.4 μM of each primer, and 1 μl of cDNA, was prepared to a final reaction volume of 20 μl. The real-time qPCR was performed on a Bio-Rad CFX96 Real-Time system, and the TBtools (Simple q-PCR summary) software was used to calculate the relative expression levels. The total RNA of 19 of 20 wheat varieties (except ZM121, for which seeds were not enough for this assay) was reverse-transcribed into cDNA for PCR amplification. PCR products were separated by electrophoresis in 3% agarose gels and stained with ethidium bromide. The gel images were obtained with a BioDoc-It imaging document system and used without any modifications (except for cropping to show the DNA band). The gene amplification data were converted into a heat map, where successful amplification in at least two out of three replicate experiments is shown in dark blue, and unsuccessful amplification is in light blue.

      Statistical analysis

    • All analyses were performed in WPS and GraphPad Prism with one-way ANOVA (Tukey's test) in homogeneous groups, assuming significant differences when p < 0.05 and p < 0.01.

    Results

      Variation of seed longevity in wheat

    • The longevity of 20 wheat varieties was evaluated based on their seed germination percentage (GP) after 12 d of CDT (Fig. 1a). Near-infrared transmittance measurements showed increased seed moisture content after CDT (Supplemental Fig. S2a), but no significant correlation was found between moisture content and GP. Ten of the varieties showed high seed longevity (ΔGP% ≤ 50%), indicating that the aging treatment time was lower than half of the inhibitory time[40] (Fig. 1a, for the definition of 'inhibitory time' see the Materials and methods section, 'Aging treatment and seed germination assays'). These results suggest that genetic differences among the wheat varieties may significantly contribute to the variation in GP (Fig. 1b).

      Figure 1. 

      The germination percentage (GP) of different wheat accessions. (a) The change of germination percentages of 20 wheat varieties after 12 d of controlled deterioration treatment (CDT) (ΔGP%). Data are the means ± standard deviations (SDs) based on three biological replicates. (b) A correlation heatmap between moisture content and GP. CK_GP indicates the GP of non-aged seeds; CDT_GP indicates the GP of seeds that CDT for 12 d; CK_MC indicates the moisture content of seeds before aging; CDT_MC indicates the moisture content of seeds after CDT for 12 d; ΔGP suggests the change of germination during CDT; ΔMC indicates the change in moisture content of seeds during CDT. (c) The GP of six wheat varieties after CDT_12D; bar = 5 cm. (d), (e) The GP of Chinese Spring seeds under NAT and CDT; bar = 5 cm; compared to NAT_0Y seeds; ns, not significant; **, p < 0.01, one-way ANOVA with Tukey's test.

      The varieties with different seed longevity were selected to investigate changes in GP after CDT. Chinese Spring was chosen for subsequent studies due to its wide use in cytogenetic analysis and moderate longevity in our study. In Chinese Spring seeds, the GP of NAT seeds decreased slowly during aging, while the viability of CDT seeds decreased significantly. After 25 d of CDT, the seeds nearly lost their germination capacity (Fig. 1d, e). Therefore, the CDT method lets us obtain seeds of different ages quickly.

      Correlation between seed longevity and RNA integrity in embryos and aleurone

    • The relationship between seed longevity and RNA integrity number (RIN) was analyzed using total RNA from the embryo and aleurone layer of Chinese Spring seeds under NAT (0, 1, 3, 5, and 8 years) and CDT (5, 15, and 25 d). The mean RIN was significantly reduced, particularly in the embryo under CDT conditions (Fig. 2a). GP was positively correlated with RIN in both the aleurone layer (R2 = 0.70) and embryo (R2 = 0.45) in Chinese Spring seeds (Fig. 2b). The RIN values were compared between the high longevity cultivar Zhengmai 366 and the low longevity cultivar Aikang 58 after 12 d of CDT, and a reduction in GP was associated with a decrease in RIN value (Supplemental Fig. S2b). These findings suggest that stored mRNAs in embryos and the aleurone layer contribute to seed longevity and germination.

      Figure 2. 

      The RNA integrity (RIN) assays of Chinese Spring seeds. (a) RIN of Chinese Spring seeds (embryo and aleurone) under natural aging treatment (NAT) for 1, 3, and 8 years (compared to NAT_0Y) and controlled deterioration treatment (CDT) for 0, 5, 15, and 25 d (compared to CDT_0D). CDT for 0-d seeds was NAT for 3 years seeds. ns, not significant; *, p < 0.05; **, p < 0.01 one-way ANOVA with Tukey's test. (b) Correlation analysis between RIN and germination percentage of Chinese Spring seeds under NAT (0, 1, 3, 8 years) and CDT (0, 5, 15, 25 d) (Pearson's R2).

      Identification of the full-length mRNAs by Nanopore sequencing

    • We used a long-read Nanopore sequencing platform to detect full-length mRNAs in aged seeds. The mRNAs were disrupted into small fragments before Illumina sequencing, so we chose Nanopore sequencing to obtain a more complete picture of the mRNA landscape in aged seeds. We analyzed samples from Chinese Spring seeds that were either NAT_0Y or subjected to CDT_25D. We obtained 205,991 and 115,606 sequences mapped to the reference transcriptome (iwgsc_ refseqv1.1_ annotation_200916 _HCLC_ cds. fa), respectively (Supplemental Table S3). We then kept 40,818 and 20,848 full-length stored mRNAs with identity ≥ 90% (Supplemental Table S3). A significantly higher amount of full-length mRNAs was detected in NAT_0Y (16,787 transcripts) than in the CDT_25D seeds (4,611 transcripts), suggesting that the mRNAs were largely degraded under CDT for 25 d (Supplemental Table S3). We identified 3,083 common full-length transcripts in NAT_0Y and CDT_25D seeds as LLR candidates (Supplemental Table S4, Fig. 3a).

      Figure 3. 

      Identification of full-length long-lived mRNA (LLRs) and validation of transcript levels by Nanopore sequencing and Illumina RNA-seq. (a) Comparative analysis of full-length mRNA in seeds of NAT_0Y and CDT_25D. The blue portion represents 3083 LLRs identified in both samples with at least one full-length transcript. (b) The transcriptomic profile of 3083 full-length LLRs identified by Nanopore sequencing was analyzed. LLRs with a TPM ≥ 1 were considered to have detectable transcript levels. These LLRs were then selected by identifying overlapping genes in all eight aging gradient samples. Aleurone-specific LLRs were annotated with green, embryo-specific LLRs with blue, and LLRs in both tissues were annotated with yellow.

      Transcriptome profile of wheat embryo and aleurone layer after CDT and NAT

    • To ensure the purity of tissue separation, we selected four genes with distinct expression patterns between embryos and aleurone layers based on the public transcriptome data from Wheatomics[45]. We performed qPCR analysis to confirm a significant difference in relative transcription levels between these two tissues (Supplemental Fig. S1c). We conducted RNA-Seq using the embryo and aleurone layer of Chinese Spring seeds after CDT and NAT to further investigate the transcriptome changes during seed aging. We generated 3.1 billion high-quality clean reads, with Q30 values ranging from 87% to 94%, and the mean GC% content was 55%~61% (Supplemental Table S5). Principal component analysis (PCA) and correlation heatmap showed that these RNA-seq samples could be separated into four categories based on aging treatments and tissues (Supplemental Fig. S1d, S1e). The results showed that gene transcription levels gradually decreased in both the embryo and the aleurone layer during seed aging (Supplemental Fig. S3a, S3b). We identified 19,736 stored mRNAs in the embryo and 21,433 in the aleurone layer with TPM ≥ 1 in both NAT and CDT seeds (Supplemental Fig. S3c; Supplemental Table S6). The transcription of 3083 LLRs detected by Nanopore sequencing was supported by Illumina RNA-seq. We identified 2,130 and 2,064 LLRs in the aleurone layer and embryo, respectively, and 1,950 LLRs were shared in both tissues (Fig. 3b; Supplemental Table S7).

      Degradation percentage of LLRs

    • To assess the stability of LLRs under NAT (0, 1, 3, 5, and 8 years) and CDT (5, 15, and 25 d) conditions, we measured the gene fold change (FC) using Illumina RNA-seq. In the aleurone layer, there were more LLRs with log2FC ≥ 0 than LLRs with log2FC < 0 under both NAT and CDT conditions (Fig. 4a). However, in the embryo, the number of LLRs with log2FC ≥ 0 decreased during seed aging and was less than that of LLRs with log2FC < 0 in CDT_25D seeds (Fig. 4b). These results suggested that the stability of LLRs differs between the aleurone layer and embryo and that LLRs in the aleurone layer were generally more stable than those in the embryo under aging conditions.

      Figure 4. 

      The degradation pattern of long-lived mRNA (LLRs). (a), (b) LLRs with different fold change (FC) among groups of comparison. (c) This refers to the count of genes that overlap with the same FC across different aging samples from the same tissue. Specifically, the samples include natural aging treatment (NAT) for 0, 1, 3, 5, and 8 years and controlled deterioration treatment (CDT) for 5, 15, and 25 d seeds. (d) Venn diagram comparisons of the most stable LLRs in the embryo and aleurone layer. The most stable LLRs were the overlapping LLRs with log2FC ≥ 1 and FDR < 0.05 between NAT_3Y and CDT (5, 15, and 25 d), which were selected from the overlapping LLRs with log2FC ≥ 1 and FDR < 0.05 (compared to NAT_0Y) as mentioned in Fig. 4c.

      Further analysis demonstrated that the LLRs had different FCs across various seed aging samples and fresh NAT_0Y seeds, likely due to differences in their Transcripts Per Million (TPMs) (Supplemental Table S6, S7). We examined the overlapping LLRs among all aged samples to identify LLRs with similar FC features in aleurone layers or embryos. We found that a significant portion of genes (14.80% from the aleurone layer and 49.00% from the embryo) were LLRs with log2FC < 0. Several LLRs log2FC ≤ 1 and FDR < 0.05 were identified in all overlapping LLR sets (Fig. 4c; Supplemental Table S8). These LLRs with log2FC < 0 were associated with translation and transportation, as shown by Go enrichment analysis (Supplemental Fig. S4a, S4b; Supplemental Table S9). In contrast, LLRs with log2FC ≥ 1, FDR < 0.05 were associated with salt stress, heat, protein folding, reactive oxygen species, protein complex oligomerization, and abscisic acid metabolism (Supplemental Fig. S4c, S4d; Supplemental Table S9). LLRs with log2FC ≥ 1 and FDR < 0.05 may be more stable (Supplemental Table S8) since elevated read counts were obtained in the degraded samples. In addition, the TPMs of LLRs with log2FC ≥ 1 and FDR < 0.05 were relatively higher in NAT_3Y seeds than in NAT_0Y (Supplemental Table S7). Therefore, these LLRs with significantly increased transcripts level that still meets the criteria of log2FC ≥ 1 and FDR < 0.05 when compared to CDT_5D, CDT_15D, and CDT_25D samples with NAT_3Y may have the highest stability during aging. Finally, we identified 24 LLRs with the highest stability during aging (Fig. 4d), among which three were low-confidence genes. So only 21 LLRs were subjected to further analysis. Analysis of these 21 transcripts in embryo and aleurone layer tissues of fresh (NAT_0Y), NAT_3Y, and CDT_25D seeds showed a uniform distribution rather than a gradual increase from 5' to 3' end, eliminating the sequencing bias of Illumina RNA-seq. This result also demonstrated the full-length characteristic of these most stable LLRs (Supplemental Fig. S5).

      Characterization of the most stable LLRs

    • The length of the coding sequence for high-confidence genes varied from 54 to 16,080 bp[45], whereas the 21 most stable LLRs identified in this study had coding sequence lengths of 336−720 bp in the aleurone layer and 195−591 bp in the embryo (Fig. 5a−c). Of all high-confidence genes, 74,181 had coding sequence lengths greater than 720 bp. In contrast, the length of 33,711 genes is not longer than 720 bp. Although the number of transcripts with length longer than 720 bp was 2.2 times greater than those shorter than 720 bp, none of them were identified as the most stable LLR, indicating that shorter transcripts may be more stable during seed aging in wheat (Fig. 5a−c). Furthermore, the average GC% contents, which is a reason for the stability of transcript, of the LLRs in the aleurone layer and embryo (approximately 75%) was higher than that of all high-confidence genes (approximately 55%), suggesting that the gene with higher GC% content may be more stable during seed aging (Fig. 5d).

      Figure 5. 

      Characterization of the most stable long-lived mRNAs (LLRs) in the embryo and aleurone layers. (a), (b), (c) Frequency analysis of the coding sequence length (x-axis) and gene numbers (y-axis). (a) Total high confidence genes. (b) The most stable LLRs in the aleurone layer. (c) The most stable LLRs in the embryo. (d) The average GC (%) content of all the high confidence genes in wheat seeds and the most stable LLRs in the wheat embryo and aleurone layer. (e) Heatmap visualization of RNA-seq data (Chinese Spring) from anther, leaf, pistils, root, shell, stem, the developing embryo (2−38 d), developing endosperm (8−32 d), NAT (0, 1, 3, 5, and 8 years) seeds, and CDT (5, 15, and 25 d) seeds. Embryo, EM; Aleurone layer, Al; Endosperm, En. LLRs in both embryo and aleurone layer,m Em&AL. (f) The relative expression levels of TraesCS7D02G026400 in root, stem, and leaf were determined by qPCR using actin as an internal reference gene. Two independent experiments were performed with triplicate samples in each experiment. The relative expression levels were calculated using the TBtools (Simple q-PCR summary) software. Using different lowercase letters to represent significant differences (p < 0.01, one-way ANOVA with Tukey's test).

      The promoters of the 21 most stable LLRs were found to contain motifs associated with various plant hormone responses, including abscisic acid response (ABRE), light response (G-box), and hormone response (Supplemental Fig. S6, Supplemental Table S10). The promoter of 17 genes contains motifs related to cis-acting regulatory elements involved in methyl jasmonate (MeJA) response (CGTCA motif and TGACG motif) (Supplemental Fig. S6). Additionally, motifs related to gibberellin response (P-BOX) and auxin response (TGA elements) were identified in three of the four LLRs that were detected in both the aleurone layer and embryo (Supplemental Fig. S6).

      The four most stable LLRs in the embryo and aleurone layer were annotated as members of the LEA 1 protein family (TraesCS7A02G439200), TSPO/MBR-related protein family (TraesCS1A02G093000), and OSIGBa0113113.5 protein (TraesCS7D02G476800, TraesCS7B02G393700) (Fig. 5e ). The expression analysis in anther, leaf, pistils, root, shell, stem, embryo, endosperm, NAT, and CDT wheat seeds indicated that TraesCS7D02G026400 was a seed-specific gene as well as an embryo-specific stable LLR in RNA seq data (Fig. 5e; Supplemental Table S11). The qPCR results showed a significantly higher relative mRNA level of TraesCS7D02G026400 in the embryo and aleurone layer compared with the root, stem, and leaf (Fig. 5f, Supplemental Table S2). None of the other 20 most stable LLRs was found to be seed-specific (Fig. 5e; Supplemental Table S11).

      Degradation of LLRs among different wheat varieties

    • We tried to design primers for the 21 most stable LLRs based on the Chinese Spring genome sequence, but their high GC content brought challenges for primer design and PCR amplification. Therefore, we selected 7 LLRs (LLR 2, 3, 10, 11, 13, 15, and 20) to measure their stability in 18 wheat varieties after aging (Fig. 6a). To evaluate the stability of LLRs, we used three genes previously shown to degrade gradually as wheat seeds aged, and they belonged to the short-lived mRNA (SLR) set in our study[20]. After natural aging for 3 years, SLRs could be amplified in more than ten varieties. However, after CDT for 25 days, all three SLRs were no longer detectable in any of the 18 wheat varieties (Fig. 6b). In contrast, all seven LLRs could be successfully amplified in more than 11 varieties after both aging treatments (Supplemental Fig. S7). These results suggest that LLRs are relatively stable during seed aging, even in different wheat varieties.

      Figure 6. 

      The transcript levels of the most stable long-lived mRNAs (LLRs) in naturally aged treatment (NAT) and controlled deterioration treatment (CDT) wheat seeds detected by RT-PCR. The number of PCR cycles varied depending on their mRNA abundance in the unaged seeds. PCR products were run in agarose gels. (a) The 21 most stable LLRs in NAT for 0 Y (NAT_0Y) and CDT for 25 d (CDT_25D) Chinese Spring seeds were analyzed, respectively (see Supplemental Table S2 for the gene list). The 10 genes in 18 wheat varieties that NAT for 3 years (NAT_3Y) seeds and CDT_25D seeds were analyzed. (See Supplemental Table S1 for the wheat cultivar code and Supplemental Table S2 for the gene list). The lower wheat cultivar code number indicates higher seed longevity in (b).

      Furthermore, to investigate the variation in seed longevity, we examined the stability of LLR13, LLR15, and LLR20 in four wheat varieties (SM691, AK58, ZM366, and XM36) with different degrees of reduction in seed germination percentage (ΔGP) after 25 d of CDT treatment. The results showed that LLR13, LLR15, and LLR20 degraded significantly in SM691 (ΔGP = 97.79%) and AK58 (ΔGP = 97.99%) but persisted in ZM366 (ΔGP = 4.11%) and XM36 (ΔGP = 10.75%) (Supplemental Fig. S7). These findings suggest that the presence or absence of specific combinations of LLRs can serve as molecular markers to estimate seed longevity, and LLR13, LLR15, and LLR20 may be promising candidates for further investigation into their roles in seed aging and longevity.

    Discussion

      The aleurone layer is essential for germination and post-germination events

    • Seed germination refers to the physiological process culminating in the emergence of the embryo from its enclosing coverings, including the endosperm, perisperm, testa, or pericarp. Starch degradation, initiated by GA secreted by the embryo during germination, is considered a post-germinative event[31,32]. The scutellum, rather than the aleurone epithelium, is mainly responsible for the synthesis of α-amylase during the initial stages of germination in wheat, rye, oats, and maize[47]. However, any malfunctioning embryo or aleurone sections can affect seed germination[48]. Seed GP was correlated with the aleurone layer RIN and the embryo RIN (Fig. 2b). The mutagenic substances formed during aging would act early during the germination of seeds. The deleterious effects of aged endosperm on a young embryo[49] might be related to the decreased activity of antioxidant enzymes, such as catalase, peroxidase, dehydrogenase, and amylase[50]. The accumulation of toxic compounds in the aged endosperm or aleurone can induce chromosomal breakage in young embryos[51]. The response of aleurone layers from normal and aged seeds to heat shock has been investigated. Only aleurone layers from normally germinated seeds could recommence substantial α-amylase synthesis during recovery[52]. One of the LLRs identified in the aleurone layer was an oleosin family protein (TraesCS7A02G234100) (Fig. 5e), which may be involved in oil body mobilization during post-germinative seedling growth and may prevent the coalescence of protein storage vacuoles[53−55]. Our study found that the aleurone layer had more stored mRNAs and LLRs in aged seeds than the embryos (Supplemental Fig. S3a, S3b; Fig. 4c). Lipid oxidation has been implicated in seed deterioration, and detailed analyses of the changes in the lipidome during long-term dry storage of a range of genotypes of oilseed rape wheat, barley and Arabidopsis support this claim[56−58]. The lipid content of wheat embryo (8%−15%) is higher than that of other seed tissues (bran, aleurone, and endosperm; 6.8%−7.5%)[59]. Additionally, the embryos and endosperms or aleurone layer have different enzymatic patterns, highlighting that the two seed compartments age independently[6]. These differences between embryos and endosperm (aleurone layer) may cause varying degradation percentages of mRNAs (Fig. 4a, b).

      Integrating NGS and full-length sequencing to obtain LLRs accurately

    • Our study observed reduced RNA integrity in the embryo and aleurone layer of aged seeds (Fig. 2a), with the lowest RIN being 5.5 (Fig. 2a). Despite a RIN lower than 3, the length of mRNA is still longer than 8000 bp[35]. Although Illumina's TPM and FC can predict mRNA degradation trends[40,44], mRNA fragmentation errors may exist with short-read sequencing. Therefore, it is difficult to determine whether the interruption of mRNAs is caused by seed aging or by the sequencing technology used, as Illumina technology can interrupt mRNAs before sequencing (Supplemental Table S5). By using the NEBNext Poly (A) mRNA magnetic isolation module and cDNA synthesis, Nanopore full-length sequencing was employed in our study to enrich and identify mRNAs that remain intact during aging[41]. Integrating Nanopore and Illumina sequencing enables the identification of LLRs with at least one full-length transcript and predicts mRNA degradation trends in aged seeds. Thus, our approach can effectively exclude the effects of mRNA fragmentation errors, leading to more accurate identification of LLRs. In conclusion, LLRs can be predicted by the FC determined by short-read sequencing[44], and fragment mRNA errors can be excluded by full-length Nanopore sequencing[36], demonstrating the integration of both sequencing technologies is a powerful tool for identifying stable mRNAs in aged seeds.

      LLRs may be related to cell survival and seed longevity

    • Poly(A) polymerase activity decreases with age, and the translational levels decrease in aged wheat embryos[60,61]. Transcript degradation of the elongation factor EF-1 occurs both in the embryo during NAT and CDT but still exists in the embryo and aleurone layer in CDT_25D (Supplemental Table S6). A longevity-related QTL (Q.Lng.ipk.2A.1) contains a candidate gene similar to the translation elongation factor EFG/EF2 protein[62]. Transcripts related to ribosomal functions, particularly translation, are overrepresented in the stable mRNAs group and may indicate the importance of reconstituting the translational machinery during germination[3]. Among the analyzed mRNAs, 21 selected LLRs were more stable (Fig. 4d). The coding sequence of these LLRs was enriched with three repeats of the sequence TCCTCCTCC, which might be related to transcription factor IIIA and ribosomal protein L5[63]. The ribosomal L34e and preprotein translocase family proteins mRNA were detected as the aleurone layer's most stable LLRs (Fig. 5e). The longevity markers 7D (Wpt-0934) and 7A (wPt-0303) also reveal the relationship between ribosomal proteins and seed longevity[64].

      In the aleurone layer, VIP1 was identified as the most stable LLR. It plays a role in the osmosensory response by binding to the 5'-AGCTGT/G-3' DNA sequence and is found in the promoters of the hypoosmolarity-responsive genes CYP707A1 and CYP707A3[65]. LEA 1, TSPO, and OSIGBa0113113.5 were identified as the most stable LLRs in both embryos and aleurone layers (Fig. 5e). The seed-specific expressed gene (TraesCS7D02G026400) is annotated as an LEA 1 family protein (Fig. 5e). The LEA 1 proteins, which have evolutionary and functional characteristics of an ancestral plant protein group, are also present in other eukaryotes and the Archaea and Bacteria domains[66]. In Arabidopsis, maize, and Medicago, LEA 1 protein is correlated with seed vigor and longevity[67−69]. Wheat seed longevity markers on 4B (wPt-1272) have identified some genes described as dehydrin-/LEA group proteins[64]. TSPO (Fig. 5e) expression seems to be correlated with LEA4-5 protein (TraesCS7A02G439200) expression in Arabidopsis[70]. TSPO is a stress-induced, posttranslationally regulated, and early secretory pathway-localized plant cell membrane protein involved in transient intracellular ABA-dependent stress signaling and has roles in apoptosis[71,72]. LLR 13, 15, and 20 were more stable in high longevity varieties than short longevity varieties after aging (Supplemental Fig. S7), suggesting that these stable LLRs may contribute to seed survival[40]. In addition to the 21 most stable mRNAs, several LLRs with log2FC ≥ 0 were identified in both the embryos and the aleurone layer, and they may be necessary for seed longevity. For example, the heat shock protein (HSP) and 1-cysteine peroxiredoxin antioxidant (PER1) were identified as LLRs (Fig. 4c, Supplemental Table S8). The heat shock protein OsHSP18.2 improved seed longevity under CDT[73]. A PER1 protein from Nelumbo nucifera enhances seed longevity and stress tolerance in Arabidopsis, and the PER1 protein is stable in high-vigor wheat after aging treatment[74,75]. A multi-omic study revealed a bZIP23-PER1A–mediated detoxification pathway to enhance seed vigor in rice[27]. These mRNAs existed after NAT and CDT, but the molecular mechanisms responsible for their role in wheat seed longevity and germination have not yet been clarified.

      Seeds translate stored mRNAs during germination using stored ribosomes, and RNA integrity is closely related to seed vigor[19,21]. The germination of dry wheat seeds correlates with the embryo and the living aleurone cell mRNAs[30]. Our study identified specific LLRs related to longevity by comparing high-vigor and low-vigor varieties, and we examined the degradation rates of mRNA by transcriptome profiling[40]. We verified full-length LLRs using Nanopore sequencing[36,44]. While LLRs have a short and high GC content, the protected manner of mRNAs results in mRNAs having variant degradation percentages[23,76]. However, fission due to free radical attacks at random bases is also evident[20,36]. Further investigation is necessary to uncover the complex roles of these LLRs in seed longevity and the mechanism of seed resurrection. Overall, our study provides valuable insights into the mechanisms of plant cell survival and may contribute to developing more effective seed storage and preservation strategies.

      Acknowledgments

      • This research was funded by the Major Program of National Agricultural Science and Technology of China (NK20220607), the National Natural Science Foundation of China (U22A20472), the National Key Research and Development Program of China (2018YFE0112000), the Sichuan Science and Technology Support Project (2021YFH0077; 2021YFYZ0027; 23NSFSC0770), the Science and Technology Support Project of Chengdu (2021-GH03-00002-HZ) and the open research fund of SKL-CGEUSC (SKL-ZD202212).

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Supplementary information
      • Supplemental Fig. S1 Tissue purity and correlation between biological replicates in Illumina RNA-seq.
      • Supplemental Fig. S2 The water content and RNA integrity number (RIN) of different varieties.
      • Supplemental Fig. S3 Identification of stored mRNA in aged wheat seeds by Illumina RNA-seq.
      • Supplemental Fig. S4 Gene Ontology (GO) enrichment of overlapping long-lived mRNAs (LLRs).
      • Supplemental Fig. S5 The sequencing depth from 5' to 3' of the most stable long-lived mRNAs (LLRs) and easily degraded mRNAs.
      • Supplemental Fig. S6 Sequence analysis of the most stable long-lived mRNAs (LLRs).
      • Supplemental Fig. S7 The correlation between longevity and the most stable long-lived mRNAs (LLRs).
      • Supplemental Table S1 Wheat accessions used in this study.
      • Supplemental Table S2 Quality of Nanopore-Seq data obtained in this study for NAT_0Y (fresh seeds) and CDT_25D seeds.
      • Supplemental Table S3 Full-length long-lived mRNAs (LLRs) identified by Nanopore-seq data.
      • Supplemental Table S4 Quality of Illumina RNA-seq data.
      • Supplemental Table S5 Identification of stored mRNAs in embryo and aleurone layer using Illumina RNA-seq.
      • Supplemental Table S6 Transcripts Per Million (TPM) analysis of full-length long-lived mRNAs (LLRs) in embryo and aleurone layers using Illumina RNA-seq.
      • Supplemental Table S7 long-lived mRNAs (LLRs) with different fold changes (FC) in the embryos and aleurone layers compared with NAT_0Y.
      • Supplemental Table S8 Gene Ontology (GO) enrichment analysis of overlapping long-lived mRNAs (LLRs) with relatively decreased and significantly increased transcript levels in embryos and aleurone layers.
      • Supplemental Table S9 Motifs in the promoter of the most stable long-lived mRNAs.
      • Supplemental Table S10 Expression profiles of the most stable long-lived mRNAs in different tissues and treatments.
      • Supplemental Table S11 List of genes and primers used for Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Real-Time Quantitative Polymerase Chain Reaction (qPCR) experiments.
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      • Copyright: © 2023 by the author(s). Published by Maximum Academic Press on behalf of Hainan Yazhou Bay Seed Laboratory. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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    Liang W, Dong H, Guo X, Rodríguez V, Cheng M, et al. 2023. Identification of long-lived and stable mRNAs in the aged seeds of wheat. Seed Biology 2:14 doi: 10.48130/SeedBio-2023-0014
    Liang W, Dong H, Guo X, Rodríguez V, Cheng M, et al. 2023. Identification of long-lived and stable mRNAs in the aged seeds of wheat. Seed Biology 2:14 doi: 10.48130/SeedBio-2023-0014
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  • Introduction
  • Materials and methods

  • Plant materials and growth conditions

  • Aging treatment and seed germination assays

  • RNA extraction and RIN value assay

  • Nanopore sequencing and identification of full-length LLRs

  • NGS and identification of stored mRNAs

  • Sequence analysis of the most stable LLRs

  • Functional analysis of LLRs

  • qPCR and RT-PCR analyses

  • Statistical analysis

  • Results

  • Variation of seed longevity in wheat

  • Correlation between seed longevity and RNA integrity in embryos and aleurone

  • Identification of the full-length mRNAs by Nanopore sequencing

  • Transcriptome profile of wheat embryo and aleurone layer after CDT and NAT

  • Degradation percentage of LLRs

  • Characterization of the most stable LLRs

  • Degradation of LLRs among different wheat varieties

  • Discussion

  • The aleurone layer is essential for germination and post-germination events

  • Integrating NGS and full-length sequencing to obtain LLRs accurately

  • LLRs may be related to cell survival and seed longevity

  • Acknowledgments
  • Conflict of interest
  • Supplementary information
  • Rights and permissions
  • About this article
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