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Plant Molecular Biology and Biotechnology Laboratory, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC, Australia

Drought is one of the significant abiotic stresses threatening crop production worldwide. Soybean is a major legume crop with immense economic significance, but its production is highly dependent on optimum rainfall or abundant irrigation. Also, in dry periods, it may require supplemental irrigation for drought-susceptible soybean varieties. The effects of drought stress on soybean including osmotic adjustments, growth morphology and yield loss have been well studied. In addition, drought-resistant soybean cultivars have been investigated for revealing the mechanisms of tolerance and survival. Advanced high-throughput technologies have yielded remarkable phenotypic and genetic information for producing drought-tolerant soybean cultivars, either through molecular breeding or transgenic approaches. Further, transcriptomics and functional genomics have led to the characterisation of new genes or gene families controlling drought response. Interestingly, genetically modified drought-smart soybeans are just beginning to be released for field applications cultivation. In this review, we focus on breeding and genetic engineering approaches that have successfully led to the development of drought-tolerant soybeans for commercial use.

Introduction

Soybean, an important legume, is one of the most widely grown food crops in the world due to its valuable seed composition. In 2019, the annual global soybean production was estimated to be above 333 million tones ( Faostat, 2019). Soybean provides an inexpensive source of protein and fats, and natural nitrogen fertilisation for the soil ( Foyer et al., 2016). Interestingly, the economic benefits derived from soybean cultivation are not just limited to the food supply; it is also an important industrial crop utilised in producing edible oils, wax, paints, dyes and fibre ( Rezaei et al., 2002; Raghuvanshi and Bisht, 2010). Also, meat substitutes based on soybean are extensively used by vegan and vegetarian consumers ( Messina and Messina, 2010; Raghuvanshi and Bisht, 2010; Tang, 2017).

Soybean is grown mainly in tropical, subtropical and temperate regions ( Fao, 2021). It is a water-intensive crop, requiring substantial water to grow and reproduce ( Bhardwaj, 1986). Consequently, rising global temperatures and changing precipitation patterns pose a significant threat to soybean production, especially in under-irrigated or rainfed areas ( Jin et al., 2017; Cotrim et al., 2021). It is known that under dry conditions or drought, soybean yield can reduce by more than 50%, causing substantial financial losses to farmers and growers ( Wei et al., 2018). Hence, drought is a significant climatic risk that calls for effective mitigation strategies to sustain the supply of soybeans worldwide.

Soybean varieties are classified into maturity groups according to their response to the photoperiod. Early maturing varieties belong to groups 0 to 3, whereas late-maturing varieties fall in groups 6 and onwards ( Zhang et al., 2007; Yang et al., 2019). Drought impacts soybeans cultivars differently, as some cultivars are more susceptible than others ( Oya et al., 2004; Maleki et al., 2013; Du et al., 2020; Dayoub et al., 2021). Also, the timing of drought stress, whether at the vegetative or the reproductive phase, is important in determining yield loss. Desclaux et al. (2000) investigated the drought-induced phenotypes of early maturity soybean varieties grown in France. They reported that drought stress at vegetative stages led to reduced plant height and a decline in seed number in the early reproductive stages and reduced seed weight in late reproductive stages. The water scarcity between flowering and early seed filling stages can affect branches' vegetative growth, resulting in decreased branch seed number and reduced branch seed yield ( Frederick et al., 2001). A report on the effect of drought on soybeans grown in the semi-arid and semi-humid regions of Huaibei regions of China reported a 73-82% decline in yield when drought stress was applied at flowering and seed filling stages ( Wei et al., 2018). Recently, Du et al. (2020) reported that long-term drought stress in reproductive stages decreases biomass allocation to reproductive organs, thereby reducing seed weight in soybean. In addition, drought also impacts the symbiotic nitrogen-fixing ability of soybeans by disturbing nitrogenase activity, which can cause carbon shortage and oxygen limitation leading to poor growth and yield ( Arrese-Igor et al., 2011; Collier and Tegeder, 2012; Kunert et al., 2016).

Plants use diverse mechanisms to overcome the adverse effects of drought, and the ability of crops to adjust using adaptive traits is termed 'drought tolerance' ( Basu et al., 2016). Decreased stomatal opening associated with reduced photosynthesis is a typical drought response observed in plants ( Liu et al., 2003; Mak et al., 2014). Abscisic acid (ABA) plays a vital role in reducing water loss under dry conditions ( Wilkinson and Davies, 2002; Kim, 2014; Wang et al., 2016). Further drought-tolerant soybeans exhibit higher ABA levels than drought-susceptible varieties ( Mutava et al., 2015). ABA, synthesised in plant roots, is transported to the guard cells of leaves, where it induces closure of stomatal openings to reduce water loss ( Wilkinson and Davies, 2002). Further, there is evidence that ABA synthesised in leaf xylem also contributes to this process ( Malcheska et al., 2017). However, reduction in the stomatal opening leads to reduced CO ₂ assimilation and photosynthesis, affecting growth and development ( Cornic and Briantais, 1991; Ohashi et al., 2006; Mutava et al., 2015; Cohen et al., 2021).

Maintenance of cell turgidity is another essential adjustment to survive drought. Under dehydration conditions, cells induce biochemical changes by synthesising necessary metabolites called osmoprotectants ( Silvente et al., 2012). These include soluble and complex sugars, sugar alcohols, organic acids and free amino acids ( Ashraf and Iram, 2005; Silvente et al., 2012; Kido et al., 2013). Osmoprotectant accumulation in the cell balances the osmotic difference between cell exteriors and the cytosol help to retain water and maintain the integrity of the cell membrane ( Yancey, 2005; Basu et al., 2016). An increase in soluble sugars, such as sucrose and fructose, improves homeostasis under stressed conditions. Further, soluble sugars are required for enhanced carbohydrate metabolism, signal transduction and synthesising enzymes and hormones needed to survive under drought ( Gupta and Kaur, 2005; Mak et al., 2014; Du et al., 2020). Metabolite profiling of biochemical compounds synthesised during drought stress revealed an increase in pinitol in the leaves of a drought-susceptible cultivar ( Silvente et al., 2012). Pinitol is a common sugar alcohol that acts as an osmoprotectant in legumes ( Ford, 1984; Streeter et al., 2001; Dumschott et al., 2019). Further, Silvente et al. (2012) reported increased levels of amino acids, such as proline, during drought stress in flowering stages. Proline helps retain water by adjusting the intracellular osmotic potential of the cells (Heerden van and Krüger, 2002). In addition, reactive oxygen species (singlet oxygen) produced in stressed cells can cause severe oxidative damage under prolonged drought conditions. Alia et al. (2001) suggested that proline acts as a scavenger of singlet oxygen. However, the role of proline in quenching singlet oxygen in stressed plants remains debated as Signorelli et al. (2013) demonstrated that proline could not quench singlet oxygen in an aqueous buffer. Figure 1A summarises the effects of drought on soybean.

Figure 1. (A) A diagram depicting various effects of drought on soybean. Drought induces morphological changes, such as loss in vegetative biomass, accompanied by a reduction in pod number, seed number, seed weight and altered biochemical composition of seeds. Further, cells regulate the effect of drought, synthesising osmolytes, such as proline and sugar alcohols, to balance the osmotic potential for maintaining cell membrane integrity. Also, in many drought-resistant soybean cultivars, roots adjust their architecture in response to water-deficit conditions by changing root length, branching and other phenotypes to absorb more soil moisture. Further, severe drought stress leads to ROS accumulation which can cause cell and tissue damage by oxidising biomolecules. (B) An outline of strategies to breed drought-smart soybeans.

On the cellular level, largely overlapping signalling mechanisms and phytohormone cross-talks mediate drought response in plants ( Basu et al., 2016). With the advent of high-throughput sequencing techniques, several genes or gene families have been identified and characterised in soybean ( Wong et al., 2013; Zhang et al., 2020, 2021). Among these, the CUC (NAC; Hussain et al., 2017), MYB ( Chen L. et al., 2021), WRKY ( Shi et al., 2018), ABA-responsive element binding (AREB; Fuganti-Pagliarini et al., 2017) and dehydration response element-binding proteins (DREB; Nguyen et al., 2019) transcription factor families are some of the prime regulators that control drought response by regulating the synthesis of drought-responsive hormones, such as ABA, ethylene and other drought signalling compounds including Brassinosteroids ( Nguyen et al., 2019; Chen L. et al., 2021).

Comparative transcriptomics have further elucidated the molecular mechanisms underlying drought response in soybean ( Ha et al., 2015; Hussain et al., 2017). For example, Hussain et al. (2017) identified 28 drought-responsive GmNAC genes in soybean and reported that only eight GmNAC genes showed high expression levels in drought-tolerant soybean variety; with drought-sensitive cultivars exhibiting lower expression levels. WRKY transcription factors have been highlighted to play vital roles in plant abiotic stress tolerance ( Ning et al., 2017; Shi et al., 2018). Shi et al. (2018) identified a drought-responsive soybean WRKY gene, GmWRKY12, whose over-expression in a transgenic hairy root assay led to increased proline levels under drought stress. Further, Wei et al. (2019) characterised another WRKY gene, GmWRKY54, which mediates drought tolerance via ABA and Ca⁺² signalling pathways. The over-expression of GmWRKY54 conferred drought tolerance in soybean ( Wei et al., 2019). Other recently identified gene families participating in soybean drought response are AT-hook motif ( Wang et al., 2021b), P-type ATPases ( Zhao et al., 2021), CCT family ( Mengarelli and Zanor, 2021) and GRAS ( Wang et al., 2020). Identifying new drought-responsive genes is vital for developing drought-smart soybeans. The term 'drought-smart' refers to soybean cultivars that can adapt to diverse types of drought, such as early drought, middle-stage drought, later-drought and seasonal drought, by combining different mechanisms of drought resistance, such as drought avoidance, drought tolerance and drought recovery. Drought avoidance is generally achieved by retaining water in plant tissues, either by restricting water loss or using water judiciously to support different plant functions. However, drought tolerance is achieved using adaptive traits, such as biochemical adjustments to maintain cell turgidity and minimise photosynthetic damage ( Basu et al., 2016). Further, cultivars with drought avoidance and drought tolerance features can recover fast upon rehydration after a short-term or seasonal drought ( Dong et al., 2019). Hence, smart soybeans are able to produce a stable yield in drought-prone areas. Traits, such as improved water retention, restricted transpiration and stable photosynthesis, are desirable to sustain biomass and yield under water-deficit conditions ( Wei et al., 2019; Kunert and Vorster, 2020; Yang et al., 2020). A combination of such traits would make a cultivar suitable for cultivation in arid or semi-arid regions ( Liu et al., 2005; Abdel-Haleem et al., 2012; Carter et al., 2016). Also, as genome sequencing of soybean cultivars gains momentum, the new genetic diversity resources will aid in developing drought-smart varieties using advanced breeding or genetic engineering methods ( Fuganti-Pagliarini et al., 2017; Golicz et al., 2018; Kajiya-Kanegae et al., 2021). Here, we review genetic improvement approaches for improving drought tolerance in soybean.

Breeding for Drought-Smart Soybean

A combination of advanced phenotyping, molecular breeding and genetic engineering approaches can be employed to breed drought-smart soybeans ( Figure 1B). Using conventional breeding techniques, pre-selected soybean germplasms (donor cultivars) with desired drought-responsive traits can be crossed to introduce favourable alleles in the resulting populations. However, extensive screening of subsequent generations is required to select a line with stable characteristics for continued cultivation ( Carter et al., 2016). Further, genetic transformation offers the possibility of targeted gene expression under constitutive or inducible promoters ( Ribichich et al., 2020). For example, Arabidopsis thaliana AtMYB44 gene was transformed in soybean using Agrobacterium-mediated transformation which resulted in improved soybeans with better yield under water-deficit conditions in the field ( Seo et al., 2012). Improvements in tissue culture regeneration of commercial soybean cultivars and optimization of Agrobacterium-mediated transformation methods will also facilitate engineering soybeans for drought tolerance ( Raza et al., 2017, 2019). Recently, more precise gene editing technique, CRISPR/cas9, has shown promising results in modifying soybean's genome in a targetted manner to obtain more specific gene modifications. CRISPR/cas9 has been successfully employed to characterise soybean drought-responsive genes using knock-down approaches. For example, CRISPR/cas9-mediated mutagenesis of soybean circadian rhythm genes (GmLCLs) generated mutant plants with decreased water loss under dehydration stress conditions ( Yuan et al., 2021). Interestingly, breeding and genetic transformation methods have successfully delivered improved soybeans that have been tested in laboratory, glasshouse or field conditions and a few of these varieties have also been approved for commercial production. In the sections below, we review recent examples of soybeans improved for drought tolerance via breeding or transgenic approaches.

Breeding and High-Throughput Phenotyping

Molecular breeding approaches for enhancing drought tolerance, such as marker-assisted selection, quantitative trait loci (QTL) mapping, GWAS and genomic selection, depend upon the extent of existing genetic diversity for the desirable traits. High genetic diversity of soybean makes it feasible to select cultivars with drought tolerance properties ( Kajiya-Kanegae et al., 2021). Some of the traits to address water deficit are slow canopy wilting, water-conserving transpiration response, dense root surface area and low stomatal conductance ( Kim, 2014; Basu et al., 2016).

Using phenotypic trait selection approaches, a high-yield and drought-tolerant cultivar was bred by the US Department of Agriculture and North Carolina State University researchers. This study involved extensive screening of soybean germplasm collected from around the world. Fifteen years of rigorous selection led to discovering a slow-wilting landrace PI 416937 from Japan and another resistant cultivar from Nepal, PI 471938. Continuous breeding efforts led to the development of a new drought-tolerant soybean variety released as 'USDA-N8002' (maturity group VIII) for field use ( Carter et al., 2016).

Recently, high-throughput phenotyping of plant populations has widened trait identification by reducing the timeline and physical labour involved in the manual screening processes ( Crusiol et al., 2021; Zhou et al., 2021). For example, remote sensing has shown great potential in analysing the genotypes of plants in a non-destructive way. These remote sensing mechanisms of phenotyping involve thermal, spectral and hyperspectral imaging techniques ( Li et al., 2014). A simple example of spectral measurement is the estimation of chlorophyll content using the soil plant analysis development metre which measures red vs. infra-red light ( Yuan et al., 2016). Hyperspectral imaging is an advanced imaging method used to detect material across the entire electromagnetic spectrum. It works on the principle that certain elements have unique spectral fingerprints that can be used to identify materials by analysing the image of the scanned object ( Crusiol et al., 2021).

Crusiol et al. (2021) employed a leaf-based hyperspectral reflectance method to distinguish soybean genotypes under different moisture conditions and at different phenological stages. Leaf-based hyperspectral reflectance was collected from soybean genotypes growing under different watering conditions over multiple cropping seasons. Short-wave infra-red wavelength (1300-2,500nm) was critical in these measurements, as it can effectively detect vegetation water status. Principal component analysis of spectral datasets of soybean genotypes showed 94% variance in the first three components, indicating that spectral data could successfully distinguish the soybean genotypes.

Multispectral and visible RGB camera imaging was also performed by Zhou et al. (2021) to estimate the yield of soybean genotypes under drought stress. They employed the unmanned aerial vehicle technique for collecting image data to develop a method of yield estimation for large breeding populations. Images of 972 breeding lines were captured at vegetative (R6), early and late reproductive phases (R1 and R6-R8). By assessing the image features related to plant height, canopy colour and canopy texture, they built a deep learning model which could explain the yield to up to 78%. The yield of slow- and fast-wilting plants belonging to three genotypes (maturity group 3, 4 and 5) was assessed, and it was found that the fast-wilting group produced less average yield (986.5kg/ha) as compared to the slow-wilting group (1,395kg/ha). Hence, high-throughput imaging for phenotyping has a remarkable potential for assessing large populations to identify plants with drought-tolerant traits.

Marker-Assisted Breeding

Marker-assisted breeding is another promising approach for developing drought-tolerant soybeans. It relies on identifying variations in chromosomal regions, known as QTLs. QTLs that make a genotype more robust than others for drought tolerance are particularly valuable for breeding. Marker-assisted breeding uses DNA markers linked to specific QTLs for selecting genotypes with the desired alleles ( Zhang et al., 2015). For example, Chen H. et al. (2021) identified QTLs related to primary root length on chromosome 16 of soybean. This QTL accounts for 30.25% variation in phenotype and will assist in developing of markers for root-length selection, which is an important trait for drought tolerance. Dhungana et al. (2021) identified QTLs associated with flooding stress at the V1-V2 stage of soybean. They analysed a recombinant inbred line (RIL) population derived from crossing a drought-susceptible (NTS116) and drought-tolerant (Danbaekkong) soybean cultivar. Using a composite interval mapping technique, they identified 10 QTLs related to flood tolerance at the V1-V2 stage of soybean. These QTLs can cause up to 30.7% phenotypic variations and can be useful for future soybean improvement programmes. Table 1 shows a list of major QTLs associated with soybean drought response. Further, other QTLs identified with soybean drought tolerance can be found at www.soybase.org, one of the prime repositories of soybean genetic resources.

Table 1. A summary of QTLs identified with soybean drought tolerance.

Genetic Engineering

Genetic engineering approaches offer viable opportunities for accelerated crop improvement ( Khan et al., 2020; Lohani et al., 2020; Arya et al., 2021). Genes controlling traits, such as flowering time, disease resistance and lipid profile, have been identified and used as targets for soybean improvement ( Haun et al., 2014; Arya et al., 2018; Ngaki et al., 2021). Similarly, high-throughput genome and transcriptome sequencing have led to identification of key transcriptional regulators of soybean drought response. Among these, the DREB ( Nguyen et al., 2019; Zhou et al., 2020), AREB ( Fuganti-Pagliarini et al., 2017), NAC ( Hussain et al., 2017; Yang et al., 2020), MYB ( Chen L. et al., 2021) and WRKY ( Ning et al., 2017; Shi et al., 2018; Wei et al., 2019) are the prime transcription factor families mediating abiotic stress responses. Gain of function and gene-knock-down approaches, such as RNAi and CRISPR/cas9, have yielded valuable information on how complex gene networks regulate dehydration stress physiology in soybean. For example, Yang et al. (2020) characterised GmNAC8 transcription factor as a positive regulator of soybean drought stress. NAC transcription factor family is primarily involved in plant growth and stress response. GmNAC8 was cloned under the control of 35s promoter for gene over-expression, and CRISPR/Cas9 was used to knock down GmNAC8. The performance of over-expression and knock-down lines was analysed under drought stress by withholding water supply for 14days. Interestingly, GmNAC8 over-expression lines had significantly high superoxide dismutase levels and proline content, which are both indicators of drought tolerance in plants. Further, as the water supply was restored, the recovery rate of over-expression lines was relatively high (up to 96%) compared to WT lines which showed only a 40% recovery rate. Interestingly, the GmNAC8 knock-down lines only had a 5 to 14% recovery rate.

MYB transcription factors regulate the biosynthesis of secondary metabolites for stress responses. Recently, Chen H. et al. (2021) characterised GmMYB14, which participates in drought tolerance and high-density soybean yield by affecting plant architecture through the Brassinosteroid pathway. GmMYB14 over-expression lines were compact with decreased plant height, internodal length, leaf surface area and petiole angle. However, the transgenic plant showed an increase in node number on the main stem and increased branch number, which contributed to enhanced yield under high-density cropping (20cm intervals) conditions. Further, under drought stress, pod number, seed number and seed weight per plant were significantly improved in soybeans over-expressing GmMYB14 compared to WT. Hence, as we face the challenge of producing more food from our limited arable land, plants that can withstand drier conditions and generate better yield under high-density cropping have the potential of ensuring future food security. Table 2 shows a list of genes or gene families recently identified to soybean drought response.

Table 2. A summary of recently characterised gene targets for engineering drought tolerance in soybean.

Dehydration responsive element binding (DREB) and AREB transcription factor family genes are known to mediate drought inducible gene expression ( Fuganti-Pagliarini et al., 2017; Zhou et al., 2020). The transgenic soybean lines over-expressing transcription factors DREB1A, DREB2A and AREB1 have been field tested for the agronomic and physiological performance under-irrigated and non-irrigated conditions ( Fuganti-Pagliarini et al., 2017). Improved water use efficiency and leaf area index were reported in 35S: AtAREB1FL lines compared to the control lines. Also, 35S: AtAREB1FL lines had the highest yield under non-irrigated conditions, similar to the yield of 35S: AtAREB1FL lines under-irrigated conditions. Interestingly, in non-irrigated conditions, the oil and protein contents of seeds were not affected by the insertion of DREB1A or DREB2A or AREB1 transcription factors.

Recently, soybeans expressing the sunflower ( Helianthus annus) transcription factor, HaHB4, were approved for production by the US Department of Agriculture. HaHB4 is a water-deficit responsive sunflower transcription factor whose over-expression in Arabidopsis led to improved drought tolerance ( Manavella et al., 2008). HaHB4 was cloned under constitutive 35S promoter and inducible HaHB4 promoter. Soybeans expressing HaHB4 were studied under glasshouse and field conditions by Ribichich et al. (2020). Transgenic lines designated as b10H performed best under field trials when genotype to environment interaction (G×E) was analysed. The b10H soybeans had better yield (seed number) under warm and dry conditions, not compensated by a decrease in seed weight. Under water-deficit conditions, b10H produced 43.4% more yield compared to WT (Williams 82). Also, the diameter of epicotyls, internode and xylem was wider in b10H soybeans as compared to WT plants. Further, b10H plants had a significantly high photosynthetic rate (at R5 and R6 stage) under warm field conditions. Molecular analysis showed that transcripts of heat shock proteins homologous to Arabidopsis thaliana HSC70-1(At5G02500), HSFB2A (At5G62020), Hsp81.4(At5G56000) and HOT5 (At5G43940) were differentially regulated in soybeans expressing HaHB4 ( Ribichich et al., 2020). Due to its exceptional field performance, the trait HaHB4 has received regulatory approvals in Argentina, Brazil, Paraguay and Canada ( Businesswire, 2021).

Microbial Inoculants for Drought-smart Soybean

Symbiotic rhizobium species associated with soybean root nodules benefit plant growth via mediating biological N fixation ( Jaiswal et al., 2021). Reduced photosynthesis under drought conditions disturbs oxygen balance in nodules triggering premature nodule senescence ( Arrese-Igor et al., 2011; Kunert et al., 2016). Symbiotic association of soybean plants with arbuscular mycorrhizal fungi, Glomus mosseae and G. intraradices, has been reported to alleviate drought-induced nodule senescence ( Porcel and Ruiz-Lozano, 2004; Takács et al., 2018). Further, it has been demonstrated that extenuation of premature nodule senescence is mediated by the induction of high glutathione reductase in soybean roots and nodules. Glutathione reductase activity likely exerts its influence by reducing oxidative damage to biomolecules ( Porcel and Ruiz-Lozano, 2004; Prabha and Sharadamma, 2019; Meena et al., 2021). Hence, arbuscular mycorrhizal fungi can play important roles in alleviating the impact of drought at root-nodule interfaces in soybeans. Recently, co-inoculation of rhizobia and mycorrhizal fungi has shown enhanced soybean tolerance to drought stress providing a cost-effective strategy for improving soybean productivity ( Igiehon et al., 2021).

Conclusion

Soybean is a crop of immense economic importance ( Johnson and Myers, 1995; Messina and Messina, 2010). Vast genetic diversity has been reported in soybean germplasm, and the increasing availability of soybean genetic resources has instigated the development of drought-smart soybeans ( Carter et al., 2016; Ribichich et al., 2020; Kajiya-Kanegae et al., 2021; Chen L. et al., 2021). Modern breeding and advanced biotechnology methods have shown promising results, and market-ready drought-tolerant soybeans have been released in some parts of the world ( Carter et al., 2016; Ribichich et al., 2020). However, soybean production is still dependent on adequate irrigation facilities in many regions, especially in under-developed and developing nations ( Droppers et al., 2021; Suriadi et al., 2021). As genetic and non-genetic improvement methods are tested on more cultivars, the dependency of soybean production on rainfall or heavy irrigation should reduce. With changing precipitation patterns and a hotter climate, drought-tolerant soybeans will play a significant role in ensuring our future food security.

Author Contributions

All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

We thank the reviewers for the suggestions to improve the manuscript. The research support from the ARC Discovery grant DP0988972 is gratefully acknowledged.

References

Suriadi, A., Zulhaedar, F., Nazam, M., and Hipi, A. (2021). "Optimal Irrigation at Various Soil Types for Soybean Production", in: IOP Conference Series: Earth and Environmental Science: IOP Publishing), 012081.
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Frontiers | Towards Developing Drought-smart Soybeans

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Drought is one of the significant abiotic stresses threatening crop production worldwide. Soybean is a major legume crop with immense economic significance, ...

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1. Relative expression levels of GmCAMTAs in different tissues of soybean plants

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1. Relative expression levels of GmCAMTAs in different tissues of soybean plants

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Methods : Heatmap analysis base on qRT-PCR (total RNA)

The total RNA was extracted from various tissues, including roots, leaves, stem, and flower, at the V4 stages of WT soybeans grown under long-day conditions (16 h light/8 h dark). The qRT-PCR analysis was performed with each GmCAMTA-specific primer (Supplementary Table S2). GmTUBULIN was used as an internal control for normalization.

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2. Various abiotic stress affected the expression of GmCAMTAs

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2. Various abiotic stress affected the expression of GmCAMTAs

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Salt, cold, drought, ABA

: analyzed in soybean seedling during abiotic stress by using qRT-PCR

salt 150 mM NaCL

cold 2 C

ABA 100 uM

GmTUBULIN gene control

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3. GmCAMTA2-OX, GmCAMTA8-OX reduced Drought tolerance in Arabidopsis

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3. GmCAMTA2-OX, GmCAMTA8-OX reduced Drought tolerance in Arabidopsis

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1. phenotype of GmCAMTA2,8 compared with WT, Vector-OX plants

2. Survival rate% against drought stress

3. Water loss from detached leaves of 3-week-old plants (180 mins)

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4. GmCAMTA2-OX, GmCAMTA8-OX regulated other gene of Arabidopsis in drought stress response 

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4. GmCAMTA2-OX, GmCAMTA8-OX regulated other gene of Arabidopsis in drought stress response 

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qRT-PCR analysis under drought stress

ABA-induced genes : AtRD29A AtRD28B

proline biosynthesis gene : AtP5CS2

stress-related marker : AtKIN1

AtTUBULIN2 gene control

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Many GmCAMTAs were expressed in the roots; however, GmCAMTA2, GmCAMTA7, GmCAMTA9, GmCAMTA14, and GmCAMTA15 were weakly expressed (Figure 3, Supplementary Figure S2A). In the leaves, the expression of most GmCAMTAs was highly induced, except for those of GmCAMTA2,GmCAMTA8, and GmCAMTA12 (Figure 3, Supplementary Figure S2B). Interestingly, GmCAMTA2,GmCAMTA8, and GmCAMTA12 were relatively highly expressed in the stem tissues as compared to other GmCAMTAs (Figure 3, Supplementary Figure S2C). Unlike the expression patterns of GmCAMTAs in roots, leaves, and stem tissues, the expression levels of GmCAMTAs were similar in the flower (Figure 3, Supplementary Figure S2D). Our results indicated that GmCAMTAs were selectively expressed in each of the plant tissues, thereby showing substantial specificity in the developmental stages.

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They showed that GmCAMTA2, GmCAMTA8, and GmCAMTA12 were especially highly expressed in stem tissues compared to other GmCAMTAs

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Salt stress : all of the GmCAMTAs were highly expressed at early times, and their expression levels increased gradually as time passed

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link : https://www.mdpi.com/1422-0067/24/14/11477

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Drought : The expression levels of most GmCAMTAs in drought stress responses were significantly increased compared to those under non-stress conditions, except GmCAMTA13 and GmCAMTA15

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Cold : GmCAMTA1 and GmCAMTA2 were highly expressed to cold stress; however, the expression levels of other GmCAMTAs were reduced during cold stress treatment

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ABA : ABA did not influence the expression of most GmCAMTAs in soybean, although GmCAMTA1, GmCAMTA5, GmCAMTA6, and GmCAMTA13 were weakly expressed under exogenous ABA conditions

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gene expressions of GmCAMTAs were differently influenced by various abiotic stresses.

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by , * and *

College of Life Sciences, Engineering Research Center of the Chinese Ministry of Education for Bioreactor and Pharmaceutical Development, Jilin Agricultural University, Changchun 130118, Jilin, China

Authors to whom correspondence should be addressed.

1. Introduction

A successful sustainable agriculture should ensure food security, must be eco-friendly and safe to humans [ 1]. With the existing population growth rate, the current food production rate needs to be increased at least up to 70% by 2050 [ 2, 3]. Despite advanced farming practices, the abiotic stresses due to drought, salinity, water and temperature fluctuations are causing 50-80% losses in crop yield, and therefore, should be as effectively managed as possible [ 4]. Soon, the warmer earth will cause a more humid atmosphere but less humid soil, leading to more frequent drought that would negatively affect the rate of photosynthesis, uptake of CO ₂, accumulation of biomass and yield [ 5, 6]. Thus, developing stress-resistant crops with stable yields under adverse conditions is an important strategy to ensure future food security [ 2, 7]. Deep insights into the mechanisms underlying signaling crosstalk mediating stress tolerance would aid plant researchers to upgrade the plant's indigenous natural machinery through biotechnology and genetic engineering.

In plants, the ubiquitous secondary messenger calcium is the key to maintain the harmonious and homeostatic conditions via signaling [ 8, 9]. Calcium ions perceive and encode the environmental, developmental or hormonal signals into a definite frequency that is decoded and relayed by the protein molecules next to them including calmodulin (CaM). By interacting with calcium, calmodulin (calcium-modulated) proteins sense and convey the signals to calmodulin-binding proteins [ 10]. Here, CaM through signal frequency readjustment channelizes them before transducing to CAMTA TFs (transcription factors), thus CaM acts like a prism (as prism dissects white light into its components). A transcription factor family reported and initially referred to as Ethylene-induced calmodulin-binding protein [ 11] or signal responsive (SR) protein, while now known as Calmodulin-binding transcription activator (CAMTA), is present in almost all eukaryotes. CAMTAs were first detected in Nicotiana tabaccum while studying calmodulin-binding proteins [ 11, 12, 13]. After their emergence, all multicellular eukaryotes studied to date have been reported to be equipped with variable number of CAMTA genes such as Arabidopsis thaliana (6) [ 13], Lycopersicum esculantum (7) [ 14], Medicago truncatula (7) [ 15], Citrus (9) [ 16], Populus trichocarpa (7) [ 17], Nicotiana tabacum (13) [ 18], Musa acuminata (5) [ 19] and Phaseolus vulgaris (8) [ 20]. Glycine max possesses 15 CAMTA genes and all of them differentially express under various stress conditions [ 21].

CAMTA TFs are an integral element of Calcium-mediated biotic/abiotic stress and hormonal signaling pathway [ 22, 23, 24, 25]. Stress signals are conveyed and modulated through Ca-CaM-CAMTA pathway and a rapid and calculated response is observed by carrying out the transcription of stimulus-specific genes [ 26]. Calmodulin Binding Transcription Activators through their CG-1 domain recognize and specifically bind the ((A/C)CGCG(C/G/T), (A/C)CGTGT)) sequence of the target genes thereby directly interacting and regulating their transcription [ 12]. The CG-1 motif is a pivotal member of a rapid stress response element (RSRE) found in the promoters of many genes that are rapidly activated in response to stress [ 27]. Previous experiments report the negative role of Arabidopsis CAMTA3 in regulating plant immunity as demonstrated in the AtMCAMTA3 (loss-of-function) mutant Arabidopsis [ 28, 29, 30]. Similarly, AtCAMTA1 and AtCAMTA3 have been shown to function in drought and regulate auxin [ 22, 31]. Moreover, AtCAMTA1 and AtCAMTA3 regulated cold response by inducing CBF (C-Repeat/DRE-Binding Factor) pathway genes as the double ATCAMTA1 and ATCAMTA2 mutant exhibited impaired freezing tolerance [ 32, 33]. The role of AtCAMTA3 in regulating SA (salicylic acid) pathway genes working in freezing tolerance was only recently determined [ 34]. Two recent studies highlighted the role of AtCAMTA3 [ 35] and AtCAMTA6 [ 36] in salt tolerance. TaCAMTA4 in wheat was demonstrated to negatively regulate defense response against Puccinia triticina [ 25].

Soybean is an important crop cultivated globally for food, feed [ 37], pharmaceutical and soil nitrogen improving purposes [ 38]. However, environmental adversities including drought affect soybean growth and yield. Earlier, the in silico analysis of GmCAMTAs was conducted, yet the potential targets of miRNA in GmCAMTA transcripts and the protein-protein interaction network were not reported [ 21]. In addition, the previous study also did not analyze the expression pattern of GmCAMTAs in soybean leaves in response to drought [ 21]. In an extension to that study and in order to decipher the role of soybean CAMTA family in drought, we first comprehensively analyzed (in silico) the GmCAMTA family including their physicochemical properties, chromosomal distribution, cis-motifs, miRNA targets and protein-protein interaction network. Secondly, we determined the spatiotemporal expression pattern of GmCAMTAs in roots and leaves of soybean under PEG stress and selected an efficient member of the GmCAMTA family to functionally characterize. We constructed the overexpression construct by cloning the 2769 bp CDS (coding sequence) of GmCAMTA12 and transformed into Arabidopsis and soybean hairy roots. Through various drought assays, we demonstrated that the transgenic Arabidopsis and chimeric soybean (OE-Overexpressing GmCAMTA12) plants exhibited enhanced tolerance and performed better under drought stress than their non-transgenic counterpart at phenotypic, physiological and molecular level. qPCR (quantitative PCR) of the downstream genes in Arabidopsis and soybean also displayed altered expression as a result of GmCAMTA12 overexpression. From our analyses, we report that GmCAMTA12 as a transcription factor plays role in drought stress by regulating the downstream genes involved in drought tolerance and could be exploited in developing drought-tolerant crops.

2. Results

2.1. Physico-Chemical Properties of GmCAMTA Proteins

Various physico-chemical properties including the number of amino acids, protein molecular weight (MW), pI (isoelectric point), number of atoms, instability and aliphatic indexes and GRAVY (grand average of hydropathy) determined online with the ProtParam tool are given in Table S1 and File S1. GmCAMTA11 and GmCAMTA9 are the shortest polypeptides comprising of 910 and 911 aa (amino acid), while GmCAMTA1 is the longest possessing 1122 aa. Overall, their average length is ~1004 aa with a range of ~200 aa mutual difference. The molecular weight of GmCAMTAs ranges between 126,989.43 and 102,394.56 kDa with an average MW of 112848.3 and the number of atoms is proportional to the molecular weight of each protein. Similarly, GmCAMTA8 has the highest pI value of 7.64 showing that GmCAMTAs have relatively lower pI. Moreover, they are also hydrophilic in nature as the GRAVY ranges between -0.625 (GmCAMTA1) and -0.394 (GmCAMTA12). Almost all of the GmCAMTAs are thermally stable as their aliphatic indexes match with that of the other globular proteins (highest in GmCAMTA12). While none of them are stable in a test tube, as the instability index of all GmCAMTAs is higher than 40. Except GmCAMTA8, the number of Asp+Glu (negatively charged aa) is higher than Arg+Lys (positively charged aa).

2.2. Phylogenetics and Structure of GmCAMTAs

As mentioned earlier, CAMTAs are multiple-stress responsive transcription factors. Enrichment of cis-motifs involved in signal response in the promoters of Medicago CAMTA genes hints that they are likely to respond variedly to various signals like other CAMTAs [ 15]. We dissected the regulatory region of GmCAMTAs (~2 kb upstream) online with PLANTCare, which detected stimulus-specific cis-motifs in their promoters. Overall, there are light (G-box, MRE and AE-box), drought (MBS), salt (MYB), pathogen (TC-rich repeats), wound (WUN, WRE), low temperature (LTR), gibberellin (GARE, P-box), auxin (AUXRE) and abscisic acid (ABRE) responsive cis-elements as shown in figure 1A. The presence of multiple cis-motifs in GmCAMTA genes represents their responsivity to multiple stimuli. Moreover, the cis-element (CG-box) is the binding site of CAMTA TFs; thus, the presence of G-box within GmCAMTA promoters indicates the interaction of one GmCAMTA TF with another. GmCAMTA12 possesses MYC, MYB, MBS and G-box, which shows its potential role in drought stress.

Using the online GSDS tool, the gene structure of all the GmCAMTAs was visualized in order to mutually compare their structural diversity. The length of GmCAMTA genes lie in the range between 3196 bp (GmCAMTA14) to 3947 bp (GmCAMTA6) with an average length of 3607 bp. The exons (yellow), introns (black lines) as well as 5' and 3' UTR (untranslated) regions (blue) of each gene are shown in figure 1B. A close observation of the number of exon-introns reveals a similar pattern, namely, three genes (GmCAMTA7, GCAMTA10 and GmCAMTA15) that are comprised of 12 exons; the rest have 13 exons and 12 introns of variable length, indicating their close mutual evolutionary relationship. This fixed numbering of intron and exon is a conserved characteristic of CAMTA, which is descended from ancestors and is also demonstrated in the CAMTA family of other species [ 23]. Similarly, except the last one/two exons, an ascending order in the exon size from 5' to 3' UTR can be observed across all the genes.

The four domains (CG-1, ANK (ankyrin repeat), IQ and CaMBD (CaM binding domain)) is the common conserved characteristic of all CAMTA TFs [ 23]. Scanning the amino acid sequences of GmCAMTAs with online protein domains illustrator tool showed the same four domains in all 15 members (figure 1C). GmCAMTA TFs through IQ (calcium-independent)/CaMBD (calcium-dependent) interact with Calmodulin, while through the CG-1 domain, they bind the DNA in a sequence-specific manner (CGCG/CGTG) at their promoter region. "ANK repeats" mediate protein-protein interactions. All these conserved domains, along with other properties, make GmCAMTA proteins, the "transcription factors". The high sequence specificity is common in the Calmodulin binding domain of Arabidopsis and soybean CAMTAs as shown in figure 1D.

An ML (Maximum Likelihood) tree was constructed which traced the evolutionary relationship among the CAMTA families of soybean, Arabidopsis, maize and tomato ( Figure 1 E). Using MEGAX (molecular evolutionary genetic analysis), the evolutionary tree was constructed from the full length aligned CAMTA protein sequences of the four species. A total of 37 CAMTAs including six from Arabidopsis, 15 from soybean, seven from tomato and nine from maize clustered into four distinct groups, I, II, III and IV. GmCAMTA1-6, AtCAMTA1-3, SlCAMTA1 and 2 and ZmCAMTA3, 6, 7a and 7b might have co-evolved and thus clustered together in group I, representing the largest clade. Similarly, GmCAMTA10, 11, 14, 15, AtCAMTA4, SlCAMTA3, 4 and ZmCAMTA1 clustered in group III showing their mutually high homology. GmCAMTA8, 9, 12 and 13 grouped with AtCAMTA5, 6, SlCAMTA5, 6 and ZmCAMTA5 making clade IV. Two orthologs (GmCAMTA7 and SlCAMTA7) comprised group II, which is the smallest clade. Clustering in the phylogenetic reconstruction indicates more mutual similarity and probably weak homology to the members of the other three bigger clusters. It is noticeable that except II, in the rest of clades, CAMTAs of the same species are at the tips of the same branches and vice versa retaining their intraspecific homology.

2.3. miRNA Targets in GmCAMTA Transcripts

miRNA target prediction is important in finding their role in plant growth development in normal as well as stress conditions [ 18]. Keeping the Expectation score ≤5, the 639 miRNAs were scanned and miRNA, where the minimum E. value was selected using the online psRNATarget tool [ 39]. A total of 10 unique miRNAs were predicted which potentially target the GmCAMTA transcripts by inhibiting translation or through cleavage ( Table S2 and File S1). Their length ranges between 19 bp (gma-miR1533) to 24 bp (gma-miR343b). The accessibility of target site (UPE), which is associated with identification of target site and energy required to cleave the transcript, varies from 11.8 (gma-miR1533) to 21.6 (gma-miR5780c). The translation of GmCAMTA1 and GmCAMTA2 transcripts is potentially inhibited by the common gma-miR5780c, while gma-miR6299 cleaves four GmCAMTA8, GmCAMTA9, GmCAMTA12 and GmCAMTA13). Similarly, GmCAMTA5 and GmCAMTA 6 have potential targets for gma-miR1533 while GmCAMTA11 and GmCAMTA14 are predicted to be cleaved by gma-miR2111b. gma-miR9726 is predicted to cleave GmCAMTA3 and gma-miR1522 GmCAMTA15. These in silico predictions require experimental validation, which would extend our understanding the mechanisms of Ca-CaM-CAMTA-mediated stress tolerance in plants.

2.4. Chromosomal Distribution and Regulatory Network of GmCAMTAs

The genome browser tool in NCBI (National Center for Biotechnology Information) mapped GmCAMTA genes to their respective chromosomes. The 15 GmCAMTA genes are unequally distributed over eight out of 20 chromosomes of soybean as shown in Figure 2. The figure depicts the complete size of each chromosome with the exact position of genes. Chromosome 8 has the highest number of GmCAMTA genes, i.e., four, while chromosome 7, 9, 11 and 18 have got only one in each. In prokaryotes, due to the absence of nucleus, transcription and translation occur simultaneously in coupling phase. On the other hand, translation of mRNA is always executed outside the nucleus in the eukaryotic cytoplasm and the proteins that work only in nucleus have a nuclear localization sequence/signal (NLS). Transcription factors also work in the nucleus; thus, after their translation in cytoplasm, they are directed to the nucleus which is mediated through their NLS. To find NLS, protein sequences of each GmCAMTA were submitted to the online cNLS mapper tool at http://nls-mapper.iab.keio.ac.jp/cgi-bin/NLS_Mapper_form.cgi. Keeping a cut off score of 5, at least one NLS in all GmCAMTAs and even more than one in some proteins were detected. Likewise, all of the six Arabidopsis CAMTAs possess only one NLS in the CG-1 domain of each protein [ 12]. In contrast, rice CAMTAs have one NLS in the C-terminal and another in N of CG-1 domain [ 40]. Experimental evidence shows that these domains perform diverse functions in the regulation of gene expression [ 41]. Table S3 (File S1) shows the nuclear localization sequence in each of the 15 GmCAMTAs and predicts that all these transcription factors are localized in the nucleus.

In order to find the interaction network of GmCAMTA proteins to relate them with other pathways, the protein sequences were individually put in the STRING (Search tool for the retrieval of interacting gene/proteins) database, which predicted a number of interactors [ 42]. Thus, they can aid in linking proteins of interest to other pathways and could lead to the discovery of novel pathways as well. The STRING database displayed a network of ~10 interactors for each GmCAMTA protein among which some sets were redundant. Thus, a total of 48 unique proteins were predicted for 15 GmCAMTAs as shown in Figure 3 ( Table S4 and File S1). This vast interaction network (experimentally determined and software predicted) indicates the complex regulatory upstream/downstream pathways of CAMTAs. However, these in silico predicted interactions require experimental validation. Besides these predicted interactions, their orthologues in other species such as Arabidopsis or other legumes could also be exploited to search other potential interactors in soybean.

2.5. GmCAMTAs as Early Drought Stress-Responsive TFs

The spatiotemporal expression in roots and leaves under 0, 1, 3, 6, 9 and 12 h of simulated drought stress is shown graphically in Figure 4. In roots, GmCAMTA2 was highly expressed during 3 h of drought followed by GmCAMTA7 and GmCAMTA10. In contrast, GmCAMTA14 was downregulated during all the five time points followed by GmCAMTA8, GmCAMTA9 and GmCAMTA11. Overall, these transcription factors upregulated abruptly during 1 and 3 h of stress and downregulated afterwards ( Figure 4A). The expression profile of GmCAMTAs in leaves at different stress durations is different as compared to roots ( Figure 4 B). In leaves, they look uniform, except GmCAMTA4, which is the highest upregulated gene followed by GmCAMTA5, GmCAMTA11 and GmCAMTA12. Interestingly, like roots, majority of these 15 genes retained the 3 h trend in the leaves as well. It is obvious that the expression was relatively high at 3 h, after which it decreased until 12 h.

The differential expression of GmCAMTA family is the result of their tightly regulated transcription. We speculate that in stress conditions, although the calcium ions continuously convey the stress signals through calcium signatures to the cytoplasm as well as the nucleus; however, the intensity/amount of these signals is weighed and adjusted by the next signal relaying molecules, such as CaM, before conveying to CAMTA transcription factors. In case, this signal transduction to CAMTA is continuing with the same intensity, yet after certain period (3 h in our case), CAMTAs' response is not the same throughout the course and seems to be unconcerned and even downregulated as the stress period continues. From the control samples (0 h) in leaves and roots, it is also obvious that the expression of all CAMTAs is active at all times. In brief, the spatiotemporal expression pattern revealed that GmCAMTAs are upregulated in the early phase of drought thus are early drought stress-responsive transcription factors.

2.6. Arabidopsis Overexpressing GmCAMTA12 Exhibited Enhanced Drought Tolerance

To evaluate the contribution of GmCAMTA12 protein in drought stress, we engineered Arabidopsis plants to constitutively express GmCAMTA12 gene under 35 s promoter. Prior to Arabidopsis transformation, the Agrobacterium tumefacians strain EHA105 transformants were verified (through PCR) to harbor the overexpression cassette. ( Figure S1 and File S1). After floral dip, several overexpressing lines were obtained of which we selected two independent stable homozygous lines (OE5 and OE12) for functional analysis. The expression of GmCAMTA12 in two transgenic Arabidopsis lines was validated through qPCR.

For drought assay, the two lines of OE GmCAMTA12 (OE5 and OE12) and the wild type (WT) plants were subjected to drought stress by withholding water for two weeks and then re-watered as shown in Figure 5A. Initially, all the plants were growing normally until water was withheld. However, upon encountering drought, nearly all the wild type and transgenic Arabidopsis stopped growth, wilted and started turning yellow afterwards. After 14 days of continuous drought treatment, most of the wild type plants were completely dried as obvious from their phenotype (dried leaves). Unlike WT, most of the OE lines retained life processes, which was evident from chlorophyll they retained in their leaves. After re-watering, majority of transgenic Arabidopsis rejuvenated but most of the wild type did not. The plants were then allowed to grow under normal conditions until seed harvest. As expected, the seed yield of WT and transgenic lines was unequal and OE lines developed more seeds than wild type Arabidopsis. Under well-watered conditions, the survival rate in soil under drought was ~100%; however withholding water for two weeks and then re-watering, less than 60% WT plants survived while OE5 and OE12 showed about 83% and 87% survival rate as shown graphically in Figure 5 D. Obviously, the constitutive overexpression of GmCAMTA12 had enhanced the drought survival efficiency of transgenic Arabidopsis leading to better growth and development.

In their root length assay on ½ MS-mannitol medium as shown in Figure 5B, WT plants grew longer roots than OE plants at 0 mM concentration of mannitol; however, on mannitol, OE plants, specifically OE12, developed longer roots than WT at all three concentrations (50, 100 and 200 mM). Interestingly, the roots of OE12 plants were the longest at 200 mM mannitol. Root length (cm) is shown graphically in Figure 5 E. As flaunted through root length assay, the comparatively longer roots of OE than WT were the result of GmCAMTA12 overexpression in T3 seeds.

Between the two overexpression lines, OE12 performed better than OE5 in both drought assays, thus, we subsequently inoculated seeds of WT and OE12 on ½ MS with various concentrations of mannitol (50, 100, 150 and 200 mM) to evaluate the germination rate ( Figure 5C). As expected, we observed higher germination rate of transgenic line OE12 than that of WT at all four concentrations of mannitol. The germination rate of OE12 was ~30% higher than WT at 50 mM mannitol. At 100 mM, the germination rate decreased in both types at a similar pace (~75% in OE and ~45% in WT). As the mannitol concentration increased to 150 and 200 mM, we saw a dramatic decline in the germination efficiency of WT (30% and 25%) as compared to OE (>65% and >60%) ( Figure 5 F). We can say that the constitutive expression on GmCAMTA12 has enhanced the germination efficiency of transgenic seeds under drought.

In order to check the performance of WT and OE lines at physiological level, the physiological indexes, such as proline and MDA contents, CAT activity and relative electrolyte leakage were determined in all plants subjected to stress. Under well-watered conditions, we observed no significant difference in the level of proline contents, which was quite low in WT and OE lines. In contrast, proline contents calculated in drought-stressed plants had significantly elevated. In WT, the average value of proline was ~400 µg/g, while in OE, it was recorded in the range of 850 to 900 µg/g ( Figure 5G). Malonaldehyde (MDA) is a well-known biomarker for sorting out stress-induced membrane damage due to oxidative stress. MDA contents in WT and OE plants during normal conditions matched mutually but its level was doubled in the drought-stressed wt plants compared with drought treated OE lines ( Figure 5H). Catalase (CAT) is a major antioxidant enzyme, which is accumulated during abiotic stresses and scavenges H ₂O ₂. The CAT activity in WT and OE plants under usual conditions was nearly equal ( Figure 5I); however, drought treatment enhanced CAT activity in transgenic plants as compare to WT. During stress, electrolytes, specifically K⁺ ions leak out of the cells through various channels and thus damage due to stress could be monitored by comparing the electrolyte leakage in WT and transgenic lines. We determined relative electrolyte leakage (REL) in WT and OE lines during normal as well as drought conditions. REL % was nearly leveled under well-watered conditions, but was more pronounced in WT as compared to OE lines during drought ( Figure 5 J). Noticeably, the amino acid sequence analysis revealed high level of sequence similarity between GmCAMTA12 and AtCAMTA5. We can speculate that GmCAMTA12 being a transcription factor interacted with the downstream target genes (including AtCAMTA5 interactors) and modulated their expression in transgenic Arabidopsis, which contributed to their better performance under drought (determined at molecular level in later section).

2.7. GmCAMTA12 Overexpression Regenerated More Developed and Drought-Efficient Hairy Roots in Soybean

For further functional validation of GmCAMTA12 in response to drought, the hairy roots system was exploited to overexpress the target gene in soybean. Prior to generating transgenic hairy roots, the Agrobacterium rhizogenes strain K599 cells harboring vector control (VC) and overexpressing GmCAMTA12 construct (OE) were verified through gene-specific PCR ( Figure S2 and File S1). After 1-2 weeks of infection of soybean seedlings with K599 (VC and OE), hairy roots had started regenerating with various frequency. Prior to subsequent experiments, we made sure that the hairy roots were transgenic by using gene-specific PCR from the genomic DNA of a small piece of hairy roots. Using qPCR, we validated the overexpression of the target gene in OE roots which was over three times higher than the VC hairy roots. After hairy roots had become long and strong enough by growing for two more weeks, the primary roots were removed and the chimeric plants (with transgenic roots and non-transgenic shoots) were shifted to fresh vermiculite.

When the transgenic roots were ~10 cm long, the VC and OE chimeric soybean plants were extirpated from vermiculite and transferred to Hoagland solution as shown in Figure 6A. After 3 days of acclimation, both VC and OE chimeric plants were treated with 6% PEG6000. The plants started to wilt with leaves curling and shoot apexes drooping on encountering drought. However, the wilting was more in VC than in OE chimeric plants as VC leaves were more wilted. GmCAMTA12 overexpression induced profuse hairy roots ( Figure 6B) due to which the aerial non transgenic part of the OE chimeric soybean plant was also more developed than the VC chimeric plants. The roots were analyzed using the root scanning system ( Figure 6C). The OE hairy roots showed higher values with total root length ( Figure 6J), surface area ( Figure 5K), root volume ( Figure 6L), number of branches ( Figure 6M) and projected area ( Figure 6 N).

The proline and MDA contents, CAT activity and relative electrolyte leakage were determined to check the impact of GmCAMTA12 overexpression at physiological level. In control samples (Hoagland), proline contents had nearly equal amount in VC and OE hairy roots; however, under drought, proline content level was recorded to be significantly higher in OE hairy roots ( Figure 6F). MDA shows the level of membrane damage as it is the final product of lipid peroxidation. In the absence of stress, MDA contents in OE type were slightly less than VC hairy roots; however, with PEG treatment, VC had substantial increase in MDA contents as compare to OE roots ( Figure 6G). In contrast, CAT activity was significantly higher in OE hairy roots than VC under drought stress ( Figure 6H). For REL, VC hairy roots had higher electrolyte leakage (%) than OE in response to drought ( Figure 6 I). Comparative physiology of OE and VC hairy roots shows that the overexpression of GmCAMTA improved the drought tolerance of OE by altering physiology.

To analyze their growth efficiency under drought stress, 0.1 g of hairy root from VC and OE hairy roots was weighed under sterile conditions and inoculated on GM media containing four various mannitol concentrations (50, 100, 150 and 200 mM) including a control (0 mM mannitol) with three replicates of each type ( Figure S3 and File S1). All the plates were kept in dark in growth room with 28 °C for 10 days of culturing after which the fresh and dry weights were determined. The control samples of both types (VC and OE) hairy roots had nearly same fresh and dry weights; however, on stress media, OE hairy roots showed better performance as the weight of OE roots was more than VC roots at all four concentrations of mannitol ( Figure 6 D,E). It further flaunted that the overexpression of GmCAMTA12 has improved the drought survival efficiency of OE roots.

2.8. Expression Analysis of GmCAMTA12 Orthologues' Regulatory Network in Arabidopsis

In Arabidopsis, AtCAMTA5 is the orthologue of GmCAMTA12; thus, the regulatory network of AtCAMTA5 was predicted with STRING database. To test our hypothesis, whether the overexpression of GmCAMTA12 TF in transgenic Arabidopsis would interact with genes in the regulatory network of AtCAMTA5, we analyzed the expression of 10 interactors predicted with STRING database ( Figure 7A). Total RNA from drought treated WT and OE plants ( Figure 7B) was isolated and reverse transcribed to cDNA. Using cDNA and gene-specific primers ( Table S6 and File S1), we conducted a qPCR which deciphered the differential expression pattern of the 10 genes in wt and OE plants ( Figure 7 C). Among them, AtNIP30 (NEFA-interacting nuclear protein-AT3G62140), which in humans is involved in negative regulation of proteasomal protein catabolic process, is slightly upregulated in response to drought. AtWRKY14 (AT1G30650) encoding WRKY transcription factor 14 possesses a DNA binding domain and specifically interacts with W box (a common elicitor-responsive cis-element). WRKY14 is also nuclear localized transcription factor and regulates many important processes through gene regulation. GmCAMTA12 overexpression upregulated AtWRKY14, which indicates that along with other transcription factors including WRKY TFs, the spectrum of CAMTA TFs regulatory networks is much wider than we think. Thus, the mutual interaction of CAMTA and WRKY should be further investigated. Peptdyl-prolyl cis-trans isomerase AtCYP59 (AT1G53720) mediates posttranslational modifications specifically protein folding. With GmCAMTA12 overexpression, the upregulation of AtCYP59 is almost equal to that of WRKY14. AtANN5 (AT1G68090) is a calcium binding protein, plays role in pollen development and is induced by cold, heat, drought and salt stresses. As expected, its expression is relatively the highest among the 10 interactors. Serine racemase (AtSR-AT4G11640) is involved in serine biosynthetic pathway. Its expression also seems to elevate with GmCAMTA12 overexpression in OE Arabidopsis. Elongator complex 3 (AtELO3-AT5G50320) is a part of Elongator multiprotein complex and regulates initiation and elongation of transcription. No apparent change in the expression level of AtELO3 in WT and OE was observed. Similar to ANN5, CaMHSP (Calmodulin Binding Heat Shock Protein-AT3G49050), also called Alpha/beta-Hydrolases superfamily protein, exhibited a higher transcript level. It is involved in the lipid metabolic pathway. B120 (G-type lectin S-receptor-like serine/threonine-protein kinase-AT4G21390) is involved in protein kinase activity and recognition of pollen. Its expression level was downregulated in transgenic Arabidopsis. AT2G43110 (U3 containing 90S pre-ribosomal complex subunit) was also upregulated with the overexpression of GmCAMTA12. AT3G19850 (BTB (for BR-C, ttk and bab) or POZ (Pox virus and Zinc finger)) domain-containing) mediates protein degradation by facilitating ubiquitination. Its expression level was elevated with GmCAMTA12 upregulation. All of these genes possess a CGCG/CGTG motif in their promoter sequences, which also validates the sequence-specific binding of CAMTA TFs. Most of these interactions are based on text-mining and should be determined experimentally.

2.9. GmCAMTA12 Overexpression Orchestrated Downstream Genes in Transgenic Hairy Roots

In order to find whether the constitutive overexpression of GmCAMTA12 modulates the genes with which GmCAMTA12 TF interacts, we selected eight genes in the GmCAMTA12 regulatory network in soybean predicted with STRING ( Figure 7D). In chimeric soybean plants possessing VC and OE hairy roots ( Figure 7E) treated with 6% PEG6000, we analyzed the expression pattern of the eight of the predicted interactors of GmCAMTA12 to know their GmCAMTA12-mediated regulation. Using gene-specific primers ( Table S7 and File S1), qPCR of the selected interacting proteins in transgenic hairy roots as well as non-transgenic leaves was carried out. The genes displayed a differential expression profile in the control and drought treated chimeric soybean plants. In VC roots ( Figure 7H), GmNIP30 (NEFA interacting protein-GLYMA19G40910) was slightly upregulated in response to drought and its expression was indifferent to GmCAMTA12 overexpression in OE roots ( Figure 7I). GmPLA1-IId (Phospholipase A1-IId - GLYMA12G15430), involved in the lipid metabolic process, was upregulated in VC roots under drought stress, while in OE roots, its upregulation was two-fold higher than that in VC. GmNAB (Nucleic Acids Binding-GLYMA18G40360) was downregulated in VC roots while upregulated with GmCAMTA12 overexpression in OE roots in response to drought. The expression of GmELO (Catalytic histone acetyltransferase subunit of the RNA polymerase II elongator complex-GLYMA06G18150) was the highest and equally expressed gene in both VC and OE roots. GmSR1 (Serine racemase-1 involved in D-Serine biosynthetic process-GLYMA05G37930) was upregulated in VC roots; however, with GmCAMTA12 overexpression, GmSRI was downregulated in OE roots under drought. Similarly, GmSR2 (Serine racemase-2-GLYMA08G01670) was repressed in the absence of GmCAMTA12 overexpression; however, in OE roots under drought, it was slightly upregulated. Simultaneously, GmUC1 (uncharacterized, but possessing a Myb-DNA binding domain-GLYMA20G32540) transcript was slightly upregulated in VC but was significantly induced in OE hairy roots in response to drought. In contrast, GmUC2 (Uncharacterized2-GLYMA17G07200) was positively regulated in VC and negatively regulated in OE roots. Unlike the roots, the expression profile of these eight genes was nearly similar in chimeric soybean leaves possessing VC ( Figure 7F) and OE ( Figure 7 G) hairy roots in response to drought. Five out of eight of these genes possess CGCG/CGTTG motif.

3. Discussion

Calcium as a ubiquitous secondary messenger orchestrates almost every cellular process in response to environmental stimulus. Plants employ the divalent cation, calcium (Ca ²⁺) in relaying these endogenous (developmental) and exogenous (environmental) signals to appropriate cellular responses. Calcium alone specifically encodes a myriad of distinct signals by using spatial and temporal Ca ²⁺ spikes as well as the frequency and amplitude of Ca ²⁺ oscillations [ 10]. Next to the secondary messenger lie the signal relaying molecules including Calmodulin, which further tune the calcium signatures and pass them to CAMTAs. Calmodulin Binding Transcription Activators have a short history of two decades, which after first being reported have been genome-wide identified in numerous plant species [ 23]. CAMTAs are important in the sense that they are the transcription factors and an intermediate in the calcium-mediated stress signaling (Ca-CaM-CAMTA).

Earlier, the identification and expression analysis of Soybean CAMTAs was conducted [ 21]; however, their functional characterization remained unexplored. Thus, in order to better comprehend the role of GmCAMTAs in drought, and take a holistic snapshot, we first attempted to fill the research gaps through comprehensive in silico analysis of GmCAMTA family. Soybean possesses 15 GmCAMTAs [ 21], the second highest number after Brassica napus, which possesses 18 genes [ 24]. Interestingly, such a large stress responsive transcription factor family must have substantial contribution to the drought tolerance of soybean. CAMTA has important role in an array of biotic/abiotic stresses as reported in earlier studies in Arabidopsis [ 22, 31, 32, 33, 34, 43], tomato [ 14, 28], tobacco [ 18] and Brassica napus [ 24], Arabidopsis [ 36] and wheat [ 25]. Dissecting GmCAMTAs with various bioinformatics tools, their gene structure depicted 13 exon pattern, which is consistent with its orthologues in Arabidopsis, maize, tomato and others [ 23]. Promoter enrichment analysis revealed the cis-elements including ABRE, DRE, G-box, W-box, WRKY, ARE and MYB, all of which respond to various stresses [ 21]. SlCAMTAs are differentially expressed during the development and ripening of tomato and the presence of ERE cis-element in all GmCAMTAs provides the basis for CAMTA role in fruit development and ripening [ 28]. Light responsive elements (G-box) are common in all GmCAMTAs and we suggest that their role in light stress should be investigated [ 44]. Additionally, their specific stress-responsive cis-motifs should be tested individually or collectively in designing stress-inducible synthetic promoters.

Protein structure is important to know for understanding the action mechanism of protein. The major basic domains, CG-1, ANK, IQ and CaMB are common in all CAMTAs and a close look into the motif sequence of CaMB domain shows residues which are highly conserved across the species. Phylogeny of CAMTAs of four species traced the evolutionary relationship among the homologues as well as orthologues, which is consistent with the previous results [ 15, 17, 18, 19, 21, 23, 24]. Some homologues show more similarity and might have co-evolved ( Figure 1). Later, the same set of proteins was found to interact with these homologues. A protein interaction network analysis found a few experimentally determined as well as predicted interactions together of 48 unique proteins of various pathways ( Figure 3). Thus, we recommend to experimentally determine those interacting proteins to unveil GmCAMTA TFs' link with the pathways of the interacting proteins. In Arabidopsis, 32 proteins were predicted with STRING in the interaction network of AtCAMTAs [ 23]. An experiment-based detailed map of all the GmCAMTA interactors would deepen our understanding of intricate mechanisms of GmCAMTA-mediated development and immunity against biotic/abiotic factors. Similarly, miRNAs are important transcriptional regulators by directly cleaving their target transcripts; thus, their potential target sites in GmCAMTAs were important to determine to understand CAMTA-mediated regulation. In silico analysis of PtCAMTAs revealed potential targets for four distinct miRNAs [ 18]. A set of 10 unique miRNAs was detected bringing more complexity in CAMTA-mediated stress response mechanisms in soybean ( Table S2 and File S1). We recommend to experimentally determine the miRNAs targeting GmCAMTAs, which might also lead to the ability to engineer soybean and other crops against drought more accurately.

The expression analysis of GmCAMTAs in soybean leaf and root reveals that although all of them express constitutively, but GmCAMTA2, GmCAMTA4, GmCAMTA5, GmCAMTA11 and GmCAMTA12 are highly upregulated against drought. Secondly, almost all GmCAMTAs expressed and peaked in 3 h after which they were repressed indicating their early responsivity to drought stress ( Figure 4). Based on the previous report [ 21], as well as our qPCR results, GmCAMTA12 was the common drought-efficient transcription factor, and thus, was selected to functionally characterize. As expected, overexpressing GmCAMTA12 in Arabidopsis and hairy roots and drought assays thereof, validated the previous and current qPCR-based results. Interestingly, GmCAMTA12 enhanced the drought survivability and growth performance of transgenic Arabidopsis ( Figure 5), as well as hairy roots ( Figure 6), which was validated at phenotypic, physiological and molecular level. Similar to GmCAMTA12, another transcription factor from soybean GmNAC85 is also drought stress-responsive and its constitutive overexpression significantly enhanced the drought tolerance in transgenic Arabidopsis [ 45]. Similarly, RNAseq of soybean overexpressing GmWRKY54 revealed that, GmWRKY54 conferred drought tolerance by enhancing ABA(Abscisic acid)/Ca ²⁺ signaling to close stomata as well as regulating numerous stress responsive transcription factors [ 6]. Similar results were recently reported about GmWRKY12 transcription factor [ 46]. In contrast, AtCAMTA1-mutant Arabidopsis exhibited enhanced drought sensitivity while also affecting the expression of other drought responsive genes [ 31]. A recent global transcriptome analysis using RNAseq revealed that a large number of genes involved in diverse stress responses are regulated either directly or indirectly by AtCAMTA3 as about 3000 genes were misregulated in the AtCAMTA3-mutant Arabidopsis [ 34]. We recommend to experimentally verify the downstream targets of GmCAMTAs through protein-protein interaction experiments such as two-hybrid/pull down assays, which might link/lead to novel pathways. GmCAMTA12 overexpression altered the expression of the genes in the regulatory network of AtCAMTA5, which is the orthologue of GmCAMTA12 in Arabidopsis. All of the 10 interacting genes possess CGCG/CGTG motif in their 2000 bp upstream region validating the sequence-specific interaction of GmCAMTAs [ 12]. In conclusion, the better performance of OE Arabidopsis and chimeric soybean plants on MS-mannitol and Hoagland-PEG was validated at physiologic and molecular level. The comparison of Proline and MDA contents, CAT activity and relative electrolyte leakage between VC and OE hairy roots shown graphically, flaunts the improved development of chimeric plants with hairy roots overexpressing GmCAMTA12. This was further verified at a molecular level by determining the transcript level of the genes in the regulatory network of GmCAMTA12. The GmCAMTA overexpression should also be analyzed in other tissues including soybean flower and seeds as well as in various developmental stages to find its integrated role in processes besides stress tolerance. However, GmCAMTA12 nuclear localization, promoter-GUS analysis, CRISPR/cas9-mediated gene-knockout and global transcriptomics of stably transformed transgenic soybean and Arabidopsis are under investigation. Taken together, a more systematic approach should be adopted to decipher the integrated role of GmCAMTA TFs with the Calcium-Calmodulin signaling crosstalk in drought.

4. Materials and Methods

4.1. In Silico Analysis

The physico-chemical properties including molecular weights, theoretical isoelectric points, Aspartate+Glutamate, Arginine+Lysine, number of atoms, instability index, aliphatic index and GRAVY (Grand average of hydrophathicity) of all GmCAMTA proteins were calculated using the ProtParam tool in the ExPASy ( https://web.expasy.org/protparam/).

To trace their evolutionary relationship, an ML tree was constructed with MEGAX using default parameters after multiply aligning the protein sequences of Glycine max, Arabidopsis thaliana, Zea mays and Solanum lycopersicum.

The exons and introns along the length of GmCAMTA genomic sequences were monitored using the online tool GSDS (Gene Structure Display Server ( http://gsds.cbi.pku.edu.cn/index.php) [ 13]. To check the cis-motifs within the regulatory regions of GmCAMTAs, (2kb upstream) 5' UTR of each gene was resolved at online database PLACE ( https://sogo.dna.affrc.go.jp/). The promoter sequences are in File S1. All the 15 GmCAMTA protein sequences were aligned with ClustalX and the conserved motifs were identified using the online SMART tool (Simple Modular Architecture Research Tool) ( http://smart.embl-heidelberg.de/). The CaMBD in Arabidopsis and Soybean CAMTA family proteins was particularly analyzed for conserved sequences. The protein alignment is shown in File S1.

The potential miRNA targets along the length of GmCAMTA transcripts were predicted online at psRNATarget server ( http://plantgrn.noble.org/psRNATarget/). To predict the proteins interacting with GmCAMTAs, each protein was separately submitted to STRING database ( https://string-db.org/). The online prediction software displayed the proteins experimentally proved/hypothetical proteins interacting with GmCAMTAs.

Primers for qPCR analysis were designed with Primer premier 5 software as well as NCBI online primer tool https://www.ncbi.nlm.nih.gov/tools/primer-blast/. The multiple alignments of GmCAMTAs CDS showed frequent conserved sequences; thus, to minimize ambiguity, the primers were designed with special care against the unique sites in each GmCAMTA CDS. Moreover, it was ensured that the primers amplify all alternative transcripts of respective gene. We attempted to design the pair targeting two exons amplifying a stretch of 200-300 bp cDNA. Those primers which failed to give amplification, created more than one peak or did not appear on agarose gel under UV were redesigned until corrected. Moreover, the primers specificity was determined with online nBLAST tool in NCBI https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome. Suzhou GENEWIZ Biological Technology Services Company, China, synthesized all primers for this experiment listed in Table S5 and File S1.

4.2. Expression Analysis

The seeds of soybean variety "Willliams 82" were washed with 75% ethanol and then sterile water. They were hydroponically cultured in Hoagland nutrient solution in a growth room with ~28 °C room temperature, 16-8 h (light-dark) photoperiod and a relative humidity of 50%. When the plants have opened their unifoliate leaf pair completely, they were subjected to 6% (PEG 6000) stress simulated drought. The plants were treated with drought for 1, 3, 6, 9 and 12 h in triplicate along with control (Hoagland). At the exact time points, we collected leaf and root samples, froze in liquid nitrogen and stored at -80 C for further processing.

Total RNA from the root and leaf samples (ground in liquid nitrogen) of soybean plants (treated with PEG for various time periods) was extracted using RNAiso Plus (Takara, Japan) according to the manufacturer's protocol. RNA concentration was determined with NanoDrop2000Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The quality of RNA samples was checked over 1% agarose gel using 0.5X TBE. (Tris/Borate/EDTA) buffer. Bright and clear bands of 25S/18S RNA showed the intact RNA. The samples that showed degraded RNA or relatively unclear bands were re-extracted until intact RNA was detected. The concentration of all good quality RNA samples was adjusted to ~500 ng/µL and it was reverse transcribed into cDNA using the PrimeScript RT reagent kit with gDNA Eraser (Takara, Japan) according to the company's protocol. After the removal of genomic DNA with gDNA eraser, 1 µg total RNA was used as template to synthesize cDNA and stored at -20 °C.

After the reverse transcription of RNA into cDNA and qPCR primer synthesis, qPCR was run according to the experimental design. Prior to the qPCR of all the samples, a standard curve was created with a reaction using a serially diluted cDNA in duplicate. Actin 11 was used as reference with leaf cDNA while Elongation factorα with that of root. Before determining the expression pattern of soybean CAMTA gene family in leaves and roots, the personal error was minimized by the standardization of experiment. An initial reaction was run on Stratagene Mx3000 P thermocycler and primers validity was determined first by confirming the single peak (amplification plot), the C t value for each gene, and then by running the reaction product on agarose gel. Each product gave a single band parallel to right marker band. A 20 µL reaction mixture contained 10 µL SYBR Premix Ex Taq, 0.4 µL ROX Reference Dye II, 0.4 µL of each gene-specific Primer, 2 µL of cDNA and 6.8 µL ddH ₂O. High PCR efficiency is indispensable for robust and more precise RT-qPCR. The formula E = 10(−1/Slope) −1 was used to calculate amplification efficiency and the slope of the calibration curve. The primers having ~100% PCR amplification efficiencies were used. The RT-qPCR profile for our samples consisted of an initial denaturation of 95 °C for 2 min followed by 40 cycles of 94 °C for 5 s and 62 °C for 20 s. The fold change in relative expression level was evaluated using 2 −ΔΔCt formula.

4.3. Gene Transformation and Drought Assays

From the bioinformatics as well as qPCR analyses, we selected the gene GmCAMTA12 (Glyma.17 G031900) to clone and transform for functional validation. The complete CDS (2769 bp) of GmCAMTA12 was PCR-amplified using cDNA as template, forward primer ACTAGTATGGCGAATAACTTAGCGG and reverse primer CCCGGGCTATGTCTTCAGTTGCATGTCAA. For amplification, LA Taq kit (Takara) was used according to the manufacturer's protocol. The PCR profile was; an initial denaturation at 94 °C for 1 min followed 35 cycles of 94 °C for 30 s, 55 °C for 30 s and 72 °C for 150 s, and a final extension at 72 °C for 10 min. Using CT101 cloning kit (Transgene, Beijing, China), the gene was cloned into pEASY ®-T1 cloning vector and transformed into Trans1-T1 chemically competent cells (Transgene) following heat shock method according to the manufacturer's instructions. The positive clones were obtained on a selective LB-Kan⁺ agar plate and screened through vector and gene specific PCR as well as restriction. After double check, three separate clones were sequenced using M13 forward and reverse primers. When the sequences of all three clones were analyzed without any point mutation, the gene was restricted from pEASY ®-T1 and sub-cloned by restricting from cloning vector using SpeI and SmaI (Takara) and ligated into the corresponding sites of expression vector pBASTA (with Kanamycin and Glyphosate resistance genes) using T4 Ligase (Promega, Madison, WI, USA) constituting the recombinant overexpression construct, pBASTA-GmCAMTA12. The pBASTA-GmCAMTA12 construct was transformed into DH5α strain of E.coli (Transgene) and positive clones were obtained on a selective LB-Kan⁺ agar plate. After a double check (PCR and restriction) of single colonies harboring the overexpression construct, the colonies were preserved in sterile 80% glycerol. The recombinant vector was transformed into chemically competent Agrobacterium tumefacians (EHA105) and Agrobacterium rhizogenes (K599) by a 5 min liquid nitrogen treatment followed by a heat shock of 5 min at 37 °C. The positive clones of EHA105 were identified on YEP Kan+Rif plates while K599 were selected using YEP Kan+Strep plates. After PCR verification, the cells were preserved at -80 °C for future use.

Arabidopsis Col seeds were kindly provided by Engineering Research Center of the Ministry of Education of Bioreactor and Pharmaceutical Development. To synchronize the germination of seeds, wt seeds were soaked in water and kept at 4 °C for 48 h and then sowed on a mixture of humus + vermiculite (3:7) in a growth room in dark at 23 °C. The germinated seedlings were then allowed to grow in 16 h photoperiod until 40 days with regular watering. We transformed the inflorescence (unopened flowers) of mature healthy Arabidopsis plants through Floral Dip method [ 47] and harvested T1 (Transgenic generation 1) seeds. The T1 seeds were germinated in excess under the same conditions as for wild type seeds. A primary screening of T1 plants (at six leaf stage) was carried out by spraying with 1% Basta (glyphosate) and the basta-survived plants were grown in separate pots in fresh soil (humus + vermiculite) under the same conditions as for wild type. Each plant represented a separate transformation event. The non-transformants died, while from the green plants, we extracted genomic DNA from leaves by 2 X CTAB and a PCR with gene specific primers using the gDNA as template was performed for secondary screening. T1 plants positive with PCR were grown to harvest T ₂ seeds. Similarly, T ₃ seeds from homozygous lines were harvested following the primary screening by 1% Basta and secondary with PCR to ensure stable transformation.

The seeds of Arabidopsis were surface sterilized by keeping in 70% ethanol for 1 min followed by 50% bleach for 10 min after which the seeds were washed six times by sterile water. The wild-type and two T ₃ transgenic Arabidopsis lines were germinated under sterile conditions on half MS plates added with various mannitol concentration. For germination, we vernalized wt and OE seeds by keeping in dark for 3 days at 4 °C. Each petri plate was marked and divided in to three parts, one of which was allotted to wt while the other two for Line-5 (OE5) and Line-12 (OE12) seeds. The seeded plates were kept in a growth chamber with 16 h photoperiod and 23 °C temperature. The germination of each line was observed and recorded for one week and the germination rate was determined by the number of seeds germinated divided by the total number of seeds. For root length analysis, wt and T3 lines were first germinated on MS media under sterile conditions and one week old seedlings of same length were transferred to square plates containing MS with various concentrations of mannitol. The plates were placed in a rack vertically so that the roots grow downwards to ground. The root growth was observed and the plants were photographed. The seeds of wild-type and two T3 transgenic Arabidopsis lines were germinated and grown for one month in soil at 23 °C, watered regularly and then subjected to drought stress by withholding water for 14 days and photographed before, during and on drought recovery after re-watering.

Various physiological parameters like MDA [ 48] and proline contents [ 49] as well as CAT activity [ 50] and relative electrolyte leakage were determined. Cell membrane integrity is lost as K⁺ ions (a chief electrolyte) leaks out of the cell leading to cell death in stress. This measurement is an indicator of the cell damage caused by stress [ 51]. To determine REL, leaf sample (~2 g) was thoroughly rinsed with deionized water and then subjected to vibration for 2 h at 25 °C in a test tube containing 10 mL deionized H ₂ O. Using a conductivity meter, we determined the conductivity of solution (C1). A second measurement of conductivity (C2) was taken after boiling the solution for 25 min and then cooled to RT. REL % was calculated using the formula (C1/C2) × 100.

Soybean hairy root is an established platform for the functional analysis of a gene by overexpression. For hairy roots, seeds of soybean variety JU 72 were washed with 75% ethanol and rinsed in sterile water a few times. The seeds were sown in pots containing autoclaved vermiculite and kept in a growth room with 16 h photoperiod and 28 °C temperature and watered regularly. In parallel, K599 from glycerol was streaked on YEP Kan + Strep plates and incubated at 28 °C for 48 h. A single colony was picked and inoculated in YEP containing Kanamycin and Streptomycin. The culture was incubated in a shaking incubator at 200 rpm and 28 °C for 48 h. A 100 µL of inoculum rom the culture was spread on YEP Kan + Strep plates and incubated at 28 °C for 48 h. When the seedlings had just sprouted, the cotyledons unfolded and the first unifoliate leaves had not yet appeared, they were ready to be infected with K599, harboring the overexpression construct. The Agrobacterium rhizogenes culture from the plate was picked with a needle and injected into the hypocotyl right at the base of cotyledon [ 52]. K599 harboring empty pBASTA were used to regeneration of VC (Vector Control) hairy roots. After infection, the seedlings were covered with a transparent plastic to ensure high humidity. Within two weeks, hairy roots started to sprout from the site of infection at variable frequencies with at least one root per seedling. The soybean plants with hairy roots were allowed to grow for two weeks after which excess of autoclaved vermiculite was added to cover the hairy roots and watered regularly. We extracted the genomic DNA from both type of the transgenic roots to confirm VC and OE through gene specific primers. The roots were scanned with a scanner and analyzed with the software.

After the hairy roots were ~10 cm long, the plants were uprooted, primary roots were cut and the chimeric plants (transgenic root and non-transgenic shoot) were transferred to fresh autoclaved vermiculite and watered regularly. It is important to notice that the shoot of each plant was cut after the second trifoliate leaves. Thus, the newly grown leaves were compared between the chimeric plants VC and OE transgenic roots. To check the performance of transgenic hairy roots under drought, plants with 10 cm long transgenic hairy roots were transferred to Hoagland solution and after acclimation, they were treated with 6% PEG6000. Proline and MDA contents, CAT activity and REL were determined in VC and OE hairy roots. Moreover, the 0.1 g of the hairy roots (VC and OE) were weighed in sterile conditions and grown on MS media at various concentrations of mannitol including 0, 50, 100, 150 and 200 mM. After 10 days, fresh and dry weights of the hairy roots were determined. The dry weight was measured keeping hairy roots at 60 °C overnight.

4.4. Expression Analysis of the GmCAMTA12 Orthologue's Regulatory Network in wt and OE Arabidopsis

The regulatory network of the orthologue of GmCAMTA12 (AtCAMTA5) in Arabidopsis was predicted online with STRING database. The genomic and proteomic sequences of these 10 interactors are given in File S1. The primers for qPCR were designed against the 10 genes with Primer-BLAST tool ( Table S6 and File S1). In parallel, RNA from wt and OE lines was extracted, quantified and reverse transcribed into cDNA with Takara kit following kit's instructions. To determine the expression pattern of the 10 genes, qPCR was run using the primers of each gene with three biological replicates. AtActin11 was used an internal reference. The data was analyzed with 2-ddCt method.

4.5. Analysis of GmCAMTA12 Regulatory Network in Chimeric Soybean Plants

Using the STRING database, the interactors of GmCAMTA12 in soybean were predicted. The primers for qPCR of eight genes designed with Primer-BLAST are given in Table S7 and File S1. The genomic and proteomic sequences of these 10 interactors are given in File S1. To determine the effect of overexpression of GmCAMTA12 on its interacting proteins, RNA from all samples was extracted using RNAiso plus (Takara) and reverse transcribed into cDNA with Takara rt kit and qPCR was run for all samples in triplicate. Actin 11 was used as internal control. Relative expression level was determined using 2-ddCt method.

Supplementary Materials

Author Contributions

Conceptualization, H.-Y.L.; methodology, W.-C.L. and M.N.; software, M.N.; validation, A.J.; investigation, M.N.; resources, H.-Y.L.; data curation, N.A.; writing-original draft preparation, M.N.; writing-review and editing, M.N. and F.-W.W.; visualization, W.-D.Q.; supervision, F.-W.W.; project administration, H.-Y.L.; funding acquisition, H.-Y.L. All authors read and approved the final manuscript.

Funding

This research was funded by the National Key R&D Program of China, grant number (2016 YFD0101005), the National Natural Science Foundation of China, grant number (31601323), the National Natural Science Foundation of Jilin Province, grant number (20170101015 JC, 20180101028 JC, 20190201259 JC) and Special Program for Research of Transgenic Plants grant number (2016 YFD0101005).

Acknowledgments

We are grateful to Haiyan Li for making all the materials available and critically reviewing the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

CAMTA

Calmodulin Binding Transcription Activator

CaM

Calmodulin (Calcium-Modulated Protein)

CML

Calmodulin-Like

CBL

Calcineurin B-like

ML

Maximum Likelihood

OE

Overexpression

PEG

Poly Ethylene Glycol

TF

Transcription Factor

VC

Vector Control

WT

Wild Type

References

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Figure 1. Comprehensive in silico analysis of the GmCAMTA family. ( A) Cis-elements in the GmCAMTA promoter region. Each type of cis-motif present in GmCAMTA promoters is shown in unique color/color pattern. ( B) Exon-intron assembly of GmCAMTA genes. ( C) Domain organization of GmCAMTA proteins. Four different domains are represented in different colored shapes. ( D) Alignment showing the conserved motif sequence of the Calmodulin Binding Domain of Arabidopsis and Soybean CAMTA TFs. Each conserved residue at the definite position along the row (throughout the orthologues) is shaded in unique color. The functional residues in CaMB domain of these CAMTAs are indicated in the motif below the alignment. In the square brackets "[ ]" are the amino acids allowed in this position of the motif; "X" represents any amino acid and the round brackets "( )" indicate the number of amino acids. ( E) ML (maximum likelihood) phylogenetic tree.

Figure 2. Chromosomal distribution of GmCAMTA genes. The 15 GmCAMTA genes are located on chromosome 5, 7, 8, 9, 15, 17 and 18.

Figure 3. Protein-protein interaction network of GmCAMTAs detected in silico using the STRING database ( https://string-db.org/). The experimentally determined interactions are denoted by pink strings, interactions on the basis of textmining are indicated by yellow strings while interactions on the basis of gene neighborhood shown by green strings.

Figure 4. Spatiotemporal expression analysis of GmCAMTA in drought. ( A) Relative fold expression of GmCAMTAs in Roots. Soybean plants were treated with 6% PEG6000 in Hoagland's solution for five different durations (1, 3, 6, 9 and 12 h) along with a control (0 h). ( B) Relative fold expression of GmCAMTAs in Leaves.

Figure 5. Phenotypic and physiological assays of wild type (WT) and OE under drought stress. ( A) Drought assay of wild type (WT) and transgenic (OE5 and OE12) Arabidopsis grown on soilrite subjected to 14 days of drought stress. ( B) Root length assay on MS-mannitol. ( C) Germination assay on MS-mannitol. ( D) Column chart showing the survival rate (%) of WT and OE in soil. ( E) Root length (cm) of WT, OE5 and OE12 lines. ( F) Germination rate (%) of WT and OE plants. ( G) Proline contents, ( H) malonaldehyde (MDA) contents, ( I) CAT activity and ( J) relative electrolyte leakage (REL %) of WT and OE plants in control and drought treatment.

Figure 6. Comparative phenotype and physiology of chimeric sobean plants. ( A) Chimeric soybean plants bearing VC (Vector control) roots and OE (overexpressing GmCAMTA12) roots in control and drought conditions. ( B) Comparison of chimeric soybean plants having VC and OE roots. ( C) VC and OE hairy roots observed with a root scanner. ( D) Fresh and ( E) dry weight of VC and OE hairy roots after culturing on MS-mannitol for 10 days. Comparison of VC and OE hairy roots in terms of ( F) Proline contents, ( G) MDA contents, ( H) CAT activity and ( I) relative electrolyte leakage between VC and OE hairy roots. ( J) Analysis of the total length, ( K) surface area, ( L) root volume, ( M) number of branches and ( N) projected area.

Figure 7. Expression analysis of genes in the regulatory network of GmCAMTA12 and AtCAMTA5. ( A) STRING-predicted regulatory network of AtCAMTA5. ( B) Drought treated WT and OE Arabidopsis. ( C) Expression profile of the 10 interactors of AtCAMTA5 in WT and OE Arabidopsis. ( D) STRING-predicted regulatory network of GmCAMTA12 in soybean. ( E) Chimeric soybean plants with VC and OE hairy roots. ( F) Expression analysis of the eight interactors of GmCAMTA12 in VC and ( G) OE leaves. ( H) Expression analysis of the eight interactors in VC and ( I) OE roots.

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Chicago/Turabian Style

# data[].attributes.attachment_link.title
Overexpression of GmCAMTA12 Enhanced Drought Tolerance in Arabidopsis and Soybean

# data[].attributes.attachment_link.description
Fifteen transcription factors in the CAMTA (calmodulin binding transcription activator) family of soybean were reported to differentially regulate in multiple stresses; however, their functional analyses had not yet been attempted. To characterize their role in stresses, we first comprehensively analyzed the GmCAMTA family in silico and thereafter determined their expression pattern under drought.

# data[].attributes.headline
CAMTAs: Calmodulin-binding transcription activators from plants to human

# data[].attributes.subject
CAMTAs: Calmodulin-binding transcription activators from plants to human

# data[].attributes.body
/review-2015/ https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2015.00648/full

# data[].attributes.attachment_link.readable_text
The circadian clock ticks in plant stress responses

Abstract

The circadian clock, a time-keeping mechanism, drives nearly 24-h self-sustaining rhythms at the physiological, cellular, and molecular levels, keeping them synchronized with the cyclic changes of environmental signals. The plant clock is sensitive to external and internal stress signals that act as timing cues to influence the circadian rhythms through input pathways of the circadian clock system. In order to cope with environmental stresses, many core oscillators are involved in defense while maintaining daily growth in various ways. Recent studies have shown that a hierarchical multi-oscillator network orchestrates the defense through rhythmic accumulation of gene transcripts, alternative splicing of mRNA precursors, modification and turnover of proteins, subcellular localization, stimuli-induced phase separation, and long-distance transport of proteins. This review summarizes the essential role of circadian core oscillators in response to stresses in Arabidopsis thaliana and crops, including daily and seasonal abiotic stresses (low or high temperature, drought, high salinity, and nutrition deficiency) and biotic stresses (pathogens and herbivorous insects). By integrating time-keeping mechanisms, circadian rhythms and stress resistance, we provide a temporal perspective for scientists to better understand plant environmental adaptation and breed high-quality crop germplasm for agricultural production.

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Introduction

The circadian clock allows organisms to anticipate cyclic changes of environmental signals and generate endogenous 24-h rhythms of physiological responses in synchronization with external and internal cues, thereby providing organisms advantages for survival (Nohales and Kay, 2016; Creux and Harmer, 2019; Bonnot et al., 2021). The circadian clock system in plants consists of time-giving input pathways mediated by environmental signals, sequentially expressed core oscillators, and numerous output pathways. The timing givers in the environment are also called " Zeitgebers", which cue time of day or season (Xu et al., 2022b). The expression of core oscillators is regulated by input signal components, and then directly or indirectly act on the rhythmic transcript accumulation of their target genes, thereby generating daily cellular, metabolic, and physiological rhythms or seasonal rhythms (Greenham and McClung, 2015; McClung, 2019; Webb et al., 2019; Sanchez et al., 2020) (Fig. 1). In this review, we will summarize the responses of the circadian clock to the resetting cues, which inevitably involve the extreme temperature and light intensity in day and night, water and nutrition deficiencies, as well as dormancy and freezing tolerance during the transition of seasons. Core oscillators of the molecular clock and the newly discovered components are also necessary, because most of them are transcriptional factors or cofactors, which directly or indirectly regulate a large number of target genes in individual cells, tissues, and organs from dawn to night. To maintain basic growth and development during daily and seasonal cycles, the circadian clock tries to tradeoff between growth and defense by altering circadian pace and robustness of 24-h rhythms. Here, we will elaborate how circadian oscillators actively participate in the stress signaling perception and transduction pathways.

The circadian system in higher plants

Environmental and internal Zeitgebers in the circadian clock system

Light and temperature, which play roles in plant morphogenesis, are the most important Zeitgebers of the circadian clock. Bright light, high temperature, and cold stimuli imposed at different times of a day significantly cause the phase shifts of the circadian rhythms. The forward shifts, backward shifts, or non-response (dead zone) in the plotted phase response curve (PRC) reflects the sensitivity of the circadian clock to the rhythmic entrainment stimulus (Bruce et al., 1960; Pittendrigh, 1960; Johnson, 1999). Bright light pulses usually cause larger phase shifts, up to 11-12 h (defined as "Type 0" PRC) (Salomé and McClung, 2005; Yuan et al., 2020). Another feature of the light pulse PRC is that there is no gradual transition between maximum phase advance and maximum phase delay, showing a big jump. However, phase shift caused by external high temperature or low temperature treatment is usually about 4-6 h. Compared to light pulse PRC, temperature pulses cause smaller phase shifts (defined as "Type 1" PRC) (Johnson, 1999; Xu et al., 2022a).

In addition, the light intensity is inversely related to the period length of circadian rhythm, in other words, the higher the light fluence, the shorter the period length (Xu et al., 2022b). It is known that blue or red light treatment can change the period length of CAB2:LUC rhythm, and phytochromes and cryptochromes play critical roles in the entrainment of the circadian clock (Somers et al., 1998). Based on the analysis of effect of light fluence on the period length, phytochrome B acts as a high-intensity red light photoreceptor in circadian input pathway, and phytochrome A acts under dim red light. In the test of blue light regime, it was found that both cryptochrome 1 and phytochrome A act to transmit low-fluence blue light cue to the clock, while cryptochrome 1 and cryptochrome 2 mediate the high-fluence blue light for circadian period control (Somers et al., 1998). Moreover, the ZEITLUPE (LOV-KELCH PROTEIN 1/ADAGIO 1)/LKP (LOV Kelch Protein2)/FKF1 (FLAVIN BINDING, KELCH REPEAT, F-Box 1/LKP3/ADO3) family of proteins in Arabidopsis contain a light-sensing LOV domain that mediates light-fluence-dependent regulation of period length and photomorphogenesis (Millar et al., 1995; Nelson et al., 2000; Somers et al., 2000; Schultz et al., 2001; Imaizumi et al., 2003).

Recent studies have revealed that internal signals act as biochemical cues to reset the circadian clock. The concentration of photosynthetic products, such as glucose and fructose, presents a 24-h circadian oscillation in leaves with the peak of enrichment is around ZT4 in the morning after light exposure (Haydon et al., 2013). The sugar pulse PRC imposed with sucrose and mannitol treatments show that photosynthetic products cause relatively small phase shifts of circadian rhythm, which belong to "Type 1" PRC. When photosynthesis is artificially blocked, the rhythmic oscillation of sugars is significantly blocked, indicating that the relay function of sugar as a light signal serves as the timing cue of the circadian clock. In addition, inorganic nitrogen and nitrogen assimilation products, such as KNO ₃, NH ₄NO ₃, glutamate, and glutamine can also be used as a timing stimulus to reset circadian rhythm at different time of a day, thereby plotting a "Type 1" nitrogen PRC (Gutierrez et al., 2008). It can be concluded that Zeitgebers perceived by plants are diverse, and the ambient light, temperature and humidity, internal carbon and nitrogen nutrition, and likely mineral elements in organelles all regulate circadian pace in a hierarchical network (Duc et al., 2009; Hermans et al., 2010; de Melo et al., 2021). The drastic fluctuations in environmental signals, such as high light and high temperature stress at noon, low temperature or even freezing at night, water and nutrients supply deficiency in the air and soil, will inevitably occur during the course of life in land plants. Therefore, to maintain rhythmic growth, cell differentiation, photoperiodic flowering, tuberization, leaf senescence, and seasonal dormancy, the circadian clock spatiotemporally fine-tunes the rhythmic activities and energy allocation to acquire resistance to daily and seasonal stresses (Fig. 1).

Multiple TTFLs of the molecular clock

The core of Arabidopsis circadian clock is composed of interlocked transcription-translation feedback loops (TTFLs), which maintain the clock pace of nearly 24 h (Hsu and Harmer, 2014) (Fig. 1). CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and LATE ELONGATED HYPOCOTYL (LHY), two homologous MYB-like transcription factors expressed around dawn, inhibit the expression of PSEUDO-RESPONSE REGULATORs ( PRR9, 7, 5, 3, 1) and evening-phased EARLY FLOWERING 3 ( ELF3), ELF4, and LUX ARRHYTHMO ( LUX), which form a ELF3-ELF4-LUX evening complex (EC) (Lau et al., 2011; Nagel et al., 2015; Kamioka et al., 2016). In turn, PRR9, 7, 5, and TIMING OF CAB EXPRESSION 1 (TOC1/PRR1) were sequentially expressed throughout the daytime and repress the transcription of CCA1 and LHY (Nakamichi et al., 2005; Nakamichi et al., 2010; Huang et al., 2012). PRR3 is a tissue-specific regulator and functions in vasculature (Para et al., 2007). The transcription of PRR9 and PRR7 is also negatively regulated by the EC complex (Huang et al., 2016). In addition, LIGHT-REGULATED WD1 (LWD1) interacts with TCP transcription factor to form a LWD1-TCP20 complex that activates the transcription of CCA1 in early morning (Wu et al., 2008; Wu et al., 2016). When TCP21, also known as CCA1 HIKING EXPEDITION (CHE), interacts with TOC1, its inhibition of the CCA1 transcription can be eliminated (Pruneda-Paz et al., 2009; Gendron et al., 2012). It was recently found that zinc finger transcription factors BBX18 and 19 dynamically recruit PRR9, 7, 5 to the promoter regions of CCA1, LHY, and RVE8, and act as negative regulators in the daytime to repress the expression of morning-phased clock genes (Yuan et al., 2021b).

The evening-phased repressor TOPLESS interacts with PRR9, 7, 5 and histone deacetylases (HDA6) to negatively regulate the expression of CCA1 (Wang et al., 2013). REVEILLE 4 (RVE4) and RVE8, MYB-LIKE factors that are expressed in the morning interact with cofactor NIGHT LIGHT-INDUCIBLE AND CLOCK-REGULATED 1 (LNK1) and LNK2 to form the morning complex and promote the expression of PRR5 and TOC1 (Xie et al., 2014). LNK1 recruits RNA polymerase II and transcription elongation factor to its target genes and affects the deposition of Tri-methylation of lysine 4 on histone H3 (H3K4me3) or chromatin modification on PRR5 and TOC1 (Ma et al., 2018). In molecular architecture of the circadian clock, core oscillators also undergo post-transcriptional modifications. Epigenetic mechanisms such as genome DNA methylation, histone deacetylation, and alternative splicing of pre-mRNA affect the expression of circadian clock genes (Ni et al., 2009; Sanchez et al., 2010; Wang et al., 2013; Seo and Mas, 2014). In addition, proteolysis and turnover of core oscillators based on phosphorylation or O-glycosylation modification are also the result of diurnal adaptation (Fujiwara et al., 2008; Wang et al., 2010; Wang et al., 2020a; Yan et al., 2021). Recently, ELF3 has been found to undergo phase separation and function as a thermosensor in response to temperature changes (Jung et al., 2020). In this article, we will continue to summarize the post-transcriptional regulation in different types of circadian gating of stress responses.

Zeitnehmer ELF3 mediates circadian gating of both light and temperature responses

Most of the core oscillators and circadian-related components involved in the molecular architecture of TTFLs respond to numerous environmental cues, including stress signaling (Harmer et al., 2000; Grundy et al., 2015; Seo and Mas, 2015; Markham and Greenham, 2021). In this review, we summarized the phenotypes of the circadian clock gene mutants under abiotic stresses (Table 1). Among them, ELF3 is regarded as a key factor in light resetting, high temperature response, and salinity tolerance of the circadian clock (McWatters et al., 2000; Covington et al., 2001; Carré, 2002; Thines and Harmon, 2010; Box et al., 2015). ELF3 was first discovered to act on the regulation of flowering time, and loss-of-function of ELF3 in elf3 alters the photoperiodic induction of flowering and diurnal control of hypocotyl growth in Arabidopsis and crops (Zagotta et al., 1996; Boden et al., 2014; Sakuraba et al., 2016; Lu et al., 2017; Jiang et al., 2019). In subsequent studies, it was found that ELF3 was intensively expressed at night, and elf3 null mutant showed an obvious light-dependent arrhythmia of circadian output (Covington et al., 2001). The altered ELF3 amount in elf3 and ELF3-OX overexpression lines significantly affect the phase shifts of strong red and blue light pulses to the circadian rhythm (Covington et al., 2001; Thines and Harmon, 2010). Therefore, ELF3 guards light resetting or sensitivity of the circadian clock to light cue.

Table 1 Physiological phenotypes of circadian clock gene mutants under abiotic stress conditions

Moreover, ELF3 is also required for the circadian clock in the responses to ambient temperature fluctuations (Thines and Harmon, 2010). The binding of evening-phased ELF3 and EC complex to the promoters of their target genes is temperature-dependent, and ELF3 and LUX regulate the expression of LNK1 under cold- or warm-night by directly binding to LNK1 promoter (Mizuno et al., 2014; Box et al., 2015). BBX18 interacts with ELF3 in thermomorphogenesis, and BBX18 together with E3 ligase XBAT31 and XBAT35 promote the degradation of ELF3 protein during warm night (Ding et al., 2018; Zhang et al., 2021a; Zhang et al., 2021b). Moreover, temperature directly controls ELF3 activity through the mechanism of protein aggregation (Jung et al., 2020). When the ambient temperature is maintained at 22 °C, ELF3 proteins diffuse, and when the temperature rises to 27 °C, ELF3 undergoes phase separation and forms protein speckles. ELF3 in crop is involved in the regulation of environmental adaptation. In short-day soybean genetic resources introduced from north to south, the delay in flowering and maturity caused by the loss-of-function of J, AtELF3 homolog, is conducive to increasing the yield of soybean in low latitudes (Lu et al., 2017). In summary, the circadian core oscillators are regulated by environmental stimuli at transcriptional and post-transcriptional levels. In addition, the circadian clock drives the self-sustaining 24-h rhythms over a broad range of physiological temperatures, a characteristic termed temperature compensation, but the underlying mechanism are largely unknown.

Circadian gating of plant thermomorphogenesis

Pittendrigh discovered the temperature independence in the clock system (Pittendrigh, 1954). Further results show that circadian period length is maintained at about 24 h over a broad range of physiological temperatures, suggesting "temperature compensation" is the core property of the circadian clocks (Pittendrigh, 1954; Zimmerman et al., 1968; Edwards et al., 2005; Xu et al., 2010). Edwards et al. completed the QTL mapping of the circadian parameters of the leaf movement rhythm at 12 °C, 22 °C, and 27 °C in recombinant inbred lines (RILs) of Arabidopsis accession Columbia (Col) by Landsberg erecta (Ler) and Cape Verde Islands (Cvi) by Ler (Edwards et al., 2005). This study identified circadian clock-controlled and flower-timing gene GIGANTEA ( GI) and an F-box E3 ubiquitin ligase gene ZEITLUPE ( ZTL) as strong candidates for two of the QTLs involving in temperature compensation.

The degradation of PRR5 and TOC1 proteins is mediated by ZTL (Fujiwara et al., 2008). GI stabilizes ZTL protein and facilitates ZTL maturation, and ZTL also contains a blue-light-absorbing LOV (Light, Oxygen, or Voltage) domain, which is critical for ZTL function on circadian rhythms (Kim et al., 2007; Cha et al., 2017; Pudasaini et al., 2017). A recent study found that in Arabidopsis Cvi, an accession line generated in Cape Verde Islands environment, the protein-protein interaction between GI Cvi and ZTLCvi is temperature-dependent, which participates in the accumulation of ZTL protein and maintains a circadian period for nearly 24 h at warm temperatures (27 °C) (Kim et al., 2020). It is also found that ZTL contributes to lengthened circadian period of the root clock (Li et al., 2020b). In ztl-3 mutant, the difference in period length between roots and shoots was significantly greater than that in wild type, indicating that ZTL may have different effects on the temperature response in multiple plant tissues. In the analysis with 30 °C, the absence of PRR9 and PRR7 in prr7 prr9 double mutant is critical to the temperature-over-compensation (Salomé et al., 2010). Moreover, compared with 22 °C, circadian amplitude of GI expression in roots was weakened at 28 °C, and the inhibitory effect of PRR9 and PRR7 on GI expression was also reduced in roots under warm temperature (Yuan et al., 2020).

The rhythmic growth of hypocotyls is jointly regulated by light, ambient temperature and the circadian clock. The sequential expression of PRR9, 7, 5, and TOC1 from dawn to early evening dynamically inhibits the hypocotyl elongation in daytime under short-day conditions (Martin et al., 2018). These PRRs interact with bHLH transcription factor PHYTOCHROME INTERACTING FACTOR (PIF3, 4) proteins and restrict PIFs activity in the morning (Soy et al., 2016; Zhu et al., 2016; Martin et al., 2018). PRR-PIF complex negatively regulate their common target genes during the daytime, such as PIF-promoted CDF5, which promote cell elongation in the morning (Martin et al., 2018). During the course of the night, TOC1 interacts with PIF4, thereby inhibiting the activation of PIF4 on its target genes. At a warm temperature of 27 °C, PIF4 mediates the increase of phytohormone auxin and promotes the elongation of hypocotyls in Arabidopsis (Gray et al., 1998; Koini et al., 2009; Franklin et al., 2011) (Fig. 2). Moreover, warm temperature specifically accelerates the rapid growth of hypocotyls at nighttime, and this common feature of hypocotyl thermo-responsiveness shows natural variation in Arabidopsis ecotypes (Box et al., 2015). In the analysis with the multiple natural accessions, the temperature-dependent expression of evening-phased gene ELF3 and LUX weakened in specific lines with faster hypocotyl growth rate at nighttime. The accumulation of PIF4 transcripts at 22 °C and 27 °C is largely affected by the functional expression of ELF3. Compared with wild type and lux-4, elf3-1 mutant lost thermal responsiveness, indicating that evening components are required for circadian gating of thermoresponsive growth (Box et al., 2015).

The circadian oscillators regulate the diel expression of PIF4 in thermomorphogenesis

Phytochrome B (phyB) and PIFs have been shown to both preferentially target G-box motifs in promotor regions of target genes. In addition to ELF3, phyB, which acts as red-light receptor in seedlings de-etiolation, also functions as a thermosensor (Jung et al., 2016; Legris et al., 2016). The rate of phyB inactivation is proportional to temperature under dark conditions. Therefore, phyB senses the temperature fluctuations and directly inhibits its target genes during the night. In the analysis of RNA-seq and ChIP-seq, it was also found that the ability of EC complex and phyB to bind target genome-wide depends on temperature (Ezer et al., 2017). This study suggests that evening-phased circadian oscillators and the phyB jointly mediate the transduction of temperature cues (Fig. 2). It was recently discovered that hypocotyl elongation of cca1 lhy mutant is not sensitive to warm temperature of 29 °C (Sun et al., 2019). Morning-phased CCA1 recruits SHORT HYPOCOTYL UNDER BLUE1 (SHB1) to PIF4 promoter and mediates red light-induced PIF4 expression, thereby maintaining the light response and warm temperature morphogenesis during the morning (Sun et al., 2019) (Fig. 2). The recently identified HMR (HEMERA)-RCB (REGULATOR OF CHLOROPLAST BIOGENESIS) protein complex promotes the accumulation of PIF4 to initiate thermomorphogenesis during daytime (Qiu et al., 2021).

Coordination of circadian timing in high-temperature tolerance

Gil et al. found that overexpression of ZTL significantly improved the acquisition of thermotolerance of seedlings, and the survival rate was close to 100% after exposure to heat shock at 45 °C for 1.5 h in darkness (Gil et al., 2017). The exposure to 40 °C for 4.5 h in darkness at the midday greatly reduced the acquired thermotolerance in ztl-105 mutant with only a 10% survival rate, compared with the wild-type survival rate of 60%, and nearly 95% of ZTL overexpression line that experienced heat shock survived. In the analysis of total proteins using an anti-ubiquitin antibody, the amount of polyubiquitinated proteins (Ub) n in heat-shocked ztl-105 were reduced by about 30% compared with that in the wild-type plants. In addition, heat shock-induced polyubiquitination is dependent on the expression of HSP90 and the protein-protein interaction between ZTL and HSP90. Analysis with high-resolution nano-LC-MS shows that ZTL is involved in protein quality control system and is responsible for the removal of high-temperature-induced harmful protein aggregates (Gil et al., 2017).

The survival rate of toc1 prr5 double mutant subjected to a 45 °C heat shock is much higher than that of wild type and TOC1 overexpression plant, indicating that the rhythmic growth controlled by evening gene TOC1 is essential for high temperature tolerance (Zhu et al., 2016). The expression of PIF4 and its target gene YUC8 were significantly promoted at ZT8-16 and ZT16-20 in toc1 and toc1 prr5 mutants, suggesting the inhibitory effects of evening-accumulated TOC1 on PIF4 expression at late afternoon and evening during thermoresponsive growth (Zhu et al., 2016) (Fig. 2). During nighttime, TOC1 and ELF3-ELF4-LUX bind to the G-box or LBS element of PIF4 promoter, respectively, to inhibit PIF4 expression (Nusinow et al., 2011). In addition, based on transcriptional profiling, RVE4 and RVE8 play a role in the early response to heat shock signaling (Li et al., 2019).

The tissue-specific expression and communication of circadian oscillators have also been studied (Endo et al., 2014; Takahashi et al., 2015; Li et al., 2020b). The circadian clocks in shoots and roots can be independently entrained by imposed light and temperature stimuli (Thain et al., 2000; Wang et al., 2020b). In the tissues of the shoot apex, vasculature, root, and root tip, the cell-autonomous circadian clock effectively controls the period length of the local rhythms such as transcript rhythmic accumulation and dynamic protein-protein interactions (Gould et al., 2018; Li et al., 2020b). The circadian clock of shoots and roots independently respond to high temperature pulses, and the circadian rhythms in both tissues have the features of temperature compensation (Li et al., 2020b). Compared to shoots, the rhythmic expression of PRR9, PRR7 and EC complex genes in roots is more sensitive to the increase of environmental temperature (Yuan et al., 2020). Recently, grafting experiment confirmed the movement of ELF4 protein from shoot to root, and the ELF4 transport was inhibited by high temperature (Chen et al., 2020). As a result, the accumulation of ELF4 protein in the roots under high temperature was reduced, which may further promote the expression of PRR9, resulting in an increase in circadian pace of the root clock. Moreover, in field-grown rice panicles, warm nighttime temperatures increase OsPRR95 expression and disrupt the circadian-associated targets based on the transcriptome analyses (Desai et al., 2021).

In short, these studies show that the clock components closely co-occur with the light and temperature signaling and accurately regulate the light and temperature-dependent growth during the day and night. Among them, multiple circadian core oscillators may also involve in plant temperature tolerance in response to climate changes.

Daily and seasonal cold acclimation and freezing tolerance

Since they are rooted into the ground and cannot move, plants have to undergo frigid temperatures and cold acclimation to enhance freezing tolerance, especially in winter. A series of studies have shown that low temperature affects the circadian rhythm and the transcription of core oscillators in Arabidopsis. For example, with the increase in the number of days of cold treatment (4 °C), the robustness of the free-running rhythm in constant light of clock gene transcripts becomes weaker or dampened compared to 20 °C (Bieniawska et al., 2008). In addition, pre-mRNA of CCA1 undergoes alternative splicing (AS), which is inhibited by cold temperatures (Filichkin and Mockler, 2012; Seo et al., 2012). The retention of intron and splicing of alternative exon in most clock genes are associated with the temperature-dependent AS. It is found that the cold temperature of 4 °C significantly alters the production of CCA1, LHY, and PRRs transcript isoforms (James et al., 2012). Seo et al. has found that cold treatment increases the CCA1α isoform amount and reduces the CCA1β isoform accumulation (Seo et al., 2012). Overexpression of CCA1α causes an increase in CBF s expression, thus enhancing the freezing resistance of plants. The hypothesis proposed in this study is that CCA1β mediates the response of CCA1 to low temperature by interfering with the formation of CCA1α-CCA1α, LHY-LHY homodimers, and CCA1α-LHY heterodimers.

In winter freezing tolerance of populus tree, the shoot apical meristem remains dormant and buds burst in early spring. When the expression of clock genes is knocked down by RNAi, the tree shows a faster clock pace of the circadian rhythm, and its acclimation to freezing is remarkably weakened (Ibanez et al., 2010). In two assessments of electrolyte leakage (− 18 °C to nearly − 70 °C) and tissue discoloration (− 18 °C and − 51 °C) to determine injury, the reduction in freezing tolerance in pttlhy mutant (down-regulation of PttLHY1 and PttLHY2) was significant compared to the wild type. Moreover, loss-of-function of PttLHY or PttTOC1 causes delayed growth cessation during the shift from 18-h light exposure of photoperiod to 15-h light exposure, indicating that the mutants lost the photoperiodic sensitivity to critical daylength. In further experiments, bud burst of pttlhy mutant was also significantly delayed when shifting from a short-day to a long-day photoperiod. All in all, this study confirmed that functional circadian oscillators are critical to photoperiodism and low temperature resistance of plants.

In addition to the greatly reduced PttLHY transcripts in early morning, in pttlhy mutant, the peak phase of FKF1 and GI rhythmic expression was largely shifted forward compared to wild type (Ibanez et al., 2010). In the mechanism of photoperiod flowering, FKF1 and GI form a complex to degrade CDF protein, which removes the repressor from inhibiting CO and FT gene transcription (Imaizumi et al., 2005; Fornara et al., 2015). FKF1and ZTL transcripts accumulation are controlled by the circadian clock. These two proteins belong to another class of blue-light photoreceptor and both own a unique structure containing a PAS domain at N-terminus, an F-box and kelch repeats at C-terminus (Nelson et al., 2000; Somers et al., 2000). ZTL and FKF1 regulate flowering time specifically in response to long days. Interestingly, the late flowering phenotype of fkf1 mutant was restored by vernalization under 4 °C in dim white light for 4 weeks (Nelson et al., 2000). This result suggests that FKF1 plays a role in freezing tolerance by regulating cold acclimation of plants.

In Arabidopsis, many circadian core oscillators are involved in low-temperature signaling (Fig. 3). It is found that prr9-11 prr7-10 prr5-10 triple mutant ( d975) and amiR-PRR9-PRR7 line are more tolerant to cold and freezing stress than the wild type (Nakamichi et al., 2009; Wang et al., 2017). Both cca1 lhy and rve4 rve8 double mutants display a freezing-sensitive phenotype (Dong et al., 2011; Kidokoro et al., 2021). As MYB-like transcription factors, CCA1 and LHY occur closely with RVE4 and RVE8 in the early morning. CCA1 and LHY act as transcription repressors in the molecular architecture, while RVE4 and RVE8 act as activators. In response to low temperature, their loss-of-function mutants also shows different effects on cold signaling. Using the analysis of subcellular localization of the protein-labeled GFP, it is found that, after cold stress treatment for 3 h, the fluorescence of CCA1, LHY protein has completely disappeared, while RVE4 and RVE8 accumulated in the nucleus specifically upon cold stress (Kidokoro et al., 2021). ChIP-qPCR result confirms that under low temperature, the binding of CCA1 and LHY is reduced, while the RVE4 and RVE8 binding is increased. From this point of view, RVEs positively regulated the target genes, and CCA1 and LHY may have an indirect effect on the regulation of cold induction of C-repeat Binding Factor gene CBF1, CBF2, and CBF3 (Dong et al., 2011; Kidokoro et al., 2021). What's worth thinking about is that in cca1-11 lhy-21 double mutant, the circadian amplitude of CBF1, CBF2, and CBF3 (also known as DROUGHT RESPONSE ELEMENT BINDING FACTOR 1B, 1C, and 1A) or CBF-targeted cold-responsive gene ( CORs) expression ( COR15A, COR47, and COR78) is significantly reduced under cold temperature (4 °C) acclimation (Dong et al., 2011). This result predicts that CCA1 and LHY may not directly regulate the transcription of genes related to cold acclimation.

MYB-like transcription factors and PRRs in the circadian clock regulate the transcription of CBFs in response to low temperatures

It has been reported that PRR9, PRR7 and PRR5 inhibit the expression of CBFs during the day and night, respectively, by binding to G-box elements (Liu et al., 2016). In addition, the binding of PIF3, 4, and 7 to E-box element inhibits the expression of CBFs during the day (Lee and Thomashow, 2012; Kidokoro et al., 2021). Under low temperature conditions, the stability of EBF1, 2 (EIN3-BINDING F-BOX) decreases, so its function on degradation of PIF3 is weakened (Jiang et al., 2017) (Fig. 3). Wang et al. found that COR27 and COR28, which are regulated by the circadian clock with peak phase occurs at night, are genetically related to PRR9 and PRR7 and jointly contribute to the resistance to freezing (− 7 °C) without prior cold acclimation (Wang et al., 2017). The results of chromatin immunoprecipitation show that the directly binding of CCA1 to the promoters of COR27 and COR28 were attenuated under cold temperature conditions. In summary, the expression of circadian genes and the stability of post-transcriptional clock proteins are sensitive to temperature fluctuations during the day or night.

The clock components identified under extreme low temperature conditions schedule multiple signaling pathways, including circadian rhythms. In d975 plants, circadian components, such as CCA1, TOC1 and GI, are expressed arrhythmically (Nakamichi et al., 2005). The d975 mutant is actually resistant to a variety of stresses such as cold, high salinity, and drought (Nakamichi et al., 2009). It is known that gi-3 plant shows an increased sensitivity to freezing stress (Cao et al., 2005). Moreover, genetic analysis in Brassica rapa shows that natural variation in GI is responsible for flowering, circadian period length, freezing resistance, and salt tolerance (Xie et al., 2015). Taken together, the circadian oscillators are associated to daily and seasonal low temperature responses.

Circadian clock gates ABA signaling in response to drought stress

The circadian clock gates plenty of hormonal cross-talks, including hormone biosynthesis, degradation, and signaling pathways (Hanano et al., 2006; Covington et al., 2008; Atamian and Harmer, 2016). Mockler et al. established diurnal and circadian expression profiling in the diurnal project (DIURNAL database, http://diurnal.cgrb.oregonstate.edu/), which is widely used to retrieve a large number of clock-controlled genes that regulate hormone metabolism, perception and responses in Arabidopsis and crops (Mockler et al., 2007). Numerous hormones are involved in the plant resistance to various abiotic and biotic stresses (Zhu, 2016). Based on the established time course transcriptome analysis, it was found that transcript enrichment of a large number of abscisic acid (ABA)- and methyl jasmonate (MJ)-responsive genes show rhythmic oscillation within a 24-h cycles (Mizuno and Yamashino, 2008). These two types of hormones constitute the most basic signaling network for plant growth and stress resistance, thus indicating that the circadian clock contributes to plant adaption to ambient stresses (Fig. 4).

Circadian clock gates ABA signaling in response to drought stress

Take drought stress as an example, when plants are faced with water deficiency, ABA accumulates and induces ABA-mediated drought responses, like the promotion of stomatal closure to reduce water loss. During day and night cycles, the ABA level in leaves oscillates robustly, accumulating during the day, reaching its maximum level around noon, then gradually decreasing to the lowest level before dawn (Lee et al., 2006). This ABA accumulation rhythm is completely synchronized with the diurnal fluctuation of transpiration rate, suggesting that ABA likely acts as an internal cyclic signal to cue water level over a 24-h day (Fig. 4c). Leaf dehydration increases the ABA-GE hydrolyzing activity of β-glucosidase, and the expression of AtBG1 gene encoding β-glucosidase dynamically determines ABA production and its diurnal oscillation. The AtBG1 mutation results in a defect in stomatal closure in ABA responses. The peak expression of ABA-induced genes mostly occurs during daytime, displaying circadian rhythmicity (Mizuno and Yamashino, 2008). All of the above indicate that the circadian clock gates ABA signaling in response to drought stress. In turn, ABA level fluctuations also influence the endogenous circadian rhythms. The exogenous application of ABA results in a slight shortening in the period length of circadian rhythm (Liu et al., 2013). In addition, ABA-treated prr mutants display more sensitive phenotype than wild-type and d975 triple plant, that is, showing stronger reduction in period length shortening and the remarkable increased water loss rate in leaves. This information indicates that the ABA gives feedback to the circadian clock and endogenous 24-h rhythms.

Functional circadian oscillators are necessary for drought responses (Fig. 4). Overexpression of TOC1 significantly blocks the tolerance to drought (Legnaioli et al., 2009). In plants overexpressing TOC1, leaves were severely dehydrated, the stomata closure was no longer sensitive to ABA treatment, and the stomata conductance was significantly higher than that of the wild type, thus reducing the survival rate under dehydration stress. ChIP analysis revealed that TOC1 overexpression inhibits ABA-related ABAR gene transcription possibly by directly binding to the ABAR promoter. Exogenous application of ABA triggers TOC1 to bind to the ABAR promoter more during the day than untreated materials and inhibit the rhythmic enrichment of ABAR in daytime (Legnaioli et al., 2009). The contribution of tissue-specific circadian clock has also been found in drought tolerance studies. TOC1 functions in tobacco ( Nicotiana attenuate) in response to drought were investigated separately in shoots and roots. (Valim et al., 2019). Under long-term drought field conditions, comparing root-only TOC1-deficient plant with wild type, TOC1 function in shoots is sufficient for wild-type allometric drought responses. Moreover, co-expression analysis in whole-transcriptome microarray profiling links the function of shoot TOC1 with red and far-red light signaling to drought responses.

Overexpression of PRR7 causes faster water loss rate in leaves, while in d975 triple mutant, dysfunction of PRR9, 7, 5 significantly reduces stomata conductance and leaf water loss rate, thereby enhancing the tolerance to drought stress (Nakamichi et al., 2009; Liu et al., 2013). Furthermore, in d975 plant treated with 10 μM ABA, the expression of ABA biosynthetic gene ABA1 and ABA-induced cycling DOF family gene CDF1 is increased, compared with the wild type treated with exogenous ABA (Liu et al., 2013). Therefore, PRRs core oscillators act as transcription repressors to negatively regulate the ABA response and signal transmission. Barley ( Hordeum vulgare) photoperiod response gene PHOTOPERIOD-H1 ( Ppd-H1) is a homolog to Arabidopsis PRR. Gol et al. found that mild drought causes a decrease in the number of spikelet and a delay in floral development in spring barley cultivars with a natural mutation in ppd-H1, but has little effect on the introgression lines carrying wild-type Ppd-H1 (Gol et al., 2021). This result suggests the critical role of circadian component PRRs in potential drought adaptations.

During water deficiency treatment, the LHY mutation significantly induces a large amount of ABA accumulation during the day and enhances the robustness of the rhythm of ABA level (Adams et al., 2018). When ChIP-seq analysis was applied to identify the targets of LHY, it was revealed that LHY likely directly binds to and inhibits and multiple target genes of ABA signaling pathways (Adams et al., 2018). The clock-controlled NCED3 gene encodes a 9-cis-epoxycarotenoid dioxygenase, which catalyze the rate-limiting step of ABA biosynthesis. LHY binds to promoters of protein phosphatase 2C genes ABI1, 2 and SnRK2.2 to inhibit their expression. LHY also binds to the ABI5 promoter to directly or indirectly promote its expression. In addition, PRR5 and PRR7 interacts with ABI5 to promote the transcriptional activation of ABI5 protein on ABA-responsive genes EM1 and EM6 (Yang et al., 2021). Studies in grain crop soybean ( Glycine max) found that loss-of-function of GmLCLs ( GmLCLa1, a2, b1, b2), four orthologues of Arabidopsis CCA1 and LHY, promote stomatal closure under drought stress and reduce leaf water loss rate, thereby enhancing the drought tolerance in soybean (Wang et al., 2021; Yuan et al., 2021a). Leaf dehydration causes circadian phase of rhythmic expression of GmLCLs to shift from early morning to noon for nearly 4 h (Yuan et al., 2021a). In addition, GmLCLs in soybean, as transcription repressors, negatively regulate the circadian rhythmic expression of ABA-responsive genes (Fig. 4 d).

In gi-1 mutant, ABA level is lower than that of wild type, and stomatal closure is weakened during the leaf dehydration, which may accelerate leaf water loss and reduce survival rate during dehydration stress (Baek et al., 2020). In terms of molecular mechanism, GI interacts with bZIP transcription factor ENHANCED EM LEVEL (EEL), and the GI-EEL complex binds to the ABA-responsive element (ABRE) of NCED3 (Baek et al., 2020). In summary, to synchronize with internal ABA response, the circadian clock is likely to orchestrate ABA biosynthesis to improve the robustness of ABA level rhythm, and to shift the circadian phase of clock and clock-controlled genes rhythmic expression in response to drought stress.

Circadian regulation of salinity responses and tolerance

The sensitivity of plants to salinity stress is time-dependent in a 24-h light-dark cycle. The application of 200 mM NaCl at nighttime has little effect on plant growth based on measurement of shoot fresh weight, while the daytime application causes a significant inhibitory effect (Park et al., 2016). Dysfunction of circadian oscillators significantly alters the resistance to salt stress. When plants were subjected to 250 mM NaCl treatment for 6 days, the bleaching rate in d975 triple mutant was only 20%, compared with 80% in wild type, indicating the contribution of PRRs in high salinity tolerance (Nakamichi et al., 2009). GI that regulates PRRs clock protein stability undergoes salt-induced degradation, and the resistance of gi-1 mutant to 150 mM NaCl treatment is stronger than that of wild type (Kim et al., 2013). In addition, overexpression of ELF3 significantly improves the resistance to 200 mM NaCl, and the ion leakage rate in ELF3-OX is much lower than that of wild-type plants (Sakuraba et al., 2017).

In terms of molecular mechanism, the circadian clock is closely related to the Salt-Overly Sensitive (SOS) signaling pathway (Zhu et al., 2010; Zhu, 2016; Yang and Guo, 2018). Under non-stress conditions, GI interacts with SOS2 to inhibit its function (Kim et al., 2013). Salt induces the degradation of GI protein, so the released SOS2 is recruited to the plasma membrane to activate SOS1 Na⁺/H⁺ antiporter activity, which extrudes Na⁺ out of the cell (Fig. 5). GI and ZTL interact to form a complex during the day, indirectly enhancing the stability of PRR5 and TOC1 proteins. If the salt stress in the daytime induces GI degradation, the cell-autonomous circadian clock will have serious functional defects. Genetic mapping and analysis of heterogeneous inbred lines show that nucleotide polymorphism of Brassica rapa GI is responsible for variation in circadian period, salt tolerance, cold response, and red light-mediated photomorphogenesis (Xie et al., 2015). Overexpression of clock evening complex component ELF3 promotes GI degradation. On the contrary, GI protein level remains higher in elf3-1 mutant (Sakuraba et al., 2017). At the same time, ELF3 promotes salt stress tolerance by repressing PIF4. PIF4 directly inhibits ANAC042 gene transcription to modulate plant growth via hormone GA and BR signaling. Also, PIF4 likely upregulates the positive aging regulator gene ANAC092/ ORESARA1 ( ORE1) and senescence-associated gene SAG29 in response to salinity stress (Sakuraba et al., 2017). Studies in soybeans have confirmed the contribution of ELF3 ortholog J in salt tolerance. The transcriptome data shows that the expression of salt-responsive genes decreases in j loss-of-function alleles, indicating the positive regulation of J in salt stress resistance (Cheng et al., 2020). In summary, GI, ZTL, and ELF3 are required in molecular mechanisms for plant to orchestrate growth, senescence, and stress resistance.

PRRs and GI regulate Na⁺ transport through transcription and post-transcriptional mechanisms, respectively

Epigenetic modification of clock components is involved in the salinity-associated mechanism. It has recently been discovered that OsPRR73 in rice ( Oryza sativa) contributes to salt tolerance (Wei et al., 2021). OsHKT2;1 was identified as the target gene of OsPRR73 by RNAseq, encoding a plasma membrane-localized Na⁺ transporter. Salt-induced expression of OSPRR73 interacts with histone deacetylase 10 (HDAC10) to alter promoter chromatin status of OsHKT2;1, thereby inhibiting OsHKT2;1 transcription (Fig. 5).

All in all, the circadian clock actively mediates the transduction of salt stress signal and physiological regulation of resistance. The rhythmic expression of core oscillators is sensitive to salt signals, and fine-tune the robustness/amplitude, circadian pace, and circadian phase of endogenous 24-h rhythm to activate the salt tolerance.

The circadian clock perceives mineral nutrition fluctuations and malnutrition

The concentration of cytosolic and chloroplastic free calcium (Ca) and magnesium (Mg) in cells oscillate in a 24-h day, indicating that ions homeostasis is regulated by the circadian clock (Johnson et al., 1995; Sai and Johnson, 1999; Feeney et al., 2016). In Arabidopsis, circadian rhythm of cytosolic free calcium ion ([Ca ²⁺] cyt) is synchronized with photoperiodic cue, and circadian peak phase occurs in daytime (Love et al., 2004; Xu et al., 2007). Plants need a variety of mineral elements for growth and development, which are transported, stored and utilized in tissues after being absorbed as metal ions from the environment. The destruction of mineral homeostasis triggers an alternation in circadian rhythms. For example, application of 10 μM CuSO ₄ causes a shortened circadian period under constant light or constant darkness and a weakened amplitude of CCA1 transcripts enrichment rhythm (Andres-Colas et al., 2010). The transcript accumulation of CCA1 and LHY in ZT0 decreases significantly under the growth conditions with 0.2, 1, 10, or 50 mM CuSO4 treatment. These results indicate that disorder of Cu homeostasis represses CCA1 and LHY expression in early morning.

In circadian phase shift assay, 0.25 μM Fe pulse causes obvious phase delay of TOC1:LUC rhythm, indicating that Fe element may act as an internal timing cue to reset the circadian clock (Salomé et al., 2013). The extremely low Fe level of 0.25 μM causes leaf chlorosis and slower circadian pace, showing a period length longer than 27 h. The circadian rhythms were further monitored under various Fe level of 0.25, 1, 5, and 25 μM, and it was found that circadian period length is inversely proportional to Fe level. It is noteworthy that, under long-term Fe deficient conditions, circadian rhythms of morning-phased CCA1, LHY, PRR9, and PRR7 expression are dampened with gradually reduced amplitude, while circadian rhythms of afternoon- and evening-phased PRR5, TOC1 and ELF3 expression are not obvious altered (Chen et al., 2013). This study suggests that the sensitivity of circadian clock to Fe fluctuations is time-dependent over a 24-h day, and Fe likely fine-tune circadian rhythm via a retrograde signaling pathway.

Circadian oscillators contribute to the transport of iron ions. Based on the analysis of chlorophyll content, the cca1 lhy double mutant and PRR7 overexpression line are more sensitive to iron excess (600 μM Fe), showing a decrease in chlorophyll content compared to the wild type (Liu et al., 2013). Moreover, the accumulation of photosynthetic protein in cca1 lhy was remarkably lower than that of wild-type plants under Fe-deficient conditions (Xu et al., 2019). The root ferric-chelate reductase (FRO) that reduces Fe ³⁺ ions to soluble Fe ²⁺ ions exhibits a circadian rhythm under constant light, with a peak phase occurring around noon. The cca1 lhy mutant exhibits a dramatic decrease in FRO enzyme activity and loss of circadian rhythm. Dex-induced CCA1 overexpression in 35S:6His-CCA1-GR transgenic line promotes transcript accumulation of iron homeostasis genes bHLH038, bHLH038, bHLH100, bHLH101, FRO2, and IRON-REGULATED TRANSPORTER1 ( IRT1), and ChIP analysis confirms the binding of CCA1 to the promoter regions of aforementioned genes (Xu et al., 2019). IRT1 is a high-affinity iron transporter responsible for the uptake of iron ions. The irt1-1 mutant exhibits a lengthened period of circadian rhythm (Hong et al., 2013). The circadian clock regulates IRT1 rhythmic expression, and the IRT1 promoter activity reaches its peak in early morning based on IRT1:LUC measurement.

TIME FOR COFFEE (TIC) is found in the nucleus and regulates the period length and amplitude of circadian rhythm, possibly by affecting the expression of LHY (Ding et al., 2007). The tic-2 mutant displays a chlorotic phenotype in response to iron status, which can be rescued by exogenous Fe application. TIC inhibits the expression of FERRITIN 1 (ferritin gene) and iron-responsive genes (Duc et al., 2009). Ferritin mediates oxidative stress response in plants (Briat et al., 2010). Under constant light conditions, FER1, FER3, and FER4 transcript accumulation exhibits circadian rhythms, with the peak expression occurring at evening (ZT12), indicating that their transcription was regulated by the circadian clock (Hong et al., 2013; Liu et al., 2013). ChIP-seq analysis in HA-PRR7/ prr7-3 material confirms that FER1, FER3 and FER4 are target genes of PRR7 protein. In summary, the circadian clock gates Fe storage and Fe signaling, thus modulating oxidative stress responses and maintaining daily organelles functions including chloroplast.

In chloroplast, Mg is a key component of chlorophyll and co-factor of many photosynthetic enzymes. Phosphorylation of KaiC protein, a circadian oscillator of cyanobacteria clock, is sensitive to the concentration of Mg (Johnson et al., 2008; Jeong et al., 2019). Low concentration of Mg induces KaiC phosphorylation, and high concentration of Mg leads to dephosphorylation of KaiC (Jeong et al., 2019). In daily photosynthesis, Mg transporter OsMGT3, which is located in chloroplast membrane, assists the Mg intake to maintain the basic functions of chloroplast. In rice chloroplasts, the Mg level oscillates in a 12-h light:12-h dark diurnal cycle with the highest accumulation (peak phase) of diel rhythm occurring in evening (ZT12-16) (Li et al., 2020a). In osmgt3 mutant, the amplitude of Mg 24-h rhythm is greatly weakened, suggesting that OsMGT3 maintains Mg homeostasis through rhythmic intake of Mg. Moreover, OsMGT3 transcripts are rhythmically enriched in the leaf blade with the peak of robust diurnal rhythm appearing at dawn. The hypothesis proposed in this study is that OsMGT3 maintain the circadian rhythmic intake of Mg in chloroplast, which contributes to Rubisco activity and photosynthetic carbon assimilation during the daytime (Li et al., 2020a). Recently, it was found that the increase in Mg concentration causes circadian period length to be shortened, indicating that magnesium level is inversely proportional to circadian pace (de Melo et al., 2021). When external supply of Mg is restricted as low as 5 or 25 μM, plant exhibits a very long period length of circadian rhythm and seedlings development is obviously slowed down. In this study, according to the Mg pulse PRC analysis, the 50 μM external pulses do not cause a phase shift of circadian rhythm. However, the studies still reveal that ion-like biochemical oscillators can affect the circadian clock.

Mechanisms linking the circadian clock and plant defense against pathogens and insect herbivores

During the course of co-evolution with microorganisms and herbivores, plants maintain a temporal resistance to biotic stresses. The circadian clock in Arabidopsis regulates the rhythmic expression of many resistance ( R)-genes related to innate immunity to resist oomycete pathogen ( Hyaloperonospora arabidopsidis, Hpa) that causes downy mildew (Zhang et al., 2013). When Hpa spores are normally disseminated in nature, CCA1 plays essential role in the resistance of wild-type Arabidopsis to Hpa Emwa1 infection at dawn (Wang et al., 2011). By contrast, cca1 mutant has more leaves with sporangiophores, regardless of whether they are infected with Hpa Emwa1 during the day or night. The R gene in Arabidopsis, RPP4, exhibits a robust circadian expression with a peak occurring around late night. Hpa Emwa1 infection causes arrhythmicity of RPP4 expression. This study reveals that the plant circadian clock predicts the natural infection time of pathogens and regulates MAMP-triggered basal immunity and RPP4-mediated programmed cell death in the hypersensitive responses.

Goodspeed et al. found that the feeding behavior of cabbage loopers ( Trichoplusia ni) shows robust circadian rhythms under light/dark cycles and continuous dark conditions (Goodspeed et al., 2012). The measurement of consumed diet indicates that feeding started during the subjective day and reaches its peak in the evening, while the amount of diet consumed in early morning is extremely low. From the results, it is speculated that insects avoid feeding from midnight to next morning, and plants are also more defensive against herbivory during the late night and dawn over a 24-h day. During the entrainment that alters light/dark cycle with in-phase or out-of-phase of plants and insects, plants show stronger resistance to herbivory with in-phase than out-of-phase with the entrainment of insects. Overexpression of CCA1 and knocking out LUX causes arrhythmicity in plant and abolished in-phase-dependent enhanced resistance to pests. This result indicates that functional circadian oscillators in plant anticipates the feeding rhythm and prepares the plant to fight against it. The circadian clock regulates the biosynthesis and signal transduction of hormones in response to environmental changes (Atamian and Harmer, 2016). Under free-running conditions, the accumulation of jasmonates (JA) and salicylate (SA) presents diametrically opposite phases in circadian rhythms, JA is enriched during subjective day, and SA is enriched during subjective night. In aos and jar1 mutants lacking active JA accumulation, plants lose their enhanced resistance to T. ni loopers during in-phase entrainment, indicating that the rhythmic enrichment of JA during the day is required for plant resistance (Goodspeed et al., 2012). Zhang et al. found that the lux-1 mutation inhibited the defense against Pseudomonas syringae mediated by JA- and SA-signaling (Zhang et al., 2019). Therefore, multiple clock components maintain resistance to biotic stress by regulating hormone signaling.

Further study has found that the circadian clock continues to function in postharvest crops to enhance pest resistance via rhythmic accumulation of antiherbivore metabolite (Goodspeed et al., 2013). The aliphatic glucosinolate accumulation in postharvest cabbage ( Brassica oleracea) is regulated by the circadian clock with the peak of rhythmic enrichment occurring in subjective day. Compared with constant light or constant darkness conditions, antiherbivore metabolite glucosinolate 4-methylsulfinylbutyl (4MSO) maintains a more robust rhythmicity under the light/dark cycles at 22 °C and 4 °C, but the expression level at 22 °C is higher than that in cold temperature. In addition, the postharvest leaf disk (lettuce and spinach) or the postharvest whole organs (zucchini, sweet potato, carrot, and blueberry) still perceives the different entrainment between in-phase and out-of-phase light/dark cycle, and has enhanced in-phase-dependent resistance to T. ni loopers incubation (Goodspeed et al., 2013). In addition, it has recently been discovered that plants resist the rhythmic feeding of green peach aphid ( Myzus persicae) through the function of CCA1 alternatively spliced isoforms (Lei et al., 2019). The mechanism hypothesis is that aphid infestation-induced CCA1 promotes the production of indole glucosinolate. From this analysis, the circadian clock regulates metabolic pathways, including the production of bioactive compounds to resist pests.

In tobacco ( Nicotiana attenuata) grown under field and glasshouse conditions, the circadian clock contributes to the nitrogen (N) incorporation of three defense compounds caffeoylputrescine (CP), dicaffeoyl spermidine (DCS), and nicotine in response to herbivore Manduca sext (Valim et al., 2020). This nicotine response can be inhibited by fatty acid-amino acid conjugates in Manduca sexta oral secretions. After knocking out tobacco clock gene TOC1, Manduca sexta oral secretion-induced JA biosynthesis was obviously reduced, and the expression of defense-related MYC2, COI1, and MYB8 gene was also reduced or phase shifted, suggesting that there is a critical role of TOC1 in relationships between herbivore-induced phenolamide and nicotine in tobacco defense (Li et al., 2018; Valim et al., 2020). Subsequent experiments confirm that mutation of TOC1 in Nicotiana attenuata shifts the N allocation from phenolamides to nicotine (Valim et al., 2020). This study indicates that plants actively allocate N-rich defense components when undergoing pest stress, and fine-tune both vegetative biomass and herbivory defense.

Concluding remarks

In response to daily and seasonal fluctuating environments, the circadian clock synchronizes many biological processes, such as the opening and closure of stomata, petal & leaf, chloroplast movement, rhythmic growth, tuberization, and photoperiodic flowering (Britz and Briggs, 1976; Holmes and Klien, 1986; Yanovsky et al., 2000; Imaizumi, 2010; Song et al., 2015). Stomata are usually open during the day and closed at night. Stomata conductance and CO ₂ assimilation exhibit rhythmic fluctuations over a 24-h day (Holmes and Klien, 1986; Dodd et al., 2005). This rhythm is maintained even under continuous light conditions, indicating that the circadian clock regulates stomata functions. The circadian amplitude of stomatal movement is known to respond to red and blue light cues in light-dark cycle (Gorton et al., 1993). While maintaining photoperiodic control of seasonal growth and reproduction, stomata is also involved in resistance to abiotic and biotic stresses (Melotto et al., 2008; Qi et al., 2018). Drought causes a decline in photosynthesis and reduces CO ₂ assimilation products, and it is known that drought-induced ABA inhibits the response of stomata to light stimuli under both light-dark cycle and continuous light (Goh et al., 2003). Many known core oscillators of the circadian clock participate extensively in oxidative stress response and disease resistance against P. syringae through the regulation of stomatal behavior (Liu et al., 2013; Zhang et al., 2013; Zhang et al., 2019). While there are still many unresolved signal pathways or networks in stomata-mediated stress responses. For example, the circadian clock and photoperiodic signals regulate the circadian rhythm of free calcium in cytoplasm and chloroplast to maintain Ca ²⁺ homeostasis (Johnson et al., 1995; Xu et al., 2007). During water deficient stress, calcium ions affect stomatal movement by regulating aquaporin and water flowing and osmotic stress can quickly increase the concentration of cytosolic free calcium (Zhang et al., 2020). These studies do not elucidate how Ca ²⁺ homeostasis is maintained in plants grown under stress conditions.

Various environmental stresses weaken and damage plants in agricultural production, therefore breeding resistant crop resources and improving plant adaptability are the keys to ensuring the crop yield. In this review we summarized the critical roles of circadian core oscillators in perception and responses to biotic and abiotic stress signals with information that may be closely relevant to future crop production, such as the longer-term adaptation of agricultural ecosystems, biomass yield, and crop storage after harvest. Using the knowledge of chronobiology to identify germplasm resources and cultivate crop varieties with regionally adaptive circadian clock genotypes and rhythmic phenotypes are the research goals. Moreover, it is known that CCA1 and TOC1 regulate the sensitivity of plants to glyphosate, an efficient herbicide (Belbin et al., 2019). Therefore, the effect of herbicide spray timing and dosage on the circadian clock and growth rhythmicity will become one of the concerns in agricultural pest control. It is pointed out in chronoculture theory that the circadian clock outputs are associated with the utilization of water irrigation, fertilizer, and pesticide use in crop production (Steed et al., 2021). Therefore, chronoculture integrates the circadian clock and time-dependent farming and management strategies. In the future, with the help of gene editing technology, the sensitivity of the circadian clock to stress cues and the circadian parameters (phase, amplitude, and period length) will be artificially altered, and it is expected that the novel germplasm resources with optimized circadian clock will improve the adaptability of crops.

Availability of data and materials

Abbreviations

ABA:

Abscisic acid

AS:

Alternative splicing

CBF1:

C-repeat/DRE binding factor 1

CCA1:

CIRCADIAN CLOCK ASSOCIATED 1

CHE:

CCA1 HIKING EXPEDITION

CO:

CONSTANS

COR:

Cold-responsive gene

EC:

Evening complex

ELF3:

EARLY FLOWERING 3

FKF1:

FLAVIN BINDING, KELCH REPEAT,F-Box 1

FT:

FLOWERING LOCUS T

GI:

GIGANTEA

JA:

Jasmonate

LHY:

LATE ELONGATED HYPOCOTYL

LNK1:

NIGHT LIGHT-INDUCIBLE AND CLOCK-REGULATED 1

LUX:

LUX ARRHYTHMO

LWD1:

LIGHT-REGULATED WD1

MJ:

Methyl Jasmonate

N:

Nitrogen

phyB:

Phytochrome B

PIF:

PHYTOCHROME INTERACTING FACTOR

PRC:

Phase response curve

PRRs:

PSEUDO-RESPONSE REGULATORs

RNAi:

RNA interference

RVE4:

REVEILLE 4

SA:

Salicylate

SAG:

Senescence-associated gene

TIC:

TIME FOR COFFEE

TOC1:

TIMING OF CAB EXPRESSION 1

TTFLs:

Transcription-translation feedback loops

ZTL:

ZEITLUPE

References

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The circadian clock ticks in plant stress responses - Stress Biology

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The circadian clock, a time-keeping mechanism, drives nearly 24-h self-sustaining rhythms at the physiological, cellular, and molecular levels, keeping them synchronized with the cyclic changes of environmental signals. The plant clock is sensitive to external and internal stress signals that act as timing cues to influence the circadian rhythms through input pathways of the circadian clock system.

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Molecular Crop Research Unit, Department of Biochemistry, Chulalongkorn University, Bangkok, Thailand

Amity Institute of Biotechnology, Amity University, Uttar Pradesh, Lucknow Campus, Lucknow, India

Plant Molecular Biology Laboratory, Department of Botany, Dayanand Anglo-Vedic (PG) College, Chhatrapati Shahu Ji Maharaj University, Kanpur, India

Omics Sciences and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand

Calcium (Ca ²⁺) ion is a critical ubiquitous intracellular second messenger, acting as a lead currency for several distinct signal transduction pathways. Transient perturbations in free cytosolic Ca ²⁺ ([Ca ²⁺] cyt) concentrations are indispensable for the translation of signals into adaptive biological responses. The transient increase in [Ca ²⁺] cyt levels is sensed by an array of Ca ²⁺ sensor relay proteins such as calmodulin (CaM), eventually leading to conformational changes and activation of CaM. CaM, in a Ca ²⁺-dependent manner, regulates several transcription factors (TFs) that are implicated in various molecular, physiological, and biochemical functions in cells. CAMTA (calmodulin-binding transcription activator) is one such member of the Ca ²⁺-loaded CaM-dependent family of TFs. The present review focuses on Ca ²⁺ as a second messenger, its interaction with CaM, and Ca ²⁺/CaM-mediated CAMTA transcriptional regulation in plants. The review recapitulates the molecular and physiological functions of CAMTA in model plants and various crops, confirming its probable involvement in stress signaling pathways and overall plant development. Studying Ca ²⁺/CaM-mediated CAMTA TF will help in answering key questions concerning signaling cascades and molecular regulation under stress conditions and plant growth, thus improving our knowledge for crop improvement.

Introduction

Plants have evolved several strategies, majorly through molecular mechanisms, to optimize growth and ameliorate tolerance toward environmental constraints ( Tripathi et al., 2020). One of the basic mechanisms is an increase in the concentration of [Ca ²⁺] cyt in response to various external stimulus ( Hepler, 2005). Ca ²⁺ signatures are considered as core regulators of many adaptive and developmental processes. They are characterized by stimulus-driven signals resulting from the cumulative action of carriers, pumps, and channels. These transporters drive Ca ²⁺ signals spatially and temporally to act inside a cell. Spatial and temporal Ca ²⁺ signals are transmitted and decoded by a bunch of Ca ²⁺-binding proteins such as calmodulin (CaM) and calcineurin. These proteins further spread relayed information to generate specific downstream response by regulating transcription factors (TFs). TFs are DNA-binding regulatory proteins involved in distinctive expression of genes that regulate developmental processes and environmental stress responses ( Hong, 2016; Mitsis et al., 2020). CaM, upon interaction with Ca ²⁺, undergoes conformational changes to modulate several TFs ( Reddy, 2001). TFs contribute to various facets of cellular processes and act as a toolkit to signals perceived from within and outside an organism ( Mitsis et al., 2020). Intriguingly, TFs constitute a major portion of eukaryotic genome, accounting for approximately 5% of the total genome (approximately 2,000 genes) in humans ( Tupler et al., 2001). In Arabidopsis thaliana, 11.8% of their genome encodes for TFs (approximately 3,000 genes) ( Arabidopsis Genome Initiative, 2000). TFs such as DREB ( Agarwal et al., 2006; Chen et al., 2008; Lata and Prasad, 2011; Jan et al., 2017), NAC ( Tran et al., 2004; Nakashima et al., 2007; Puranik et al., 2012; Marques et al., 2017), bHLH ( Sun et al., 2018; Wang et al., 2020), WRKY ( Park et al., 2005; Phukan et al., 2016), and MYB ( Levy et al., 2005; Roy, 2016; Zhang et al., 2018) had been previously reported to play crucial roles in stress biology and plant growth. In a similar vein, CAMTA (calmodulin-binding transcription activator) in a Ca ²⁺/CaM-driven modus had been involved in carrying out important functions by modulating plant stress responses and overall development ( Bouche et al., 2002, 2005; Galon et al., 2010a; Liu et al., 2015; Shkolnik et al., 2019). The present review recapitulates the progress made in comprehending the involvement of Ca ²⁺/CaM-mediated CAMTA regulation in stress adaptation and plant development.

Calcium: A Second Messenger and Its Interaction With Calmodulin

A second messenger is a molecule that acts to transmit signals from a receptor to a target ( Bush, 1995). Ca ²⁺ is one of the best characterized second messengers. It is hypothesized that Ca ²⁺, as a messenger ion, emerged early during cell evolution ( Edel et al., 2017). The role of Ca ²⁺ as a primary nutrient in sustaining the structural integrity of cell walls and modulating diverse physiological processes such as biotic and abiotic stresses had been elucidated well by extensive biochemical and genetic studies ( Poovaiah et al., 1993; Zielinski, 1998; Reddy, 2001; Snedden and Fromm, 2001; Sanders et al., 2002; Harper et al., 2004; Reddy et al., 2004; Bouche et al., 2005; Hepler, 2005; Aghdam et al., 2012; Thor, 2019; Tian et al., 2020). Ca ²⁺ spikes are usually the outcome of two opposing reactions occurring in cells: Ca ²⁺ influx (entry) via dedicated channels or Ca ²⁺ efflux (exit) via specific pumps ( Xiong et al., 2006; Tuteja and Mahajan, 2007). The regulation of nanomolar concentration of Ca ²⁺ is achieved by two mechanisms. First, Ca ²⁺-ATPases pump [Ca ²⁺] cyt to the exterior or into organelles such as vacuoles and endoplasmic reticulum. Second, the opening of Ca ²⁺-permeable ion channels results in the Ca ²⁺ influx to the cytosol ( Medvedev, 2005; Tuteja and Mahajan, 2007; Swarbreck et al., 2013; Demidchik and Shabala, 2018). Transmembrane Ca ²⁺ gradients maintain sufficient energy to drive Ca ²⁺ flux passively into the cytosol from the apoplast or the organelles ( Medvedev, 2005; Swarbreck et al., 2013; Demidchik and Shabala, 2018).

Much evidence indicates that Ca ²⁺-mediated signaling is implicated in the relay of stress signals such as light ( Kim et al., 2003; Liang et al., 2009; Hochmal et al., 2015; Hou et al., 2019), temperature ( Teige, 2019), salt ( Rahman A. et al., 2016; Manishankar et al., 2018; Seifikalhor et al., 2019), cold ( Shi et al., 2014; Yuan et al., 2018b), and gravity ( Kordyum, 2003; Toyota et al., 2008; Salmi et al., 2011); oxidative signals such as pathogen attack ( Kiep et al., 2015; Aldon et al., 2018; Tian et al., 2019) and reactive oxygen species ( Mori and Schroeder, 2004; Monshausen et al., 2009; Kurusu et al., 2015); and hormone signals such as ethylene ( Li et al., 2018; Zhu et al., 2018), abscisic acid (ABA) ( Edel and Kudla, 2016; Chen et al., 2017; Yuenyong et al., 2018), gibberellins ( Abbasi et al., 2004; Nakata et al., 2009; Li et al., 2013), and auxins ( Vanneste and Friml, 2013; Hazak et al., 2019). In plants, oodles of Ca ²⁺-binding proteins function as Ca ²⁺ sensors decoding complex Ca ²⁺ signatures ( Kudla et al., 2018). These binding proteins sense changes in [Ca ²⁺] cyt and/or [Ca ²⁺] nuc, regulating downstream signaling processes and hence drawing physiological response against them ( Day et al., 2002; Boonburapong and Buaboocha, 2007). Ca ²⁺ sensors are basically proteins with highly conserved one or multiple helix-turn-helix structures-EF-hand ( Nakayama et al., 2000; Day et al., 2002). Approximately, 250 EF-hand comprising putative Ca ²⁺ sensors are known in A. thaliana, which accounts for about 1% of the reported proteome ( Day et al., 2002). Ca ²⁺ sensors are divided into three categories, namely, (i) CaM and CMLs (calmodulin-like proteins) ( Bender and Snedden, 2013), (ii) CBLs (calcineurin-B-like proteins) ( Luan, 2009), and (iii) CPKs (Ca ²⁺-dependent protein kinases) and CCaMK (calcium and calcium/calmodulin-dependent protein kinase) ( Cheng et al., 2002).

Calmodulins are a class of extensively studied Ca ²⁺ sensors and are known to regulate diverse cellular processes in plants, including stress responses and plant development ( Zielinski, 1998; Bouche et al., 2002; Bergey et al., 2014; Zeng et al., 2015; Dubrovina et al., 2019). CaM is a 17-kDa dumbbell-shaped cytosolic acidic protein, with a supple joint in the middle ( Meador et al., 1992). CaMs contain four EF-hand motifs and are highly conserved among eukaryotes ( Zielinski, 1998; Reddy et al., 2002). It has been ascertained that around 300 proteins interact with CaMs in plants ( Reddy et al., 2002; Bouche et al., 2005; Popescu et al., 2007). Among the known protein-protein interactions, CaMs are implicated to have maximum interacting partners ( Lee et al., 2010). Conformational changes upon Ca ²⁺/CaM association lead to contact of hydrophobic surface within each domain ( Liu et al., 2017). This ultimately elevates the CaM's Ca ²⁺ sensor bustle, thus resulting in an interaction with its target proteins ( Snedden and Fromm, 2001). CaM can act either directly by interacting with key target enzymes or indirectly with the help of specific kinases ( Zielinski, 1998; Mt and Mn, 2008; Villalobo et al., 2019). Interestingly, Ca ²⁺ ions interact with CaM in a cooperative manner, such that even small changes in the level of [Ca ²⁺] cyt lead to huge alteration in the levels of active CaM ( Beccia et al., 2015; Fernandes and Oliveira-Brett, 2017). Various CaM proteins display differential expression and possess variable affinity to Ca ²⁺, including their downstream target proteins ( McCormack et al., 2005; Popescu et al., 2007; Chinpongpanich et al., 2015). Many Ca ²⁺- and Ca ²⁺/CaM-associated TFs, implicated in stress signaling, are found in plants ( Singh and Virdi, 2013; Shen et al., 2020). However, few TFs are specifically transcribed to distinct Ca ²⁺ signal durations and amplitude. For example, the binding of Ca ²⁺ can directly regulate the activity of certain TFs such as the DREAM (downstream regulatory element antagonist modulator) protein ( Carrion et al., 1999). Similarly, AtNIG1 ( A. thaliana NaCl-inducible gene 1) is also classified as a Ca ²⁺-dependent TF, which is targeted in the nucleus, indicating that it is a nuclear Ca ²⁺-binding protein ( Kim and Kim, 2006). Taking Ca ²⁺/CaM-associated TFs into account, AtGT2L, a member of GT-2 subfamily, upon interaction with Ca ²⁺/CaM, is responsive to freezing and salinity stresses in plants ( Xi et al., 2012). WRKY is also implicated to be a Ca ²⁺/CaM-dependent gene ( Park et al., 2005; Yan et al., 2018). AtWRKY7 is well reported to be involved in Ca ²⁺/CaM-dependent gene regulation and has a role in pathogen incursion ( Park et al., 2005). Experimental results have revealed that CaM binds exclusively to the Ca ²⁺-regulated CaM-binding domain (CaMBD) of AtWRKY7 ( Park et al., 2005). In addition, WRKY45, WRKY43, WRKY53, and WRKY50 bind to various isoforms of CaM in a Ca ²⁺-driven behavior ( Popescu et al., 2007). Myb is another TF that is well characterized as a Ca ²⁺/CaM-dependent protein. It acts upstream to a number of defense-responsive, salinity, and drought-receptive genes ( Do Heo et al., 1999; Stracke et al., 2001; Li et al., 2019). CaM also supports transcriptional repression interceded by CBNAC/NTL9 ( Kim et al., 2007). The TF ZmNAC84 has been reported to interact physically with ZmCCaMK both in vivo and in vitro. ZmNAC84, after ABA induction, has a partially overlapping expression profile with ZmCCaMK. Its overexpression renders drought tolerance and resistance to oxidative stress induction owing to drought ( Zhu et al., 2016). Moreover, Cam7, also known as ZBF3, is a CaM isoform in A. thaliana that works as a transcriptional regulator. The upstream elements to TSS of light-inducible genes are directly targeted by ZBF3 to promote photomorphogenesis ( Yadav et al., 2005; Kushwaha et al., 2008). In a similar context, recently, CaM proteins have also been implicated in salt stress. "Khao Dawk Mali 105" rice variety, which overexpressed OsCam1-1, shows differential expression of several genes implicated in salt stress signaling, hormonal regulation, lipid/carbohydrate/secondary metabolism, photosynthesis, etc. Owing to OsCam1-1 overexpression, the rate of photosynthesis declines in transgenic rice, whereas content of sucrose and starch increases under salt stress. This study further revealed that CaM under salt stress condition boosts several metabolic enzymes implicated in energy pathways of plant cells, which either conserve or generate energy under limited photosynthesis ( Yuenyong et al., 2018). Thus, Ca ²⁺/CaM acts as an intermediate complex between stimulus perception and gene expression ( Figure 1).

Figure 1. Decoding of calcium signatures: calcium signaling and its sensors with associated transcription factors.

Calmodulin-Binding Transcription Activator

Calmodulin-binding transcription activator also known as SR (signal responsive) protein ( Yang and Poovaiah, 2000) or EICBP (ethylene-induced CaM-binding proteins) ( Reddy et al., 2000) is considered as the largest and best characterized family of CaM-binding TFs ( Finkler et al., 2007; Kim et al., 2009). The presence of this novel protein that binds to DNA was for the very first time identified during the process of isolation of partial cDNA clone (CG-1) from parsley ( Petroselinum crispum) ( e Silva, 1994). It was shown that CGCG was the signature motif required for the binding of this protein to DNA. Upon exposure to UV-containing white light, the CG-1 coding mRNA was rapidly and transiently accumulated in parsley cultured cells; however, until then, CG-1 was not designated as a Ca ²⁺/CaM-binding protein ( e Silva, 1994). Later, Yang and Poovaiah (2000) identified NtER1 as a CaM-binding protein engaged in ethylene-regulated plant death and senescence, indicating its potential application in prolonged storage life of horticultural crops. They also showed that CaM binds with a very high affinity ( Kd∼12 nM) in a Ca ²⁺-dependent manner to NtER1 ( Yang and Poovaiah, 2000). Yang and Poovaiah (2002) further reported a homolog of NtER1 and five related genes in Arabidopsis ( AtSRs). Their results evidently suggested AtSRs as a CaM-binding gene family with CGCG as a core motif in their promoters. The study also indicated probable implication of CAMTAs in numerous signal transduction pathways in plants ( Yang and Poovaiah, 2002). Since then, CAMTA TF had been reported in various multicellular organisms. With the advancement in computational techniques, proteins similar to CAMTA had been reported in unicellular eukaryotes such as the ciliates, Paramecium tetraurelia and Tetrahymena thermophila ( Finkler et al., 2007). The orthologs of CAMTA had also been reported in Arabidopsis ( Yang and Poovaiah, 2002; Bouche et al., 2005); humans ( Song et al., 2006); flies ( Han et al., 2006); and in various crop plants such as tobacco ( Yang and Poovaiah, 2000), rice ( Choi et al., 2005), tomato ( Yang et al., 2012), grapevine ( Shangguan et al., 2014), corn ( Yue et al., 2015), strawberry ( Leng et al., 2015), barrelclover ( Yang et al., 2015), soybean ( Wang et al., 2015), poplar ( Wei et al., 2017), and cotton ( Pant et al., 2018). Deciphering the CAMTA gene expression patterns and functional redundancies in model plants and various crops laid a foundation for further investigation of its role, which can be beneficially applied to agricultural sector. CAMTA as a TF has been extensively studied for its involvement in stress and developmental biology ( Galon et al., 2008; Doherty et al., 2009; Pandey et al., 2013; Sun et al., 2020) (discussed in CAMTA Transcriptional Response Under Abiotic Stress Conditions and CAMTA Transcriptional Response Under Biotic Stress Conditions).

The most important characteristic of this TF is its affinity for CaM, indicating its participation in Ca ²⁺ sensing. Ca ²⁺ signatures are effectively decoded by CAMTAs to generate explicit gene expression patterns ( Liu et al., 2015). Liu et al. deciphered how different Ca ²⁺ signatures are decoded by CAMTAs for specific gene expression patterns. The group devised a dynamic model based on thermodynamic and kinetic principles for understanding the Ca ²⁺-CaM-CAMTA binding and the subsequent gene expression patterns. The modeling analysis unraveled that Ca ²⁺ signals in the form of elevated [Ca ²⁺] cyt are non-linearly amplified upon binding of Ca ²⁺, CaM, and CAMTAs. They further combined experimental data with mathematical modeling to comprehend the information flow from Ca ²⁺ signatures to CAMTA−regulated gene expression patterns ( Liu et al., 2015). This study provided great insight about the involvement of Ca ²⁺/CaM in regulation of CAMTAs. The succeeding section focuses on the transcriptional machinery of CAMTA with respect to its molecular and physiological functions in regulating stress responses and overall plant development.

CAMTA Domain Organization

Calmodulin-binding transcription activators were reported to consist of multiple functional domains ( Finkler et al., 2007). These domains have evolutionary conserved amino acid sequence, organized into a specific conserved order. The predicted functional domains of CAMTA include (a) NLS (nuclear localization signals) for targeting protein into nucleus. These signals have been reported in almost all the CAMTAs, but their localization varies in different organisms; (b) CG-1 domain, which is a unique domain implicated in DNA binding; for any protein to be characterized as CAMTA, the presence of CG-1 domain is obligatory; (c) TIG, which is involved in non-specific DNA interactions in TFs ( Aravind and Koonin, 1999) and also implicated in protein dimerization ( Muller et al., 1995); (d) ankyrin (ANK) repeats, which are tandem repeats of about 33 amino acids in a variety of eukaryotic and viral proteins and contribute in protein-protein interactions ( Sedgwick and Smerdon, 1999; Rubtsov and Lopina, 2000); (e) CaMBD, which is implicated in the association of Ca ²⁺-loaded CaM to CAMTAs; (f) IQ motifs, which are implicated in the binding of CaM and CaM-like proteins and are regions of low complexity with a consensus motif IQXXXRGXXX ( Bahler and Rhoads, 2002). In some cases, the presence of transcription activation domains (TADs) has also been mapped. The presence of TAD was reported in AtCAMTA1 ( Bouche et al., 2002), HsCAMTA2 ( Song et al., 2006), and DmCAMTA ( Han et al., 2006). However, owing to lack of sequence homology in this domain, it cannot be inferred that all CAMTAs possess TAD. All these domains are pivotal for the transcription of CAMTA. Nonetheless, certain variations exist; for example, the mapping of Ca ²⁺-loaded CaM domain on AtCAMTA1 reveals a binding site adjacent to IQ motif, indicating multiple CaM binding sites ( Bouche et al., 2002). Similarly, in OsCAMTA, the Ca ²⁺-dependent CaMBD and Ca ²⁺-independent CaM dissociation domains were mapped on C-termini ( Choi et al., 2005). Recently, in Gossypium species, GrCAMTA5.2, GrCAMTA5.3, and GhCAMTA3D.1, GhCAMTA5D.1 were depicted as TIG lacking CAMTAs in Gossypium raimondii and Gossypium hirsutum, respectively ( Pant et al., 2018). Similar to Gossypium study, five TaCAMTAs ( Triticum aestivum CAMTA), namely, TaCAMTA1-A/B/D and TaCAMTA5-A/D, also lacked TIG domain ( Yang et al., 2020). The domain organization of six Arabidopsis CAMTAs, analyzed bioinformatically, is shown in Figure 2.

CAMTA Transcriptional Response Under Abiotic Stress Conditions

The underpinning mechanisms of CAMTA under abiotic stress conditions had been reasonably elucidated ( Pandey et al., 2013; Noman et al., 2019; Shkolnik et al., 2019). Under normal conditions, where plants are not exposed to any stress, there is minimal induction of CAMTA genes. This could be either because the genes in this family of TFs share functional redundancy or the genes in this family are expressed under certain specific environmental conditions. Taking the model plant A. thaliana into account, six CAMTAs were reported: CAMTA1, CAMTA2, CAMTA3, CAMTA4, CAMTA5, and CAMTA6. It was shown that all these genes were rapidly and differentially triggered by environmental cues such as high temperatures, salinity, H ₂O ₂, physical wounds; and hormones such as ethylene, ABA, methyl jasmonate (MeJA), and salicylic acid (SA) ( Yang and Poovaiah, 2002; Kim et al., 2013, 2017; Pandey et al., 2013; Shkolnik et al., 2019). Considering the first member of Arabidopsis CAMTA gene family, AtCAMTA1, its role has been reported in auxin signaling (increased expression of AUX/IAA-IAA29), transport, and homeostasis. For transgenic plants possessing AtCAMTA1 promoter, GUS construct displayed cell-specific expression profiles of auxin. The involvement of CAMTA1 in auxin signaling is established by chemical impairment of polar auxin transport. Gene expression profiling of auxin transport inhibitor 1-N-naphthylphthalamic acid to plants also supports the responsiveness of CAMTA1 in auxin pathways. Genome-wide transcriptome of camta1 mutant mapped 63 upregulated genes. Further analysis revealed that 17 genes were involved in auxin signaling. Moreover, on analysis of hypocotyl elongation, the camta1 mutants were hyperresponsive to auxin exposure as compared to wild type ( Galon et al., 2010a). It was further reported that CAMTA1, CAMTA2, and CAMTA3 are negative regulators of auxin and are associated with genes responsible for red light and high light responses, whereas CAMTA4, CAMTA5, and CAMTA6 are positive regulators of auxins and are associated with genes responsible for blue light and darkness responses ( Galon et al., 2010b). In a similar vein, phytohormonal regulation of CAMTAs is also extended to brassinosteroid (BR) signaling in which BZR1 (involved in BR signaling cascade) has CAMTA5 as an associated protein ( Wang et al., 2013).

Additionally, camta1 responded severely to cold stress, indicating its role in cold stress management. CAMTA protein provides a much-needed connecting link between Ca ²⁺ signatures and cold acclimatization. CBF TF plays a pivotal role in cold acclimation of plants. In A. thaliana, CBF genes induce approximately 100 more genes (termed as CBF regulon) upon exposure to low temperatures ( Maruyama et al., 2004; Vogel et al., 2005). CAMTA binds to consensus sequence located in the promoter of the CBF2 gene. The 1,000-bp upstream region of CBF2 gene comprises seven conserved consensus motifs: CM1 to CM7. The CAMTA signature domain CG-1 matches the CM2 motif of CBF2. CBF2 expression is impaired in camta3 mutants and impaired to a much higher extent in camta1 and camta3 double mutant. These are indications of the intersecting functions of different CAMTA proteins. Therefore, CAMTA controls CBF regulon, which confers freezing tolerance to plants ( Doherty et al., 2009). Moreover, CAMTA1, CAMTA2, and CAMTA3 work in recital to induce CBF1, CBF2, and CBF3 and confer freezing tolerance to plants at low temperatures. Furthermore, at warmer temperatures, these three CAMTAs collectively inhibit SA biosynthesis. It had been previously shown that exposure to low temperatures increases SA levels ( Scott et al., 2004). SA biosynthesis at low temperatures involves ICS (isochorismate synthase) pathway, encircling ICS1, CBP60g, and SARD1 genes. At warm temperatures, CAMTA1, CAMTA2, and CAMTA3 suppress the accretion of ICS1, CBP60g, and SARD1. Such suppression by CAMTA proteins was not observed at low temperatures ( Kim et al., 2013). More neoteric inventions have elucidated that the suppression of genes involved in SA biosynthesis by CAMTA at high temperatures (22°C) is overcome when plants are infected by biotrophic or hemibiotrophic pathogens and grown at low temperatures (4°C) for more than a week. Suppression of SA pathway by CAMTA3 under normal condition involves an N-terminal repression module (NRM). NRM works autonomously of CaM binding to CaMBD or the IQ. This finding is different owing to the fact that CAMTA3 repressional assertion involves the binding of CaM to CaMBD. To sum up, the repression activity of CAMTA3 at low temperatures and pathogen infection is governed by related mechanisms ( Kim et al., 2017). To further elaborate, three dehydration-responsive element (DRE) binding protein 1/C-repeat binding factors (DREB1/CBFs) are master regulators of cold-responsive gene expression. DREB1 stimulates cold-responsive transcriptional cascade, eventually inducing several genes implicated in cold stress response. Rapid and gradual temperature decreases are sensed as cold stress by plants, which results in the activation of DREB1 genes. CAMTA3 and CAMTA5 are responsive to rapid decline in temperatures by inducing the expression of DREB1s. However, CAMTA3 and CAMTA5 do not respond to gradual declines in temperatures. In contrast to circadian clock associated 1 and late elongated hypocotyl (acts as transcriptional activators to regulate DREB1 expression only during the day), AtCAMTA3 and AtCAMTA5 function both during the day and night ( Kidokoro et al., 2017).

Drought and salinity are considered as few of the worst conditions in agriculture and horticulture. The role of CAMTA1 has been extended to drought response ( Pandey et al., 2013). The mutants had impaired photosystem II efficiency and water use efficiency, hence making plants susceptible to drought. The mutants had relatively low water content and retarded growth. Microarray analysis revealed drought revival, osmotic balance, apoptosis, DNA methylation, and photosynthesis as investigative pathways in drought-treated camta1 mutants. It has also been shown that CAMTA1 regulates a broad spectrum of stress-inducible genes such as RD26, ERD7, RAB18, LTPs, COR78, CBF1, and HSPs. To ascertain the claims, CAMTA recognition cis-element was found to be enriched in the 1,000-bp upstream regions of the listed genes ( Pandey et al., 2013). Very recently, CAMTA6 has been shown to be associated with sodium (Na⁺) homeostasis during early seed germination. The camta6 mutants accumulate less NaCl and display tolerance to salinity and ABA. AtHKT1 (high affinity K⁺ transporter 1 encoding a Na⁺/K⁺ transporter) expression was limited to radicles and did not increase upon subjecting camta6 to salt stress or ABA treatment. In addition, even the transcriptome of CAMTA6 under control and salt stress condition revealed 1,020 upregulated and 1,467 downregulated salt-responsive genes in the wild type ( Shkolnik et al., 2019). With regard to context, although CAMTA2 in plants has been less explored, studies highlight the regulation of AtALMT1 (aluminum-activated malate transporter 1) by CAMTA2 in association with WRKY46 ( Tokizawa et al., 2015; Wu et al., 2019). Next, considering the involvement of CAMTAs in plant development, the role of CAMTA1 and CAMTA5 is also extended in pollen development. The AVP1 promoter activity has been reported to be activated by AtCAMTA1 in cultured cells. AtCAMTA1 triggers the GUS reporter expression driven by P281 promoters of AVP1 but does not stimulate the GUS reporter expression in mutants with disrupted despaired CGCG-box, indicating that AtCAMTA1 attaches to CGCG-box and elevates AVP1 expression. AtCAMTA1 in collaboration with AtCAMTA5, cooperatively elevates their gene function and enhance AVP1 expression during pollen development ( Mitsuda et al., 2003). Apart from A. thaliana, CAMTAs from other plant species have also been implicated very well in abiotic stress responses. Various in silico and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) approaches were deployed to assess the involvement of CAMTAs under different abiotic stress conditions. For example, Zea mays (corn) ZmCAMTAs in drought, cold, salt, and hormonal signaling ( Yue et al., 2015); Fragaria ananassa (Strawberry) FaCAMTAs in heat, cold, salt, and ethylene stress ( Leng et al., 2015); T. aestivum (wheat) TaCAMTAs in cold, salt, heat, and drought stress ( Yang et al., 2020); Citrus sinensis (sweet orange) CitCAMTAs in salinity, dehydration, and hormonal response ( Zhang et al., 2019); and Linum usitatissimum (Flax) LuCAMTAs in drought, low temperature, and light responses ( Ali et al., 2020) (discussed in detail in Recent Discoveries of CAMTAs in Various Crop Plants).

CAMTA Transcriptional Response Under Biotic Stress Conditions

Transient Ca ²⁺ levels are known to be elevated during pathogen attack ( Ma and Berkowitz, 2007), which eventually activates CaM. Similar to the role of CAMTA1 in abiotic stress tolerance, the role of CAMTA3 in response to biotic stress has been elucidated to a great extent ( Galon et al., 2008; Laluk et al., 2012; Benn et al., 2016; Kim et al., 2017, 2020; Jacob et al., 2018). Ca ²⁺-loaded CaM regulates the activity of CAMTA3 either by negative regulation of CAMTA3 or by positive regulation of a negative repressor TF ( Galon et al., 2008). The bacterial pathogen Pseudomonas syringae and the fungal pathogen Botrytis cinerea did not heavily affect camta3 in comparison to their effect in control plants. This is suggestive of the fact that CAMTA3 is responsible for suppression of biotic defense responses, which may be primarily achieved either by binding of CAMTA3 with the promoters of suppressed genes or by expressing a TF that is repressed. Transcriptomics approach on camta3 revealed an attenuated expression of six genes and an enhanced expression of 99 genes out of which 32 genes are related to defense response such as WRKY33, PR1, chitinase, etc. Apart from model plants, the role of CAMTA3 in biotic response was also extended to crop plants. OsCBT encoding CaM-binding protein was functionally characterized in plant defense. Oscbt-1 mutants were significantly tolerant to rice blast fungus Magnaporthe grisea and the bacterial pathogen Xanthomonas oryzae ( Choi et al., 2005; Koo et al., 2009). Furthermore, for the identification of transcriptional network governed by OsCBT, transcriptome analysis between wild-type (WT) and oscbt-1 rice grown in pathogen-free conditions was performed. The analysis revealed 81 up-regulated genes and 200 down-regulated genes. Majority of these genes were implicated in biotic stress responses. The gene expression patterns of fungal pathogen response were also found to be significantly affected in oscbt-1 mutant, suggesting the probability of OsCBT in regulating rice defense response ( Chung et al., 2020).

Pathogen attack on plants causes intracellular Ca ²⁺ spikes; however, how Ca ²⁺ mediates this interaction-based SA level fluctuation remains unclear. CAMTA3 has been reported to link Ca ²⁺ transients to SA-mediated immune response. CAMTA3 also interacts with EDS1 promoter to impair its expression ( Du et al., 2009). Further research has led to the establishment of the role of CAMTA3 in plant defense and ethylene-induced senescence ( Nie et al., 2012). Edr2 (enhanced disease resistance 2) suppressors were screened to establish the components of EDR2 pathways. This led to the identification of gain-of-function mutation in signal responsive1 ( SR1) or CAMTA3. Edr2 mutants have enhanced tolerance for Golovinomyces cichoracearum (powdery mildew), indicating that EDR2 is a negative regulator for powdery mildew. The sr1-4D mutation restrains edr2-mediated powdery mildew tolerance. Post mildew infection, as compared to wild type, the edr2 plants displayed necrotic lesions with little powder. However, the edr2 sr1-4D plants phenotypically resemble that of the control plants with oodles of conidia on leaves. The sr1-4D gain-of-function also represses elevated ethylene induced senescence. Thus, CAMTA3 negatively impacts plant immune responses. It also directly interacts with the promoter of NDR1 (non-race specific disease resistance1), a key module in resistance to P. syringae-mediated plant immunity. Moreover, CAMTA3 also regulates EIN3 (ethylene insensitive3), which contributes to ethylene-induced senescence ( Nie et al., 2012).

As discussed previously, CAMTA1, 2, and 3 play pivotal roles in SA-mediated plant immunity. Pip (pipecolic acid) regulates the process of systemic acquired resistance (SAR) in plants ( Bernsdorff et al., 2016). CAMTA1, 2, and 3 suppress the synthesis of Pip by inducing ALD1 (AGD2-like defense response protein 1). ALD1 is known to encode an enzyme required for Pip biosynthesis. The induction of ALD1 results in an accumulation of Pip, which in turn increases the concentration of NPR1 (SA receptor protein). Thus, CAMTA123 triple mutation induces plant defense machinery and instigates SAR ( Kim et al., 2020). Moreover, CBP60g and SARD1 TFs are regulators of SA and NHP ( N-hydroxypipecolic acid). Both SA and NHP have been extensively linked to plant immunity. CBP60g has been identified as a direct target of CAMTA3 through chromatin immuno-precipitation (ChIP) and electrophoretic mobility shift assay (EMSA) assays. Thus, CAMTAs suppress SA and NHP biosynthesis by regulating SARD1 and CBP60g expression ( Lenzoni et al., 2018; Sun et al., 2020).

CAMTA3, as defense against insect herbivory, has also been studied in plants ( Qiu et al., 2012). Loss-of-function mutation of AtSR1 suppresses resistance to feeding by Trichoplusia ni (generalist herbivore), sustaining radically elevated larval weight gains and jasmonate (JA) accumulation owing to wound. The responsiveness of AtSR1 mutant to T. ni is attributed to low glucosinolate (GS) levels with remarkably decreased levels in indol-3-ylmethyl (I3M) and 4-methylsulfinylbutyl (4MSOB), the two well-characterized herbivory deterrents. Induction of the various genes implicated in GS metabolism such as IQD1, MYB51, and AtST5a also changes with alterations in AtSR1 transcript levels. Thus, AtSR1 through Ca ²⁺ signaling cascade acts as a key module of plant resistance to insect herbivory. Additionally, Ca ²⁺/CaM-dependent signaling plays a major role in GS metabolism through CAMTA3 involvement ( Laluk et al., 2012). Furthermore, upon pathogen exposure, plants allocate their energy resources (at the expense of growth) to defend themselves against invading pathogens. Yuan et al. (2018a) well-documented AtSR1 as a negative regulator of plant immune responses ( PTI-, ETI-, SA-, and JA-mediated signaling pathways) and a positive regulator of plant growth (auxin and BR signaling pathways). EMSA and ChIP assays demonstrated that AtSR1 maintains a strong balance between plant immunity and growth by interacting with the CGCG motif present in the upstream sequences of its potential target genes ( Yuan et al., 2018a). The same has also been previously established by Li et al. (2014), who identified seven SISR/CAMTA genes in tomato ( Solanum lycopersicum). Functional analysis using VIGS indicated that both SlSR1 and SlSR3L negatively regulate plant defense responses. SISRs were explicitly induced upon P. syringae pv. tomato ( Pst) DC3000 and B. cinerea infection. The knockouts of SlSR1 or SlSR3L displayed higher tolerance to Pst DC3000 and B. cinerea with subsequent accumulation of H ₂O ₂. Moreover, the genes implicated in pattern-triggered immunity, defense responses, and ethylene and SA pathways were significantly elevated ( Li et al., 2014).

To cope with environmental constraints, plants have synchronized mechanisms underlying GSR (general stress response) networks and stress-specific networks ( Benn et al., 2014). RSRE (rapid stress response element), the functional motif of GSR, has been extensively studied to explore the new horizons of stress signaling ( Walley and Dehesh, 2010). CAMTA3 has been very well studied as a pivotal modulator of RSRE-mediated GSR ( Bjornson et al., 2014). The function of CAMTA in RSRE response was hypothesized on the basis that Ca ²⁺ ion inducers, flagellin22 (flg22) and oligogalacturonic acid, enhance the RSRE activity, and Ca ²⁺ ion chelator, EGTA, reduces the RSRE activity upon wounding. CAMTA3 lies downstream of MEKK1, facilitating the regulation of peak time and amplitude of plant GSR and, in conjugation with CAMTA2 and CAMTA4, stimulates RSRE ( Benn et al., 2014; Bjornson et al., 2014). Moreover, the methylerythritol cyclodiphosphate (MEcPP)-triggered induction of GSR by transduction of CAMTA3 has also been reported ( Benn et al., 2016). CCaMKS and CDPKs all together act to relay the Ca ²⁺ signals essential for RSRE activation. Induction of RSRE by Ca ²⁺ outburst is in accordance with the defined and established role of this transducer in stress signaling ( Dodd et al., 2010). CAMTA3 conceivably has also been hypothesized to negatively regulate PTI (PAMP-triggered immunity) and defense by direct targeting of BAK1 and JIN1 through suppression of JA signaling pathway ( Rahman H. et al., 2016). CAMTA3 also controls the transcriptional regulation of early convergence of leucine-rich repeat-containing protein (NLR) and pattern recognition receptor (PRR) signaling. CAMTAs, being a probable target of pathogen effectors, promote pathogen virulence. Furthermore, camta3 mutants activate NLRs, resulting in host cell death. Deciphering the NLR-driven CAMTA activity underlines the connection of CAMTA in plant innate immunity ( Lolle et al., 2017; Jacob et al., 2018). Hence, ever since its discovery, CAMTAs have been implicated in various developmental and cellular processes either themselves acting as a key TF or targeting another TF through direct or indirect association. The overall involvement of CAMTAs in diverse molecular processes is shown diagrammatically in Figure 3.

Figure 3. Working model of CAMTA in plant cells.

Recent Discoveries of CAMTAs in Various Crop Plants

The function of CAMTAs has been established in crop plants like corn ( Z. mays), strawberry ( F. ananassa), barrelclover ( Medicago truncatula), poplar ( Populus trichocarpa), soybean ( Glycine max), grapevine ( Vitis vinifera), tomato ( S. lycopersicum), and cotton ( Gossypium spp.), where they were previously and largely unknown ( Yang et al., 2013, 2015; Shangguan et al., 2014; Leng et al., 2015; Wang et al., 2015; Yue et al., 2015; Wei et al., 2017; Pant et al., 2018). Nine ZmCAMTAs have been reported, upon scanning the entire genome of maize. These CAMTAs were shown to share homology with rice CAMTA. Moreover, an exceedingly high degree of sequence and structural pattern existed among the ZmCAMTAs, indicating that their origin is from a single ancestral sequence. All these CAMTAs possess highly tissue-specific expression and display slight diversity in their gene organization. The qRT-PCR of ZmCAMTAs under various abiotic stress (drought, salt, and cold), biotic stress (RBSDV-rice black-streaked dwarf virus infection), and stress hormones (ABA, auxin, SA, and MeJA) reveals its role as a stress-responsive gene. The occurrence of many stress-related cis-regulatory element in the 1.5-kb promoter region of ZmCAMTAs further confirmed its role in stress regulation ( Yue et al., 2015). Taking into account tomato ( S. lycopersicum), a horticulturally important crop, signal-responsive (SR)/CAMTA TF is well implicated in postharvest biology. SlSRs, in response to stress conditions, also coordinate Ca ²⁺ signaling with signal transduction pathways implicated in stress during fruit ripening and storage, thus indicating its significant role in horticultural sector ( Yang et al., 2013). As previously discussed, SlSRs are involved in plant defense responses and drought management. In S. lycopersicum, SlSR1 and SlSR3L negatively regulate plant defenses against B. cinerea and Pst DC3000. Conversely, SlSR1L is involved in the positive regulation of drought stress responses ( Li et al., 2014).

In F. ananassa, 4 CAMTAs, namely, FaCAMTA1, FaCAMTA3, FaCAMTA4, and FaCAMTA5, have been reported, upon the exploitation of BLAST and HMMER tool bioinformatically, using the CAMTA family domain file. All these CAMTAs possessed known domain organization comprising CG1, TIG, ANK repeats, and IQ motifs. Under heat, cold, salt, and ethylene stresses, the FaCAMTAs exhibit distinct expression patterns ( Leng et al., 2015). CAMTA genes have also been identified in M. truncatula genome, specifying its role in nodule organogenesis. The MtCAMTA genes display tissue-specific expression and respond to various stress-related hormones. Upon Sinorhizobium meliloti infection, the expression profiling of the MtCAMTA genes revealed that there is an alleviation in the suppression of most MtCAMTA genes expression, indicating the CAMTA involvement in early nodulation signaling pathway. Moreover, the promoters of early rhizobial infection response genes display the presence of CAMTA binding motifs ( Yang et al., 2015). Similarly, seven CAMTAs were identified in the genome of P. trichocarpa, and their expression patterns were analyzed in roots and leaves. The qRT-PCR analysis showed that their expression was induced by pathogenic infection with Alternaria alternata; mannitol, NaCl, and cold stress; and phytohormones including SA, ABA, and MeJA ( Wei et al., 2017). This was further substantiated as their promoter analysis revealed that most PtCAMTAs contain phytohormone or stress-related cis-regulatory elements. In G. max, 15 CAMTA genes were identified, sharing sequence homology with their Arabidopsis counterparts. All GmCAMTAs were profoundly expressed in root and leaf tissue. Their transcript abundance was induced significantly upon dehydration, cold, salinity, and hormonal treatments such as ABA, SA, and MeJA. In concurrence to their receptiveness to these signals, the promoter regions of these genes have been reported to be enriched with stress-related cis-regulatory elements ( Wang et al., 2015). Complete functional analysis of GmCAMTA12, in relation to drought stress, has been performed. The promoter region of GmCAMTAs contained ABRE, SARE, G-box, and W-box cis-regulatory elements. Arabidopsis T3 overexpression lines of GmCAMTA12 display enhanced drought tolerance and increased survival and germination rate under drought. GmCAMTA12 overexpression lines performed better in terms of physiological parameters such as proline and malondialdehyde contents, catalase activity, and electrolyte leakage ( Noman et al., 2019). In V. vinifera, 10 SR/CAMTAs were identified bioinformatically as Ca ²⁺/CaM-binding protein, belonging to four gene groups: VvCAMTA1, VvCAMTA3, VvCAMTA4, and VvCAMTA5 and localized on 5, 7, 1, and 5 chromosomes, respectively. Expression analysis of VvCAMTAs depicted its role mainly in Ca ²⁺ signal relay, with significantly high expression in bud, fruit, and inflorescence ( Shangguan et al., 2014). Additionally, in four different Nicotiana species ( Nicotiana tabacum, Nicotiana sylvestris, Nicotiana tomentosiformis, and Nicotiana benthamiana), 29 CAMTAs (13 in N. tabacum, 6 in N. sylvestris, and 5 each in N. tomentosiformis and N. benthamiana) were identified by evolutionary and expression analyses, which provided great insights into CAMTAs origin, expansion, and response toward stress conditions and plant development ( Kakar et al., 2018). NtabCAMTAs are among the early responsive genes to biotic stress ( cucumber mosaic virus-M -M strain of CMV, potato virus Y -Mn strain of PVY, and fungal pathogen black shank or Phytophthora nicotianae) and played pivotal roles in plant defense. NtabCAMTAs were also significantly responsive to abiotic conditions such as cold, drought, and cadmium stress ( Kakar et al., 2018).

The identification and functional characterization of CAMTA gene family were also extended to cotton. Six CAMTA members were identified in Gossypium arboreum, 7 in G. raimondii, and 9 in G. hirsutum. Segmental duplication and purifying selection play key roles in the expansion and evolution of CAMTAs in cotton genome. Expression profiling indicated that cotton CAMTAs were predominantly expressed in various stages of fiber development (0, 6, 9 12, and 25 days after anthesis). Precisely, the specific involvement of GhCAMTA2A.2 and GhCAMTA7A in maintaining fiber strength has been reported ( Pant et al., 2018). CAMTA gene family has also been classified in Phaseolus vulgaris (common bean). P. vulgaris contained 11 chromosomes, of which five chromosomes harbored eight CAMTA genes with 11 to 12 introns per gene. PvulCAMTAs shared similarity with GmCAMTAs owing to similar genome organization. In silico analysis revealed that PvulCAMTAs are involved in salt stress signaling and were further substantiated through RNA-seq and qRT-PCR analyses ( Buyuk et al., 2019). In concurrence to the above study, PvulCAMTA1 has also been characterized to impart drought tolerance ( Saeidi et al., 2019). CAMTA gene family had also been well reported in citrus ( Zhang et al., 2019). Nine CAMTA genes were found in the citrus ( C. sinensis and Citrus clementina) genomes. Most of the CitCAMTAs were found to be highly conserved during evolution. All CitCAMTA genes (except CitCAMTA4) were expressed in minimum one plant tissue corroborating their involvement in leaf, root, stem, seed, cotyledon, and fruit development. Hormone and stress experiments revealed varying expression profiles of CitCAMTAs, indicating stress adaptation. Upon salt stress (0- to 24-h treatment), CitCAMTA1, CitCAMTA5, and CitCAMTA9 were significantly expressed. The expression of CitCAMTA7 first increased and then decreased as the time of salt treatment progressed. The expression of CitCAMTA3 was suppressed at all time points. For the remaining CitCAMTAs, i.e., CitCAMTA2, CitCAMTA6, and CitCAMTA8, there were no obvious changes observed. Upon dehydration stress (0- to 12-h treatment), CitCAMTA5, CitCAMTA6, and CitCAMTA8 levels slightly declined, whereas for CitCAMTA3 and CitCAMTA9, the expression levels were up-regulated reaching a maximum at 12 h of treatment. CitCAMTA1 and CitCAMTA2 showed moderate level of expression under dehydration stress. While most of the CitCAMTAs were responsive to salt and dehydration stress, CitCAMTA4 remain unaltered. Lastly, the authors investigated the involvement of CitCAMTAs in hormonal signaling (SA, ETH, IAA, 6- BA, ABA, MeJA, and GA3). All the CitCAMTAs (except CitCAMTA4) were responsive to at least one phytohormone. CitCAMTA1, CitCAMTA5, CitCAMTA7, and CitCAMTA9 positively correlated to all the hormones, whereas CitCAMTA3 showed a negative correlation. These observations led the authors to hypothesize the involvement of CitCAMTAs in abiotic stress pathways and hormonal regulation ( Zhang et al., 2019). Genome-wide identification also led to the identification of 15 wheat CAMTA genes, which were classified into three groups A, B, and C containing seven, six, and two TaCAMTAs, respectively ( Yang et al., 2020). Most of the TaCAMTAs contained stress-responsive cis-regulatory elements. All the 15 TaCAMTAs expressed differentially in various tissues and under different abiotic stresses. The expression analysis of TaCAMTA1-A and 1-D revealed that they were significantly expressed under drought, cold, heat, and salt stress. TaCAMTA genes belonging to the same homologous group had similar expression profiles under particular stress condition. For example, TaCAMTA1-A/B/D had almost similar expression profile under drought stress, TaCAMTA5-A/D under salt stress, and TaCAMTA1-A/B/D under heat stress. This observation implies that homologous TaCAMTA genes from the same group might share same functionality. However, few homologous TaCAMTA genes belonging to the same group displayed varying expression profiles under drought, cold, heat, and salinity stress. For instance, TaCAMTA3-A/D was significantly expressed upon cold stress, but the expression profiles of TaCAMTA1-B and TaCAMTA3-B were comparatively stable. This might be due to the functional differentiation in some homologous TaCAMTA genes. Additionally, in the study, it was depicted that 584 genes in the wheat genome can be potential targets of TaCAMTA, indicating the probable role of CAMTA in stress biology and plant development ( Yang et al., 2020). Furthermore, TaCAMTA4 had been demonstrated to function as negative regulator of defense response against Puccinia triticina ( Wang et al., 2019). TaCAMTA4 was found to be homologous to AtCAMTA4 and has been very well implicated as a CaM-binding protein in the wheat- P. triticina interaction system. This was proved by cloning and functionally characterizing TaCAMTA4 by using the CaM-encoding gene ( TaCaM4-1) as a bait, for subsequent screening of cDNA library from P. triticina-infected Triticum leaves. The EMSA results revealed that TaCaM4-1 binds to TaCAMTA4 by the C-terminal CaMBD in Ca ²⁺-dependent manner. Bimolecular fluorescence complementation (BiFC) analysis showed cytoplasm and nucleus as the probable interaction site of TaCAMTA4 and TaCaM4-1 ( Wang et al., 2019). In a similar vein, nine CAMTA genes were identified in Flax ( L. usitatissimum) upon genome-wide identification study. These nine CAMTA proteins were classified into three groups, depending on the phylogenetic analysis. Various hormonal (ABA and SA) and stress-related (drought, low temperature, and light) cis-regulatory elements were found to be enriched in promoter region of LuCAMTAs genes. In addition, the miRNA target analysis showed different miRNA families (miRN30, miRN9, miR2275, miR164, miR159, miR164, miRN15, miR395, miR156, miRN28, and miR164) as probable targets of the LuCAMTAs genes (except LuCAMTA9) ( Ali et al., 2020).

Conclusion and Future Perspective

Improving crop yield and productivity for the ever-growing population has been a major challenge all over the world. The demand for ever-increasing food supply has put forth a tremendous pressure on the agriculture sector. The advent of new biotechnological tools and techniques has robustly contributed to crop improvement and horticultural science ( Limera et al., 2017; Adlak et al., 2019). In this regard, several TFs, including CAMTA, had been identified and functionally characterized. Ever since the discovery of CAMTAs, its role had been comprehensively reported in developmental and stress biology in various model and crop plants ( Table 1). The objective ahead is to understand the fundamental functionality and molecular mechanism behind CAMTA protein expression and downstream processing. A deeper understanding of the mechanisms underlying the involvement of Ca ²⁺ and CaM in CAMTA transcript expression would provide great insights into its probable function in various tissues and response to environmental cues. In addition, deciphering the probable functions and mechanisms underlying the complex regulatory property of CAMTA proteins through its ability to transduce Ca ²⁺ signatures via CaM can improve our knowledge of phenotypic plasticity in plants. However, because of the possible functional redundancy or at least overlapping functions, molecular characterization of individual CAMTA genes still poses a great challenge ahead for researchers. Rigorously developed CRISPR/Cas9-mediated genome editing techniques that rapidly generate mutations at multiple loci using one single-guide RNA will effectively help characterizing each of the gene family members. Nonetheless, the involvement of CAMTA as stress-related gene and its subsequent study in this direction would widen and provide vast knowledge for improvement in crop yield, productivity, and strategies to cope with adverse conditions in arid or semiarid regions of the world.

Table 1. Reported functions of CAMTA in plants.

Author Contributions

ZI drafted the manuscript. MSI helped in drawing the figures. SPS edited the manuscript. TB reviewed the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This work was funded using research grants from the Second Century Fund (C2F Post-Doc fellowship). The fellowship for ZI was supported by C2F, Chulalongkorn University.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

ZI is thankful to TB for providing the facilities and infrastructure.

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Calcium (Ca2+) ion is a critical ubiquitous intracellular second messenger, acting as a lead currency for several distinct signal transduction pathways. Tran...

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Abstract

Agrobacterium, the natures' genetic engineer, has been used as a vector to create transgenic plants. Agrobacterium-mediated gene transfer in plants is a highly efficient transformation process which is governed by various factors including genotype of the host plant, explant, vector, plasmid, bacterial strain, composition of culture medium, tissue damage, and temperature of co-cultivation. Agrobacterium has been successfully used to transform various economically and horticulturally important monocot and dicot species by standard tissue culture and in planta transformation techniques like floral or seedling infilteration, apical meristem transformation, and the pistil drip methods. Monocots have been comparatively difficult to transform by Agrobacterium. However, successful transformations have been reported in the last few years based on the adjustment of the parameters that govern the responses of monocots to Agrobacterium. A novel Agrobacterium transferred DNA-derived nanocomplex method has been developed which will be highly valuable for plant biology and biotechnology. Agrobacterium-mediated genetic transformation is known to be the preferred method of creating transgenic plants from a commercial and biosafety perspective. Agrobacterium-mediated gene transfer predominantly results in the integration of foreign genes at a single locus in the host plant, without associated vector backbone and is also known to produce marker free plants, which are the prerequisites for commercialization of transgenic crops. Research in Agrobacterium-mediated transformation can provide new and novel insights into the understanding of the regulatory process controlling molecular, cellular, biochemical, physiological, and developmental processes occurring during Agrobacterium-mediated transformation and also into a wide range of aspects on biological safety of transgenic crops to improve crop production to meet the demands of ever-growing world's population.

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Vinod Goyal

Present address: Bioseed Research India Pvt. Ltd., Hyderabad, 500033, India

Authors and Affiliations

National Research Centre on Plant Biotechnology, Lal Bahadur Shastri Building, Pusa Campus, New Delhi, 110012, India

Shweta Mehrotra & Vinod Goyal

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Correspondence to Shweta Mehrotra.

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Mehrotra, S., Goyal, V. Agrobacterium-Mediated Gene Transfer in Plants and Biosafety Considerations. Appl Biochem Biotechnol 168, 1953-1975 (2012). https://doi.org/10.1007/s12010-012-9910-6
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Agrobacterium-Mediated Gene Transfer in Plants and Biosafety Considerations - Applied Biochemistry and Biotechnology

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Agrobacterium, the natures' genetic engineer, has been used as a vector to create transgenic plants. Agrobacterium-mediated gene transfer in plants is a highly efficient transformation process which is governed by various factors including genotype of the host plant, explant, vector, plasmid, bacterial strain, composition of culture medium, tissue damage, and temperature of co-cultivation.