Abstract
Rising soil and water salinity endanger plant growth and crop productivity, putting global food security at risk. As plants are sessile, their adaptation to rapidly changing environments is slow, endangering their survival. As a result, mitigation efforts should shift to develo** smart crops capable of withstanding dynamic and heterogeneously distributed salinity. Recent breakthroughs in bioinformatics and high throughput genomics can cost-effectively accelerate the introduction of superior varieties for saline regions. Sugar plays an essential role in biomass accumulation and is thus a viable target for forage crop improvement programs. Sugars Will Eventually be Exported Transporter (SWEET) gene family transcribes for source-sink carbon allocation in the form of sugar in higher plants. However, little is known about SWEET’s role in maize's phenotypes of agronomic interest for forage production. Here, through a genome-wide analysis, we identified and characterized 19 SWEET genes that are expressed across various shoot phenotypes. Eleven of the genes are salt-responsive, and ZmSWEET7 is most abundant in high-sugar-yielding varieties compared to low-sugar varieties. Homologous overexpression of the ZmSWEET7 increases the maximum quantum yield of photosystem II photochemistry (FV/FM), CO2 assimilation rate (A), soluble sugar content, and dry matter, with the quantum yield for CO2 fixation efficiency (phiCO2) showing the most significant increase. There is a strong positive association between phiCO2 and soluble sugar content, dry matter, and FV/FM in ZmSWEET7 overexpressing mutants compared to the wild. These findings indicate that ZmSWEET7-mediated CO2 fixation efficiency rather than assimilation rate plays a positive pleiotropic role in C accumulation in the form of sugar or dry matter via increased FV/FM. This work lays a strong foundation for salt-tolerant forage maize genetic improvement.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Maize is cultivated extensively due to its versatility as a forage, feed, and food crop, as well as its capacity to produce great yields (Erenstein et al. 2022). Globally, maize is produced each year more than any other grain with a total global production exceeding 1 billion tons (Ranum et al. 2014). Maize’s significant intraspecific genetic diversity in abiotic stress performance and ease of cross-pollination makes it a crop of choice for crop improvement programs for African vast marginal regions where maize is an important staple. As an annual crop, maize is subject to seasonal abiotic and biotic pressures, with climate change and unsustainable land use presenting the most serious threats to maize production (Masuka et al. 2012). The most significant obstacle to maize production is escalating water scarcity and soil deterioration (Falkenmark 2013), and salinity is a key factor in soil degradation in Africa (Thiam et al. 2021). For example, salinization accounts for 50% of irrigated land and is constantly increasing (Thomas and Middleton, 1993).
Due to the complexities of tackling salinity holistically, current research on maize production should be directed toward in-planta and in-vivo phenoty**, genoty**, and genetic breeding. For instance, so far, studies of critical candidate genes involved in growth and response to saline conditions have been summarized in various forage crops such as sorghum (Sorghum bicolor) (Amombo et al. 2022), turfgrasses (Fan et al. 2020; Amombo et al. 2017), alfalfa (Medicago sativa) (Bhattarai et al. 2020), and white clover (Trifolium repens) (Wang et al. 2009). It is worth noting that most of the reports focus on tolerance/resistance rather than forage quality. Furthermore, the most introduced improved cultivars do not have all the beneficial qualities that make them appropriate for farming in the vast arid regions of Africa and other continents. As a result, contemporary germplasm genetic improvement programs should be exploited to accelerate the introduction of improved cultivars with high forage value as well as resistance to salt and drought.
Sugar is an incredibly critical biomolecule to consider because it plays a significant role in carbohydrate accumulation which is a major component of forage quality (Ruckle et al. 2017). Sugar transport is essential for C translocation between sources and sinks during and after photosynthesis (Lemoine et al. 2013). Sugar transport in plants is controlled by gene families which consist of several proton-coupled sugar/H+ symporters, sugar/H+ antiporters, and uniporters (Bazzone et al. 2018). Among the transporters, sucrose transporters (SUTS) have received considerable interest from maize researchers (Slewinski et al. 2009; Leach et al. 2017). Besides SUTS, Sugars Will Eventually Be Exported Transporters (SWEET) is a novel and underexplored class of genes with highly conserved functions in sugar distribution between photosynthetic leaves and agronomically important sinks with high potential for forage improvement (Anjali et al. 2020). Distinguished by their conserved MtN3/saliva domain, the SWEET gene family has been identified and analyzed at the genome-wide level in several model plant species, including barrel clover (Medicago truncatula) (Hu et al. 2019), rockcress (Arabidopsis thaliana), and black cottonwood (Populus trichocarpa) (Zhang et al. 2021) which has provided a foundation for studying important forage crops like maize.
Understanding the transcriptional regulation and function of this gene in maize through a forward genetic approach, as well as map** its chromosomal location via reverse genetics provide a rich genetic resource for the targeted development of superior varieties with high tolerance to salinity and drought, as well as high sugar content, which is an important component of livestock production. Therefore, this work looked at the gene architectures, conserved motif compositions, and chromosomal location of SWEET genes in maize. Furthermore, we investigated the tissue-specific expression of ZmSWEETs and their transcriptional regulation and functional analysis in two varieties with contrasting sugar amounts under normal and saline conditions.
Materials and Methods
In Silico ZmSWEET Gene Identification
The most recent versions of maize genome annotations were obtained from the genome assembly (https://maizegdb.org), while the SWEET full-length and homeodomain amino acid sequences were obtained from the Arabidopsis Information Resource (TAIR: http://www.arabidopsis.org/), aligned with multiple sequence alignment program MAFFT v5.3 (Katoh et al. 2019), and then loaded to sequence homologs searching software HMMER v3.0 (Potter et al. 2018) for the construction of Hidden Markov models (HMM). Using an E-value threshold of 1.0, the HMM profiles were used as queries against annotated maize protein databases. For further query, a BLASTP search with an E-value threshold of 0.01 was also conducted using both the full-length and homeodomain amino acid sequences of Arabidopsis SWEET to detect extra putative SWEET genes. The protein sequences obtained using the two approaches described above were combined, and redundant entries were manually removed. Pfam (https://pfam.xfam.org/) and SMART (http://smart.embl.de/) were used to examine the target sequences for the presence of the conserved homeobox domain (Finn et al. 2016). For evolution analysis, a bootstrapped phylogenetic tree was generated using the neighbor-joining technique, while the genetic distance was computed using MEGA v 5.1 software (http://www.megasoftware.net/).
To understand the gene structure, the exon–intron architectures were examined and visualized using the Gene Structure Display Server (GSDS: http://gsds.cbi.pku.edu.cn/); while MEME 4.9.1 (http://meme-suite.org/) was used to find conserved motifs in model SWEET and maize SWEET genes, WebLogo (Crooks et al. 2004) (http://weblogo.berkeley.edu/logo.cgi) was used to visualize them. The following parameters were set: the frequency of motif occurrences was set to zero or one per sequence; the maximum number of motifs was set to eight; the optimal motif width was set to six and one hundred, and the optimal number of sites for each motif was set to two and two hundred.
Chromosome Distribution and Subcellular Localization
The SWEET gene annotation information in the maize genome database was used to evaluate the location of the maize SWEET family members on maize chromosomes. The plant genome duplication database service (http://chibba.agtec.uga.edu/duplication/index/locket) was used to find duplicate gene pairs. The Clustalw algorithm was used to identify the amino acid sequence of the partially repeated SWEET gene (Thompson et al. 1994). The pre-built ngLOC model database (http://ngloc.unmc.edu/) was used to retrieve a web-based interface for predicting subcellular localization. SWEET protein sequences in FASTA format were used to create predictions, and maize species were used as the default. The MLCS (Multi-Localization Confidence Score) (King et al. 2012) was used to determine the prediction level of the top two sites.
Plant Materials and Growth Conditions
Two maize varieties (INRA16595 and Dracma) with contrasting sugar yields were selected (Fig. S1). From our phenoty** experiment, we observed that INRA16595 (a local germplasm-designated variety V1) is a high-sugar-yielding variety sourced from the National Institute for Agricultural Research (INRA) in Morocco, while Dracma (the silage variety designated variety V10) is the farmer-preferred commercial variety sourced from Syngenta with significantly lower sugar content but higher dry mass. We conducted a pot experiment simulating field saline conditions. The seeds were sown in plastic pots filled with commercial soil and were placed under the light with a 14 h photoperiod, a dark/light temperature cycle of 25/30 °C, and relative humidity of 55–65%. The plants were irrigated every other day with 500 mL of deionized water (control), 4 dS/m water (low), and 8 dS/m (medium) salinity for each variety. For the salt treatments, the extra water flowing at the bottom was reirrigated back until there was no further flow through. Sampling for RNA extraction was done at the transition between the tassel stage and the earliest reproductive stage. During this growth stage, transcriptional regulation of flowering and sensitivity to environmental stress is high. Leaves were collected and frozen at − 80°C for RNA extraction. The experiment consisted of six biological replicates.
RNA Extraction, cDNA Synthesis, and RT-qPCR
Young clean shoots were removed and homogenized in liquid nitrogen. With some modifications, the total RNA was isolated using the Canvax Total RNA extraction Kit (Carl Stuart UK Limited, Surrey, United Kingdom). The RNA double bands were confirmed using 1% agarose electrophoresis on the horizontal electrophoresis system (Avantor, Hamilton Street, Allentown, PA, USA) dyed using ethidium bromide and visualized on an ultraviolet gel imager (Mini 6, G: box, Syngene, MD, USA). The HiScript II One-Step qPCR SYBR Green kit (Red Maple Hi-tech Industry Park, Nan**g, PRC) was used for reverse transcription to cDNA and qPCR. The cDNA synthesis and qPCR were prepared as follows: 10 μL 2 × One-Step SYBR green mix, 1 μL One-Step SYBR Green enzyme mix, 0.4 μL 50 × ROX reference dye 1, 0.4 μL gene-specific primer forward (10 μM), 0.4 μL gene-specific primer reverse (10 μM), 2 μL template RNA, and topped up with RNase-free ddH2O to 20 μL. Then the qPCR was operated by AriaMx Real-Time PCR (qPCR) instrument (Agilent, Santa Clara, CA, USA) with melting curves inspection at the end of each reaction. The PCR reaction consisted of the following steps: reverse transcription (1 cycle, 55 °C, 15 min), initial denaturation (1 cycle, 95 °C, 30 s), cycling reaction (40 cycles, 96 °C, 10 s and 60 °C for 30 s), melting curve (1 cycle, 95 °C, 15 s, 60 °C, 60 s, 95 °C, 15 s). Elongation Factor 1 (EF1) gene was used as a reference, each reaction had three duplications. The values of the relative expressions for SWEET family genes were calculated using the 2−∆∆Ct method. Table S1 shows primers used for real-time PCR in this study.
Homologous Overexpression of ZmSWEET7
Immature Dracma maize embryos were prepared following D'Halluin et al. (1992) protocol with significant modifications. Briefly, the seeds were isolated surface sterilized, and treated with 0.3% macerozyme for 3 min at pH 5.6. The embryos were washed and placed into a disposable cuvette containing 200 µL of phosphate buffer saline. In each cuvette, 15 μL of plasmid DNA with ZmSWEET7 insert from variety V1, and the GUS reporter gene were introduced into the enzyme-treated embryo. After 1 h of incubation, the cuvettes were placed in an ice bath for 10 min after which electroporation was performed by discharging one pulse with a field strength of 375 Vlcm from a 900-pF capacitor (BTX Twin Waveform Electroporation Systems, Holliston, MA, USA). Then, embryos were washed and transplanted back into a nutrient medium for further growth. Following germination, the seedlings were transplanted to pots with commercial soil and watered every other day with a specified amount of water. The following treatment was imposed: The mutant and wild types were both treated to 8 dS/m of saline water, while the control was treated with deionized water (EC = 0 dS/m). Excess saline water was trapped beneath the surface and reirrigated until no more water trickled and EC measurements were taken frequently to maintain constant salinity. Plants started to exhibit phenotypic differences after 8 days of treatment when sampling for physiological analysis began. To confirm electroporation success, fresh leaf samples were collected from the pots and cut into quarters. The sections were transferred to 0.5 mL of X-Gluc stain and incubated overnight at 37 °C. The stain was then rinsed in warm 70% ethanol until the chlorophyll color disappeared. The GUS stain was examined using a dissecting microscope (VWR, SN 545036).
Phenotypic Analysis
The phenotypic selection was based on the following criteria: (a) salt sensitivity (chlorophyll a fluorescence), (b) CO2 dynamics (quantum efficiency of CO2 assimilation (phiCO2), intra and extracellular CO2, CO2 conductance, and CO2 assimilation rate), (c) water status (relative water content (RWC), transpiration rate, and water conductivity), and (d) yield (total sugar and dry matter). Photosynthesis measurements were done using the combined induction kinetics and gas exchange measurement using Li-COR 6800 equipment (Li-COR Biosciences, Lincoln, NE, USA). All the measurements took place in the morning after the extra dark adaptation. Measurements were performed on the ear leaves in each pot. Chlorophyll fluorescence measurements were performed both on the control, low, and medium salinity treatments on the same day with minimum time wastage. After the adaptation of leaves to darkness, a light pulse at a flow rate of 500 µ mol/m2/s was applied with the help of a light-emitting diode. The fast fluorescence kinetics (F0 to FM) was recorded to 1 s. For each variety and treatment, at least 6 repetitions were applied. The measured data were analyzed by the JIP test according to (Strasser et al. 1995). CO2 assimilation was measured using the CO2 response curve at various CO2 concentrations i.e., 0, 200, 400, 600, 800, and 1000 µ mol mol−1.
The Deans et al. (2018) approach was used to determine the total soluble sugars. In summary, dried harvested shoot samples were finely milled, and 20 mg of the powder was transferred into a glass test tube blended with 1 ml of 0.1 M H2SO4 and heated for 1 h in a water bath. The samples were chilled in a lukewarm water bath before being centrifuged for 10 min at 15,000 rpm. A 15 µL of the supernatant was transferred to a clean glass test tube and 400 mL of distilled water was added. 400 mL of 5% phenol was added, followed by 2 mL of concentrated H2SO4. The reaction mixture was vortexed and incubated for 30 min at room temperature. A spectrophotometer was used to measure absorbance at 490 nm. The glucose standard curve was used to calculate the values.
For RWC from fully expanded leaves, 1.5 cm wide by 4 cm long portions were cut with scissors from the area between the mid-vein and the edge. Three samples (replications) were collected from each plot, each sample representing a different plant. To avoid physiological changes, sampling proceeded quickly. Each sample was placed in a pre-weighed airtight plastic vial with its basal part to the bottom. The vials were immediately placed in a cold box but not frozen and transported to the laboratory as soon as possible. In the laboratory, the vials were weighed to obtain fresh weight (FW), after which the leaves were immediately hydrated in deionized water to full turgidity for 4 h under normal room light and temperature. After hydration, the leaves were removed from the water and dried using tissue paper to remove any residual surface moisture and immediately weighed to obtain the fully turgid weight (TW). The leaves were oven-dried for 24 h at 80 °C and weighed to obtain the dry weight (DW). The RWC was calculated as RWC (%) = [(FW–DW)/(TW–DW)] × 100, where FW is Sample fresh weight, TW is Sample turgid weight, and DW is Sample dry weight. Harvesting took place 40 days following silking, at silage harvest maturity just before senescence. The fresh plant samples were oven-dried at 60 °C and dry weight measurements were taken.
Data Analysis
All the experimental data for phenoty** and gene expression consisted of six replicates. Means and standard deviation were analyzed using SPSS version 16, while the multivariate and chlorophyll a fluorescence curves were analyzed using Origin lab Pro version 2022b.
Results
Together, the HMM search using SWEET domains as queries, as well as BLAST using AtSWEET and O. sativa OsSWEET sequences as queries revealed a total of 19 ZmSWEETs. The ZmSWEETs were designated by their orthologous genes in A. thaliana. The genes encoded amino acids with sizes ranging from 14,609.5 to 153,183.84 kDa and isoelectric points (pI) ranging from 5.33 to 9.79 which indicated that most of the ZmSWEET proteins were basic proteins. All the ZmSWEET proteins were hydrophobic proteins with a grand average of hydropathicity, (GRAVY) above 0. These results indicated that the basic properties of the proteins encoded by members of the maize ZmSWEET gene family were different (Table 1). Multiple sequence alignment showed that the 7 alpha-helical transmembrane domains (7-TMs) were basically conserved in ZmSWEETs, while ZmSWEET17 lacked the TM1 domain (Fig. S2).
Phylogeny and Gene Structure
We performed a phylogenetic study of the discovered genes with other species i.e., A. thaliana; O. sativa; Vitis vinifera, and Litchi chinensis to better understand their probable unique functions. Maize and the other species were classified into four evolutionary clades. Clade 4 was the smallest clade in any of the investigated species with only three ZmSWEET genes. Clade III had the most evolutionary-related genes, with 9 from maize, indicating that SWEET members from other species and maize may be linked. There is a tandem duplication of ZmSWEET4 (SWEET4 and SWEET4C) distributed in clades II and I (Fig. 1).
The motif organization was investigated to better comprehend the detected maize gene structure. All ZmSWEET genes had 5 to 10 motifs. The number of motifs in SWEET varied among clades. ZmSWEET6, ZmSWEET8, ZmSWEET17, and ZmSWEET18 from clade III, for example, had the most motifs (10), while ZmSWEET10 from clade II had just five. Within the same group, small variations in gene features were also seen. For example, ZmSWEET15 in clade I had 8 motifs, but ZmSWEET13 in the same clade had just 7. Untranslated areas were found in all 19 individuals, the great majority of whom belonged to clade III. The arrangement of motifs in a gene family may reveal information about its functional development (Fig. 2A). Cis-regulatory elements are distinct DNA sequences located upstream of gene coding regions that regulate gene expression by interacting with transcription factors. In this study, we observed that light responsiveness had the most cis-regulatory elements among all the genes, suggesting a sugar transport-light link in maize. The second most prevalent cis-regulatory element is linked to stress-signaling hormones such as MeJA, salicylic acid, abscisic acid, and gibberellins, as well as endosperm and meristem control. Other notable features were seen in metabolism and regulation, circadian control, MYB binding, and anaerobic control (Fig. 2B).
Chromosomal Distribution and Segmental Duplication
All the SWEET genes in maize were found on 9 of the 10 maize chromosomal pairs. The number of genes found on each chromosome ranged from one on chromosomes 2 and 6 to four on chromosomes 7 and 9. Members of comparable gene clades were found on the same chromosomes. Clade 1 ZmSWEET1 and ZmSWEET2 members, for example, were all found on chromosome 1. ZmSWEET3 is the sole maize gene on chromosome 2, whereas its group III orthologous counterparts such as ZmSWEET5, ZmSWEET6, ZmSWEET7, and ZmSWEET4 are all situated on chromosome 3. Chromosome 9 similarly contained only one gene, ZmSWEET17 (Fig. 3A). The BLASTP and MCScanX algorithms grouped ZmSWEET into four duplication events. For example, ZmSWEET5 and ZmSWEET13, ZmSWEET1 and ZmSWEET17, ZmSWEET7 and ZmSWEET16, and ZmSWEET8 and ZmSWEET3 can be produced by fragment duplication. Based on the findings, these ZmSWEET genes were most likely created through gene segmental rather than tandem duplications (Fig. 3B).
Predicted and Confirmed Expression in Agronomic Phenotypes
Expression prediction revealed that ZmSWEET16, ZmSWEET7, and ZmSWEET10 were found to have considerably higher expression levels in the tassel inflorescence, whereas ZmSWEET15, ZmSWEET11, and ZmSWEET19 were found to be strongly expressed in the vascular leaf. ZmSWEET3 is the sole highly expressed gene in maize endosperm, indicating a potential function in grain filling; ZmSWEET10 and ZmSWEET11 are also strongly expressed in the pericarp. In the shoot axis internode, ZmSWEET15 and ZmSWEET11 are co-expressed. As a result, we considered these SWEET genes to be candidates for further functional analysis (Fig. 4A). Through qPCR, all 19 ZmSWEETs were detected in the shoots. We, therefore, tested the expression of the genes in shoot tissues of agronomic importance. The leaf sheath has the most upregulated genes. Among the most upregulated genes are ZmSWEET10 and ZmSWEET19 in the leaf sheath, while ZmSWEET5 and ZmSWEET9 are upregulated in the tassel. The highest upregulation is ZmSWEET7 in the shoot internode which is also upregulated in the leaf sheath and whole leaf. Consistent with bioinformatic prediction, most of the genes are downregulated (Fig. 4B).
Expression of SWEET Under NaCl Treatment
We compared the expression profile of salt-responsive genes in the shoots, and 11 genes are responsive to salt treatment in both varieties by real-time qPCR and gene-specific primers. In V1, three genes ZmSWEET3, ZmSWEET11, and ZmSWEET1 are upregulated by salinity. All these genes are downregulated by salinity in Dracma. The only upregulated gene by salinity in Dracma is ZmSWEET7 which is also highly upregulated in V1. Also, with more than sixfold change compared to the control, ZmSWEET7 stands out as the most upregulated gene. Interestingly, this gene was also predicted to be of high abundance in the shoot axis internode. Due to its distribution in traits of agronomic interest as well as high expression under salinity, we, therefore, considered ZmSWEET7 our target for further functional analysis under salt stress (Fig. 5).
Phenotypic Variation in Wild and Mutant Samples
The GUS::ZmSWEET7 fusion protein could be visualized as dark blue colorization on the epidermal cells under fluorescent microscopy (Fig. 6A) indicating integration of ZmSWEET7 promoters. The morphology of the seedlings varies after transplanting and salt treatments. The plant height, number of leaves, and leaf length are notable morphological variations. The control has the greatest height and longest leaves, followed by the mutant (Fig. 6B, C). This trend was consistent up to silage maturity.
There is a significant increase in the total soluble sugar content in the mutants (46.78 mg/g DW) compared to the wild (38.66 mg/g DW) which despite being a significant increase is still below the one observed in V1 from the previous phenoty** study. This can be attributed to the fact that ZmSWEET7 is just one of the several genes in V1 that play a role in sugar accumulation. An increase in the sugar content under saline conditions could also be an osmotic stress adaptive mechanism since sugars act as important compatible osmolytes under osmotic stress caused by salinity and drought. It was, therefore, necessary to determine the water status of plants by measuring the RWC. There is an insignificant decline in the RWC of salt-treated wild type compared to the control. Mutants exhibited a slightly significant increase (Fig. 7).
Chlorophyll a Fluorescence and CO2 Assimilation Rate and Efficiency
The results revealed that different treatments resulted in considerably varied slow kinetics of chlorophyll a fluorescence behavior. For instance, the wild type had the lowest F0 of 496.7, whereas the control treatment had a much higher F0 of 890. The F0 was medium in the mutant at 579.8. Generally, after the first F0, all treatments experienced an O-J increase that occurred between 0.00001 and 0.0001 s. To achieve the greatest FM, the J-I-P phase of the fluorescence induction curve rise time spanned from 0.0001 to 0.001 s. The control had the highest FM value of 1948.72, followed by the mutant, and the wild type had the lowest of 996.18. Notably, the J-I-P rise was much delayed in the wild type (Fig. 8A). The mutant exhibited the highest rise in A between 0 and 200 ppm followed by the wild type. However, the wild type reached the plateau at the earliest (Fig. 8B). A similar trend is observed in the dry matter weight where the mutant, despite being lower than the control, exhibits a significantly higher DW than the wild type (Fig. 8C). The mutant and control FV/FM levels do not differ significantly. However, the wild type had much lower values (Fig. 8D), demonstrating that ZmSWEET7 could protect photosystem II in maize.
Further analysis of the photosynthesis parameters indicated that GUS::ZmSWEET7 mutants experienced a significant increase in the phiCO2 compared to the control and wild type with a small difference between the control and wild type. Salt treatment caused a significant decline in the stomatal conductance to water and CO2 in both wild and mutant types which coincided with the transpiration rate. However, the conductance despite being lower than the control was significantly higher than the wild type. The same pattern is observed in intracellular and extracellular CO2 (Fig. 9).
Multivariate analysis reveals a strong correlation between A and phiCO2 in both salt-treated wild and mutants. In the wild type, A is strongly positively correlated with the dry matter, intracellular and extracellular CO2 but weakly correlated with the soluble sugar content. Transpiration was the only positive correlation with sugar, while there is a strong negative correlation between transpiration and FV/FM. The phiCO2, on the other hand, is strongly positively correlated with dry weight (0.65), moderately correlated with extracellular CO2, and there is almost zero correlation with the soluble sugar content. There is also a strong negative correlation between phiCO2 and FV/FM. In the mutants, A is strongly positively correlated with the extracellular CO2 and the FV/FM. However, there is still a very low correlation between A and soluble sugar, while the strongest negative correlation is observed with transpiration rate. The phiCO2 on the other hand displays strong positive correction with the DW and FV/FM and soluble sugar content. There is a negative correlation however between conductance to CO2 and intracellular CO2, while the extracellular CO2 exhibited a slightly positive correlation (Fig. 10). These findings indicate that ZmSWEET7-mediated increase in phiCO2 plays a positive pleiotropic role in C accumulation in the form of sugar or dry matter via increased FV/FM.
Discussion
Despite maize's economic and agronomic importance, its study as a forage in Africa has often lagged compared to other forages such as Napier grass (Pennisetum purpureum) (Balehegn et al. 2021), particularly in terms of develo** genotypes with high abiotic stress tolerance while retaining high nutritive value. As a C4 crop, maize uses sunlight to generate carbon-based macromolecules in its foliar tissues, which is the basis of energy flow through the trophic levels in the ecosystem (Dusenge et al. 2019). Among the photosynthates, sugars are the most prevalent carbon molecules which play an integral role in biomass accumulation (Aluko et al. 2021). The important connection between soluble sugars and forage quality has been well documented by Capstaff et al. (2018). As primary consumers, herbivorous livestock actively manage their sugar intake and assimilation from forages to meet physiological demands, which is critical to their survival and productivity (Sarwar et al. 1992). Therefore, forage quality and soluble sugar content are intricately connected. Perennial ryegrass (Lolium perenne), for example, is a popular forage crop whose excellent digestibility in livestock has been associated with its high soluble sugar content (Ruckle et al. 2017). Besides, sugars are also required for plant response to biotic and abiotic stress (Ciereszko. 2018; O'Hara et al. 2012; Eveland et al. 2012; Lastdrager et al. 2014). Thus, sugar distribution and accumulation patterns are key aspects to consider when selecting excellent fodder for optimum livestock production.
In this study, using a genome-wide approach, we identified 19 ZmSWEET genes in maize. Their AA lengths were varied and distributed across various organelles. The varied structure of ZmSWEETs indicates that they have distinct functions in different biological processes or under different growth settings. Gene structure analysis revealed that the bulk of ZmSWEET genes had 10 motifs. This was higher than those in closely related species like sorghum (Miao et al. 2017). To validate the conserved motif analysis, we conducted a phylogenetic analysis. These genes were classified into four clades (Clades I–IV) based on their phylogenetic evolutionary relationship, which corresponded to the classification of SWEETs in the model species A. thaliana. Our findings revealed that gene members in each clade had a unique conserved motif indicating that they may play a variety of roles in maize. However, there are disparities in the number of subfamilies revealed; for example, in Clade III, 9 members of ZmSWEET were discovered, compared to only one member of AtSWEET and OsSWEET. In addition, the number of duplicated gene pairs differed across clades compared to Arabidopsis. For instance, we observe four duplication events on chromosomes 2–4, 3–8, and 1–9. This suggests that during maize evolution, gene duplication and divergence events happened more in a segmental rather than in a tandem manner compared to A. thaliana and O. sativa.
Due to the intricate connection between SWEET and sugar, we validated the bioinformatics results and tested the expression level of SWEETs in phenotypes of agronomic importance. ZmSWEET7 was highly distributed in almost all tested phenotypes. While the highest expression level is observed in the ZmSWEET19, ZmSWEET19 is an ortholog of AtSWEET2 whose vacuolar transcription regulates sugar release by decreasing the access of glucose sequestered in the vacuole, minimizing carbon export (Chen et al. 2015). In this study, the transcript levels of ZmSWEET19 were highest in the leaf sheath indicating that these genes play an important role in sugar accumulation in an organ-specific manner. We also looked at the expression patterns of SWEET family genes from distinct phylogenic clades in salt stress to determine if functional differentiation occurred. A total of 11 genes were responsive to salt stress. Among them, all the ZmSWEET genes in clades I, II, and IV can respond to salt stress conditions, highlighting the common involvement of these three phylogenic clades in maize salt stress response. Most of the genes are upregulated by salinity in V1 compared to Dracma with ZmSWEET7 having the highest upregulation level. Interestingly, the highly expressed ZmSWEET19 during normal conditions was not strongly salt inducible.
Therefore, we probed the potential role of ZmSWEET7 in salt tolerance by overexpressing it in Dracma through direct immature embryo electroporation. Dracma varieties that overexpress the gene have increased sugar content and RWC compared to the wild type but lower than the control. The RWC represents a plant's water state and is inextricably linked with the osmotic potential (Paulino et al. 2020), and recently, the RWC has been documented to be among the most reliable physiological marker for salt-stressed plants (Soltabayeva et al. 2021). Plants under salt stress, like those under water stress, experience physiological drought because of increased osmotic pressure in the surrounding soil. To overcome this, plants must enhance their osmotic pressure by accumulating osmotically compatible solutes, and sugars are among the osmotically active substances. Interestingly, there is a 20.98% gain (from 38.66 to 46.78 mg/g DW) from the wild type, which despite being a significant increase is still below observed in V1 from the previous phenoty** study. This can be attributed to the fact that ZmSWEET7 is just one of several genes in variety V1 that play a role in sugar accumulation. An increase in the sugar content under saline conditions could therefore also be an osmotic stress adaptive mechanism since sugars act as important compatible osmolytes. Turner (2018) reviewed osmotic adjustment studies on crops for the past 40 years and found that sugar had an essential function in enhancing crops' ability to withstand osmotic stress. Chimenti et al. (2006) reported notable intraspecies variation in osmotic adjustment in maize, which could be utilized to select drought-tolerant lines. The differential accumulation of soluble sugars and their positive association with water content in this study suggest that like in drought, soluble sugar could be utilized as a phenotypic marker for salt tolerance in maize.
Photosynthesis is sensitive to environmental changes and can determine the abiotic tolerance level of a plant as well as biomass buildup, both of which are essential for crop forage yield (Yadav et al. 2020). Chlorophyll fluorescence has been demonstrated to be one of the most sensitive physiological measures when subjected to salt stress, making it a reliable phenotypic marker (Papageorgiou and Govindjee 2004). As a result, we investigated the FV/FM from chlorophyll a fluorescence curve of Dracma variety that overexpresses ZmSWEET7. The findings demonstrated that different treatments resulted in significantly different behavior. Following the initial F0, all treatments saw an O-J rise suggesting that salinity modifications had no effect on this phase. This phase represents the photochemical step of Chl fluorescence induction. Thus, higher F0 values in the mutant relative to the wild type showed a larger physical separation of the PSII reaction center from their corresponding pigment antennae, which has been shown to contribute to better salt tolerance by restricting energy input into the electron transport chain (Srivastava et al. 1997). The J-I-P phase of the fluorescence induction curve rising time was set at 0.0001–0.001 s to produce the highest FM. Notably, the wild-type J-I-P increase was substantially delayed, demonstrating that this phase relates to plastoquinone accumulation, whereas it increased in the mutant. The large increase in I translates to slower electron transit to the PSI acceptors. In mutants, there is also a higher plateau, indicating a bigger number of PSI end acceptors, which are typically linked with alternative electron transfer routes that function as electron sinks.
In-planta CO2 dynamics define the pathway for carbon accumulation which in turn determines plant growth and production (McCarthy et al. 2010). An assimilation rate curve plotted versus intercellular CO2 concentration can reveal numerous insights into the biochemistry of a leaf or plant. For instance, a ZmSWEET7-mediated increase in stomatal conductance, which regulates gas exchange (CO2 and water), can allow maize to increase their CO2 uptake and subsequently enhance photosynthesis, while its negative correlation with Ci and A could be a strategy of increased CO2 and water use efficiency under saline conditions as evidenced by higher intracellular CO2 and assimilation rate and lower transpiration rate, respectively. The photosynthetic quantum yield efficiency is a crucial but seldom measured biophysical quantity which estimates both net carbon intake and net oxygen evolution concurrently (Du et al. 2018). The high correlation between phiCO2 and dry matter accumulation and sugar in mutants indicates that SWEET mediates carbon accumulation via increased efficiency of CO2 fixation per unit of supplied CO2. There have not been many maize studies that explicitly relate PSII with phiCO2. The positive association between phiCO2 and PSII, on the other hand, supports Edwards et al.’s (1993) claim that, across a wide variety of environmental conditions, fluorescence characteristics may be employed to predict accurately and rapidly CO2 assimilation rates in maize.
In conclusion, through a genome-wide analysis, we discovered 19 SWEET genes in maize and analyzed their chemical structure, chromosomal distribution, phylogeny, and promoter regions. To validate the work, we conducted a molecular stress physiology experiment which indicated that the 19 genes are expressed differentially in various phenotypes of agronomic interest, while 11 are salt inducible. Among the salt-inducible genes, ZmSWEET7 is the most upregulated in the high-sugar variety and its homologous overexpression in low-sugar variety enhanced the phiCO2 which is positively associated with DW and soluble sugar accumulation and FV/FM under saline conditions. Although dry matter and total sugar are quantitative traits controlled by multiple genes, this study provides insights into the potential role of ZmSWEET7 in these important carbon dynamics under saline conditions especially via phiCO2. To understand the specific transcriptional role of this gene, complete knock-out studies using genome editing will be needed.
References
Aluko OO, Li C, Wang Q, Liu H (2021) Sucrose utilization for improved crop yields: a review article. Int J Mol Sci 22(9):4704. https://doi.org/10.3390/ijms22094704
Amombo E, Li H, Fu J (2017) Research advance on tall fescue salt tolerance: from root signaling to molecular and metabolic adjustment. J Amer Soc Hort Sci 142:337–345. https://doi.org/10.21273/JASHS04120-17
Amombo E, Ashilenje D, Hirich A, Kouisni L, Oukarroum A, Ghoulam C, El Gharous M, Nilahyane A (2022) Exploring the correlation between salt tolerance and yield: research advances and perspectives for salt-tolerant forage sorghum selection and genetic improvement. Planta 255(3):71. https://doi.org/10.1007/s00425-022-03847-w
Anjali A, Fatima U, Manu MS, Ramasamy S, Senthil-Kumar M (2020) Structure and regulation of SWEET transporters in plants: an update. Plant Physiol Biochem. https://doi.org/10.1016/j.plaphy.2020.08.043
Balehegn M, Ayantunde AA, Amole T, Njarui D, Nkosi BD, Müller FL, Meeske R, Tjelele TJ, Malebana IM, Madibela OR, Boitumelo WS, Lukuyu B, Weseh A, Minani E, Adesogan AT (2021) Forage conservation in Sub-Saharan Africa: review of experiences, challenges, and opportunities. Agron J. https://doi.org/10.1002/agj2.20954
Bazzone A, Klaus F (2018) Sugar transporters of the major facilitator superfamily: SSM-based electrophysiology reveals common principles and differences. In: Gordon Research Conference, Ligand Recognition and Molecular Gating
Bhattarai S, Biswas D, Fu WB, Biligetu B (2020) Morphological, physiological, and genetic responses to salt stress in alfalfa: a review. Agronomy 10(4):577. https://doi.org/10.3390/agronomy10040577
Capstaff NM, Miller AJ (2018) Improving the yield and nutritional quality of forage crops. Front Plant Sci 9:535. https://doi.org/10.3389/fpls.2018.00535
Chen HY, Huh JH, Yu YC, Ho LH, Chen LQ, Tholl D, Frommer WB, Guo WJ (2015) The Arabidopsis vacuolar sugar transporter SWEET2 limits carbon sequestration from roots and restricts Pythium infection. Plant J 83(6):1046–58. https://doi.org/10.1111/tpj.12948
Chimenti CA, Marcantonio M, Hall AJ (2006) Divergent selection for osmotic adjustment in improved drought tolerance in maize (Zea mays L.) in both early growth and flowering phases. Field Crop Res 95:305–315. https://doi.org/10.1016/j.fcr.2005.04.003
Ciereszko I (2018) Regulatory roles of sugars in plant growth and development. Acta Soc Botanicorum Poloniae. https://doi.org/10.5586/asbp.3583
Crooks GE, Hon G, Chandonia JM, Brenner SE (2004) WebLogo: a sequence logo generator. Genome Res 14:1188–1190. https://doi.org/10.1101/gr.849004
D’Halluin K, Bonne E, Bossut M, De Beuckeleer M, Leemans J (1992) Transgenic maize plants by tissue electroporation. Plant Cell 4(12):1495–505. https://doi.org/10.1105/tpc.4.12.1495
Deans CA, Sword GA, Lenhart PA, Burkness E, Hutchison WD, Behmer ST (2018) Quantifying plant soluble protein and digestible carbohydrate content, using corn (Zea mays) as an exemplar. J Vis Exp 138:58164. https://doi.org/10.3791/58164
Du N, Gholami P, Kline DI, DuPont CL, Dickson AG, Mendola D, Martz T, Allen AE, Mitchell BG (2018) Simultaneous quantum yield measurements of carbon uptake and oxygen evolution in microalgal cultures. PLoS ONE 13(6):e0199125. https://doi.org/10.1371/journal.pone.0199125
Dusenge ME, Duarte AG, Way DA (2019) Plant carbon metabolism and climate change: elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration. New Phytol 221(1):32–49. https://doi.org/10.1111/nph.15283
Edwards GE, Baker NR (1993) Can CO2 assimilation in maize leaves be predicted accurately from chlorophyll fluorescence analysis? Photosynth Res 37(2):89–102. https://doi.org/10.1007/BF02187468
Erenstein O, Moti J, Boddupalli P, Khondoker M, Kai S (2022) Global maize production, consumption, and trade: trends and R&D implications. Food Secur. https://doi.org/10.1007/s12571-022-01288-7
Eveland AL, Jackson DP (2012) Sugars, signalling, and plant development. J Exp Bot 63(9):3367–77. https://doi.org/10.1093/jxb/err379
Falkenmark M (2013) Growing water scarcity in agriculture: future challenge to global water security Phil. Trans R Soc A 371:20120410–20120410
Fan J, Zhang W, Amombo E, Hu L, Kjorven JO, Chen L (2020) Mechanisms of environmental stress tolerance in turfgrass. Agronomy 10(4):522. https://doi.org/10.3390/agronomy10040522
Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, Potter SC, Punta M, Qureshi M, Sangrador-Vegas A, Salazar GA, Tate J, Bateman A (2016) The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res 44(D1):D279–D285. https://doi.org/10.1093/nar/gkv1344
Hu B, Wu H, Huang W, Song J, Zhou Y, Lin Y (2019) SWEET gene family in Medicago truncatula: genome-wide identification, expression and substrate specificity analysis. Plants (basel) 8(9):338. https://doi.org/10.3390/plants8090338
Katoh K, Rozewicki J, Yamada KD (2019) MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform 20(4):1160–1166. https://doi.org/10.1093/bib/bbx108
King BR, Vural S, Pandey S, Barteau A, Guda C (2012) ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes. BMC Res Notes 5:351. https://doi.org/10.1186/1756-0500-5-351
Lastdrager J, Johannes H, Sjef S (2014) Sugar signals and the control of plant growth and development. J Expt Bot. https://doi.org/10.1093/jxb/ert474
Leach KA, Tran TM, Slewinski TL, Meeley RB, Braun DM (2017) Sucrose transporter2 contributes to maize growth, development, and crop yield. J Integr Plant Biol 59(6):390–408. https://doi.org/10.1111/jipb.12527
Lemoine R, La Camera S, Atanassova R, Dédaldéchamp F, Allario T, Pourtau N, Bonnemain JL, Laloi M, Coutos-Thévenot P, Maurousset L, Faucher M, Girousse C, Lemonnier P, Parrilla J, Durand M (2013) Source-to-sink transport of sugar and regulation by environmental factors. Front Plant Sci 4:272. https://doi.org/10.3389/fpls.2013.00272
Masuka B, Araus JL, Das B, Sonder K, Cairns JE (2012) Phenoty** for abiotic stress tolerance in maize. J Integr Plant Biol 54(4):238–49. https://doi.org/10.1111/j.1744-7909.2012.01118.x
McCarthy HR, Oren R, Johnsen KH, Gallet-Budynek A, Pritchard SG, Cook CW, LaDeau SL, Jackson RB, Finzi AC (2010) Re-assessment of plant carbon dynamics at the Duke free-air CO2 enrichment site: interactions of atmospheric CO2 with nitrogen and water availability over stand development. New Phytol 185:514–528. https://doi.org/10.1111/j.1469-8137.2009.03078.x
Miao H, Sun P, Liu Q, Miao Y, Liu J, Zhang K, Hu W, Zhang J, Wang J, Wang Z, Jia C, Xu B, ** Z (2017) Genome-wide analyses of SWEET family proteins reveal involvement in fruit development and abiotic/biotic stress responses in banana. Sci Rept 7:3536. https://doi.org/10.1038/s41598-017-03872-w
O’Hara L, Matthew P, Astrid W (2012) How do sugars regulate plant growth and development? New insight into the role of trehalose-6-phosphate. Mol Plant. https://doi.org/10.1093/mp/sss120
Papageorgiou GC, Govindjee G (2004) Chlorophyll A fluorescence: a signature of photosynthesis. Springer, Dordrecht
Paulino MKSS, de Souza ER, Lins CMT, Dourado PRM, Leal LYC, Monteiro DR, Junior FEAR, Silva CUC (2020) Influence of vesicular trichomes of Atriplex nummularia on photosynthesis, osmotic adjustment, cell wall elasticity and enzymatic activity. Plant Physiol Biochem. https://doi.org/10.1016/j.plaphy.2020.07.036
Potter SC, Luciani A, Eddy SR, Park Y, Lopez R, Finn RD (2018) HMMER web server: 2018 update. Nucleic Acids Res 46:200–204. https://doi.org/10.1093/nar/gky448
Ranum P, Peña-Rosas JP, Garcia-Casal MN (2014) Global maize production, utilization, and consumption. Ann NY Acad Sci 1312:105–112. https://doi.org/10.1111/nyas.12396
Ruckle ME, Meier MA, Frey L, Eicke S, Kölliker R, Zeeman SC, Studer B (2017) Diurnal leaf starch content: an orphan trait in forage legumes. Agronomy 7:16. https://doi.org/10.3390/agronomy7010016
Sarwar M, Firkins JL, Eastridge ML (1992) Effects of varying forage and concentrate carbohydrates on nutrient digestibilities and milk production by dairy cows. J Dairy Sci 75(6):1533–42. https://doi.org/10.3168/jds.S0022-0302(92)77910-7
Slewinski TL, Meeley R, Braun DM (2009) Sucrose transporter1 functions in phloem loading in maize leaves. J Exp Bot 60(3):881–92. https://doi.org/10.1093/jxb/ern335
Srivastava A, Guissé B, Greppin H. Strasser RJ (1997) Regulation of antenna structure and electron transport in PSII of Pisum sativum under elevated temperature probed by the fast polyphasic chlorophyll a fluorescence transient: OKJIP. Biochim Biophys Acta 1320:95–106. https://doi.org/10.1016/S0005-2728(97)00017-0
Soltabayeva A, Ongaltay A, Omondi JO, Srivastava S (2021) Morphological, physiological and molecular markers for salt-stressed plants. Plants (Basel) 10(2):243. https://doi.org/10.3390/plants10020243
Strasser BJ, Strasser RJ (1995) Measuring fast fluorescence transients to address environmental questions: the JIP-test. In: Mathis P (ed) Photosynthesis: from light to biosphere. KAP Press, Dordrecht, p 977–980. https://doi.org/10.1007/978-94-009-0173-5_1142
Thiam S, Villamor GB, Faye LC, Sène JHB, Diwediga B, Kyei-Baffour N (2021) Monitoring land use and soil salinity changes in coastal landscape: a case study from Senegal. Environ Monit Assess 193(5):259. https://doi.org/10.1007/s10661-021-08958-7
Thomas David SG, Middleton N (1993) Salinization: new perspectives on a major desertification issue. J Arid Environ 24:95–105. https://doi.org/10.1006/jare.1993.1008
Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-speci®c gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680. https://doi.org/10.1093/nar/22.22.4673
Turner NC (2018) Turgor maintenance by osmotic adjustment: 40 years of progress. J Exp Bot 69(13):3223–3233. https://doi.org/10.1093/jxb/ery181
Wang J, Drayton MC, George J, Cogan NOI, Baillie RC, Hand ML, Kearney GA, Erb S, Wilkinson T, Bannan NR, Forster JW, Smith KF (2009) Identification of genetic factors influencing salt stress tolerance in white clover (Trifolium repens L.) by QTL analysis. TAG Theor App Genet 120(3):607–619. https://doi.org/10.1007/s00122-009-1179-y
Yadav S, Modi P, Dave A, Vijapura A, Patel D, Patel M (2020) Effect of abiotic stress on crops. In: Hasanuzzaman M, Filho MCMT, Fujita M, Nogueira TAR (eds) Sustainable crop production. IntechOpen, London
Zhang L, Wang L, Zhang J, Song C, Li Y, Li J, Lu M (2021) Expression and localization of SWEETs in populus and the effect of SWEET7 overexpression in secondary growth. Tree Physiol 41(5):882–899. https://doi.org/10.1093/treephys/tpaa145
Acknowledgements
The authors would like to thank the OCP Phosboucraa Foundation for funding this project. Special thanks to the laboratory staff at ASARI for their precious support in the setup of the experiments.
Funding
This work was supported by financial assistance from OCP Phosboucraa Foundation under the Grant Number: FPB_SPA005_2020.
Author information
Authors and Affiliations
Contributions
EA, AO, CG, and AN planned the study and contributed to conceptualization and methodology. EA performed bioinformatics, laboratory experiments, and wrote the first draft of the manuscript. DSA contributed to collection of agronomic and physiological data. AN supervised the work and acquired the funding. AH, LK, AO, CG, KM, MEG, and AN contributed to reviewing and editing all the versions of the manuscript.
Corresponding author
Ethics declarations
Competing Interests
The authors declare that they have no competing interest.
Additional information
Handling Editor: Vinay Kumar.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Amombo, E., Ashilenje, D.S., Hirich, A. et al. Insights on the SWEET Gene Role in Soluble Sugar Accumulation via the CO2 Fixation Pathway in Forage Maize Under Salt Stress. J Plant Growth Regul (2023). https://doi.org/10.1007/s00344-023-11112-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00344-023-11112-x