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2024 Volume 4
Article Contents
Abstract
Maize is a major staple crop and a model species for plant biology and genetic studies
Hundreds of genomic regions were selected during maize domestication
Domestication genes for morphological traits
Teosinte branched1 (tb1) and increased apical dominance in maize
Teosinte glume architecture 1 (tga1) and reduced glumes in maize
Grassy tillers1 (gt1) and suppression of prolificacy in maize
UPA2 and leaf angle
GLOSSY15 (Gl15) and vegetative phase change
Domestication genes related to identity and morphology of inflorescence
Domestication genes of kernel-related traits
ZmSh1 and seed shattering
Nutritional quality
Domestication genes related to disease resistance and abiotic stresses
Pre-Columbia spread of maize from tropical to temperate
Selective genes underlying agronomic trait improvement during modern maize breeding
Key genes/pathways regulating shade avoidance responses and plant architecture in maize
Complementary and differential selection of heterotic groups during modern maize breeding
Perspectives
Acknowledgments
Conflict of interest
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ARTICLE   Open Access    

Enhancing salt stress tolerance of forage sorghum by foliar application of ortho-silicic acid

  • 1.

    Department of Botany & Plant Physiology, CCS Haryana Agricultural University, Hisar 125004, Haryana, India

  • 2.

    Forage Section, Department of Genetics & Plant Breeding, CCS Haryana Agricultural University, Hisar 125004, Haryana, India

  • 3.

    Department of Molecular Biology, Biotechnology and Bioinformatics, CCS Haryana Agricultural University, Hisar 125004, Haryana, India

  • 4.

    Maharshi Dayanand University, Rohtak 124001, Haryana, India

  • 5.

    Centre of Research Impact and Outreach, Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India

More Information
  • Received: 27 April 2024
    Revised: 08 June 2024
    Accepted: 03 July 2024
    Published online: 16 July 2024
    Grass Research  4,  Article number: e016 (2024)  |  Cite this article
  • Soil salinity poses a significant threat to global food security as salt-affected soils are expected to increase more under the influence of climate change. Sorghum is the world's 5th most important cereal crop and is moderately salt tolerant. Salt stress causes osmotic stress in sorghum and induces several physiological changes, such as membrane disruption, reactive oxygen species (ROS) generation, nutrient imbalance, decreased photosynthetic activity, and decreased stomatal aperture. This research focused on minimizing the detrimental effects of soil salinity on crop productivity by exploring the potential of ortho-silicic acid (OSA) as a mitigating agent for salt stress and analyzing its impact on growth, physiological and biochemical attributes. A pot experiment was performed under control and 4, 6, and 8 dS·m−1 NaCl with OSA concentrations of 1.5 and 2.5 mg·L−1. Results indicated that OSA application improved growth attributes, including fresh weight, plant height, dry weight, and leaf area, under various salt stress levels. Physiological attributes such as photosynthesis rate, transpiration rate, stomatal conductance, and relative water content were 23.7%, 32.4%, 51.3%, and 6.4% higher under 2.5 mg·L−1 OSA treatment, respectively compared to control. Nutritional attributes such as crude protein, fiber, total soluble sugars, and lignin were also improved under OSA treatment. The concentration of 2.5 mg·L−1 OSA treatment was found to be more effective under saline and non-saline conditions for increasing sorghum productivity. This research offers a promising strategy to increase crop productivity and resilience in the face of escalating soil salinity due to climate change.

  • Maize is a major staple crop and a model species for plant biology and genetic studies

    As a major staple crop, today maize accounts for approximately 40% of total worldwide cereal production (http://faostat.fao.org/). Since its domestication ~9,000 years ago from a subgroup of teosinte (Zea mays ssp. parviglumis) in the tropical lowlands of southwest Mexico[1], its cultivating area has greatly expanded, covering most of the world[2]. Human's breeding and utilization of maize have gone through several stages, from landraces, open-pollinated varieties (OPVs), double-cross hybrids (1930s-1950s) and since the middle 1950s, single-cross hybrids. Nowadays, global maize production is mostly provided by single-cross hybrids, which exhibit higher-yielding and better stress tolerance than OPVs and double-cross hybrids[3].

    Besides its agronomic importance, maize has also been used as a model plant species for genetic studies due to its out-crossing habit, large quantities of seeds produced and the availability of diverse germplasm. The abundant mutants of maize facilitated the development of the first genetic and cytogenetic maps of plants, and made it an ideal plant species to identify regulators of developmental processes[4−6]. Although initially lagging behind other model plant species (such as Arabidopsis and rice) in multi-omics research, the recent rapid development in sequencing and transformation technologies, and various new tools (such as CRISPR technologies, double haploids etc.) are repositioning maize research at the frontiers of plant research, and surely, it will continue to reveal fundamental insights into plant biology, as well as to accelerate molecular breeding for this vitally important crop[7, 8].

    Hundreds of genomic regions were selected during maize domestication

    During domestication from teosinte to maize, a number of distinguishing morphological and physiological changes occurred, including increased apical dominance, reduced glumes, suppression of ear prolificacy, increase in kernel row number, loss of seed shattering, nutritional changes etc.[9] (Fig. 1). At the genomic level, genome-wide genetic diversity was reduced due to a population bottleneck effect, accompanied by directional selection at specific genomic regions underlying agronomically important traits. Over a century ago, Beadle initially proposed that four or five genes or blocks of genes might be responsible for much of the phenotypic changes between maize and teosinte[10,11]. Later studies by Doebley et al. used teosinte–maize F2 populations to dissect several quantitative trait loci (QTL) to the responsible genes (such as tb1 and tga1)[12,13]. On the other hand, based on analysis of single-nucleotide polymorphisms (SNPs) in 774 genes, Wright et al.[14] estimated that 2%−4% of maize genes (~800−1,700 genes genome-wide) were selected during maize domestication and subsequent improvement. Taking advantage of the next-generation sequencing (NGS) technologies, Hufford et al.[15] conducted resequencing analysis of a set of wild relatives, landraces and improved maize varieties, and identified ~500 selective genomic regions during maize domestication. In a recent study, Xu et al.[16] conducted a genome-wide survey of 982 maize inbred lines and 190 teosinte accession. They identified 394 domestication sweeps and 360 adaptation sweeps. Collectively, these studies suggest that maize domestication likely involved hundreds of genomic regions. Nevertheless, much fewer domestication genes have been functionally studied so far.

    Figure 1.  Main traits of maize involved in domestication and improvement.
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    Domestication genes for morphological traits

    Teosinte branched1 (tb1) and increased apical dominance in maize

    During maize domestication, a most profound morphological change is an increase in apical dominance, transforming a multi-branched plant architecture in teosinte to a single stalked plant (terminated by a tassel) in maize. The tillers and long branches of teosinte are terminated by tassels and bear many small ears. Similarly, the single maize stalk bears few ears and is terminated by a tassel[9,12,17]. A series of landmark studies by Doebley et al. elegantly demonstrated that tb1, which encodes a TCP transcription factor, is responsible for this transformation[18, 19]. Later studies showed that insertion of a Hopscotch transposon located ~60 kb upstream of tb1 enhances the expression of tb1 in maize, thereby repressing branch outgrowth[20, 21]. Through ChIP-seq and RNA-seq analyses, Dong et al.[22] demonstrated that tb1 acts to regulate multiple phytohormone signaling pathways (gibberellins, abscisic acid and jasmonic acid) and sugar sensing. Moreover, several other domestication loci, including teosinte glume architecture1 (tga1), prol1.1/grassy tillers1, were identified as its putative targets. Elucidating the precise regulatory mechanisms of these loci and signaling pathways will be an interesting and rewarding area of future research. Also worth noting, studies showed that tb1 and its homologous genes in Arabidopsis (Branched1 or BRC1) and rice (FINE CULM1 or FC1) play a conserved role in repressing the outgrowth of axillary branches in both dicotyledon and monocotyledon plants[23, 24].

    Teosinte glume architecture 1 (tga1) and reduced glumes in maize

    Teosinte ears possess two ranks of fruitcase-enclosed kernels, while maize produces hundreds of naked kernels on the ear[13]. tga1, which encodes a squamosa-promoter binding protein (SBP) transcription factor, underlies this transformation[25]. It has been shown that a de novo mutation occurred during maize domestication, causing a single amino acid substitution (Lys to Asn) in the TGA1 protein, altering its binding activity to its target genes, including a group of MADS-box genes that regulate glume identity[26].

    Grassy tillers1 (gt1) and suppression of prolificacy in maize

    Prolificacy, the number of ears per plants, is also a domestication trait. It has been shown that grassy tillers 1 (gt1), which encodes an HD-ZIP I transcription factor, suppresses prolificacy by promoting lateral bud dormancy and suppressing elongation of the later ear branches[27]. The expression of gt1 is induced by shading and requires the activity of tb1, suggesting that gt1 acts downstream of tb1 to mediate the suppressed branching activity in response to shade. Later studies mapped a large effect QTL for prolificacy (prol1.1) to a 2.7 kb 'causative region' upstream of the gt1gene[28]. In addition, a recent study identified a new QTL, qEN7 (for ear number on chromosome 7). Zm00001d020683, which encodes a putative INDETERMINATE DOMAIN (IDD) transcription factor, was identified as the likely candidate gene based on its expression pattern and signature of selection during maize improvement[29]. However, its functionality and regulatory relationship with tb1 and gt1 remain to be elucidated.

    UPA2 and leaf angle

    Smaller leaf angle and thus more compact plant architecture is a desired trait for modern maize varieties. Tian et al.[30] used a maize-teosinte BC2S3 population and cloned two QTLs (Upright Plant Architecture1 and 2 [UPA1 and UPA2]) that regulate leaf angle. Interestingly, the authors showed that the functional variant of UPA2 is a 2-bp InDel located 9.5 kb upstream of ZmRAVL1, which encodes a B3 domain transcription factor. The 2-bp Indel flanks the binding site of the transcription factor Drooping Leaf1 (DRL1)[31], which represses ZmRAVL1 expression through interacting with Liguleless1 (LG1), a SBP-box transcription factor essential for leaf ligule and auricle development[32]. UPA1 encodes brassinosteroid C-6 oxidase1 (brd1), a key enzyme for biosynthesis of active brassinolide (BR). The teosinte-derived allele of UPA2 binds DRL1 more strongly, leading to lower expression of ZmRAVL1 and thus, lower expression of brd1 and BR levels, and ultimately smaller leaf angle. Notably, the authors demonstrated that the teosinte-derived allele of UPA2 confers enhanced yields under high planting densities when introgressed into modern maize varieties[30, 33].

    GLOSSY15 (Gl15) and vegetative phase change

    Maize plants exhibit salient vegetative phase change, which marks the vegetative transition from the juvenile stage to the adult stage, characterized by several changes in maize leaves produced before and after the transition, such as production of leaf epicuticular wax and epidermal hairs. Previous studies reported that Glossy15 (Gl15), which encodes an AP2-like transcription factor, promotes juvenile leaf identity and suppressing adult leaf identity. Ectopic overexpression of Gl15 causes delayed vegetative phase change and flowering, while loss-of-function gl15 mutant displayed earlier vegetative phase change[34]. In another study, Gl15 was identified as a major QTL (qVT9-1) controlling the difference in the vegetative transition between maize and teosinte. Further, it was shown that a pre-existing low-frequency standing variation, SNP2154-G, was selected during domestication and likely represents the causal variation underlying differential expression of Gl15, and thus the difference in the vegetative transition between maize and teosinte[35].

    Domestication genes related to identity and morphology of inflorescence

    A number of studies documented evidence that tassels replace upper ears1 (tru1) is a key regulator of the conversion of the male terminal lateral inflorescence (tassel) in teosinte to a female terminal inflorescence (ear) in maize. tru1 encodes a BTB/POZ ankyrin repeat domain protein, and it is directly targeted by tb1, suggesting their close regulatory relationship[36]. In addition, a number of regulators of maize inflorescence morphology, were also shown as selective targets during maize domestication, including ramosa1 (ra1)[37, 38], which encodes a putative transcription factor repressing inflorescence (the ear and tassel) branching, Zea Agamous-like1 (zagl1)[39], which encodes a MADS-box transcription factor regulating flowering time and ear size, Zea floricaula leafy2 (zfl2, homologue of Arabidopsis Leafy)[40, 41], which likely regulates ear rank number, and barren inflorescence2 (bif2, ortholog of the Arabidopsis serine/threonine kinase PINOID)[42, 43], which regulates the formation of spikelet pair meristems and branch meristems on the tassel. The detailed regulatory networks of these key regulators of maize inflorescence still remain to be further elucidated.

    Domestication genes of kernel-related traits

    Kernel row number (KRN) and kernel weight are two important determinants of maize yield. A number of domestication genes modulating KRN and kernel weight have been identified and cloned, including KRN1, KRN2, KRN4 and qHKW1. KRN4 was mapped to a 3-kb regulatory region located ~60 kb downstream of Unbranched3 (UB3), which encodes a SBP transcription factor and negatively regulates KRN through imparting on multiple hormone signaling pathways (cytokinin, auxin and CLV-WUS)[44, 45]. Studies have also shown that a harbinger TE in the intergenic region and a SNP (S35) in the third exon of UB3 act in an additive fashion to regulate the expression level of UB3 and thus KRN[46].

    KRN1 encodes an AP2 transcription factor that pleiotropically affects plant height, spike density and grain size of maize[47], and is allelic to ids1/Ts6 (indeterminate spikelet 1/Tassel seed 6)[48]. Noteworthy, KRN1 is homologous to the wheat domestication gene Q, a major regulator of spike/spikelet morphology and grain threshability in wheat[49].

    KRN2 encodes a WD40 domain protein and it negatively regulates kernel row number[50]. Selection in a ~700-bp upstream region (containing the 5’UTR) of KRN2 during domestication resulted in reduced expression and thus increased kernel row number. Interestingly, its orthologous gene in rice, OsKRN2, was shown also a selected gene during rice domestication to negatively regulate secondary panicle branches and thus grain number. These observations suggest convergent selection of yield-related genes occurred during parallel domestication of cereal crops.

    qHKW1 is a major QTL for hundred-kernel weight (HKW)[51]. It encodes a CLAVATA1 (CLV1)/BARELY ANY MERISTEM (BAM)-related receptor kinase-like protein positively regulating HKW. A 8.9 Kb insertion in its promoter region was find to enhance its expression, leading to enhanced HKW[52]. In addition, Chen et al.[53] reported cloning of a major QTL for kernel morphology, qKM4.08, which encodes ZmVPS29, a retromer complex component. Sequencing and association analysis revealed that ZmVPS29 was a selective target during maize domestication. They authors also identified two significant polymorphic sites in its promoter region significantly associated with the kernel morphology. Moreover, a strong selective signature was detected in ZmSWEET4c during maize domestication. ZmSWEET4c encodes a hexose transporter protein functioning in sugar transport across the basal endosperm transfer cell layer (BETL) during seed filling[54]. The favorable alleles of these genes could serve as valuable targets for genetic improvement of maize yield.

    In a recent effort to more systematically analyze teosinte alleles that could contribute to yield potential of maize, Wang et al.[55] constructed four backcrossed maize-teosinte recombinant inbred line (RIL) populations and conducted detailed phenotyping of 26 agronomic traits under five environmental conditions. They identified 71 QTL associated with 24 plant architecture and yield related traits through inclusive composite interval mapping. Interestingly, they identified Zm00001eb352570 and Zm00001eb352580, both encode ethylene-responsive transcription factors, as two key candidate genes regulating ear height and the ratio of ear to plant height. Chen et al.[56] constructed a teosinte nested association mapping (TeoNAM) population, and performed joint-linkage mapping and GWAS analyses of 22 domestication and agronomic traits. They identified the maize homologue of PROSTRATE GROWTH1, a rice domestication gene controlling the switch from prostrate to erect growth, is also a QTL associated with tillering in teosinte and maize. Additionally, they also detected multiple QTL for days-to-anthesis (such as ZCN8 and ZmMADS69) and other traits (such as tassel branch number and tillering) that could be exploited for maize improvement. These lines of work highlight again the value of mining the vast amounts of superior alleles hidden in teosinte for future maize genetic improvement.

    ZmSh1 and seed shattering

    Loss of seed shattering was also a key trait of maize domestication, like in other cereals. shattering1 (sh1), which encodes a zinc finger and YABBY domain protein regulating seed shattering. Interesting, sh1 was demonstrated to undergo parallel domestication in several cereals, including rice, maize, sorghum, and foxtail millet[57]. Later studies showed that the foxtail millet sh1 gene represses lignin biosynthesis in the abscission layer, and that an 855-bp Harbinger transposable element insertion in sh1 causes loss of seed shattering in foxtail millet[58].

    Nutritional quality

    In addition to morphological traits, a number of physiological and nutritional related traits have also been selected during maize domestication. Based on survey of the nucleotide diversity, Whitt et al.[59] reported that six genes involved in starch metabolism (ae1, bt2, sh1, sh2, su1 and wx1) are selective targets during maize domestication. Palaisa et al.[60] reported selection of the Y1 gene (encoding a phytoene synthase) for increased nutritional value. Karn et al.[61] identified two, three, and six QTLs for starch, protein and oil respectively and showed that teosinte alleles can be exploited for the improvement of kernel composition traits in modern maize germplasm. Fan et at.[62] reported a strong selection imposed on waxy (wx) in the Chinese waxy maize population. Moreover, a recent exciting study reported the identification of a teosinte-derived allele of teosinte high protein 9 (Thp9) conferring increased protein level and nitrogen utilization efficiency (NUE). It was further shown that Thp9 encodes an asparagine synthetase 4 and that incorrect splicing of Thp9-B73 transcripts in temperate maize varieties is responsible for its diminished expression, and thus reduced NUE and protein content[63].

    Domestication genes related to disease resistance and abiotic stresses

    Teosintes is known to confer superior disease resistance and adaptation to extreme environments (such as low phosphorus and high salinity). de Lange et al. and Lennon et al.[64−66] reported the identification of teosinte-derived QTLs for resistance to gray leaf spot and southern leaf blight in maize. Mano & Omori reported that teosinte-derived QTLs could confer flooding tolerance[67]. Feng et al.[68] identified four teosinte-derived QTL that could improve resistance to Fusarium ear rot (FER) caused by Fusarium verticillioides. Recently, Wang et al.[69] reported a MYB transcription repressor of teosinte origin (ZmMM1) that confers resistance to northern leaf blight (NLB), southern corn rust (SCR) and gray leaf spot (GLS) in maize, while Zhang et al.[70] reported the identification of an elite allele of SNP947-G ZmHKT1 (encoding a sodium transporter) derived from teosinte can effectively improve salt tolerance via exporting Na+ from the above-ground plant parts. Gao et al.[71] reported that ZmSRO1d-R can regulate the balance between crop yield and drought resistance by increasing the guard cells' ROS level, and it underwent selection during maize domestication and breeding. These studies argue for the need of putting more efforts to tapping into the genetic resources hidden in the maize’s wild relatives. The so far cloned genes involved in maize domestication are summarized in Table 1. Notably, the enrichment of transcription factors in the cloned domestication genes highlights a crucial role of transcriptional re-wiring in maize domestication.

    Table 1.  Key domestication genes cloned in maize.
    GenePhenotypeFunctional annotationSelection typeCausative changeReferences
    tb1Plant architectureTCP transcription factorIncreased expression~60 kb upstream of tb1 enhancing expression[18−22]
    tga1Hardened fruitcaseSBP-domain transcription factorProtein functionA SNP in exon (K-N)[25, 26]
    gt1Plant architectureHomeodomain leucine zipperIncreased expressionprol1.1 in 2.7 kb upstream of the promoter region increasing expression[27, 28]
    Zm00001d020683Plant architectureINDETERMINATE DOMAIN transcription factorProtein functionUnknown[29]
    UPA1Leaf angleBrassinosteroid C-6 oxidase1Protein functionUnknown[30]
    UPA2Leaf angleB3 domain transcription factorIncreased expressionA 2 bp indel in 9.5 kb upstream of ZmRALV1[30]
    Gl15Vegetative phase changeAP2-like transcription factorAltered expressionSNP2154: a stop codon (G-A)[34, 35]
    tru1Plant architectureBTB/POZ ankyrin repeat proteinIncreased expressionUnknown[36]
    ra1Inflorescence architectureTranscription factorAltered expressionUnknown[37, 38]
    zflPlant architectureTranscription factorAltered expressionUnknown[40, 41]
    UB3Kernel row numberSBP-box transcription factorAltered expressionA TE in the intergenic region;[44−46]
    SNP (S35): third exon of UB3
    (A-G) increasing expression of UB3 and KRN
    KRN1/ids1/Ts6Kernel row numberAP2 Transcription factorIncreased expressionUnknown[47, 48]
    KRN2Kernel row numberWD40 domainDecreased expressionUnknown[50]
    qHKW1Kernel row weightCLV1/BAM-related receptor kinase-like proteinIncreased expression8.9 kb insertion upstream of HKW[51, 52]
    ZmVPS29Kernel morphologyA retromer complex componentProtein functionTwo SNPs (S-1830 and S-1558) in the promoter of ZmVPS29[53]
    ZmSWEET4cSeed fillingHexose transporterProtein functionUnknown[54]
    ZmSh1ShatteringA zinc finger and YABBY transcription factorProtein functionUnknown[57, 58]
    Thp9Nutrition qualityAsparagine synthetase 4 enzymeProtein functionA deletion in 10th intron of Thp9 reducing NUE and protein content[63]
    ZmMM1Biotic stressMYB Transcription repressorProtein functionUnknown[69]
    ZmHKT1Abiotic stressA sodium transporterProtein functionSNP947-G: a nonsynonymous variation increasing salt tolerance[70]
    ZmSRO1d-RDrought resistance and productionPolyADP-ribose polymerase and C-terminal RST domainProtein functionThree non-synonymous variants: SNP131 (A44G), SNP134 (V45A) and InDel433[71]
     | Show Table
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    Pre-Columbia spread of maize from tropical to temperate

    After its domestication from its wild progenitor teosinte in southwestern Mexico in the tropics, maize has now become the mostly cultivated crop worldwide owing to its extensive range expansion and adaptation to diverse environmental conditions (such as temperature and day length). A key prerequisite for the spread of maize from tropical to temperate regions is reduced photoperiod sensitivity[72]. It was recently shown that CENTRORADIALIS 8 (ZCN8), an Flowering Locus T (FT) homologue, underlies a major quantitative trait locus (qDTA8) for flowering time[73]. Interestingly, it has been shown that step-wise cis-regulatory changes occurred in ZCN8 during maize domestication and post-domestication expansion. SNP-1245 is a target of selection during early maize domestication for latitudinal adaptation, and after its fixation, selection of InDel-2339 (most likely introgressed from Zea mays ssp. Mexicana) likely contributed to the spread of maize from tropical to temperate regions[74].

    ZCN8 interacts with the basic leucine zipper transcription factor DLF1 (Delayed flowering 1) to form the florigen activation complex (FAC) in maize. Interestingly, DFL1 was found to underlie qLB7-1, a flowering time QTL identified in a BC2S3 population of maize-teosinte. Moreover, it was shown that DLF1 directly activates ZmMADS4 and ZmMADS67 in the shoot apex to promote floral transition[75]. In addition, ZmMADS69 underlies the flowering time QTL qDTA3-2 and encodes a MADS-box transcription factor. It acts to inhibit the expression of ZmRap2.7, thereby relieving its repression on ZCN8 expression and causing earlier flowering. Population genetic analyses showed that DLF1, ZmMADS67 and ZmMADS69 are all targets of artificial selection and likely contributed to the spread of maize from the tropics to temperate zones[75, 76].

    In addition, a few genes regulating the photoperiod pathway and contributing to the acclimation of maize to higher latitudes in North America have been cloned, including Vgt1, ZmCCT (also named ZmCCT10), ZmCCT9 and ZmELF3.1. Vgt1 was shown to act as a cis-regulatory element of ZmRap2.7, and a MITE TE located ~70 kb upstream of Vgt1 was found to be significantly associated with flowering time and was a major target for selection during the expansion of maize to the temperate and high-latitude regions[77−79]. ZmCCT is another major flowering-time QTL and it encodes a CCT-domain protein homologous to rice Ghd7[80]. Its causal variation is a 5122-bp CACTA-like TE inserted ~2.5 kb upstream of ZmCCT10[72, 81]. ZmCCT9 was identified a QTL for days to anthesis (qDTA9). A Harbinger-like TE located ~57 kb upstream of ZmCCT9 showed the most significant association with DTA and thus believed to be the causal variation[82]. Notably, the CATCA-like TE of ZmCCT10 and the Harbinger-like TE of ZmCCT9 are not observed in surveyed teosinte accessions, hinting that they are de novo mutations occurred after the initial domestication of maize[72, 82]. ZmELF3.1 was shown to underlie the flowering time QTL qFT3_218. It was demonstrated that ZmELF3.1 and its homolog ZmELF3.2 can form the maize Evening Complex (EC) through physically interacting with ZmELF4.1/ZmELF4.2, and ZmLUX1/ZmLUX2. Knockout mutants of Zmelf3.1 and Zmelf3.1/3.2 double mutant presented delayed flowering under both long-day and short-day conditions. It was further shown that the maize EC promote flowering through repressing the expression of several known flowering suppressor genes (e.g., ZmCCT9, ZmCCT10, ZmCOL3, ZmPRR37a and ZmPRR73), and consequently alleviating their inhibition on several maize florigen genes (ZCN8, ZCN7 and ZCN12). Insertion of two closely linked retrotransposon elements upstream of the ZmELF3.1 coding region increases the expression of ZmELF3.1, thus promoting flowering[83]. The increase frequencies of the causal TEs in Vgt1, ZmCCT10, ZmCCT9 and ZmELF3.1 in temperate maize compared to tropical maize highlight a critical role of these genes during the spread and adaptation of maize to higher latitudinal temperate regions through promoting flowering under long-day conditions[72,81−83].

    In addition, Barnes et al.[84] recently showed that the High Phosphatidyl Choline 1 (HPC1) gene, which encodes a phospholipase A1 enzyme, contributed to the spread of the initially domesticated maize from the warm Mexican southwest to the highlands of Mexico and South America by modulating phosphatidylcholine levels. The Mexicana-derived allele harbors a polymorphism and impaired protein function, leading to accelerated flowering and better fitness in highlands.

    Besides the above characterized QTLs and genes, additional genetic elements likely also contributed to the pre-Columbia spreading of maize. Hufford et al.[85] proposed that incorporation of mexicana alleles into maize may helped the expansion of maize to the highlands of central Mexico based on detection of bi-directional gene flow between maize and Mexicana. This proposal was supported by a recent study showing evidence of introgression for over 10% of the maize genome from the mexicana genome[86]. Consistently, Calfee et al.[87] found that sequences of mexicana ancestry increases in high-elevation maize populations, supporting the notion that introgression from mexicana facilitating adaptation of maize to the highland environment. Moreover, a recent study examined the genome-wide genetic diversity of the Zea genus and showed that dozens of flowering-related genes (such as GI, BAS1 and PRR7) are associated with high-latitude adaptation[88]. These studies together demonstrate unequivocally that introgression of genes from Mexicana and selection of genes in the photoperiod pathway contributed to the spread of maize to the temperate regions.

    The so far cloned genes involved in pre-Columbia spread of maize are summarized in Fig. 2 and Table 2.

    Figure 2.  Genes involved in Pre-Columbia spread of maize to higher latitudes and the temperate regions. The production of world maize in 2020 is presented by the green bar in the map from Ritchie et al. (2023). Ritchie H, Rosado P, and Roser M. 2023. "Agricultural Production". Published online at OurWorldInData.org. Retrieved from: 'https:ourowrldindata.org/agricultural-production' [online Resource].
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    Table 2.  Flowering time related genes contributing to Pre-Columbia spread of maize.
    GeneFunctional annotationCausative changeReferences
    ZCN8Florigen proteinSNP-1245 and Indel-2339 in promoter[73, 74]
    DLF1Basic leucine zipper transcription factorUnknown[75]
    ZmMADS69MADS-box transcription factorUnknown[76]
    ZmRap2.7AP2-like transcription factorMITE TE inserted ~70 kb upstream[77−79]
    ZmCCTCCT-domain protein5122-bp CACTA-like TE inserted ~2.5 kb upstream[72,81]
    ZmCCT9CCT transcription factorA harbinger-like element at 57 kb upstream[82]
    ZmELF3.1Unknownwo retrotransposons in the promote[84]
    HPC1Phospholipase A1 enzymUnknown[83]
    ZmPRR7UnknownUnknown[88]
    ZmCOL9CO-like-transcription factorUnknown[88]
     | Show Table
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    Selective genes underlying agronomic trait improvement during modern maize breeding

    Subsequent to domestication ~9,000 years ago, maize has been continuously subject to human selection during the post-domestication breeding process. Through re-sequencing analysis of 35 improved maize lines, 23 traditional landraces and 17 wild relatives, Hufford et al.[15] identified 484 and 695 selective sweeps during maize domestication and improvement, respectively. Moreover, they found that about a quarter (23%) of domestication sweeps (107) were also selected during improvement, indicating that a substantial portion of the domestication loci underwent continuous selection during post-domestication breeding.

    Genetic improvement of maize culminated in the development of high planting density tolerant hybrid maize to increase grain yield per unit land area[89, 90]. To investigate the key morphological traits that have been selected during modern maize breeding, we recently conducted sequencing and phenotypic analyses of 350 elite maize inbred lines widely used in the US and China over the past few decades. We identified four convergently improved morphological traits related to adapting to increased planting density, i.e., reduced leaf angle, reduced tassel branch number (TBN), reduced relative plant height (EH/PH) and accelerated flowering. Genome-wide Association Study (GWAS) identified a total of 166 loci associated with the four selected traits, and found evidence of convergent increases in allele frequency at putatively favorable alleles for the identified loci. Moreover, genome scan using the cross-population composite likelihood ratio approach (XP-CLR) identified a total of 1,888 selective sweeps during modern maize breeding in the US and China. Gene ontology analysis of the 5,356 genes encompassed in the selective sweeps revealed enrichment of genes related to biosynthesis or signaling processes of auxin and other phytohormones, and in responses to light, biotic and abiotic stresses. This study provides a valuable resource for mining genes regulating morphological and physiological traits underlying adaptation to high-density planting[91].

    In another study, Li et al.[92] identified ZmPGP1 (ABCB1 or Br2) as a selected target gene during maize domestication and genetic improvement. ZmPGP1 is involved in auxin polar transport, and has been shown to have a pleiotropic effect on plant height, stalk diameter, leaf length, leaf angle, root development and yield. Sequence and phenotypic analyses of ZmPGP1 identified SNP1473 as the most significant variant for kernel length and ear grain weight and that the SNP1473T allele is selected during both the domestication and improvement processes. Moreover, the authors identified a rare allele of ZmPGP1 carrying a 241-bp deletion in the last exon, which results in significantly reduced plant height and ear height and increased stalk diameter and erected leaves, yet no negative effect on yield[93], highlighting a potential utility in breeding high-density tolerant maize cultivars.

    Key genes/pathways regulating shade avoidance responses and plant architecture in maize

    Shade avoidance syndrome (SAS) is a set of adaptive responses triggered when plants sense a reduction in the red to far-red light (R:FR) ratio under high planting density conditions, commonly manifested by increased plant height (and thus more prone to lodging), suppressed branching, accelerated flowering and reduced resistance to pathogens and pests[94, 95]. High-density planting could also cause extended anthesis-silking interval (ASI), reduced tassel size and smaller ear, and even barrenness[96, 97]. Thus, breeding of maize cultivars of attenuated SAS is a priority for adaptation to increased planting density.

    Extensive studies have been performed in Arabidopsis to dissect the regulatory mechanism of SAS and this topic has been recently extensively reviewed[98]. We recently showed that a major signaling mechanism regulating SAS in Arabidopsis is the phytochrome-PIFs module regulates the miR156-SPL module-mediated aging pathway[99]. We proposed that in maize there might be a similar phytochrome-PIFs-miR156-SPL regulatory pathway regulating SAS and that the maize SPL genes could be exploited as valuable targets for genetic improvement of plant architecture tailored for high-density planting[100].

    In support of this, it has been shown that the ZmphyBs (ZmphyB1 and ZmphyB2), ZmphyCs (ZmphyC1 and ZmphyC2) and ZmPIFs are involved in regulating SAS in maize[101−103]. In addition, earlier studies have shown that as direct targets of miR156s, three homologous SPL transcription factors, UB2, UB3 and TSH4, regulate multiple agronomic traits including vegetative tillering, plant height, tassel branch number and kernel row number[44, 104]. Moreover, it has been shown that ZmphyBs[101, 105] and ZmPIF3.1[91], ZmPIF4.1[102] and TSH4[91] are selective targets during modern maize breeding (Table 3).

    Table 3.  Selective genes underpinning genetic improvement during modern maize breeding.
    GenePhenotypeFunctional annotationSelection typeCausative changeReferences
    ZmPIF3.1Plant heightBasic helix-loop-helix transcription factorIncreased expressionUnknown[91]
    TSH4Tassel branch numberTranscription factorAltered expressionUnknown[91]
    ZmPGP1Plant architectureATP binding cassette transporterAltered expressionA 241 bp deletion in the last exon of ZmPGP1[92, 93]
    PhyB2Light signalPhytochrome BAltered expressionA 10 bp deletion in the translation start site[101]
    ZmPIF4.1Light signalBasic helix-loop-helix transcription factorAltered expressionUnknown[102]
    ZmKOB1Grain yieldGlycotransferase-like proteinProtein functionUnknown[121]
     | Show Table
    DownLoad: CSV

    In a recent study to dissect the signaling process regulating inflorescence development in response to the shade signal, Kong et al.[106] compared the gene expression changes along the male and female inflorescence development under simulated shade treatments and normal light conditions, and identified a large set of genes that are co-regulated by developmental progression and simulated shade treatments. They found that these co-regulated genes are enriched in plant hormone signaling pathways and transcription factors. By network analyses, they found that UB2, UB3 and TSH4 act as a central regulatory node controlling maize inflorescence development in response to shade signal, and their loss-of-function mutants exhibit reduced sensitivity to simulated shade treatments. This study provides a valuable genetic source for mining and manipulating key shading-responsive genes for improved tassel and ear traits under high density planting conditions.

    Complementary and differential selection of heterotic groups during modern maize breeding

    Nowadays, global maize production is mostly provided by hybrid maize, which exhibits heterosis (or hybrid vigor) in yields and stress tolerance over open-pollinated varieties[3]. Hybrid maize breeding has gone through several stages, from the 'inbred-hybrid method' stage by Shull[107] and East[108] in the early twentieth century, to the 'double-cross hybrids' stage (1930s−1950s) by Jones[109], and then the 'single-cross hybrids' stage since the 1960s. Since its development, single-cross hybrid was quickly adopted globally due to its superior heterosis and easiness of production[3].

    Single-cross maize hybrids are produced from crossing two unrelated parental inbred lines (female × male) belonging to genetically distinct pools of germplasm, called heterotic groups. Heterotic groups allow better exploitation of heterosis, since inter-group hybrids display a higher level of heterosis than intra-group hybrids. A specific pair of female and male heterotic groups expressing pronounced heterosis is termed as a heterotic pattern[110, 111]. Initially, the parental lines were derived from a limited number of key founder inbred lines and empirically classified into different heterotic groups (such as SSS and NSS)[112]. Over time, they have expanded dramatically, accompanied by formation of new 'heterotic groups' (such as Iodent, PA and PB). Nowadays, Stiff Stalk Synthetics (SSS) and PA are generally used as FHGs (female heterotic groups), while Non Stiff Stalk (NSS), PB and Sipingtou (SPT) are generally used as the MHGs (male heterotic groups) in temperate hybrid maize breeding[113].

    With the development of molecular biology, various molecular markers, ranging from RFLPs, SSRs, and more recently high-density genome-wide SNP data have been utilized to assign newly developed inbred lines into various heterotic groups, and to guide crosses between heterotic pools to produce the most productive hybrids[114−116]. Multiple studies with molecular markers have suggested that heterotic groups have diverged genetically over time for better heterosis[117−120]. However, there has been a lack of a systematic assessment of the effect and contribution of breeding selection on phenotypic improvement and the underlying genomic changes of FHGs and MHGs for different heterotic patterns on a population scale during modern hybrid maize breeding.

    To systematically assess the phenotypic improvement and the underlying genomic changes of FHGs and MHGs during modern hybrid maize breeding, we recently conducted re-sequencing and phenotypic analyses of 21 agronomic traits for a panel of 1,604 modern elite maize lines[121]. Several interesting observations were made: (1) The MHGs experienced more intensive selection than the FMGs during the progression from era I (before the year 2000) to era II (after the year 2000). Significant changes were observed for 18 out of 21 traits in the MHGs, but only 10 of the 21 traits showed significant changes in the FHGs; (2) The MHGs and FHGs experienced both convergent and divergent selection towards different sets of agronomic traits. Both the MHGs and FHGs experienced a decrease in flowering time and an increase in yield and plant architecture related traits, but three traits potentially related to seed dehydration rate were selected in opposite direction in the MHGs and FHGs. GWAS analysis identified 4,329 genes associated with the 21 traits. Consistent with the observed convergent and divergent changes of different traits, we observed convergent increase for the frequencies of favorable alleles for the convergently selected traits in both the MHGs and FHGs, and anti-directional changes for the frequencies of favorable alleles for the oppositely selected traits. These observations highlight a critical contribution of accumulation of favorable alleles to agronomic trait improvement of the parental lines of both FHGs and MHGs during modern maize breeding.

    Moreover, FST statistics showed increased genetic differentiation between the respective MHGs and FHGs of the US_SS × US_NSS and PA × SPT heterotic patterns from era I to era II. Further, we detected significant positive correlations between the number of accumulated heterozygous superior alleles of the differentiated genes with increased grain yield per plant and better parent heterosis, supporting a role of the differentiated genes in promoting maize heterosis. Further, mutational and overexpressional studies demonstrated a role of ZmKOB1, which encodes a putative glycotransferase, in promoting grain yield[121]. While this study complemented earlier studies on maize domestication and variation maps in maize, a pitfall of this study is that variation is limited to SNP polymorphisms. Further exploitation of more variants (Indels, PAVs, CNVs etc.) in the historical maize panel will greatly deepen our understanding of the impact of artificial selection on the maize genome, and identify valuable new targets for genetic improvement of maize.

    Perspectives

    The ever-increasing worldwide population and anticipated climate deterioration pose a great challenge to global food security and call for more effective and precise breeding methods for crops. To accommodate the projected population increase in the next 30 years, it is estimated that cereal production needs to increase at least 70% by 2050 (FAO). As a staple cereal crop, breeding of maize cultivars that are not only high-yielding and with superior quality, but also resilient to environmental stresses, is essential to meet this demand. The recent advances in genome sequencing, genotyping and phenotyping technologies, generation of multi-omics data (including genomic, phenomic, epigenomic, transcriptomic, proteomic, and metabolomic data), creation of novel superior alleles by genome editing, development of more efficient double haploid technologies, integrating with machine learning and artificial intelligence are ushering the transition of maize breeding from the Breeding 3.0 stage (biological breeding) into the Breeding 4.0 stage (intelligent breeding)[122, 123]. However, several major challenges remain to be effectively tackled before such a transition could be implemented. First, most agronomic traits of maize are controlled by numerous small-effect QTL and complex genotype-environment interactions (G × E). Thus, elucidating the contribution of the abundant genetic variation in the maize population to phenotypic plasticity remains a major challenge in the post-genomic era of maize genetics and breeding. Secondly, most maize cultivars cultivated nowadays are hybrids that exhibit superior heterosis than their parental lines. Hybrid maize breeding involves the development of elite inbred lines with high general combining ability (GCA) and specific combining ability (SCA) that allows maximal exploitation of heterosis. Despite much effort to dissect the mechanisms of maize heterosis, the molecular basis of maize heterosis is still a debated topic[124−126]. Thirdly, only limited maize germplasm is amenable to genetic manipulation (genetic transformation, genome editing etc.), which significantly hinders the efficiency of genetic improvement. Development of efficient genotype-independent transformation procedure will greatly boost maize functional genomic research and breeding. Noteworthy, the Smart Corn System recently launched by Bayer is promised to revolutionize global corn production in the coming years. At the heart of the new system is short stature hybrid corn (~30%−40% shorter than traditional hybrids), which offers several advantages: sturdier stems and exceptional lodging resistance under higher planting densities (grow 20%−30% more plants per hectare), higher and more stable yield production per unit land area, easier management and application of plant protection products, better use of solar energy, water and other natural resources, and improved greenhouse gas footprint[127]. Indeed, a new age of maize green revolution is yet to come!

    Acknowledgments

    This work was supported by grants from the Key Research and Development Program of Guangdong Province (2022B0202060005), National Natural Science Foundation of China (32130077) and Hainan Yazhou Bay Seed Lab (B21HJ8101). We thank Professors Hai Wang (China Agricultural University) and Jinshun Zhong (South China Agricultural University) for valuable comments and helpful discussion on the manuscript. We apologize to authors whose excellent work could not be cited due to space limitations.

  • The authors declare that they have no conflict of interest. Haiyang Wang is an Editorial Board member of Seed Biology who was blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of this Editorial Board member and his research groups.

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  • Cite this article

    Pankaj, Devi S, Dhaka P, Kumari G, Satpal, et al. 2024. Enhancing salt stress tolerance of forage sorghum by foliar application of ortho-silicic acid. Grass Research 4: e016 doi: 10.48130/grares-0024-0014
    Pankaj, Devi S, Dhaka P, Kumari G, Satpal, et al. 2024. Enhancing salt stress tolerance of forage sorghum by foliar application of ortho-silicic acid. Grass Research 4: e016 doi: 10.48130/grares-0024-0014
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Enhancing salt stress tolerance of forage sorghum by foliar application of ortho-silicic acid

  • 1.

    Department of Botany & Plant Physiology, CCS Haryana Agricultural University, Hisar 125004, Haryana, India

  • 2.

    Forage Section, Department of Genetics & Plant Breeding, CCS Haryana Agricultural University, Hisar 125004, Haryana, India

  • 3.

    Department of Molecular Biology, Biotechnology and Bioinformatics, CCS Haryana Agricultural University, Hisar 125004, Haryana, India

  • 4.

    Maharshi Dayanand University, Rohtak 124001, Haryana, India

  • 5.

    Centre of Research Impact and Outreach, Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India

  • Received: 27 April 2024
  • Revised: 08 June 2024
  • Accepted: 03 July 2024
  • Published online: 16 July 2024
Grass Research  4,  Article number: e016  (2024)  |  Cite this article

Abstract: Soil salinity poses a significant threat to global food security as salt-affected soils are expected to increase more under the influence of climate change. Sorghum is the world's 5th most important cereal crop and is moderately salt tolerant. Salt stress causes osmotic stress in sorghum and induces several physiological changes, such as membrane disruption, reactive oxygen species (ROS) generation, nutrient imbalance, decreased photosynthetic activity, and decreased stomatal aperture. This research focused on minimizing the detrimental effects of soil salinity on crop productivity by exploring the potential of ortho-silicic acid (OSA) as a mitigating agent for salt stress and analyzing its impact on growth, physiological and biochemical attributes. A pot experiment was performed under control and 4, 6, and 8 dS·m−1 NaCl with OSA concentrations of 1.5 and 2.5 mg·L−1. Results indicated that OSA application improved growth attributes, including fresh weight, plant height, dry weight, and leaf area, under various salt stress levels. Physiological attributes such as photosynthesis rate, transpiration rate, stomatal conductance, and relative water content were 23.7%, 32.4%, 51.3%, and 6.4% higher under 2.5 mg·L−1 OSA treatment, respectively compared to control. Nutritional attributes such as crude protein, fiber, total soluble sugars, and lignin were also improved under OSA treatment. The concentration of 2.5 mg·L−1 OSA treatment was found to be more effective under saline and non-saline conditions for increasing sorghum productivity. This research offers a promising strategy to increase crop productivity and resilience in the face of escalating soil salinity due to climate change.

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    Introduction

    • Soil salinity is a major restraint to agricultural production worldwide, and has been recognized as a major challenge to food security by the Food and Agricultural Organization (FAO, 2022). According to the reports, salt-affected soils are estimated to be around 1 billion ha, globally[1]. According to CSSRI, Karnal Haryana, salt-affected soils are estimated to increase by ~16 mha in India by the year 2050, due to anthropological changes. Soil salinization has made a significant part of land area unproductive or less productive[2]. This land degradation in salt-affected areas in India are threatening the development and economic growth of the country. In arid and semi-arid regions of India, desertification is reaching irreversible levels due to environmental degradation. Continued usage of improper farming techniques and activities has intensified soil salinization[3−5]. However, there is a dire need to adapt promising approaches in agriculture, which have the potential to meet global food demand while reducing the environmental footprint of agricultural systems[6,7]. The estimates show that 50% of arable land around the world will become salt-affected by the year 2050, therefore escalating food insecurity[8,9]. According to the Central Ground Water Board (CGWB), 84.87% of groundwater resources are classified as 'saline' in Haryana, largely due to overexploitation of groundwater resources and improper agricultural practices, which has led to the depletion of groundwater reserves and increased salinity. Soil salinity has a significant negative impact on crops, reducing their growth, yield and quality. This is due to the interference of salt with the nutrient and water uptake ability of plants, disrupting the morphological, physiological, biochemical, molecular, and metabolic processes resulting in a lowering of osmotic potential and an increase in ion toxicity[10,11]. An increase in the toxicity of ions causes the production of reactive oxygen species (ROS), contributing to a decrease in protein content and accumulation of proline and other osmolytes[12]. The global population is also expected to grow to 9.8 billion by 2050, which will require at least a 50% rise in agricultural production from 2012 levels.

      Sorghum bicolor (L.) Moench, is a C4 crop of the family Poaceae and is ranked among the top five cereal crops globally. It is a multipurpose crop that is mainly grown for food, fodder, fuel, and bioethanol production[13]. It is well known for its adaptability to moderately salt-stressed conditions and is therefore widely grown in arid and semi-arid areas. It is known for its high nutritional content, gluten-free nature, and low environmental impact, making it an ideal choice for promoting sustainable food systems. According to the United States Department of Agriculture (USDA) report of 2022, the world's total sorghum production is 58.6 million tonnes, with India contributing 7.51% of the world's production. In India, Maharashtra is the largest producer of sorghum, contributing 47%, followed by Karnataka (22%) and Rajasthan (8%). In Haryana, sorghum is mainly grown as a fodder crop, with an area of 40.3 thousand hectares and a production of 21.3 thousand tonnes, with an average yield of 528 kg per hectare, according to the Department of Agriculture (DOA) of Haryana. The year 2023 has also been declared as the International Year of Millets to raise awareness about potential nutritional benefits, resilience, and the role of millets in sustainable agriculture. It is anticipated to emerge as a vital crop in the current climate change scenario due to its significant tolerance to high temperatures, water deficit, and saline conditions, which makes it an indispensable feed reserve[14]. Sorghum is a rich source of various phytochemicals including tannins, phenolic acids, anthocyanins, phytosterols, and policosanols and its fractions possess high antioxidant activity in vitro relative to other cereals. It is gluten-free and can be a good alternative for those with celiac disease or gluten sensitivity. It is also low on the glycemic index, making it a good choice for people with diabetes.

      Silicon (Si) is a beneficial element which acts as a mitigator of abiotic and biotic stresses and increases growth and production in higher plants[15,16]. It is present in the earth's crust in the form of SiO2 in high concentration but it can't be utilized by plants in this form. Plants uptake Si in the form of ortho-silicic acid (OSA) or mono-silicic acid (H4SiO4). It is the only biologically active form of silicon and is easily soluble in water. Silixol is a stable formulation of OSA which contains 0.8% ortho-silicic acid, and has been generally used in foliar spray as a Si fertilizer in different concentrations which vary from crop to crop to enhance plant growth. Si helps in the alleviation of salt stress by reducing uptake of Na+[17] and Cl− ions[18] which contributes to the maintenance of optimum K+/Na+ ratio under saline conditions. The effect of Si on the transcriptional regulation of genes involved in water transport and stress-related pathways, including the jasmonic acid pathway, ABA-dependent or independent regulatory pathway, and phenyl propanoid pathway, has been proposed and the role of Si in stress mitigation in rice, wheat, maize, tomato, and soybean have been established by various researchers[19]. Silicon also alleviates salt-induced osmotic stress by upregulating the aquaporin's activity, thereby increasing the root hydraulic conductance[20]. Si is associated with higher tolerance due to involvement in the protection of various essential mechanisms such as energy metabolism, photosynthesis, transcription/translation, and hormonal signaling under salinity using proteomics studies[21]. Si supplementation via both foliar and root application effectively attenuated salinity in sorghum and sunflower plants by contributing to an antioxidative defense mechanism leading to Na+ detoxification[22]. Si application also enhanced nutrient uptake and nutrient use efficiency (NUE) contributing to higher dry weight accumulation[23]. The foliar application of manganese combined with silicon resulted in increased quantum efficiency of PSII, micronutrient uptake, relative chlorophyll index and subsequently enhanced dry mass accumulation in corn and sorghum[24]. The above findings indicate that silicon plays a crucial role in enhancing crop resilience and its nutritional quality under salinity stress. Therefore, the present investigation with the aim to mitigate the negative impact of salt stress on the growth, physiological, and biochemical parameters of sorghum by exogenous application of ortho-silicic acid was undertaken.

    Materials and methods

    • Seeds of two sorghum genotypes (CSV33MF and SSG 59-3) were collected from the Forage Section, Department of Genetics and Plant Breeding, Chaudhary Charan Singh Haryana Agricultural University, Hisar (Haryana) and were sown in pots under screen house conditions in screen house of the Department of Botany and Plant Physiology on July 17, 2021. The seeds were surface sterilized in 1% sodium hypochlorite (NaOCl) solution for 5 min and were sown in plastic pots containing 10 kg of dune sand. Before sowing pots were saturated with desired salt levels i.e., control (0), 4, 6, and 8 dS·m−1. The nutrient solution was given at regular intervals according to the method of Arnon[25]. Ortho-silicic acid (1.5 and 2.5 mg·L−1) was applied exogenously with the help of a manual sprayer 30 d after sowing (DAS). Three plants per pot were maintained to study several parameters.

      Growth/morphological attributes

    • Fresh weight (FW) of leaf, root and stem, fresh weight (FW) of leaf, root and stem height (HT), dry weight (DW) and leaf area (cm2·plant−1) at 40 Days After Sowing (30 DAS and 10 Days after OSA application).

      Physiological attributes

    • Photosynthesis rate, transpiration rate, and stomatal conductance were calculated using a third leaf from the top by using a plant Infrared Gas Analyzer (IRGA, LCi-SD, ADC Bioscience, USA)[26].

      Relative water content (RWC) was calculated according to Barrs & Weatherley[27] using the formula:

      RWC(%)=Freshweight−DryweightTurgidweight−Dryweight×100

      To assess the water potential of leaves, a pressure chamber (Model 3005, Soil Moisture Corporation, Santa Barbara, CA, USA) was used[28]. The third leaf from the top was separated from the plant with the help of a sharp edge knife and sealed in the pressure chamber one by one with the cut end protruding outside, and pressure was developed until the sap just appeared at the end.

      1,000mmol⋅kg−1=2.5MPa=25bars

      Chlorophyll fluorescence in plants was measured on a sunny day using a chlorophyll fluorometer)[29]. For 2 min, a fully expanded leaf was acclimated to darkness using a clip, and the leaf-adapted darkness was then continuously irradiated for 1 s (1,500 mol·m−2·s−1) by an array of three light-emitted diodes in the sensor.

      Relative stress injury was quantified by determining the ratio of ion leakage into the external aqueous medium to the total ion concentration of the stressed tissue, as assessed through the electrical conductivity of the external medium[30]. The membrane injury was calculated as:

      RSI(%)=1−Ec1Ec2×100

      Biochemical attributes

    • Dried samples were ground to 1 mm particles and crude protein (CP), lignin, and fiber were analyzed by near-infrared reflectance spectroscopy (NIRS) using the method given by Fekadu et al.[31]. All results are reported as % dry mass (% DM).

      Total soluble sugars were determined using the method given by Yemm & Willis[32].

      Statistical analysis

    • The data was analyzed statistically for ANOVA using complete randomized design (CRD) by using SPSS 13.0 (Statistical Package for the Social Sciences) (SPSS Inc., Chicago, IL, USA) using Tukey's HSD test at 0.05 significance level and was expressed as mean ± standard errors. Treatments were compared with CD values at a 5% level of significance. Pearson's correlation analysis was conducted to explore potential associations among the traits, aiming to identify any potential relationships between variables. Correlation analysis was done using different packages of R software version 4.0.5.

    Results

      Growth attributes

    • The growth attributes were significantly influenced by both salt stress and OSA treatments. The data is presented in Tables 1 & 2. According to Table 1, the effect of ortho-silicic acid (OSA) on the fresh weight (FW), plant height (HT), dry weight (DW), and leaf area of sorghum genotype CSV 33 MF was evaluated under different levels of salt stress.

      Table 1.  Effect of ortho-silicic acid (OSA) on fresh weight stem (FWS), fresh weight leaf (FWL), fresh weight root (FWR), plant height (PH), dry weight stem (DWS), dry weight leaf (DWL), dry weight root (DWR) and leaf area (LA) of Sorghum genotype CSV 33MF grown under different levels of salt stress.

      Salt level OSA
      (mg·L−1)
      		 FWS
      (g·plant−1)      		 FWL
      (g·plant−1)      		 FWR
      (g·plant−1)      		 PH
      (cm)      		 DWS
      (g·plant−1)      		 DWL
      (g·plant−1)      		 DWR
      (g·plant−1)      		 LA
      (cm2)
      0 dS·m−1 0 29.73cd ± 0.64 11.40bc ± 0.55 10.06abcd ± 0.29 114.0ab ± 8.39 9.01bc ± 0.19 2.84abc ± 0.14 3.05abc ± 0.09 689.0abc ± 33.4
      1.5 33.90ab ± 0.55 12.50ab ± 0.47 11.66ab ± 0.23 129.7a ± 4.18 9.97ab ± 0.16 3.12ab ± 0.12 3.38ab ± 0.08 756.6ab ± 28.7
      2.5 34.83a ± 0.46 13.76a ± 0.61 12.13a ± 0.27 136.0a ± 5.29 10.85a ± 0.14 3.43a ± 0.15 3.67a ± 0.07 832.0a ± 35.2
      4 dS·m−1 0 28.20de ± 0.83 11.16bc ± 0.38 9.86bcde ± 0.73 112.0ab ± 3.79 8.26cd ± 0.25 2.70bcd ± 0.18 2.98abc ± 0.22 663.6bc ± 43.4
      1.5 30.83bcd ± 0.44 12.06ab ± 0.17 11.43abc ± 0.23 120.3a ± 7.36 9.03bc ± 0.15 2.92abc ± 0.05 3.20ac ± 0.05 717.3ab ± 11.2
      2.5 32.26abc ± 0.23 12.56ab ± 0.32 11.76ab ± 0.29 124.3a ± 5.90 9.49abc ± 0.06 3.14ab ± 0.08 3.41ab ± 0.23 769.6ab ± 18.4
      6 dS·m−1 0 21.03gh ± 0.43 8.60de ± 0.06 7.80ef ± 0.42 82.6cd ± 4.42 5.81ef ± 0.55 2.18d ± 0.01 2.36cde ± 0.13 479.6de ± 03.0
      1.5 24.10fg ± 0.40 9.16d ± 0.55 9.33de ± 0.69 93.0bc ± 1.16 6.50e ± 0.53 2.36cd ± 0.28 2.48cde ± 0.36 499.3d ± 29.6
      2.5 25.20ef ± 0.55 9.76cd ± 0.27 9.40cde ± 0.12 95.3bc ± 1.33 6.73de ± 0.38 2.46cd ± 0.07 2.75bcd ± 0.07 548.3cd ± 16.0
      8 dS·m−1 0 14.40i ± 1.46 5.56f ± 0.37 5.90f ± 0.32 47.7e ± 2.33 3.85g ± 0.45 1.40e ± 0.05 1.80e ± 0.21 302.0f ± 12.2
      1.5 17.00i ± 0.89 6.03f ±0.13 6.76f ± 0.41 58.7de ± 4.33 4.64fg ± 0.27 1.47e ± 0.04 2.01de ± 0.10 318.0f ± 05.0
      2.5 17.83hi ± 0.53 6.70ef ±0.15 7.16f ± 0.46 63.0de ± 2.08 4.88fg ± 0.16 1.54e ± 0.01 2.11de ± 0.10 340.0f ± 05.1
      MSE 1.43 0.43 0.505 12.5 0.298 0.045 0.085 49.8
      C.D 2.03 1.11 1.20 13.9 0.92 0.36 0.49 82.8
      S.E (m) 0.69 0.38 0.41 4.74 0.31 0.12 0.17 28.2
      S.E (d) 0.98 0.54 0.58 6.71 0.45 0.17 0.24 39.9
      Data having the same letters in the column do not differ significantly while groups with different letters suggest a significant difference (Tukey's HSD test p < 0.05) with error degree of freedom = 24; MSE (Mean Square Error) at 5%. * Values are presented as mean ± standard error (n = 3).

      Table 2.  Effect of ortho-silicic acid (OSA) on fresh weight stem (FWS), fresh weight leaf (FWL), fresh weight root (FWR), plant height (PH), dry weight stem (DWS), dry weight leaf (DWL), dry weight root (DWR) and leaf area (LA) of Sorghum genotype SSG 59-3 grown under different levels of salt stress.

      Salt level OSA
      (mg·L−1)      		 FWS
      (g·plant−1)      		 FWL
      (g·plant−1)      		 FWR
      (g·plant−1)      		 PH
      (cm)      		 DWS
      (g·plant−1)      		 DWL
      (g·plant−1)      		 DWR
      (g·plant−1)      		 LA
      (cm2)
      0 dS·m−1 0 29.10a ± 0.47 11.53abc ± 1.47 10.03abc ± 0.69 118.7b ± 4.8 8.82b ± 0.14 2.88ab ± 0.37 3.04ab ± 0.21 697.0c ± 31.5
      1.5 31.57a ± 0.69 13.77a ± 0.76 10.56ab ± 0.32 132.3ab ± 1.3 9.55ab ± 0.20 3.42a ± 0.18 3.23ab ± 0.14 829.0ab ± 15.6
      2.5 32.56a ± 0.54 13.97a ± 0.43 11.23a ± 1.02 140.0a ± 2.5 10.47a ± 0.42 3.63a ± 0.12 3.38a ± 0.40 879.0a ± 19.7
      4 dS·m−1 0 27.73ab ± 1.65 11.27abc ± 1.33 9.67abc ± 0.57 116.7bc ± 4.1 8.06bc ± 0.51 2.82abc ± 0.33 2.92ab ± 0.33 681.7c ± 23.7
      1.5 29.67a ± 1.30 12.50ab ± 0.35 11.10a ± 0.35 128.0ab ± 2.9 8.58b ± 0.40 3.12a ± 0.08 3.14ab ± 0.10 755.0bc ± 19.9
      2.5 31.80a ± 1.67 13.30a ± 0.50 10.26ab ± 0.30 132.3ab ± 0.9 9.01ab ± 0.41 3.32a ± 0.12 3.21ab ± 0.19 804.0ab ± 30.1
      6 dS·m−1 0 19.27cd ± 0.38 8.03cdef ± 0.70 7.67cdef ± 0.24 87.0d ± 1.5 5.77d ± 0.12 1.94cdef ± 0.17 2.29bc ± 0.16 360.7de ± 16.2
      1.5 21.60c ± 0.66 8.50cde ± 0.45 8.40bcde ± 0.32 98.0d ± 1.5 6.29d ± 0.16 2.06bcde ± 0.11 2.42abc ± 0.12 389.3d ± 03.2
      2.5 22.90bc ± 1.78 8.93bcd ± 0.43 8.83abcd ± 0.09 100.7cd ± 5.1 6.56cd ± 0.37 2.18bcd ± 0.13 2.57abc ± 0.23 434.7d ± 14.8
      8 dS·m−1 0 12.60e ± 0.50 4.30f ± 0.25 5.70f ± 0.40 53.3e ± 1.2 3.75e ± 0.22 1.12f ± 0.07 1.66c ± 0.08 247.7f ± 17.0
      1.5 14.37de ± 0.68 4.63ef ± 0.73 5.87ef ± 0.18 62.7e ± 1.6 4.04e ± 0.21 1.23ef ± 0.09 1.72c ± 0.15 269.0ef ± 25.5
      2.5 15.90de ± 0.47 5.07def ± 0.72 6.77def ± 0.46 66.0e ± 2.8 4.21e ± 0.04 1.28def ± 0.10 1.85c ± 0.06 284.7ef ± 08.1
      MSE 3.22 1.76 0.772 22.9 0.272 0.100 0.123 19.82
      C.D 3.04 2.25 1.49 9.28 0.88 0.54 0.60 59.8
      S.E (m) 1.04 0.77 0.51 3.16 0.30 0.18 0.20 20.4
      S.E (d) 1.46 1.08 0.72 4.47 0.43 0.26 0.29 28.8
      Data having the same letters in the column do not differ significantly while groups with different letters suggest a significant difference (Tukey's HSD test p < 0.05) with error degree of freedom = 24; MSE (Mean Square Error) at 5%. * Values are presented as mean ± standard error (n = 3).
      Fresh weight

    • The fresh weight of stem, leaf, and root showed a drastic decline under salt stress. OSA was able to mitigate the negative effects of salt stress to some extent. At control (0), the highest percent increase in FW was observed at 2.5 mg·L−1 of OSA, with stem FW increasing by 17.1%, leaf FW by 20.7%, and root FW by 20.6% in CSV 33MF. Exogenous OSA application also resulted in increased FW of stem, leaf, and root at 4 dS·m−1 with the highest increase observed at 2.5 mg·L−1, with stem FW increasing by 14.4%, leaf FW by 12.5 %, and root FW by 19.3%. Under 8 dS·m−1 salt level, the highest decline in fresh weight was observed which was mitigated to some extent by OSA application which increased FW of stem, leaf, and root. The highest percent increase in FW was observed at 2.5 mg·L−1 of OSA, with stem FW increasing by 23.8%, leaf FW by 20.5%, and root FW by 21.4%. Similar trends were observed in Table 2 demonstrating the impact of salt stress and OSA on sorghum genotype SSG 59-3. The imposition of elevated salt stress levels resulted in a significant decrease in the fresh weight of leaves, with the maximum reduction of 62.6% observed at 8 dS·m−1 in comparison to the control. However, the application of ortho-silicic acid (OSA) at concentrations of 1.5 and 2.5 mg·L−1 led to an increase in the fresh weight of leaves, with more pronounced enhancements observed at 2.5 mg·L−1.

      Dry weight

    • The dry weight of the leaf, stem and root showed a declining trend with the increasing salt level from control to 8 dS·m−1. Fold change was found maximum in leaf dry weight at 8 dS·m−1 of salt stress i.e. 0.59 and 0.55 in CSV33MF and SSG 59-3 respectively, with respect to the control. Dry weight enhancement was noticed after exogenous OSA application. Maximum increase was observed at 2.5 mg·L−1 OSA application. After the application of 2.5 mg·L−1 of OSA, the percent increase was maximum at the control of salt stress in both genotypes i.e. CSV33MF (20.6%) and SSG 59-3 (11.2%) with respect to the control. Likewise, the dry weight of leaves exhibited a declining trend with escalating salt stress levels in the SSG 59-3 genotype. The highest percentage decline of 45.39% was observed at 8 dS·m−1 of salt stress compared to the control. However, the foliar application of OSA resulted in an increment in the dry weight of leaves, with the maximum enhancement observed at 2.5 mg·L−1. Similar results were also obtained for dry weight of stem and root under different levels of salt stress and foliar application of ortho-silicic acid. The contribution towards dry weight was more from the stem as compared to the root.

      Plant height

    • The plant height was reduced in both genotypes due to the effect of salt stress. At 0 dS·m−1 salt level, plant height was recorded highest (136.0 ± 5.29 cm) at 2.5 mg·L−1 of OSA demonstrating a fold change of 1.19 as compared to control conditions of treatment. The plant height also showed a percent increase of 12.6% and 15.7% at 1.5 mg·L−1 and 2.5 mg·L−1 of OSA, respectively as compared to the control at 6 dS·m−1. The plant height exhibited a significant percent increase of 32.2% at 8 dS·m−1 after the exogenous application of 2.5 mg·L−1 OSA as compared to the control of the treatment. Plant height also experienced a significant reduction with increasing levels of salt stress in the SSG 59-3 genotype. At 8 dS·m−1, there was a higher percentage decline of 55.4% compared to the control level. The foliar application of OSA at concentrations of 1.5 and 2.5 mg·L−1 resulted in significant enhancements in plant height, with the maximum increase of 23.8% observed at 8 dS·m−1 at 2.5 mg·L−1 OSA.

      Leaf area

    • Leaf area per plant decreased with increasing salt levels ranging from control to 8 dS·m−1. The highest decline in leaf area was observed at 8 dS·m−1 of salt level, but on comparison of both genotypes, SSG 59-3 (64.4%) had a maximum decrease in leaf area per plant as compared to CSV 33MF (56.2%) with respect to control. Leaf area per plant was increased after the foliar spray of ortho-silicic acid at 1.5 and 2.5 mg·L−1, but the maximum rise in leaf area was observed at 2.5 mg·L−1 of OSA. The leaf area showed a significant percent increase of 20.7% at 2.5 mg·L−1 of OSA compared to the control at 0 dS·m−1. After exogenous application of 2.5 mg·L−1 OSA, the leaf area also exhibited a significant percent increase of 15.9% at 4 dS·m−1 and 14.3 % at 6 dS·m−1 in CSV33MF genotype. The most drastic reduction in leaf area was observed at 8 dS·m−1 which was 56.16% in CSV33MF and 64.56% in SSG 59-3. Exogenous application of 2.5 mg·L−1 OSA mitigated the effects of salt stress on leaf area and increased leaf area by fold change of 1.13 in CSV33MF and 1.15 in SSG 59-3.

      Overall, both genotypes exhibited reductions in fresh weight, dry weight, plant height, and leaf area per plant as salt stress levels increased. Nevertheless, the application of OSA mitigated these adverse effects and led to improvements in these growth parameters, with greater enhancements observed at 2.5 mg·L−1 of OSA.

      Physiological attributes

    • The leaf water potential values became increasingly negative as salt levels were incremented from the control to 8 dS·m−1 in both genotypes at 40 Days after sowing (DAS) (Tables 3 & 4). A comparison of the two genotypes revealed that SSG 59-3 exhibited the most negative value (−1.15 MPa) compared to CSV33MF (−1.13 MPa) at the 8 dS·m−1 salt level. The water potential of the leaves became less negative when different concentrations of ortho-silicic acid were applied to both genotypes. SSG 59-3 displayed a less mean negative value (−0.50 MPa) compared to CSV33MF (−0.57 MPa) after the application of 2.5 mg·L−1 OSA at control. The relative water content (RWC) progressively decreased with increasing salt stress levels at 40 DAS in both genotypes. The maximum decrease in RWC was observed at the 8 dS·m−1 salt level in both genotypes, with a decrease of 18.3% in CSV33MF and 18.5% in SSG 59-3 compared to their respective controls. The foliar application of 2.5 mg·L−1 of ortho-silicic acid increased the RWC at each level of salt stress in both genotypes. In CSV33MF, the RWC increased from 81.2% to 85.5% at 4 dS·m−1, from 71.6% to 76.1% at 6 dS·m−1, and from 66.2% to 72.0% at 8 dS·m−1. A similar enhancement in RWC was observed in SSG 59-3. Relative Stress Injury exhibited an increasing trend with the imposition of salt stress, ranging from the control to 8 dS·m−1. The maximum leakage was estimated at the 8 dS·m−1 salt level in both genotypes, with SSG 59-3 showing more damage (42.3%) compared to CSV33MF (36.8%) with respect to the control. The foliar application of 2.5 mg·L−1 of ortho-silicic acid prevented electrolyte leakage to some extent.

      Table 3.  Effect of ortho-silicic acid (OSA) on water potential (Ψw), Relative Water Content (RWC), Relative Stress Injury (RSI), Transpiration Rate (E), Stomatal Conductance (gs), Assimilation Rate (A), Chlorophyll Fluoroscence (CHLF) and Chlorophyll content (CHL) of Sorghum genotype CSV33MF grown under different levels of salt stress.

      Salt level OSA (mg·L−1) Ψw (MPa) RWC (%) RSI (%) E (mmol H2O·m−1·s−1) gs (mmol H2O·m−1·s−1) A (μmol CO2·m−1·s−1) CHLF (Fv/Fm) CHL (SPAD)
      0 dS·m−1 0 −0.673bc ± 0.023 84.45abc ± 5.49 11.13f ± 0.39 3.58ab ± 0.07 0.103abc ± 0.012 11.12bcd ± 0.80 0.729abc ± 0.011 36.6abc± 4.3
      1.5 −0.583ab ± 0.015 85.89ab ± 2.14 10.54f ± 0.72 3.88ab ± 0.25 0.120ab ± 0.010 12.09abc ± 1.10 0.739ab ± 0.001 39.2ab ± 2.6
      2.5 −0.496a ± 0.012 87.74a ± 2.10 10.45f ± 0.33 4.14a ± 0.06 0.147a ± 0.009 14.68a ± 0.31 0.772a ± 0.008 42.5a ± 1.4
      4 dS·m−1 0 −0.783de ± 0.013 81.19abcd ± 1.98 21.30de ± 2.12 3.35bc ± 0.17 0.093abc ± 0.012 10.78bcd ± 0.53 0.703bcde ± 0.008 33.5bcde± 1.3
      1.5 −0.700cd ± 0.012 84.20abc ± 3.03 20.63de ± 1.91 3.69ab ± 0.07 0.107abc ± 0.009 11.87abc ± 0.25 0.710bcd ± 0.006 35.0bcd ± 1.9
      2.5 −0.667bc ± 0.018 85.50ab ± 1.38 18.30e ± 0.55 4.04ab ± 0.13 0.127ab ± 0.015 12.55ab ± 0.31 0.719abcd ± 0.004 38.7abc ± 2.3
      6 dS·m−1 0 −0.996h ± 0.009 71.59de ± 4.01 28.97bc ± 0.55 1.80ef ± 0.10 0.050bc ± 0.010 7.84de ± 1.29 0.679cde ± 0.010 28.7def ± 1.3
      1.5 −0.893fg ± 0.003 74.32cde ± 0.39 27.30bc ± 0.72 2.09de ± 0.12 0.063bc ± 0.009 8.94cde ± 1.03 0.723abcd ± 0.003 32.5cde ± 2.3
      2.5 −0.833ef ± 0.018 76.07bcde ± 2.15 24.03cd ± 0.07 2.71cd ± 0.23 0.083abc ± 0.012 9.27bcde ± 0.45 0.736abc ± 0.003 34.3bcd ± 1.9
      8 dS·m−1 0 −1.127i ± 0.023 66.17e ± 4.00 36.80a ± 0.61 1.09f ± 0.11 0.030c ± 0.010 6.21e ± 0.24 0.650e ± 0.005 21.8g ± 1.2
      1.5 −1.010h ± 0.012 70.30e± 1.48 34.23a ± 0.61 1.57ef ± 0.16 0.047bc ± 0.009 7.31e ± 0.33 0.665de ± 0.009 25.6fg ± 1.0
      2.5 −0.957gh ± 0.035 71.97de ± 4.42 31.87ab ± 1.12 2.16de ± 0.06 0.070abc ± 0.010 8.20de ± 0.10 0.687bcde ± 0.015 27.5efg ± 2.7
      MSE 0.001 5.89 3.02 0.06 0.0008 1.372 0.0004 4.84
      C.D 0.052 5.13 2.96 0.42 0.031 1.98 0.023 3.73
      S.E (m) 0.018 1.76 1.00 0.14 0.011 0.68 0.008 1.27
      S.E (d) 0.025 2.49 1.42 0.20 0.015 0.96 0.011 1.80
      Data having the same letters in the column do not differ significantly while groups with different letters suggest a significant difference (Tukey's HSD test p < 0.05) with error degree of freedom = 24; MSE (Mean Square Error) at 5%. * Values are presented as mean ± standard error (n = 3).

      Table 4.  Effect of ortho-silicic acid (OSA) on water potential (Ψw), Relative Water Content (RWC), Relative Stress Injury (RSI), Transpiration Rate (E), Stomatal Conductance (gs), Assimilation Rate (A), Chlorophyll Fluoroscence (CHLF) and Chlorophyll content (CHL) of Sorghum genotype SSG 59-3 grown under different levels of salt stress.

      Salt level OSA (mg·L−1) Ψw (MPa) RWC (%) RSI (%) E (mmol H2O·m−1·s−1) gs (mmol H2O·m−1·s−1) A (μmol CO2·m−1·s−1) CHLF (Fv/Fm) CHL (SPAD)
      0 dS·m−1 0 −.713bc ± 0.026 82.47ab ± 1.98 12.93f ± 0.43 3.30bc ± 0.13 0.097abc ± 0.009 10.85abc ± 0.61 0.713abc ± 0.006 33.5cd ± 0.7
      1.5 −0.630ab ± 0.015 84.84a ± 2.44 11.77f ± 0.73 3.74ab ± 0.08 0.117a ± 0.009 11.54ab ± 0.30 0.738a ± 0.014 37.4ab ± 1.1
      2.5 −0.570a ± 0.025 86.48b ± 1.69 11.33f ± 0.33 4.02a ± 0.15 0.136a ± 0.015 13.34a ± 0.76 0.751a ± 0.001 40.1a ± 0.6
      4 dS·m−1 0 −0.837cd ± 0.017 79.29ab ± 1.88 22.83e ± 0.58 3.15c ± 0.06 0.093abcd ± 0.008 10.14bcd ± 1.17 0.710abc ± 0.001 31.1de ± 0.5
      1.5 −0.743bc ± 0.015 82.32ab ± 1.72 22.06e ± 0.80 3.42bc ± 0.07 0.103ab ± 0.012 11.90ab ± 0.12 0.714abc ± 0.001 34.2bcd ± 0.6
      2.5 −0.713bc ± 0.020 84.59a ± 2.10 19.57e ± 0.58 3.58abc ± 0.09 0.128a ± 0.015 12.71ab ± 0.16 0.724ab ± 0.006 37.3abc ± 0.4
      6 dS·m−1 0 −1.023ef ± 0.052 69.96cd ± 0.71 32.47c ± 0.60 1.54ef ± 0.16 0.030e ± 0.010 6.84e ± 0.21 0.657cd ± 0.009 26.4fg ± 1.0
      1.5 −0.943de ± 0.029 74.45bc ± 0.78 30.60c ± 0.78 2.04de ± 0.08 0.040cde ± 0.010 7.40de ± 0.07 0.700abc ± 0.001 29.5ef ± 0.9
      2.5 −0.877d ± 0.009 75.76bc ± 1.78 26.90d ± 0.10 2.55d ± 0.08 0.057bcde ± 0.009 8.27cde ± 0.08 0.715ab ± 0.008 32.2de ± 0.5
      8 dS·m−1 0 −1.150f ± 0.042 63.96d ± 0.68 42.33a ± 0.69 0.92g ± 0.14 0.017e ± 0.008 5.81e ± 0.23 0.637d ± 0.001 20.0h ± 0.8
      1.5 −1.063ef ± 0.044 68.76cd ± 1.09 39.40ab ± 0.71 1.32fg ± 0.06 0.020e ± 0.009 6.12e ± 0.33 0.665cd ± 0.014 23.5gh ± 0.9
      2.5 −1.030ef ± 0.010 69.45cd ± 1.82 36.63a ± 1.30 1.61ef ± 0.07 0.037de ± 0.010 7.11e ± 0.70 0.678bcd ± 0.001 25.7fg ± 0.8
      MSE 0.002 8.28 1.45 0.31 0.0008 0.96 0.0004 1.65
      C.D 0.084 4.88 2.04 0.16 0.031 1.30 0.021 2.18
      S.E (m) 0.029 1.66 0.69 0.05 0.011 0.45 0.007 0.74
      S.E (d) 0.040 2.35 0.98 0.07 0.015 0.63 0.010 1.05
      Data having the same letters in the column do not differ significantly while groups with different letters suggest a significant difference (Tukey’s HSD test p < 0.05) with error degree of freedom = 24; MSE (Mean Square Error) at 5%. * Values are presented as mean ± standard error (n = 3).

      The chlorophyll content also declined with increasing salt stress levels from the control (0) to 8 dS·m−1 in both genotypes. The maximum fold change was observed at the 8 dS·m−1 salt level, contributing to a fold change of approximately 0.6 in both CSV33MF and SSG 59-3 compared to the control. A progressive increase in chlorophyll content was observed in both genotypes with the application of different concentrations of ortho-silicic acid. The fold change was maximum at 2.5 mg·L−1 of ortho-silicic acid in CSV33MF (1.285) and SSG 59-3 (1.207) at the 8 dS·m−1 salt level compared to the control. Chlorophyll fluorescence or photochemical quantum yield also declined with the imposition of salt stress in both genotypes at 40 DAS. The quantum yield values decreased from the control to 8 dS·m−1, ranging from 0.73 to 0.65 in CSV33MF and 0.71 to 0.64 in SSG 59-3, respectively. The application of 2.5 mg·L−1 of ortho-silicic acid resulted in a progressive increase in quantum yield at each salt level, as well as in the control. The maximum increase was noticed at the 6 dS·m−1 salt level in CSV33MF, where it increased from 0.68 to 0.74.

      The rate of photosynthesis decreased with increasing salt stress levels from the control (0) to 8 dS·m−1 in both genotypes at 40 DAS. The percent decrease in photosynthetic rate was 44.2% in CSV33MF and 46.5% in SSG 59-3 at the 8 dS·m−1 salt level compared to the control. The foliar application of 2.5 mg·L−1 of ortho-silicic acid enhanced the rate of photosynthesis in both genotypes. The percent increase was noticed at each level of salt stress in CSV33MF (14.1%, 15.4%, and 32.2% at 4, 6, and 8 dS·m−1, respectively) compared to the control. Similar increments were also observed in genotype SSG 59-3. A progressive decline was noticed in the rate of transpiration with an increase in salt levels from the control to 8 dS·m−1. The fold change in transpiration rate was highest at the 8 dS·m−1 salt level, with 0.305 in CSV33MF and 0.282 in SSG 59-3 compared to the control. At the highest salt level (8 dS·m−1), the application of 2.5 mg·L−1 of ortho-silicic acid showed a significant increase in transpiration rate (88.7% in CSV33MF and 73.1% in SSG 59-3). Stomatal conductance also decreased with an increase in salt levels in both genotypes. The percent decline in stomatal conductance observed at 4, 6, and 8 dS·m−1 salt levels was 5.0%, 50.2%, and 70.1%, respectively, in CSV33MF, and 5.2%, 68.4%, and 84.4% in SSG 59-3 compared to their respective controls. The foliar application of ortho-silicic acid (1.5 and 2.5 mg·L−1) led to an improvement in stomatal conductance in both genotypes, while the maximum increase was observed at 2.5 mg·L−1 OSA at 8 dS·m−1 leading to a fold change of 2.33 in the CSV33MF genotype.

      Biochemical parameters

    • A decrease in protein content was observed with the increase in salt levels from control to 8 dS·m−1 at 40 DAS as described in Fig. 1. In the sorghum genotypes CSV33MF and SSG 59-3, protein content decreased from 8.89 to 6.61 and 8.59 to 6.72% DM, respectively, with the onset of salt stress (0 to 8 dS·m−1). The foliar application of ortho-silicic acid (2.5 mg·L−1) increased protein content under both stressed and control conditions. The maximum increase in protein content (23.9% in CSV33MF and 15.2% in SSG 59-3) was observed with 2.5 mg·L−1 OSA at 8 dS·m−1 salt level compared to their respective controls.

      Figure 1. 

      Effect of ortho-silicic acid (OSA) on protein (% DM), fiber (% DM), Total Soluble Sugar (TSS) (% DM) and lignin (% DM) of Sorghum genotypes CSV33MF and SSG 59-3 grown under different levels of salt stress at 40 DAS. Data having the same letters in the column do not differ significantly while groups with different letters suggest a significant difference (Tukey's HSD test p < 0.05) with error degree of freedom = 48.

      The total soluble sugar (TSS) content of both sorghum genotypes was significantly influenced by both salt stress (4, 6, and 8 dS·m−1) and OSA (1.5 and 2.5 mg·L−1). The results revealed that plants grown under stress conditions exhibited higher TSS values compared to the control. The maximum TSS content was estimated at 8 dS·m−1 salt level in both genotypes (CSV33MF and SSG 59-3). Significant increases in TSS content were observed with the application of ortho-silicic acid at 1.5 and 2.5 mg·L−1 concentrations. The highest increase was observed in CSV33MF at 8 dS·m−1 salt level, where TSS content increased from 7.40 to 7.97 at 2.5 mg·L−1 OSA.

      The fiber content significantly decreased with increasing levels of salt stress from control to 8 dS·m−1 in both genotypes at 40 DAS. The decrease was 14.1% in CSV33MF and 10.8% in SSG 59-3 at 8 dS·m−1 compared to the control. Application of all concentrations of ortho-silicic acid caused an increase in fiber content in both genotypes under stressed and control conditions, but the maximum increment was observed with 2.5 mg·L−1 OSA in CSV33MF compared to SSG 59-3 under control conditions. Lignin content increased with each increment in salt levels in both genotypes at 40 DAS. The percentage increase in lignin content was 5.6%, 14.9%, and 18.3% at 4, 6, and 8 dS·m−1 salt levels, respectively, in CSV33MF compared to their respective controls. Foliar application of OSA (1.5 and 2.5 mg·L−1) led to an increase in lignin content in both genotypes, with the maximum increase observed at 2.5 mg·L−1. The percentage increase was higher at 8 dS·m−1 salt level in CSV33MF (4.5%) and at 6 dS·m−1 in SSG 59-3 (6.8%) compared to their respective controls.

    Discussion

    • Salt stress has a detrimental influence on plant development (germination, vegetative growth, and reproductive development), physiology and biochemical aspects along with its water and nutrient uptake either directly or indirectly. A comprehensive review of the literature revealed a scarcity of well-maintained work on the effects of ortho-silicic acid on the morpho-physiological and biochemical responses of sorghum to salt stress. The nutritional quality of sorghum also relies on these components, therefore evaluation of these parameters under salt stress might reveal the relative importance of these traits in maintaining nutritional quality which can be further improved by crop improvement program of forage sorghum for salt stress tolerance.

      Morpho-physiological changes in response to OSA treatment under salt stress

    • Growth variables are an indispensable tool for assessing crop productivity across various species. It is influenced by an array of factors, including genetic makeup, physiological and biochemical factors along with environmental factors. Under salt stress, both the genotypes of sorghum (CSV33MF and SSG 59-3) exhibited a significant decline in plant height, fresh and dry weight, leaf number, leaf area per plant, and specific leaf weight. This reduction in growth parameters intensified with increasing salt concentration, validating findings also reported by Bimurzayev et al.[33]. The presence of salts in the soil lowers the solute potential, leading to a reduction in water imbibition by roots, which subsequently impairs plant growth and development. This decrease in growth could be linked to negative effects on physiological processes such as osmotic imbalance, ion stresses, transpiration, and photosynthesis[34]. Increased salt tolerance in plants is often associated with higher antioxidant activity and cell membrane protection, contributing to decreased lipid peroxidation as well as improved leaf photosynthetic rate and stomatal conductance. For this, in-depth investigations are warranted. For instance, a recent opportunity has suggested that mapping the movement of cell apoplastic ion and metabolite patterns could offer novel insights into the function of guard cells[35], particularly under salinity. This understanding could shed light on the maintenance of photosynthesis and biomass accumulation with silicon application under salinity. OSA has shown potential in alleviating salt stress by regulating antioxidant activity. Foliar application of OSA mitigates salt-induced damage and enhances photosynthetic characteristics of plants, evidenced by improved photosynthetic pigment content, photosynthetic rate, stomatal conductance, and intercellular CO2 concentration[36]. Under abiotic stress, OSA can promote meristem division and plant development through improved nutrient uptake and reduced oxidative damage caused by ROS generation[37]. Additionally, OSA contributes to plant growth by enhancing multiple adaptive responses, including organic acids and phenol exudation, accumulation of compatible solutes and hormonal regulation[38].

      Biochemical changes in response to OSA treatment under salt stress

    • In leaves, intense soluble sugar accumulation was observed following salt treatment in all the genotypes of wheat and barley. This accumulation is attributed to increased invertase activity, which converts sucrose to fructose and glucose, thus raising the total soluble sugar content[39]. Elevated levels of sugar metabolites in the leaves of salt-tolerant cultivars, functioning as osmo-protectants, helping the plants cope with osmotic stress[40].

      Silicon (Si) plays a crucial role in altering the gene expression of Si transporters (Lsi1 and Lsi2) and stress-related proteins, leading to increased silica accumulation and higher levels of compatible solutes in Si-supplemented plants[41]. Similarly, maize plants grown from seeds treated with Si for 12 h showed increased growth, leaf-relative water content, and levels of photosynthetic pigments, soluble sugars, soluble proteins, total free amino acids, potassium, and activities of SOD, CAT, and POD enzymes, compared to untreated plants[42]. Increased lignin deposition has been observed in Si-treated (Si+) plants compared to untreated (Si−) plants. Deposition of lignin at the inner tangential cell wall starts together with the formation of the silica aggregates in Si+ roots[43]. Upregulation of phenylpropanoid biosynthesis-related genes under -Si conditions. Due to this -Si plants exhibit increased mechanical strength and calorific value in cell walls due to elevated lignin deposition. Limiting the Si supply also significantly increased the thioglycolic acid lignin content and thioacidolysis-derived syringyl/guaiacyl monomer ratio[44]. Flores et al. reported that silicon application improved the physiological quality, dry mass, and fibre production of Sorghum bicolor[45]. The accumulation of large amounts of silicon in the endoderm tissues of sorghum increased the crop's tolerance to stress including water deficit as the endoderm cells play an important role in water transportation by roots. Also, the application of silicon increased the protein content in sorghum seeds. The study focused on the nutritional and functional properties of sorghum-based products and the potential valorization of sorghum bran. The addition of silicon, along with micro minerals and soybeans, enhanced the protein content in polished sorghum-based products[46]. In the present study, OSA alleviated the negative impact of stress on growth and yield, consistent with previous findings. Si application enhanced levels of photosynthetic pigments, relative water content, protein content, and carbohydrate content in both wheat varieties (WH-1105 and KRL-210). It also reduced proline content, malondialdehyde (MDA) content (an indicator of lipid peroxidation), and electrolyte leakage, thereby improving salt stress tolerance. Furthermore, Si reduces Na+ absorption, improves ion balance, and alleviates the adverse effects of salt stress in two licorice species, as demonstrated by previous studies[47,48].

      All morphological parameters were observed to have a highly significant negative correlation with RSI, while conferring a highly significant positive correlation with A, gs, RWC, E and CHL. Figure 2 provides information regarding the correlation values and patterns among the different traits varying across all the treatments. The data is highly significant for approximately all parameters having a p-value of < 0.001. Correlations between different traits were determined using Pearson correlation coefficients (PCCs). Strong correlation was observed among all the physiological traits except RSI, and with the growth traits. Among the biochemical traits, protein and fibre showed a positive correlation with physiological and growth traits whereas lignin and TSS showed a negative correlation.

      Figure 2. 

      Pattern of correlation and level of significance observed among different traits across all the treatments in both sorghum genotypes. TSS (Total Soluble Sugar), PROTEIN, FIBRE, LIGNIN, A (Assimilation Rate), gs (Stomatal Conductance), DW (Dry Weight of Stem, Leaf and Root), FW (Fresh Weight of Stem, Leaf and Root), WP (Water Potential), E (Transpiration Rate), RWC (Relative Water Content); PLANTHT (Plant Height), Fv/Fm (Chlorophyll Fluoroscence), CHL (Chlorophyll Content).

    Conclusions

    • Salt stress is a major environmental factor limiting global plant growth and productivity. The sorghum varieties CSV33MF and SSG 59-3, grown in northern India, are significantly affected by salinity and sodicity. Silicon (Si) has shown potential as an abiotic stress mitigator. Salt stress negatively impacted morpho-physiological and biochemical traits in both sorghum genotypes, with SSG 59-3 experiencing greater reduction. Foliar application of ortho-silicic acid (OSA) improved various growth parameters, with 2.5 mg·L−1 OSA being more effective than 1.5 mg·L−1. OSA significantly mitigated the effect of salt stress up to 6 dS·m−1 and demonstrated improved productivity at all salt levels. OSA enhances osmotic balance and water use efficiency (WUE), preventing salt-induced damage in plants. Sorghum is a vital crop in the context of climate change due to its abiotic stress resilience ensuring food security in increasingly adverse conditions. These findings suggest the potential inference of using exogenous ortho-silicic acid to mitigate salt stress.

    Author contributions

    • The authors confirm contribution to the paper as follows: study conception and design: Kumari G, Satpal, Lakra N, Arya SS; data collection, data analysis and interpretation of results, draft manuscript preparation, writing, review and editing: Pankaj, Devi S, Ahlawat YK, Dhaka P. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • All data generated or analyzed during this study are included in this published article, and are available from the corresponding author upon reasonable request.

      Acknowledgments

      • This work was supported by the Chaudhary Charan Singh Haryana Agricultural University, Haryana, India.

      Conflict of interest

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

      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (2)  Table (4) References (48)
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    Pankaj, Devi S, Dhaka P, Kumari G, Satpal, et al. 2024. Enhancing salt stress tolerance of forage sorghum by foliar application of ortho-silicic acid. Grass Research 4: e016 doi: 10.48130/grares-0024-0014
    Pankaj, Devi S, Dhaka P, Kumari G, Satpal, et al. 2024. Enhancing salt stress tolerance of forage sorghum by foliar application of ortho-silicic acid. Grass Research 4: e016 doi: 10.48130/grares-0024-0014
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