Maize multi-omics reveal leaf water status controlling of differential transcriptomes, proteomes and hormones as mechanisms of age-dependent osmotic stress response in leaves

Niu, Liangjie; Wang, Wenkang; Li, Yingxue; Wu, **aolin; Wang, Wei

doi:10.1007/s44154-024-00159-9

Maize multi-omics reveal leaf water status controlling of differential transcriptomes, proteomes and hormones as mechanisms of age-dependent osmotic stress response in leaves

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Published: 18 March 2024

Volume 4, article number 19, (2024)
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Maize multi-omics reveal leaf water status controlling of differential transcriptomes, proteomes and hormones as mechanisms of age-dependent osmotic stress response in leaves

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Liangjie Niu¹,
Wenkang Wang¹,
Yingxue Li¹,
**aolin Wu¹ &
…
Wei Wang ORCID: orcid.org/0000-0002-3711-5181¹

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Abstract

Drought-induced osmotic stress severely affects the growth and yield of maize. However, the mechanisms underlying the different responses of young and old maize leaves to osmotic stress remain unclear. To gain a systematic understanding of age-related stress responses, we compared osmotic-stress-induced changes in maize leaves of different ages using multi-omics approaches. After short-term osmotic stress, old leaves suffered more severe water deficits than young leaves. The adjustments of transcriptomes, proteomes, and hormones in response to osmotic stress were more dynamic in old leaves. Metabolic activities, stress signaling pathways, and hormones (especially abscisic acid) responded to osmotic stress in an age-dependent manner. We identified multiple functional clusters of genes and proteins with potential roles in stress adaptation. Old leaves significantly accumulated stress proteins such as dehydrin, aquaporin, and chaperones to cope with osmotic stress, accompanied by senescence-like cellular events, whereas young leaves exhibited an effective water conservation strategy mainly by hydrolyzing transitory starch and increasing proline production. The stress responses of individual leaves are primarily determined by their intracellular water status, resulting in differential transcriptomes, proteomes, and hormones. This study extends our understanding of the mechanisms underlying plant responses to osmotic stress.

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Introduction

The frequency and duration of drought have risen by 29% globally since 2000 (UN report 2022), significantly affecting crop growth and causing considerable yield loss (Gupta et al. 2020). Plants have developed complex mechanisms to respond to drought in an age- and tissue-dependent manner (Skirycz et al. 2010; Rankenberg et al. 2021; Zhang et al. 2022).

As the principal photosynthetic organs, leaves are the most susceptible to abiotic stress, especially drought. Under drought stress, plants generally exhibit tissue-specific responses by altering physiology, modifying root growth, and closing stomata to reduce leaf water loss (Gupta et al. 2020). Drought signals promote reactive oxygen species (ROS) scavenging, osmotic regulation, and cell wall remodeling. Moreover, abscisic acid (ABA), brassinosteroid, and ethylene pathways play critical roles in stress sensing (Zhang et al. 2020), by activating a variety of stress-responsive genes that trigger stomatal closure and improve water balance (Liu et al. 2023). However, the molecular regulatory mechanisms underlying drought responses in leaves are largely unclear, particularly with respect to leaf age.

Leaf stress responses are mainly determined by the developmental age (Rankenberg et al. 2021). Young leaves are significantly less affected by abiotic stress than old leaves (Bielczynski et al. 2017). Age-dependent tolerance has been observed in various plants and abiotic stresses, such as drought (Sperdouli et al. 2021), salinity (Guo et al. 2018), heat (** leaf. B Cross section of different leaves under the control and stress conditions. Left: cross section of leaves. Right: cross section of main veins. The sections were stained with safranin-fast green. Scale bars, 50 μm. C Relative water content (RWC) of different leaves under the control and stress conditions. D Water loss rate of different leaves in vitro. E RWC of L3 with or without mature leaves. F Measurement of Chl fluorescence, Fv/Fm, and Chl content. Scale bars, 2 cm. G Detection of reactive oxygen species (ROS), superoxide anions (O₂^-), malondialdehyde (MDA) and proline. Scale bars, 1 cm. H Measurement of antioxidant enzyme systems including catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), and reduced glutathione (GSH). Data were obtained from at least three biological replicates. Different letters above the columns indicate statistical significance in ANOVA (P < 0.05) using GraphPad Prism 8.0 software

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Stomatal closure occurred rapidly upon osmotic stress without visible differences in stomatal parameters across the leaves (Supplementary Fig. 1). Photosynthesis was severely inhibited in mature leaves compared to L3, as indicated by decreased Chl fluorescence, Fv/Fm, and Chl content (Fig. 1F), whereas L3 maintained a similar Fv/Fm before and after stress. Stress resistance of the photosynthetic apparatus decreased sharply with increasing leaf age. L3 accumulated proline at higher levels than mature leaves under both control and stress conditions (Fig. 1G). It is well known that water deficit-induced stomatal closure leads to light capture exceeding the CO₂ absorption rate, causing increased ROS production. L1 had higher ROS (especially O₂^-) levels than the younger leaves (Fig. 1G). Malondialdehyde (MDA) content increased in stressed leaves (Fig. 1G). Conversely, ROS-scavenging enzymes were elevated, especially those of catalase (CAT) and superoxide dismutase (SOD), in L3 (Fig. 1H). The content of glutathione (GSH), an important antioxidant component in plants, was increased in all stressed leaves (Fig. 1H).

Taken together, these results indicate that mature L1 and L2 were more adversely affected by osmotic stress, whereas L3 were less affected because of their more effective water conservation strategy.

Hormone level changes in different leaves under osmotic stress

We compared the stress-induced quantitative changes in the major hormones in the leaves of three ages (Fig. 2). The ABA content was extremely low in control leaves regardless of age, representing 0.3−0.5% of that in stressed leaves. The ABA levels increased significantly in stressed leaves (L1 > L2> L3) and were positively correlated with age. Gibberellic acid 1 (GA1) levels increased in stressed leaves, especially in L2 and L3. IAA, zeatin (a cytokinin, CK), SA, and GA15, decreased or showed no significant changes upon osmotic stress, except for isopentenyladenine (a CK) levels, which increased at L3 (Fig. 2). Compared with the significantly increased ABA levels, cis- and trans-zeatin were significantly suppressed in mature leaves under osmotic stress (Fig. 2). This revealed that the dominant growth-promoting hormone shifted toward the stress hormone ABA, thereby initiating a series of stress-related responses for leaf adaptation to water deficit.

Transcriptomic changes in different leaves under osmotic stress

Transcriptomic changes in leaves under osmotic stress were analyzed using RNA-seq. Quality checks confirmed the high quality of the clean reads in the three biological replicates (Supplementary Dataset 1). Reverse transcription quantitative PCR (RT-qPCR) revealed transcript levels of the 19 selected genes similar to those detected by sequencing (Supplementary Fig. 2). Tissue-level differences accounted for the largest variance (>83%) in the principal component analysis (PCA), whereas the control and treated samples were indistinguishable, especially for mature leaves (Fig. 3A).

We identified 3195, 2673, and 1653 stress-responsive DEGs in L1, L2, and L3, respectively (Fig. 3B; Supplementary Dataset 2). The Venn diagram (Fig. 3B) revealed 1315, 633, and 365 specific DEGs in L1, L2, and L3, respectively, which may be age dependent. Only 888 DEGs were common to all leaves and probably independent of age, the majority of which were upregulated.

These DEGs were mainly enriched in 35 KEGG pathways, seven of which were common to all leaves, including hormone signal transduction; the mitogen-activated protein kinase (MAPK) signaling pathway; phenylpropanoid biosynthesis; starch and sucrose metabolism; alanine, aspartate, and glutamate metabolism; flavonoid biosynthesis; and carotenoid biosynthesis (Fig. 3C). Each leaf included a distinct group of pathways under osmotic stress, such as porphyrin and Chl metabolism in L1; amino sugar and nucleotide sugar metabolism in L2; and phenylalanine and butanoate metabolism in L3 (Fig. 3C).

GO analysis identified 511, 463, and 332 terms in L1, L2, and L3, respectively (Supplementary Dataset 3). Stress-responsive DEGs were enriched in biological processes such as transcriptional regulation, carbohydrate metabolism, protein phosphorylation/dephosphorylation, oxidation/reduction process, proteolysis/ubiquitin-dependent protein catabolic process, oxidative stress, and defense response (Fig. 3D). The occurrence of upregulated DEGs related to proteolysis, ubiquitin-dependent protein catabolic processes, and transmembrane transport was higher in L1 than in L2 and L3. Molecular function terms such as DNA/nucleotide/ATP binding, catalytic activity, hydrolase/transferase/monooxygenase activity, metal ion binding, and transcription factor (TF) activity were linked to upregulated DEGs, thus favoring the adjustment of biological processes to osmotic stress (Fig. 3D). The cell components mainly included the membrane (43.05%), nucleus (21.80%), cytoplasm (8.84%), cell wall (5.64%), and chloroplasts (4.46%). The downregulated DEGs related to chloroplasts were obvious in mature leaves, but not in L3 leaves, consistent with the significantly decreased photosynthesis in mature leaves under osmotic stress (Fig. 1F).

Taken together, osmotic stress caused more extensive transcriptomic adjustments in L1, and multiple metabolic pathways and regulatory responses in leaves were affected, reflecting the physiological changes (Fig. 1), in line with the current understanding of plant stress responses.

Proteomic changes in different leaves under osmotic stress

We analyzed the proteomic responses of each leaf to osmotic stress. The identified 23676 peptides represented 6112 kinds of distinct proteins, 98.8% of which matched the corresponding genes detected by RNA-seq. Similar to the transcriptome data, tissue-level differences accounted for the largest variance (approximately 50%) of PCA in leaf proteomes (Fig. 4A), whereas the control and treated proteomes of L3 showed the least variance.

A paired comparison of the control and treated leaves led to the identification of 143, 86, and 58 stress-responsive DAPs in L1, L2, and L3, respectively (Supplementary Dataset 4; Fig. 4B). The DAPs number was only equivalent to 4.48% (L1), 3.22% (L2) and 3.51% (L3) of the corresponding DEGs number. This indicated that the proteomes could maintain a relatively steady state compared to the transcriptomes under short-term osmotic stress, especially for L3.

Most of these DAPs were age-related, with 124, 69, and 41 DAPs detected in L1, L2, and L3, respectively (Fig. 4B). More abundant DAPs represented a larger proportion of mature leaves. Only one common DAP (galactinol-sucrose galactosyltransferase) accumulated independently of age, with log₂ fold-change (FC) values of 1.32, 1.26 and 1.6 in L1, L2, and L3, respectively. This enzyme transfers the galactosyl moiety of galactinol to sucrose, generating raffinose and inositol, which are involved in abiotic stress adaptation (Li et al. 2021).

The KEGG enrichment analysis revealed that the upregulated DAPs were enriched in 18 pathways (Fig. 4C). Translation, carbohydrate metabolism, amino acid metabolism, energy metabolism, secondary metabolite synthesis, protein folding, sorting, and degradation, transport and catabolism, lipid metabolism, and environmental adaptation were more significant in L1. Many pathways were common between the DAPs and DEGs. The predicted protein localization mainly included the chloroplasts, cytosol, and nuclei, with notable differences across the leaves (Fig. 4D).

Unexpectedly, an overwhelming majority of DAPs were not encoded by the corresponding DEGs (Supplementary Fig. 3), indicating a great inconsistency between protein abundance and transcription levels. The DAPs encoded by the DEGs included aquaporin TIP1-1, Protein EARLY RESPONSIVE TO DEHYDRATION 15, peroxidase, and the small ribosomal subunit protein uS2c. Although the abundance of 2-isopropylmalate synthase in L1 increased significantly, its expression was downregulated under osmotic stress. This enzyme participates in leucine biosynthesis, thus, a decrease in transcription but an increase in synthesis would maintain amino acid biosynthesis during leaf adaptation to water deficits.

Metabolic pathways associated with osmotic stress tolerance in leaves

KEGG and GO analyses identified crucial DEGs and DAPs involved in the osmotic stress response in maize leaves. We further analyzed these DEGs and DAPs with respect to the enriched pathways. Similar to previous studies (Shi et al. 2013; Kanojia et al. 2020; Singh et al. 2022), we mainly focused on upregulated DEGs and more abundant DAPs, highlighting their important roles in abiotic stress resistance in plants (Zhang et al. 2022).

Primary and energy metabolism

Photosynthesis and glycolysis provide essential compounds and energy for maintaining cellular homeostasis. Under osmotic stress, almost all DEGs linked to photosynthesis were downregulated, including Chl a/b binding protein, PsbQ, PsaN, OHP, cytb₆f, ATP synthase, and RuBisCO (Supplementary Fig. 4A, B; Supplementary Dataset 5). These genes were suppressed the most in L1 and the least in L3, consistent with decreased photosynthesis. Only PsbP, ferredoxin, and triose-phosphate isomerase were upregulated in stressed leaves. Osmotic stress stimulated genes related to Chl degradation, such as pheophorbide-a oxygenase and senescence-inducible chloroplast stay-green proteins, but inhibited genes related to Chl synthesis, especially in L1 (Supplementary Fig. 4C). At the proteomic level, two Chl a/b-binding proteins and PsbP showed inconsistent changes in their corresponding gene levels, with PsbP being more abundant only in L3 (Supplementary Fig. 4A; Supplementary Dataset 5).

In photorespiration, the gene glycolate oxidase linked to peroxisomes was significantly downregulated in L1, but not in L3, whereas two genes encoding mitochondrial glycine cleavage system H protein2 (GDH2) were over-represented (Supplementary Fig. 4D), especially in mature leaves. Thus, the upregulated GDH2 enhances the release of CO₂ that can enter the Calvin cycle again, thereby protecting the photosynthetic apparatus under water deficit conditions.

Transitory starch is the main storage form of photosynthetic products; it accumulates in starch granules (SG) in chloroplasts during the day, and is hydrolyzed to soluble sugars at night (AbdElgawad et al. 2020). Microscopy observation indicated that SG existed only in vascular bundle sheath cells but not in mesophyll cells (Fig. 5A, B). Upon 4 h stress, SG degraded during light significantly more in L3 than in mature leaves, and the isolated SG (1.4 to 2.5 μm) from L3 were reduced in size compared with those from mature leaves (Fig. 5C). Meanwhile, maltose, glucose, and sucrose content increased in stressed leaves (Fig. 5D). After removing mature leaves, L3 accumulated adequate SG, comparable to the control (Fig. 5E).

Most genes linked to starch and sugar metabolism were upregulated in the stressed leaves (Supplementary Fig. 5A; Supplementary Dataset 6). Particularly, four β-amylases involved in starch degradation were upregulated, of which ZmBAM8 had unusually high log₂FC of 8.96, 8.63 and 6.84 in L1, L2 and L3, respectively. Most genes linked to sugar conversion and transport were also over-represented in stressed leaves, e.g., sucrose-phosphate synthase, three UDP-glycosyltransferase genes and β-frucotofuranosidase (Supplementary Fig. 5B; Supplementary Dataset 6). Interestingly, sucrose transporter 3 (Suc3) was only upregulated in L1. In Arabidopsis, sucrose export from the source tissues is mainly controlled by SUC2 activity (Tong et al. 2022). Hence, the upregulation of Suc3 enhanced sucrose export from L1 to young leaves under osmotic stress. These results imply that SG degradation during the day plays a vital role in leaf stress adaptation, especially in L3, which differs from starch degradation at night to sustain plant growth and metabolism.

The glycolytic tricarboxylic acid (TCA) cycle plays a central role in energy metabolism. Key genes linked to glycolytic TCA were over-presented in stressed leaves, especially in L1, e.g., pyruvate dehydrogenase kinase, NADH dehydrogenase, alternative oxidase AOX3 precursor, cyclic oxidative cytochrome C-2, and isocitrate lyase (Supplementary Fig. 5C; Supplementary Dataset 6). Therefore, when chloroplast energy production declines under osmotic stress, mitochondrial and cytosolic energy pathways are enhanced to supply energy for plant stress adaptation.

Differential defense response to osmotic stress via specific DEGs/DAPs

Acute osmotic stress caused a reduction in leaf RWC (Fig. 1C), leading to dynamic alterations in gene expression and protein synthesis (Figs. 3 and 4). The heat map displayed significant changes in the selected DEGs and DAPs in individual leaves (Fig. 6), which were mainly involved in stress signaling, TF regulation, stress tolerance, ROS scavenging, and the synthesis and degradation of proteins and lipids.

A large number of MAPK cascade components were over-represented under osmotic stress, especially in L1 (Fig. 6A; Supplementary Dataset 7), including MAPK, MAPK2, MAPK8, phospholipase C, cysteine-rich receptor-like kinase, leucine-rich repeat receptors, lectin receptor-like kinase, G-proteins, 14-3-3 proteins, and phosphoinositides, which are known to encode key signal molecules that regulate plant development, metabolism, and stress response (Zhu 2016). Many genes associated with calcium sensing, binding, transport, and responsiveness were upregulated under osmotic stress, especially in L1 (Fig. 6A; Supplementary Dataset 7); encoding for plasma membrane calcium-transporting ATPase, Ca-binding protein, calmodulin, and chloroplastic GTP diphosphokinase. In particular, calcium-transporting ATPase 5 was upregulated with high log₂FC values of 5.05, 5.10 and 3.67 in L1, L2 and L3, respectively.

The upregulated MAPK and Ca²⁺ signaling pathways would trigger the activity of TFs, heat shock proteins (HSPs) and other stress proteins. The stress-responsive TFs genes include many MYB, WRKY, APETALA2/ethylene-responsive factor (AP2/ERF), bHLH, dehydration-responsive element-binding proteins (DREB), and heat shock factor (HSF) (Supplementary Dataset 7), which are implicated in plant responses to multiple abiotic stresses (Zhu 2016; Zhang et al. 2022). Overall, more upregulated WRKY, AP2/ERF, MYB, bHLH and HSF genes were observed in L1 than in L2 and L3. The upregulated bHLH genes were eight, five, and four in L1, L2, and L3, respectively. The bHLH93 is upregulated in mature leaves, and its homolog is involved in the differentiation of stomatal guard cells (Ohashi-Ito and Bergmann 2006).

Stress proteins such as HSPs and dehydrin play a vital role in conferring abiotic stress tolerance (Szlachtowska and Rurek 2023). Many genes encoding HSPs, chaperones, and dehydrin were upregulated more in L1 than in L2 and L3 (Fig. 6A). Among the 27 identified HSPs, 20 exhibited higher levels in L1. The chloroplast chaperone ClpD was upregulated in all stressed leaves; however, ClpD only increased in L1. Five different dehydrin genes were highly induced by osmotic stress, with the average log₂FC of 5.48, 4.69, 3.17 in L1, L2, and L3, respectively. Immunoblotting revealed increased dehydrin levels in mature leaves under osmotic stress. Similarly, aquaporin TIP1-1 (Fig. 6B) increased in mature leaves, consistent with TIP1-1 expression levels, likely permitting the rapid influx of water from old leaves into young leaves under water-deficient conditions.

A common strategy of plant responses to intracellular osmotic fluctuations is to accumulate osmolytes, such as proline. Delta-1-pyrroline-5-carboxylate synthase (P5CS) controlling the rate-limiting step in glutamate-derived proline biosynthesis. Three P5CS were upregulated in all stressed leaves (Fig. 6A; Supplementary Dataset 7). Meanwhile, a MYB that positively regulates proline synthesis and transport, was only upregulated in L1 (Supplementary Dataset 7).

Protein and lipid degradation pathways were enriched more evidently in L1 under osmotic stress (Fig. 3). Many genes involved in ubiquitin-mediated protein degradation, such as cysteine protease, aspartate protease, ubiquitin-protein ligase, and RING-type E3 ubiquitin transferase were upregulated in stressed leaves, with higher levels in L1 (Fig. 6C). Protease do-like 5, which is involved in chloroplast protein degradation, increased only in L1 (Fig. 6D). Genes linked to glycerolipid degradation (α/β-hydrolases) and fatty acid degradation (acetyl-CoA acetyltransferase) were upregulated in mature stressed leaves (Fig. 6C). Similarly, acetyl-CoA acetyltransferase was significantly upregulated in mature stressed leaves. Sphingolipids are fundamental components of the plant membrane system and are involved in multiple cellular and stress-response processes (Huby et al. 2020). The expression of sphingolipid biosynthesis-related genes, such as ASC1-like protein 1 decreased in all stressed leaves, particularly in L1 (Fig. 6C; Supplementary Dataset 7).

Hormones signal transduction

Hormone signal transduction was enriched in all leaves under osmotic stress (Fig. 3C). Numerous DEGs have been linked to the biosynthesis and signaling of auxins, ABA, zeatin, jasmonic acid (JA), and ethylene. Most of the DEGs (8 of 11) related to the ABA biosynthesis and response, were upregulated in all stressed leaves. The 9-cis-Epoxycarotenoid dioxygenase (NCED) is a key enzyme in ABA biosynthesis. HAV22 and ABF4 (ABRE binding factor 4) are involved in ABA-activated signaling. Four NCED and two HAV22 genes, as well as ABF4, were upregulated by osmotic stress, with higher levels observed in mature leaves (Supplementary Fig. 6). Likewise, MYB139 was upregulated in mature leaves (Supplementary Dataset 7), and its homolog AtMYB44 repressed genes encoding phosphatase 2C in response to ABA (Jung et al. 2008). The expression levels of ABA-related genes positively correlated with leaf age, which was consistent with the actual ABA levels (Fig. 2C).

One SA-related gene was downregulated in all leaves, which was consistent with the reduced SA levels in all leaves (Fig 2; Supplementary Fig. 6). Most genes regulating JA biosynthesis, including LOX1 (3), LOX3 (1), AOS (1), and OPR1 (2), were upregulated in L1, followed by L2, but were downregulated in L3, except for one LOX1, which was inconsistent with decreased JA levels in stressed leaves.

Ethylene is a well-known senescence-inducing hormone in plants (Rankenberg et al. 2021). Many TFs genes AP2/ERF were overrepresented under osmotic stress in L1 (17), followed by L2 (9), and L3 (7) (Supplementary Fig. 6). Three ACO genes (involved in ethylene biosynthesis) and two ethylene-responsive protein (ERP) genes were upregulated to comparable levels in all leaves (Supplementary Fig. 6). Without assaying ethylene levels, we could not compare the expression levels of ethylene-related genes.

Remarkably, genes related to hormones that regulate cell division and growth showed decreased expression in all stressed leaves, especially in L1 (Supplementary Fig. 6). Of the 15 auxin-related genes, the upregulated genes were more abundant in L2 (5) and L3 (5), whereas the downregulated genes were more abundant in L1 (6). Of the 11 zeatin-related genes, seven, two, and one were under-represented in L1, L2, and L3, respectively, consistent with zeatin changes (Fig. 2). GA-related genes were downregulated in all leaves, whereas GA20 oxidase 1 was upregulated.

Secondary metabolism

Secondary metabolites such as terpenoids, phenolics, and flavonoids play critical roles in plant adaptation to abiotic stress (Winkel-Shirley 2002). Multiple phenylpropanoid synthesis-related genes were upregulated in all the stressed leaves (Supplementary Fig. 7). In particular, many genes linked to the biosynthesis of terpenoids, betaine, lignin, and wax, were upregulated, especially in L1 (Supplementary Dataset 7). Genes encoding UDP-glucose 4-epimerase, UDP-glucose 6-dehydrogenase, UDP-glucuronate decarboxylase, and cellulose synthase which are associated with cell wall synthesis, were upregulated in mature leaves. In particular, MYB20 which is involved in the regulation of secondary cell wall biogenesis, showed a high log₂FC of 7.10, 7.54, 5.40 in L1, L2 and L3, respectively. By contrast, the genes encoding glycosyl hydrolase family 10 protein and mannan endo-1,4-β-mannosidase that are related to cell wall degradation were upregulated in mature leaves (Supplementary Fig. 7; Supplementary Dataset 7). Of the 15 genes linked to flavonoid biosynthesis, five were upregulated in L1. Five MYBs that regulate flavonoid and riboflavin biosynthesis were upregulated in all the stressed leaves. Moreover, the ZF-HD homeobox protein gene ZHD15 which controls flavonoid biosynthesis, was highly expressed in L1 (log₂FC 6.11) and L2 (log₂FC 3.05), but slightly increased in L3 (log₂FC 0.90).

Discussion

Many studies have reported that old leaves are more susceptible to abiotic stress compared with young leaves (Wang et al. 2012; Kanojia et al. 2020). However, the mechanisms underlying this phenomenon in maize remain unclear. In this study, we aimed to gain a systematic understanding of age-related stress responses in maize leaves using a multi-omics approach. Mannitol has been used to induce osmotic stress (Nikonorova et al. 2018, Kalve et al. 2020) in the drought-resistant maize line C7-2t. As expected, old L1 was affected significantly more by osmotic stress relative to young L3, with greater levels and species alterations in DEGs, DAPs, and hormones (Figs. 1, 2, 3 and 4). These differences constitute the molecular basis of the age-related stress response mechanisms in maize leaves (Fig. 7).

First, the age-related osmotic stress response was primarily determined by intracellular water status in individual leaves in the coordinated whole-plant defense responses. Osmotic stress caused by drought is the primary signal that triggers a cascade of complex secondary reactions and metabolic regulatory disorders in plants (Zhang et al. 2022). When exposed to short-term osmotic stress in the roots, maize plants respond with stomatal closure, to mitigate water loss via transpiration. The water deficit status in individual leaves varied with age, and the RWC reduction was significantly higher in L1 than in L3 (Fig. 1C). Leaf RWC reflects the physiological consequences of cellular water deficit (Nevo and Chen 2010). Hence, L3 exhibited a more effective water conservation strategy than L1 in the coordinated whole-plant stress response, owing to the effects of both water potential and osmotic adjustment (Fig. 7).

The effect of osmotic stress on maize leaves of different ages evidently differs from that of other abiotic stresses, such as heat and cold. For instance, heat treatment generates a similar temperature effect on leaves of different ages in maize (Karim et al. 1999) and Arabidopsis (** stress-resistant maize. Front Plant Sci 11:576385. https://doi.org/10.3389/fpls.2020.576385 " href="/article/10.1007/s44154-024-00159-9#ref-CR31" id="ref-link-section-d33499037e2107">2020).

DAPs and DEGs exhibited weak consistency of 21.7% in L1, 17.4% in L2, and 15.5% in L3 (Supplementary Fig. 3). The consistency ranges from 27% to 40% (Muers et al. 2011; Vogel et al. 2012; Walley et al. 2016; Jia et al. 2018), depending on the species, cell type, and developmental stage. For example, it is quite low in immature tomato fruits (Osorio et al. 2011) and in the ‘sink-source’ transition zone of maize leaves (Ponnala et al. 2014). Other factors that influence this correlation include translational control, protein degradation, modifications, and complex formation (Vogel et al. 2012). Weak consistency between transcripts and proteins implies active reprogramming of protein translation under osmotic stress.

Third, metabolic activities and stress signaling pathways were significantly influenced by osmotic stress in an age-related manner. Photosynthesis is sensitive to abiotic stress (AbdElgawad et al. 2020). Compared with L1, L3 maintained relatively high levels of photosynthetic proteins and activity under osmotic stress (Supplementary Fig. 4A), as observed in heat-stressed maize leaves (Karim et al. 1999). In Arabidopsis, young leaves are less vulnerable to drought than older leaves, largely because young leaves maintain an almost normal PSII function to avoid photooxidative damage (Sperdouli et al. 2021). Under osmotic stress, the decreased photosynthesis (Fig. 1F, Supplementary Fig. 4A, B) was partly compensated for by the increased glycolytic TCA (Supplementary Fig. 5) to support the stress response and metabolic activities, such as during leaf senescence (Luo et al. 2020). Thus, maintaining sufficient photosynthetic activity in L3 enhanced stress tolerance. In addition, differential secondary metabolism was observed in all the stressed leaves.

Plants accumulate specific primary metabolites as an adaptive response to abiotic stress (Urrutia et al. 2021; Zhang et al. 2022). Starch and sucrose metabolisms were implicated in the leaf response to osmotic stress (Figs. 3C, D and 4C). Transitory starch is a major determinant of plant growth and responses to abiotic stresses, including salt, drought, and cold (Thalmann and Santelia 2017; Dong and Beckles 2019; AbdElgawad et al. 2020). Arabidopsis leaves accumulated transitory starch in the chloroplasts of mesophyll cells (Crumpton-Taylor et al. 2012), whereas maize leaves accumulated SG only in the chloroplasts of vascular bundle sheath cells (Supplementary Fig. 5A, B). Short-term osmotic stress cause rapid starch decreased in Arabidopsis leaves kept under dark conditions, whereas long-term stress significantly increases starch content (Liu et al. 2019). We found that transitory starch levels declined rapidly under osmotic stress, even in daylight, especially in L3 (Supplementary Fig. 5A–C), which was accompanied by an increase in soluble sugars (Supplementary Fig. 5D). Young leaves did not rely on mature leaves to produce transitory starch (Supplementary Fig. 5E), which contributed to the maintenance of photosynthesis in maize leaves under drought (AbdElgawad et al. 2020). In addition, three MYB genes were more suppressed by osmotic stress in L3 and L2 than in L1, and their homolog OsMYB30 suppresses β-amylase expression in rice (Lv et al. 2020), was used here. Plants were grown in 1/4 Hoagland medium under controlled conditions (27°C, 65% relative humidity, 16 h light: 8 h dark photoperiod, and 150 μmol m^-2s^-1). At 10 d after germination, half of the seedlings were subjected to 0.3 M mannitol stress (in 1/4 Hoagland medium) for 4 h under 5 h light; the other half served as the control. Afterwards, the fully expanded leaf (L1), the newly-developed leaf (L2), and the develo** leaf (L3), were harvested separately from the control and stressed plants. Each sample consisted of three biological replicates. In total, 18 samples from three age groups and two conditions were compared pairwise between the control and stressed samples to determine significant FCs in transcripts, proteins, and hormones. Moreover, mature L1 and L2 were removed and only L3 was retained in the seedlings to evaluate its effects on starch metabolism.

The RWC, as well as proline, and MDA contents were determined as previously described (Zhang et al. 2020). Leaf water loss was evaluated using detached leaves (Liu et al. 2023). The H₂O₂ levels and GSH, SOD, POD, and CAT activities were assayed as described previously (Yang et al. 2021). Proteins were extracted using a phenol-based method (Wu et al. 2014). Chl, protein, starch, and soluble sugars were quantified as previously described (Niu et al. 2019a). Chl fluorescence and the maximum photochemical efficiency of PSII (Fv/Fm) were measured using a multicolor fluorescence device after the seedlings were dark-adapted for 15 min. Stomatal characteristics were observed as described (Liu et al. 2023). Dehydrin and β-amylase were detected with specific antiserums (AS07 206A, AS15 2895, Agrisera) by immunoblotting leaf protein (Niu et al. 2019a), using anti-actin antibodies to confirm equal protein loading. All experiments were performed three times independently.

Light and electron microscopy

For light microscopy, leaf tissue was fixed, dehydrated, embedded, and sectioned as previously described (Du et al. 2019). The sections were stained with safranin-fast green or iodine and observed under a light microscope. The SG were isolated from the leaves according to Niu (et al. 2019a), and observed using scanning electron microscopy. For transmission electron microscopy, the leaves were transversely cut using an ultramicrotome. Sections were mounted onto aluminum electron microscope stubs, coated with a thin layer of gold using a vacuum sputter coater, and observed using an HT7700 system (Hitachi, Japan). Images were processed using the CaseViewer software (v. 3.3.6).

Hormone profiling by targeted metabolomics

Leaf tissue (50 mg) was ground in 1.0 ml of 50% acetonitrile (plus internal standard) in a grinding tube with magnetic beads (50 Hz, 5 min), followed by rotary extraction (1 h, 4°C). After centrifugation (12000g, 15 min, 4°C), the supernatant was filtrated, freeze-dried and redissolved in 50 μl of 50% acetonitrile. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed using a QTRAP 6500+ system (SCIEX) at the BGI (Shenzhen, China). Samples (10 μl) were injected into the HSS T3 Column (Waters, USA) and eluted using eluent A (0.02% formic acid) and eluent B (0.02% formic acid + acetonitrile). The solvent gradient was set as 0–3 min 2% B, 3–15 min 80% B, 15–18 min 98% B, 18–20 min 2% B. Ion source parameters were 500 °C, 5000 V, curtain gas 30 psi, gas I 40 psi, gas II 40 psi, collision gas 8 psi for positive mode, and 450°C, –4500 V, 35, 55, 45, and 9 psi for the negative mode. Multiple reaction monitoring was performed for metabolite identification and quantification using the Novogene database.

Transcriptome profiling by RNA-Seq

Total RNA was isolated from 100 mg of leaf tissue using an RNA extraction kit (Qiagen, Hilden, Germany). The mRNA was enriched by polyA selection and sequenced using the BGIseq500 system at BGI (Shenzhen, China). Raw reads were filtered using the SOAPnuke software to remove adapters and low-quality reads. HISAT2 was used to align clean reads to the maize reference genome (GCF_000005005.2_B73_RefGen_v4) (Kim et al. 2015). DEGs with |log₂FC| ≥ 1 (q ≤ 0.05) were identified by DESeq2 (Love et al. 2014). GO and KEGG enrichment analyses for DEGs were used to identify stress-related metabolic pathways (Liu et al. 2023). The sequence data were deposited in the European Nucleotide Archive (accession no. E-MTAB-11331). The expression levels of the 19 selected transcripts were verified using RT-qPCR (primers listed in Supplementary Table 1), with ZmUBI as an internal control.

Proteome profiling by data-independent acquisition (DIA) proteomics

Leaf proteins were extracted using a phenol-based method (Wu et al. 2014). After reduction and alkylation, 100 μg proteins were digested overnight at 37 °C with 0.5 µg sequencing-grade trypsin (Niu et al. 2019b). The resultant peptides were desalted, vacuum-dried, redissolved in mobile phase A (5% acetonitrile, pH 9.8), and separated on a Gemini high-pH C18 column using a Shimadzu LC-20AB HPLC system. Next, the peptides were reconstituted in mobile phase A (2% acetonitrile and 0.1% formic acid) and separated using a Thermo UltiMate 3000 UHPLC. After enrichment and desalting, the samples were placed on a tandem self-packed C18 column and separated using mobile phase B (98% acetonitrile and 0.1% formic acid). The separated peptides were ionized using a nanoESI source and passed through an Oritrap Exploris 480 tandem mass spectrometer (Thermo Fisher Scientific) for data-dependent acquisition (DDA) mode detection. The DDA data were identified using the Andromeda search engine against UniProtKB (January 27, 2022). DAPs were identified based on |log₂FC| ≥ 1 (q ≤ 0.05) for significant difference by MSstats in R (Choi et al. 2014). Finally, GO and KEGG enrichment analyses of DAPs were used to identify stress-related metabolic pathways. The proteome data were deposited in the ProteomeX Change Consortium (accession no. PXD037099).

Statistical analyses

Spearman correlation coefficient was computed using the ‘test. Corr’ in R package; principal component analysis (PCA) and analysis of variance (ANOVA) were calculated using ‘metaX’ in R package (Wen et al. 2017). Each experiment was performed in at least three biological replicates. To determine statistical significance of the raw data, an ANOVA (P < 0.05) test or Student's t-test (P < 0.05) were performed using GraphPad Prism 8.0 software.

Availability of data and materials

All data included in this study are available as Supporting Information. RNA-Seq data were deposited in the European Nucleotide Archive (accession No. E-MTAB-11331, https://www.ebi.ac.uk/ena/browser/text-search?query=E-MTAB-11331). Proteome data were deposited in the ProteomeXchange Consortium (accession No. PXD037099, http://www.ebi.ac.uk/pride, Username: reviewer_pxd037099@ebi.ac.uk; Password: joOfUdnI).

Abbreviations

ABA:: Abscisic acid
ABF4:: ABRE binding factor 4
AP2/ERF:: APETALA2/ethylene-responsive factor
CAT:: Catalase
CK:: Cytokinin
DAPs:: Differentially abundant proteins
DDA:: Data-dependent acquisition
DEGs:: Differentially expressed genes
DIA:: Data-independent acquisition
DREB:: Dehydration-responsive element-binding proteins
ERP:: Ethylene-responsive protein
FC:: Fold-change
GA1:: Gibberellic acid 1
GDH2:: Glycine cleavage system H protein2
GSH:: Glutathione
HSF:: Heat shock factor
HSPs:: Heat shock proteins
JA:: Jasmonic acid
LC-MS/MS:: Liquid chromatography-tandem mass spectrometry
MAPK:: Mitogen-activated protein kinase
MDA:: Malondialdehyde
NCED:: 9-cis-epoxycarotenoid dioxygenase
PCA:: Principal component analysis
POD:: Peroxidase
P5CS:: Delta-1-pyrroline-5-carboxylate synthase
ROS:: Reactive oxygen species
RT-qPCR:: Reverse transcription quantitative PCR
RWC:: Relative water content
SA:: Salicylic acid
SG:: Starch granules
SOD:: Superoxide dismutase
Suc3:: Sucrose transporter 3
TCA:: Tricarboxylic acid

References

AbdElgawad H, Avramova V, Baggerman G, Van Raemdonck G, Valkenborg D, Van Ostade X, Guisez Y, Prinsen E, Asard H, Van den Ende W, Beemster GTS (2020) Starch biosynthesis contributes to the maintenance of photosynthesis and leaf growth under drought stress in maize. Plant Cell Environ 43:2254–2271. https://doi.org/10.1111/pce.13813
Article CAS PubMed Google Scholar
Berens ML, Wolinska KW, Spaepen S, Ziegler J, Nobori T, Nair A, Krüler V, Winkelmüller TM, Wang Y, Mine A, Becker D, Garrido-Oter R, Schulze-Lefert P, Tsuda K (2019) Balancing trade-offs between biotic and abiotic stress responses through leaf age-dependent variation in stress hormone cross-talk. Proc Natl Acad Sci U S A 116:2364–2373. https://doi.org/10.1073/pnas.1817233116
Article ADS CAS PubMed PubMed Central Google Scholar
Bielczynski LW, Łącki MK, Hoefnagels I, Gambin A, Croce R (2017) Leaf and plant age affects photosynthetic performance and photoprotective capacity. Plant Physiol 175:1634–1648. https://doi.org/10.1104/pp.17.00904
Article CAS PubMed PubMed Central Google Scholar
Chaumont F, Tyerman SD (2014) Aquaporins: highly regulated channels controlling plant water relations. Plant Physiol 164:1600–1618. https://doi.org/10.1104/pp.113.233791
Article CAS PubMed PubMed Central Google Scholar
Choi M, Chang CY, Clough T, Broudy D, Killeen T, MacLean B, Vitek O (2014) MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30:2524–2526. https://doi.org/10.1093/bioinformatics/btu305
Article CAS PubMed Google Scholar
Crumpton-Taylor M, Grandison S, Png KM, Bushby AJ, Smith AM (2012) Control of starch granule numbers in Arabidopsis chloroplasts. Plant Physiol 158:905–916. https://doi.org/10.1104/pp.111.186957
Article CAS PubMed Google Scholar
Dong S, Beckles DM (2019) Dynamic changes in the starch-sugar interconversion within plant source and sink tissues promote a better abiotic stress response. J Plant Physiol 234–235:80–93. https://doi.org/10.1016/j.jplph.2019.01.007
Article CAS PubMed Google Scholar
Du C, Chai L, Wang Z, Fan H (2019) Response of proteome and morphological structure to short-term drought and subsequent recovery in Cucumis sativus leaves. Physiol Plant 167:676–689. https://doi.org/10.1111/ppl.12926
Article CAS PubMed Google Scholar
Gong F, Yang L, Tai F, Hu X, Wang W (2014) “Omics” of maize stress response for sustainable food production: opportunities and challenges. OMICS 18:714–732. https://doi.org/10.1089/omi.2014.0125
Article CAS PubMed PubMed Central Google Scholar
González-Villagra J, Rodrigues-Salvador A, Nunes-Nesi A, Cohen JD, Reyes-Díaz MM (2018) Age-related mechanism and its relationship with secondary metabolism and abscisic acid in Aristotelia chilensis plants subjected to drought stress. Plant Physiol Biochem 124:136–145. https://doi.org/10.1016/j.plaphy.2018.01.010
Article CAS PubMed Google Scholar
Guo R, Bai Z, Zhou J, Zhong X, Gu F, Liu Q, Li H (2018) Tissue physiological metabolic adaptability in young and old leaves of reed (Phragmites communis) in Songnen grassland. Plant Physiol Biochem 128:99–105. https://doi.org/10.1016/j.plaphy.2018.05.012
Article CAS PubMed Google Scholar
Gupta A, Rico-Medina A, Caño-Delgado AI (2020) The physiology of plant responses to drought. Science 368:266–269. https://doi.org/10.1126/science.aaz7614
Article ADS CAS PubMed Google Scholar
Huby E, Napier JA, Baillieul F, Michaelson LV, Dhondt-Cordelier S (2020) Sphingolipids: towards an integrated view of metabolism during the plant stress response. New Phytol 225:659–670. https://doi.org/10.1111/nph.15997
Article PubMed Google Scholar
Jia H, Sun W, Li M, Zhang Z (2018) Integrated analysis of protein abundance, transcript level, and tissue diversity to reveal developmental regulation of maize. J Proteome Res 17:822–833. https://doi.org/10.1021/acs.jproteome.7b00586
Article CAS PubMed Google Scholar
Jung C, Seo JS, Han SW, Koo YJ, Kim CH, Song SI, Nahm BH, Choi YD, Cheong JJ (2008) Overexpression of AtMYB44 enhances stomatal closure to confer abiotic stress tolerance in transgenic Arabidopsis. Plant Physiol 146:623–635. https://doi.org/10.1104/pp.107.110981
Article CAS PubMed PubMed Central Google Scholar
Kalve S, Sizani BL, Markakis MN, Helsmoortel C, Vandeweyer G, Laukens K, Sommen M, Naulaerts S, Vissenberg K, Prinsen E, Beemster GTS (2020) Osmotic stress inhibits leaf growth of Arabidopsis thaliana by enhancing ARF-mediated auxin responses. New Phytol 226:1766–1780. https://doi.org/10.1111/nph.16490
Article CAS PubMed Google Scholar
Kanojia A, Gupta S, Benina M, Fernie AR, Mueller-Roeber B, Gechev T, Dijkwel PP (2020) Developmentally controlled changes during Arabidopsis leaf development indicate causes for loss of stress tolerance with age. J Exp Bot 71:6340–6354. https://doi.org/10.1093/jxb/eraa347
Article CAS PubMed PubMed Central Google Scholar
Karim MA, Fracheboud Y, Stamp P (1999) Photosynthetic activity of develo** leaves of Zea mays is less affected by heat stress than that of developed leaves. Physiol Plant 105:685–693. https://doi.org/10.1034/j.1399-3054.1999.105413.x
Article CAS Google Scholar
Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12(4):357–360. https://doi.org/10.1038/nmeth.3317
Article CAS PubMed PubMed Central Google Scholar
Li CH, Tien HJ, Wen MF, Yen HE (2021) Myo-inositol transport and metabolism participate in salt tolerance of halophyte ice plant seedlings. Physiol Plant 172:1619–1629. https://doi.org/10.1111/ppl.13353
Article CAS PubMed Google Scholar
Liu K, Zou W, Gao X, Wang X, Yu Q, Ge L (2019) Young seedlings adapt to stress by retaining starch and retarding growth through ABA-Dependent and -independent pathways in Arabidopsis. Biochem Biophys Res Commun 515:699–705. https://doi.org/10.1016/j.bbrc.2019.06.023
Article CAS PubMed Google Scholar
Liu H, Song S, Liu M, Mu Y, Li Y, Xuan Y, Niu L, Zhang H, Wang W (2023) Transcription factor ZmNAC20 improves drought resistance by promoting stomatal closure and activating expression of stress-responsive genes in maize. Int J Mol Sci 24:4712. https://doi.org/10.3390/ijms24054712
Article CAS PubMed PubMed Central Google Scholar
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8
Article CAS PubMed PubMed Central Google Scholar
Luo J, Havé M, Clément G, Tellier F, Balliau T, Launay-Avon A, Guérard F, Zivy M, Masclaux-Daubresse C (2020) Integrating multiple omics to identify common and specific molecular changes occurring in Arabidopsis under chronic nitrate and sulfate limitations. J Exp Bot 71:6471–6490. https://doi.org/10.1093/jxb/eraa337
Article CAS PubMed Google Scholar
Lv Y, Yang M, Hu D, Yang Z, Ma S, Li X, **ong L (2017) The OsMYB30 transcription factor suppresses cold tolerance by interacting with a jaz protein and suppressing β-amylase expression. Plant Physiol 173:1475–1491. https://doi.org/10.1104/pp.16.01725
Article CAS PubMed PubMed Central Google Scholar
Muers M (2011) Gene expression: Transcriptome to proteome and back to genome. Nat Rev Genet 12:518. https://doi.org/10.1038/nrg3037
Article CAS PubMed Google Scholar
Nevo E, Chen G (2010) Drought and salt tolerances in wild relatives for wheat and barley improvement. Plant Cell Environ 33:670–685. https://doi.org/10.1111/j.1365-3040.2009.02107.x
Article CAS PubMed Google Scholar
Nikonorova N, Van den Broeck L, Zhu S, van de Cotte B, Dubois M, Gevaert K, Inzé D, De Smet I (2018) Early mannitol-triggered changes in the Arabidopsis leaf (phospho)proteome reveal growth regulators. J Exp Bot 69:4591–4607. https://doi.org/10.1093/jxb/ery261
Article CAS PubMed PubMed Central Google Scholar
Niu L, Ding H, Hao R, Liu H, Wu X, Hu X, Wang W (2019a) A rapid and universal method for isolating starch granules in plant tissues. Plant Cell Environ 42:3355–3371. https://doi.org/10.1111/pce.13631
Article CAS PubMed Google Scholar
Niu L, Wu Z, Liu H, Wu X, Wang W (2019b) 2-DE-based proteomic analysis of protein changes associated with etiolated mesocotyl growth in Zea mays. BMC Genomics 20:758. https://doi.org/10.1186/s12864-019-6109-z
Article CAS PubMed PubMed Central Google Scholar
Niu L, Liu L, Wang W (2020) Digging for stress-responsive cell wall proteins for develo** stress-resistant maize. Front Plant Sci 11:576385. https://doi.org/10.3389/fpls.2020.576385
Article PubMed PubMed Central Google Scholar
Ohashi-Ito K, Bergmann DC (2006) Arabidopsis FAMA controls the final proliferation/differentiation switch during stomatal development. Plant Cell 18:2493–2505. https://doi.org/10.1105/tpc.106.046136
Article CAS PubMed PubMed Central Google Scholar
Osorio S, Alba R, Damasceno CM, Lopez-Casado G, Lohse M, Zanor MI, Tohge T, Usadel B, Rose JK, Fei Z, Giovannoni JJ, Fernie AR (2011) Systems biology of tomato fruit development: combined transcript, protein, and metabolite analysis of tomato transcription factor (nor, rin) and ethylene receptor (Nr) mutants reveals novel regulatory interactions. Plant Physiol 157:405–425. https://doi.org/10.1104/pp.111.175463
Article CAS PubMed PubMed Central Google Scholar
Park SY, Yu JW, Park JS, Li J, Yoo SC, Lee NY, Lee SK, Jeong SW, Seo HS, Koh HJ, Jeon JS, Park YI, Paek NC (2007) The senescence-induced staygreen protein regulates chlorophyll degradation. Plant Cell 19:1649–1664. https://doi.org/10.1105/tpc.106.044891
Article CAS PubMed PubMed Central Google Scholar
Pazmiño DM, Rodríguez-Serrano M, Romero-Puertas MC, Archilla-Ruiz A, Del Río LA, Sandalio LM (2011) Differential response of young and adult leaves to herbicide 2,4-dichlorophenoxyacetic acid in pea plants: role of reactive oxygen species. Plant Cell Environ 34:1874–1889. https://doi.org/10.1111/j.1365-3040.2011.02383.x
Article CAS PubMed Google Scholar
Ponnala L, Wang Y, Sun Q, van Wijk KJ (2014) Correlation of mRNA and protein abundance in the develo** maize leaf. Plant J 78:424–440. https://doi.org/10.1111/tpj.12482
Article CAS PubMed Google Scholar
Rabas AR, Martin CE (2003) Movement of water from old to young leaves in three species of succulents. Ann Bot 92(4):529–536. https://doi.org/10.1093/aob/mcg171
Article CAS PubMed PubMed Central Google Scholar
Rankenberg T, Geldhof B, van Veen H, Holsteens K, Van de Poel B, Sasidharan R (2021) Age-dependent abiotic stress resilience in plants. Trends Plant Sci 26:692–705. https://doi.org/10.1016/j.tplants.2020.12.016
Article CAS PubMed Google Scholar
Safavi-Rizi V, Franzaring J, Fangmeier A, Kunze R (2018) Divergent N deficiency-dependent senescence and transcriptome response in developmentally old and young Brassica napus leaves. Front Plant Sci 9:48. https://doi.org/10.3389/fpls.2018.00048
Article PubMed PubMed Central Google Scholar
Shi X, Gupta S, Lindquist IE, Cameron CT, Mudge J, Rashotte AM (2013) Transcriptome analysis of cytokinin response in tomato leaves. PLoS One 8:e55090. https://doi.org/10.1371/journal.pone.0055090
Article ADS CAS PubMed PubMed Central Google Scholar
Singh P, Kumar K, Jha AK, Yadava P, Pal M, Rakshit S, Singh I (2022) Global gene expression profiling under nitrogen stress identifies key genes involved in nitrogen stress adaptation in maize (Zea mays L.). Sci Rep 12:4211. https://doi.org/10.1038/s41598-022-07709-z
Article ADS CAS PubMed PubMed Central Google Scholar
Skirycz A, De Bodt S, Obata T, De Clercq I, Claeys H, De Rycke R, Andriankaja M, Van Aken O, Van Breusegem F, Fernie AR, Inzé D (2010) Developmental stage specificity and the role of mitochondrial metabolism in the response of Arabidopsis leaves to prolonged mild osmotic stress. Plant Physiol 152:226–244. https://doi.org/10.1104/pp.109.148965
Article PubMed PubMed Central Google Scholar
Sperdouli I, Moustaka J, Ouzounidou G, Moustakas M (2021) Leaf age-dependent photosystem ii photochemistry and oxidative stress responses to drought stress in arabidopsis thaliana are modulated by flavonoid accumulation. Molecules 26:4157. https://doi.org/10.3390/molecules26144157
Article CAS PubMed PubMed Central Google Scholar
Szlachtowska Z, Rurek M (2023) Plant dehydrins and dehydrin-like proteins: characterization and participation in abiotic stress response. Front Plant Sci 14:1213188. https://doi.org/10.3389/fpls.2023.1213188
Article PubMed PubMed Central Google Scholar
Teng YS, Chan PT, Li HM (2012) Differential age-dependent import regulation by signal peptides. PLoS Biol 10:e1001416. https://doi.org/10.1371/journal.pbio.1001416
Article CAS PubMed PubMed Central Google Scholar
Thalmann M, Santelia D (2017) Starch as a determinant of plant fitness under abiotic stress. New Phytol 214:943–951. https://doi.org/10.1111/nph.14491
Article CAS PubMed Google Scholar
Tong C, Li C, Cao XY, Sun XD, Bao QX, Mu XR, Liu CY, Loake GJ, Chen HH, Meng LS (2022) Long-distance transport of sucrose in source leaves promotes sink root growth by the EIN3-SUC2 module. PLoS Genet 18:e1010424. https://doi.org/10.1371/journal.pgen.1010424
Article CAS PubMed PubMed Central Google Scholar
Urrutia M, Blein-Nicolas M, Prigent S, Bernillon S, Deborde C, Balliau T, Maucourt M, Jacob D, Ballias P, Bénard C, Sellier H, Gibon Y, Giauffret C, Zivy M, Moing A (2021) Maize metabolome and proteome responses to controlled cold stress partly mimic early-sowing effects in the field and differ from those of Arabidopsis. Plant Cell Environ 44:1504–1521. https://doi.org/10.1111/pce.13993
Article CAS PubMed PubMed Central Google Scholar
Valente MA, Faria JA, Soares-Ramos JR, Reis PA, Pinheiro GL, Piovesan ND, Morais AT, Menezes CC, Cano MA, Fietto LG, Loureiro ME, Aragão FJ, Fontes EP (2009) The ER luminal binding protein (BiP) mediates an increase in drought tolerance in soybean and delays drought-induced leaf senescence in soybean and tobacco. J Exp Bot 60:533–546. https://doi.org/10.1093/jxb/ern296
Article CAS PubMed Google Scholar
Vogel C, Marcotte EM (2012) Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 13:227–232. https://doi.org/10.1038/nrg3185
Article CAS PubMed PubMed Central Google Scholar
Walley JW, Sartor RC, Shen Z, Schmitz RJ, Wu KJ, Urich MA, Nery JR, Smith LG, Schnable JC, Ecker JR, Briggs SP (2016) Integration of omic networks in a developmental atlas of maize. Science 353:814–818. https://doi.org/10.1126/science.aag1125
Article ADS CAS PubMed PubMed Central Google Scholar
Wang H, Zhang M, Guo R, Shi D, Liu B, Lin X, Yang C (2012) Effects of salt stress on ion balance and nitrogen metabolism of old and young leaves in rice (Oryza sativa L.). BMC Plant Biol 12:194. https://doi.org/10.1186/1471-2229-12-194
Article CAS PubMed PubMed Central Google Scholar
Wang YM, Zhang H, **ong YT, Zhu Q, Ding YC, Zhao S, Zhang XH, Uchimiya M, Yuan XY (2018) Leaf aging effects on copper and cadmium transfer along the lettuce-snail food chain. Chemosphere 211:81–88. https://doi.org/10.1016/j.chemosphere.2018.07.141
Article ADS CAS PubMed Google Scholar
Wen B, Mei Z, Zeng C, Liu S (2017) metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics 18:183. https://doi.org/10.1186/s12859-017-1579-y
Article CAS PubMed PubMed Central Google Scholar
Winkel-Shirley B (2002) Biosynthesis of flavonoids and effects of stress. Curr Opin Plant Biol 5:218–223. https://doi.org/10.1016/s1369-5266(02)00256-x
Article CAS PubMed Google Scholar
Wu X, **ong E, Wang W, Scali M, Cresti M (2014) Universal sample preparation method integrating trichloroacetic acid/acetone precipitation with phenol extraction for crop proteomic analysis. Nat Protoc 9:362–374. https://doi.org/10.1038/nprot.2014.022
Article CAS PubMed Google Scholar
**ang Q, Rathinasabapathi B (2022) Differential tolerance to heat stress of young leaves compared to mature leaves of whole plants relate to differential transcriptomes involved in metabolic adaptations to stress. AoB Plants 14:plac024. https://doi.org/10.1093/aobpla/plac024
Article CAS PubMed PubMed Central Google Scholar
Xu FQ, Xue HW (2019) The ubiquitin-proteasome system in plant responses to environments. Plant Cell Environ 42:2931–2944. https://doi.org/10.1111/pce.13633
Article CAS PubMed Google Scholar
Yang H, Zhao Y, Chen N, Liu Y, Yang S, Du H, Wang W, Wu J, Tai F, Chen F, Hu X (2021) A new adenylyl cyclase, putative disease-resistance RPP13-like protein 3, participates in abscisic acid-mediated resistance to heat stress in maize. J Exp Bot 72:283–301. https://doi.org/10.1093/jxb/eraa431
Article CAS PubMed Google Scholar
Zhang ZS, Yang C, Gao HY, Zhang LT, Fan XL, Liu MJ (2014) The higher sensitivity of PSI to ROS results in lower chilling-light tolerance of photosystems in young leaves of cucumber. J Photochem Photobiol B 137:127–134. https://doi.org/10.1016/j.jphotobiol.2013.12.012
Article CAS PubMed Google Scholar
Zhang Q, Liu H, Wu X, Wang W (2020) Identification of drought tolerant mechanisms in a drought-tolerant maize mutant based on physiological, biochemical and transcriptomic analyses. BMC Plant Biol 20:315. https://doi.org/10.1186/s12870-020-02526-w
Article CAS PubMed PubMed Central Google Scholar
Zhang H, Zhu J, Gong Z, Zhu JK (2022) Abiotic stress responses in plants. Nat Rev Genet 23:104–119. https://doi.org/10.1038/s41576-021-00413-0
Article CAS PubMed Google Scholar
Zhu JK (2016) Abiotic stress signaling and responses in plants. Cell 167:313–324. https://doi.org/10.1016/j.cell.2016.08.029
Article CAS PubMed PubMed Central Google Scholar

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Funding

This work was supported by the National Natural Science Foundation of China (32272026 and U1904107 to W.W.), and the National Key Laboratory of Wheat and Maize Crop Science, Henan agricultural University, China (39990101 to W.W.).

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Authors and Affiliations

National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou, 450046, China
Liangjie Niu, Wenkang Wang, Yingxue Li, **aolin Wu & Wei Wang

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Liangjie Niu
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Wenkang Wang
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Yingxue Li
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**aolin Wu
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Wei Wang
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Contributions

WW and XLW conceived of and designed the project. LJN prepared the plant material for the omics work. LJN conducted the experiments analyzed data. WKW, YXL, XLW and WW contributed to data interpretation. LJN wrote the manuscript with input from all of the co-authors, who revised and approved the final manuscript.

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Correspondence to **aolin Wu or Wei Wang.

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Supplementary Fig. 1. Comparison of stomata characteristics of different leaves. The abaxial surfaces of leaves in 10-d-old maize seedlings were observed under a microscope. Supplementary Fig. 2. Reverse transcription quantitative-PCR (RT-qPCR) validation of expression changes of 19 DEGs in different maize leaves. Ten-d-old seedlings were subjected to osmotic stress for 4 h, and each leaf was used for RNA extraction and PCR analysis (triplicate). Mean relative expression levels of DEGs were normalized to a value of 1.0 in L1 under control condition with ZmUBI as a reference gene. Error bars indicated the SE values of three biological replicates. Asterisks show significant difference in expression changes as assessed by Student's t-test (* P < 0.05, ** P < 0.01, *** P < 0.001). Supplementary Fig. 3. Correlation analyses of transcriptomic and proteomic data sets in different maize leaves under control and osmotic stress conditions. Ten-d-old seedlings were subjected to osmotic stress for 4 h, and each leaf was used for transcriptomic and proteomic analyses. A Number correlations between proteins and mRNAs and between DAPs and DEGs. B Expression correlations between proteins and mRNA and between DAPs and DEGs. NDEGs, no differences in gene expression; NDAPs, no differences in protein expression. C Classification distribution map of term associated genes significantly enriched in GO and KEGG pathway. The numbers in parentheses represent the number of proteins associated with the term, and the color of the heat map represents the proportion of the protein associated with the GO and KEGG terms. Supplementary Fig. 4. DEGs and DAPs involved in photosynthesis, photorespiration and Chl metabolism in different-age maize leaves under osmotic stress. Ten-d-old seedlings were subjected to osmotic stress for 4 h, and each leaf was used for transcriptomic and proteomic analyses. A Light reaction. B Calvin cycle. C DEGs involved in Chl biosynthesis and degradation. D Photorespiration. Supplementary Fig. 5. DEGs and DAPs involved in starch and sugar metabolism and glycolysis in different maize leaves under osmotic stress. Ten-d-old seedlings were subjected to osmotic stress for 4 h, and each leaf was used for analyses. A starch metabolism. B Sugars metabolism. CGlycolysis-TCA. Supplementary Fig. 6. The transcript levels of DEGs related to phytohormones in different maize leaves under osmotic stress. Ten-d-old seedlings were subjected to osmotic stress for 4 h, and each leaf was used for analyses. Abbreviations: 2OGDD, 2-oxoglutarate-dependent dioxygenase; ACO, 1-aminocyclopropane-1-carboxylate oxidase; ABF4, ABRE binding factor 4; AUX1, auxin transporter 1; AOS, allene oxide synthase; CKO, CK oxidase; CKX, CK oxidase/dehydrogenase; ERP, ethylene-responsive protein; GA20OX1, GA20 oxidase; GA2OX1, GA2 oxidase 1; GEM, GL2 expression modulator; GH3.17, IAA-amido synthetases; GRAM, GRAM domain-containing protein; GT, glycosyltransferase; HVA22, abscisic acid-responsive HVA22 family protein; ILR1, IAA-leucine resistant 1; LOX, Lipoxygenase; NCED, nine-cis-epoxycarotenoid dioxygenase; JMT, jasmonic acid carboxyl methyltransferase; OPR1, 12-oxophytodienoate reductase 1; PIN, PIN-formed; SUAR, auxin-responsive SAUR family protein; SCL, scarecrow-like transcription factor. Data represent means ± SD of three biological replicates. Significant differences in expression levels are indicated with different letters (P < 0.05, ANOVA). Supplementary Fig. 7. DEGs related to secondary metabolism in different maize leaves under osmotic stress. Ten-d-old seedlings were subjected to osmotic stress for 4 h, and each leaf was used for analyses. A DEGs related to wall synthesis and degradation. B DEGs related to various secondary metabolites. C DAPs related to secondary metabolism. Abbreviations: DBAT, 10-deacetylbaccatin III 10-O-acetyltransferase; DXR, 1-deoxy-D-xylulose-5-phosphate reductoisomerase; HMGR, 3-hydroxy-3-methylglutaryl-coenzyme A reductase; XTH, xyloglucan endotransglucosylase/hydrolase. Supplementary Fig. 8. Programmed cell death (PCD) detection in different maize leaves under osmotic stress. Ten-d-old seedlings were subjected to osmotic stress for 4 h, and each leaf was used for analyses. PCD was detected using Fluorescein (FITC) Tunel Cell Apoptosis Detection Kit (Servicebio, Wuhan, China). The blue signal represents staining with propidium iodide (PI), green signal represents TUNEL-positive nuclei of dead cells due to PCD. The images were detected with a Zeiss LSM880 confocal laser scanning microscope. Scale bars, 100 mm.

Additional file 2:

Supplementary Table 1. Primers used in this study.

Additional file 3:

Supplementary Dataset 1. Gene sequence assembly, quality check, alternative splicing, and expression levels of maize leaf transcriptomes.

Additional file 4:

Supplementary Dataset 2. DEGs in maize leaves of different ages under osmotic stress. Supplementary Dataset 3. GO terms of DEGs in maize leaves of different ages under osmotic stress. Supplementary Dataset 4. DAPs in maize leaves of different ages under osmotic stress. Supplementary Dataset 5. DEGs related to photosynthesis. Supplementary Dataset 6. DEGs related to carbohydrate and glycolytic-TCA metabolism. Supplementary Dataset 7. DEGs related to stress response.

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Niu, L., Wang, W., Li, Y. et al. Maize multi-omics reveal leaf water status controlling of differential transcriptomes, proteomes and hormones as mechanisms of age-dependent osmotic stress response in leaves. Stress Biology 4, 19 (2024). https://doi.org/10.1007/s44154-024-00159-9

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Received: 23 January 2024
Accepted: 06 March 2024
Published: 18 March 2024
DOI: https://doi.org/10.1007/s44154-024-00159-9

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Maize multi-omics reveal leaf water status controlling of differential transcriptomes, proteomes and hormones as mechanisms of age-dependent osmotic stress response in leaves

Abstract

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Introduction

Hormone level changes in different leaves under osmotic stress

Transcriptomic changes in different leaves under osmotic stress

Proteomic changes in different leaves under osmotic stress

Metabolic pathways associated with osmotic stress tolerance in leaves

Primary and energy metabolism

Differential defense response to osmotic stress via specific DEGs/DAPs

Hormones signal transduction

Secondary metabolism

Discussion

Light and electron microscopy

Hormone profiling by targeted metabolomics

Transcriptome profiling by RNA-Seq

Proteome profiling by data-independent acquisition (DIA) proteomics

Statistical analyses

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