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Article

Identification of the Maize LEA Gene Family and Its Relationship with Kernel Dehydration

by
Yaping Zhang
,
Xiaojun Zhang
,
Liangjia Zhu
,
Lexin Wang
,
Hao Zhang
,
Xinghua Zhang
,
Shutu Xu
* and
Jiquan Xue
*
Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
*
Authors to whom correspondence should be addressed.
Plants 2023, 12(21), 3674; https://doi.org/10.3390/plants12213674
Submission received: 7 August 2023 / Revised: 21 October 2023 / Accepted: 22 October 2023 / Published: 25 October 2023
(This article belongs to the Special Issue Crop Stress Tolerance and Green Production: Celebrating the 90th Anniversary of the Founding of Northwest A&F University)
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Abstract

:
Maize, the most widely planted and highest yielding of the three major crops in the world, requires the development and breeding of new varieties to accommodate the shift towards mechanized harvesting. However, the moisture content of kernels during harvest poses a significant challenge to mechanized harvesting, leading to seed breakage and increased storage costs. Previous studies highlighted the importance of LEA (Late Embryogenesis Abundant) members in regulating kernel dehydration. In this study, we aimed to gain a better understanding of the relationship between the LEA family and grain dehydration in maize. Through expression pattern analysis of maize, we identified 52 LEA genes (ZmLEAs) distributed across 10 chromosomes, organized into seven subgroups based on phylogenetic analysis, gene structure, and conserved motifs. Evolutionary and selective pressure analysis revealed that the amplification of ZmLEA genes primarily resulted from whole-genome or fragment replication events, with strong purifying selection effects during evolution. Furthermore, the transcriptome data of kernels of two maize inbred lines with varying dehydration rates at different developmental stages showed that 14 ZmLEA genes were expressed differentially in the two inbreds. This suggested that the ZmLEA genes might participate in regulating the kernel dehydration rate (KDR) in maize. Overall, this study enhances our understanding of the ZmLEA family and provides a foundation for further research into its role in regulating genes associated with grain dehydration in maize.

1. Introduction

Maize is the most widely planted and highest yielding cereal crop in the world, including in China [1]. As production shifts from manual to mechanized operations due to changes in production methods and labor shortages, there is a need for maize varieties suitable for mechanized management, including sowing, watering, fertilization, and harvesting [2]. According to the State Ministry of Agriculture and Rural Affairs (https://www.moa.gov.cn/ (accessed on 20 December 2020)), China’s comprehensive mechanized production rate of maize reached 89.76% in 2020, with mechanized sowing rates (ratio of mechanically sown area to total cultivated area) above 90%. However, the mechanized harvesting rate (ratio of mechanically harvested area to cultivated area) remains below 85%, with most of the maize harvested as ears rather than kernels, resulting in increased costs for air-drying and storage [3]. Therefore, the development of new maize varieties suitable for mechanized kernel harvesting is essential. Currently, most maize varieties have 25–40% of kernel moisture content (KMC), which exceeds the standard KMC (<25%) required for kernel mechanized harvesting [4]. KMC is considered a limiting factor for mechanized harvesting [5]. Thus, understanding the genetic basis of KMC is crucial for breeding kernel varieties suitable for mechanized harvest in the genomic era.
During maize kernel development, the KMC at harvest is determined by the kernel dehydration rate (KDR) before and after physiological maturity [6]. According to a previous study by our group, one LEA (Late Embryogenesis Abundant) gene (Zm00001eb238300) was identified as being associated with the KDR by multiple omics [7]. Additionally, in the model plant Arabidopsis, LEA proteins have been reported to regulate kernel development and dehydration processes, especially members of the LEA_4 subfamily [8]. The LEA proteins were first isolated from cotyledons during embryonic development and were found to accumulate during seed maturation in cotton in 1981 [9], which was considered an indicator of seed maturity [10]. Similar proteins have been found in other plants, including Arabidopsis [8], sorghum [11], rice [12], and wheat [13]. They are rapidly synthesized and accumulate when plants encounter adverse environmental conditions, such as low temperatures, drought, and dehydration [14]. Normally, LEA proteins exist in an unstructured conformation under hydration conditions but adopt a structured conformation when an organism experiences water scarcity [15], allowing it to adapt to adverse environments.
Moreover, LEA proteins participate in various regulatory networks in Arabidopsis. Firstly, they interact with sugar materials to create a glassy state within cells, preventing excessive seed dehydration [16]. Secondly, as molecular chaperones, LEA proteins stabilize protein structures, protecting other proteins and membranes from aggregation, which is crucial for stress tolerance, especially dehydration and low-temperature stress [17]. Additionally, LEA proteins enhance stress tolerance by buffering the increase in cellular ions, regulating both internal and external cell osmotic potentials [18]. In summary, LEA proteins function as dehydration defenders, regulating water in plants during maturation, dehydration, and stress responses [19].
Based on multi-omics analyses, including genome-wide association analyses, transcriptomics, and proteomics, our team discovered that LEA family members are involved in regulating KMC and KDR during different development stages in maize kernels [7]. Previous studies also showed that abscisic acid (ABA) enhances the dehydration tolerance during maize kernel dehydration by regulating the abundance of LEA proteins [20]. Therefore, we conducted a systematic analysis of the LEA family in maize, including family identification and evolution, based on the B73 reference genome (http://maizegdb.org (accessed on 20 December 2022), Zm-B73-REFERENCE-NAM-5.0). Moreover, transcriptome data were collected to identify differentially expressed maize LEA genes (ZmLEAs) in two maize inbred lines with significantly different dehydration rates during late seed development. Further, the proteome data of kernels in five periods for the line KA105 from our lab were used to analyze the time-solved accumulation of ZmLEA proteins in maize. The aim of this study was to provide a theoretical basis for understanding the role of LEA family members in regulating KMC and KDR during maize kernel development and to guide germplasm improvement for mechanized maize kernel harvesting.

2. Materials and Methods

2.1. Identification of ZmLEA Genes

The maize genome data were downloaded from Maize GDB (http://maizegdb.org (accessed on 20 December 2022)), sorghum and rice LEA gene sequences from NCBI (https://ncbi.nlm.nih.gov (accessed on 20 December 2022)), and Arabidopsis LEA family gene sequences from the online database TAIR (https://arabidopsis.org (accessed on 20 December 2022)). To identify ZmLEAs, we extracted the published LEA protein sequences from Arabidopsis, rice, and sorghum as references and compared them with the B73 reference genome (version 5.0), using e-values (e < E−5) to obtain the maize LEA family members with TBtools software (version 1.112) [21]. Subsequently, to decrease the false positives, the retrieved candidate genes were compared again with entries in the SwissProt database (https://expasy.org/resources/uniprotkb-swiss-prot (accessed on 20 December 2022)). Finally, the conserved structural domains of the proteins were further screened using CD-Search and Pfam with e (e < E−10) as the threshold value, and 52 ZmLEAs were obtained after filtering out redundant genes.

2.2. Multiple Sequence Alignment and Phylogenetic Analysis

The acquired ZmLEA protein sequences of maize were aligned with multiple amino acid sequences from maize, Arabidopsis, rice, and sorghum using the ClustalW tool. The comparison findings were utilized to build ML (Maximum Likelihood) phylogenetic trees for the four species using MEGA11 (Bootstrap: 1000; alternative model: WAG). The evolutionary tree was visualized using the online tool EvolView (http://www.evolgenius.info/evolview.html (accessed on 22 December 2022)).

2.3. Characterization of the ZmLEA Proteins

The ExPASy website (https://web.expasy.org/protparam/ (accessed on 20 December 2022)) was used to analyze and physically characterize the molecular weight, isoelectric point, and overall average hydrophilicity of the ZmLEA proteins. Then, the BUSCA annotation system (https://busca.biocomp.unibo.it/ (accessed on 20 December 2022)) was used to predict the subcellular location of each ZmLEA protein.

2.4. Chromosome Localization and Homology Analysis

The chromosome locations of the LEA gene sequences in Arabidopsis, sorghum, and rice were obtained from TAIR and NCBI (https://www.ncbi.nlm.nih.gov (accessed on 20 December 2022)). The related LEA proteins were annotated in the Ensembl Plants databases. Interspecies covariance analysis and visualization were performed for LEA genes from maize, sorghum, rice, and Arabidopsis using the software programs TBtools (version 1.112) [21] and MCScanX (http://chibba.pgml.uga.edu/mcscan2/#tm (accessed on 20 December 2022)) [22].

2.5. Analysis of Gene Structure, Conserved Motifs, and cis-Acting Elements

The conserved motifs of ZmLEA proteins were examined using the online MEME software (https://meme-suite.org/meme/tools/meme (accessed on 20 December 2022)). The gene structures and protein conserved motifs of ZmLEA genes were visualized and detected using the TBtools software [21]. The cis-acting elements were predicted by subjecting the 2000 bp sequence upstream of the ZmLEA promoters to PlantCARE (https://bioinformatics.psb.ugent.be/webtools/plantcare/html/ (accessed on 20 December 2022)) and then visualized using the TBtools software (version 1.112).

2.6. GO Annotation Analysis

The amino acid sequences of the ZmLEA proteins were submitted to the online software eggNOG (http://eggnog-mapper.embl.de/ (accessed on 20 December 2022)), followed by GO annotation and enrichment analysis conducted using TBtools software for the ZmLEA genes.

2.7. Transcriptome and Proteome Data Collection and Analysis

The transcriptome and proteome data were obtained from a previous study [7]. The fast-dehydrating maize inbred line (KA105) and the slow-dehydrating maize inbred line (KB020) were used as the study materials. Their average KMC at different kernel development times, determined over two years (2018 and 2019), is shown in Table S1. The two inbred lines were planted in June 2018 at the Guancun Maize Experimental Base of Northwest Agriculture and Forestry University, Yangling District, Shaanxi Province, China (E 34°32′, N 108°05′). A completely randomized block experimental design was adopted, with a row length of 4.5 m, a row spacing of 0.6 m, four rows planted in each plot, two replications, and a density of 75,000 plants hm−2, and the field management conditions were the same as those used locally. All ears were bagged at the pre-sprouting stage and then hand-pollinated on the same date to minimize the environment noise. Kernels in the middle of well-pollinated ears at 14, 21, 28, 35, 42, 49, 56, and 63 days after pollination were sampled immediately for two duplicates—one was used to determine the KMC using an oven at 35 °C, and the other was frozen in liquid nitrogen. All samples were stored in a freezer at −80 °C for RNA and protein extraction. Three replicates were set up for sample at each time. Based on parameters including the false discovery rate (FDR), an adjusted p-value of 0.05, and a fold change (FC) of 2 or 0.5 (namely, −log2(FC) > 1) using edgeR, the differentially expressed genes were screened [23]. A differential gene expression heatmap and a differential protein expression heatmap were created using the OmicShare tool (https://www.omicshare.com/tools (accessed on 20 December 2022)).

3. Results

3.1. Identification of the Members of ZmLEA Family in Maize

To obtain candidate ZmLEA genes in maize, we compared maize protein sequences with known LEA protein sequences from three other species: Arabidopsis, sorghum, and rice. Then, the candidate ZmLEA genes were compared again with entries in the SwissProt database (https://expasy.org/resources/uniprotkb-swiss-prot (accessed on 20 December 2022)). After eliminating the redundant genes, 52 genes with conserved structural domains were retained, which were sequentially named ZmLEA1–ZmLEA52 according to the distribution order of these genes on the 10 chromosomes of the B73 reference genome (Table 1).
Most of these 52 ZmLEA genes were harbored on chromosomes 1 and 3, which contained 9 ZmLEA genes each, while chromosome 9 contained only 1 ZmLEA gene. Notably, the ZmLEA genes showed a preferential distribution at both sides of the chromosomes, and some regions had two or four assembled ZmLEA genes. For example, ZmLEA35–28 were located on the same region at the end of long arm on chromosome 6 (Figure 1). Further, the 52 ZmLEA genes were divided into seven subfamilies based on sequence homology and conserved motifs in the Pfam database, i.e., ZmLEA_1, ZmLEA_2, ZmLEA_3, ZmLEA_4, ZmLEA_5, dehydrins (DHN), and seed maturation protein (SMP) subfamilies [8].

3.2. Evolutionary Analysis of LEA Family Genes

To analyze the evolution process of LEA family genes, phylogenetic trees were constructed using genes from maize, Arabidopsis, rice, and sorghum (Figure 2). The evolutionary results showed that LEA_2, with 30 members, was the most represented in maize, as well as in the other three species. Interesting, only one gene (Sb02g028010) from sorghum was found in the LEA_6 subfamily. A comparison of the LEA genes in the four species revealed greater similarity among monocotyledon species (maize, sorghum, and rice) than with the dicotyledon Arabidopsis (Figure 3A). Maize had 59 and 48 LEA homologous gene pairs compared to sorghum and rice, respectively, and only 13 LEA homologous gene pairs with Arabidopsis. Specifically, the sorghum genome contained 42 homologous ZmLEA genes, the rice genome 38 homologous ZmLEA genes, and the Arabidopsis genome only 8 homologous ZmLEA genes. This homology between maize and the other three species indicates that LEAs evolved before the division of monocotyledons and dicotyledons, and this evolution occurred concurrently with species evolution. Furthermore, the highest homology was observed between maize and sorghum, which is consistent with the evolution of these two species.
As maize is a paleo-tetraploid plant, gene duplications are normal. Here, we found that there were 18 collinear genes among the 52 LEA family genes in the maize genome, which were found to be distributed on chromosomes 1, 3, 5, and 8 (Figure 4). These collinear genes belong to the four subfamilies LEA_2, LEA_3, SMP, and DHN. Among them, the LEA_2 subfamily appeared to comprise the largest proportion, with 12 LEA genes (40% in LEA_2), while the three remaining subfamilies only had 2 collinear genes each. This indicates that gene duplication also contributed to the expansion of the maize LEA family. In addition, we calculated the Ka (the ratio of the number of synonymous substitutions per synonymous sites) and Ks (the ratio of the number of non-synonymous substitutions per non-synonymous sites) values of the 18 collinear gene pairs in maize, which indicated the direction of evolution. The Ka/Ks ratio of these collinear gene pairs was consistently <1, ranging from 0.21 (ZmLEA13/51) to 0.85 (ZmLEA14/42), with an average value of 0.50 (Figure 3B). Notably, ZmLEA14/42 displayed a high Ka/Ks ratio (0.85), suggesting a rapid post-replication evolution for this gene pair. Overall, the Ka/Ks analysis indicated a strong purifying selection effect in the maize LEA family, aiding in the elimination of environmental disadvantages resulting from non-synonymous mutations.

3.3. Physicochemical Characterization of Maize ZmLEA Genes

To better understand the ZmLEA family, the physicochemical characteristics were analyzed on the ExPASy website (Table 1). For the 52 ZmLEA gene sequences, the length ranged from 393 to 3269 base pairs (bp), except for the longest gene (ZmLEA45), which was 12,839 bp. As for the related encoded protein sequences, most contained fewer than 400 amino acids (aa), except for ZmLEA29, containing 604 aa, and ZmLEA47, containing 470 aa. Moreover, 65% (34) of the ZmLEA proteins had relatively high isoelectric points (PI > 7), especially in the LEA_2 subfamily. Regarding the hydrophilic mean values, the members of the subfamilies exhibited low hydrophilic mean values (GRAVY < 0), with the exception of half of the members of the LEA_2 subfamily. Furthermore, the DHN and SMP subfamilies showed strong hydrophilicity, with a low cysteine and a high lysine content in their amino acid sequences.
In addition, the subcellular location of different ZmLEA subfamilies was predict using the BUSCA annotation system (https://busca.biocomp.unibo.it/ (accessed on 20 December 2022)), and distinct subcellular locations were found. The proteins of the LEA_3 subfamily were found in mitochondria, while the LEA_5 proteins appeared localized in the nucleus. Other subfamilies showed localization in more than one cell region. For LEA_2, 43% of the proteins were nucleus-localized, with most having high PI (>7) and substantial hydrophobicity. This suggests that various ZmLEA subfamily proteins serve diverse functions in different organelles.

3.4. Prediction Analysis of the Structure and Function of ZmLEAs

Using the obtained protein sequences of the 52 ZmLEA genes, we extracted the gene structure of all genes according to the B73 reference genome version 5.0, based on the maizegdb database (http://www.maizegdb.org (accessed on 20 December 2022)). We found that members of the same subfamily had similar exon counts and exon and intron structures, but considerable variance existed between subfamilies (Figure 5). The majority of ZmLEA_2 subclade genes are around 2000 bp in length and have only one intron, except for ZmLEA9 (3269 bp), with two introns.Further, the conserved motifs in the ZmLEA proteins were analyzed, which also showed remarkably similar motifs within a given subfamily but diversities between different subfamilies (Figure 6). Due to fewer members and shared motifs occurring within the LEA_1 (Zm00001eb308610) and LEA_5 (Zm00001eb286150 and Zm00001eb286170) subfamilies, the motifs in the genes of these two subfamilies are not presented in the results.
Meanwhile, the cis-acting elements of these 52 ZmLEAs were predicted using the 2000 bp sequence upstream of the promoter of each gene. Nine cis-acting elements were primarily identified, i.e., auxin responsiveness, light responsiveness, gibberellin responsiveness, ABA (abscisic acid) responsiveness, MeJA (methyl jasmonate) responsiveness, SA (salicylic acid) responsiveness, low-temperature responsiveness, defense and stress responsiveness, and wound responsiveness elements (Figure 7A). The light responsiveness elements occurred more than twice in all genes, but other elements were only randomly present in a few genes. In sum, two types of elements were noted: one responded to adverse environments, and the other responded to hormones, including ABA, MeJA, and SA. Lastly, a GO analysis was conducted to annotate the gene functions; these genes showed enrichment for defense responses (Figure 7B), which is consistent with the cis-element analysis. This implies that the ZmLEA gene family members react to stress and adversity.

3.5. Expression and Protein Abundance in Two Maize Inbred Lines with Different Kernel Dehydration Rates

To validate the relationship between ZmLEA expression patterns and protein abundance with KMC and KDR, we collected and analyzed the transcriptome and proteome data of kernels obtained in a previous study in our lab [7]. For the transcriptome data, the kernels of two inbreds, analyzed 21, 28, 35, 42, 49, and 56 days after pollination (DAP), were used to screen the differentially expressed ZmLEA genes. The first inbred line was KA105, which shows rapid dehydration, and the other inbred line was KB020, with the characteristic of slow dehydration (Table S1). We found 14 ZmLEA genes with differential expression during kernel development, especially at DAP42, DAP49, and DAP56 (Figure 8). In addition, the expression levels of the ZmLEA genes changed with the kernel development, and most showed an upwards trend (Table S2). For example, ZmLEA9 and ZmLEA19 exhibited an obviously declining trend in expression throughout kernel development in both inbred lines, while the other 12 ZmLEA genes accumulated with kernel development and displayed a significantly increasing trend. Comparing the expression of ZmLEA genes during the same period in the two maize inbred lines with different dehydration rates, the ZmLEA27, ZmLEA34, and ZmLEA46 genes showed significantly higher expression in the slow-dehydrating inbred line (KB020) than in the fast-dehydrating inbred line (KA105). Conversely, the remaining 11 genes exhibited significantly higher expression in KA105 compared to KB020. These findings suggest that the ZmLEA gene family plays an essential role in seed development, particularly in the later stages.
For the proteome data, the kernels of KA105 at DAP28, DAP35, DAP42, DAP49, and DAP56 were collected, and 15 ZmLEAs showed differential abundance (Figure 9). Among them, 13 proteins, including ZmLEA1, tended first to decrease and then to increase, with DAP35 being a turning point. ZmLEA43, ZmLEA44, and ZmLEA48 showed a trend of decreased expression at DAP49. This is consistent with the trends observed in the transcriptome data. In addition, ZmLEA48 showed a consistently high expression, while ZmLEA20 showed a lower protein expression at DAP28 and DAP42. Based on this comprehensive transcriptome and proteome analysis, we suggest that ZmLEAs participate in regulating the KMC and KDR at the transcript or protein level.

4. Discussion

The LEA gene family is vast and complex, present not only in common crops like wheat, sorghum, and rice, but also in vascular and non-vascular plants, fungi, bacteria, and invertebrates [24]. In a model plant, the LEA gene family has been understood more clearly than in other species [25]. To better understand the LEA gene family in maize, which is the most widely planted and highest yield crop in the world, we conducted a comparative genome analysis to identify LEA members in maize. In a previous study [26], Li and Cao identified 32 ZmLEA genes and divided them into nine subfamilies based on the B73 reference genome version 3.0. Considering the high accuracy and integrity of the B73 reference genome version 5.0, we identified 52 ZmLEA genes in maize, which were categorized into seven subfamilies by comparison of the B73 reference genome (version 5.0) with the identified LEA genes in Arabidopsis, rice, and sorghum. In Li and Cao’s study [26], some ZmLEAs were not annotated correctly, which resulted in missing some ZmLEAs, such as ZmLEA29 (Zm00001eb238300), which was identified in this study but not in theirs. Additionally, the sole member of the ZmLEA_6 subfamily (GRMZM2G322351 or Zm00001eb316370) was not defined as an LEA family member. The results of this study indicated that the highest proportion of genes was found in the ZmLEA_2 subfamily, containing 30 members, while the lowest proportion was found in the ZmLEA_1 subfamily, which only contained 1 gene (Table 1). By analyzing the phylogenetic relationship of the LEA genes in these four species, we found the LEA_6 subfamily only existed in sorghum. It was also reported that the LEA_6 subfamily is not found in the algal and rice genomes [27,28]; it now can be stated as missing in maize. The results of the covariance analysis in four species—maize, sorghum, rice, and Arabidopsis thaliana—showed that the LEA family was mainly represented by the LEA_2 subfamily and the LEA_4 subfamily.
According to the physicochemical characterization of the ZmLEA genes, we found most of the ZmLEA proteins were hydrophilic, according to their hydrophilicity mean (GRAVY) values (Table 1), similar to the LEA proteins in Arabidopsis and Brassica napus [8,29]. Many studies showed that the high hydrophilicity can be due to the disordered nature of the LEA proteins in their natural state, which is why the LEA proteins are also referred to as disordered proteins [30]. The 30 members of the ZmLEA_2 subfamily were found to include both hydrophilic and hydrophobic proteins, a result that is consistent with previous studies on Arabidopsis, rice, etc. [8,27]. The genes of the LEA_2 subfamily are considered to have low homology with those of other subfamilies, as shown by their gene structure, which could explain the atypical features of the LEA_2 subfamily members (Figure 5). Furthermore, most members of the ZmLEA family contain only one intron and have a relatively short length (Figure 5). This is consistent with previous studies showing that stress response-related genes have almost no introns [31,32]. It is also consistent with the gene structures of LEA genes that have been reported in other plants. For instance, ~60% of the LEA genes in wheat lack introns, while over half of the LEA genes in Arabidopsis contain only one intron per member [33]. From the point of view of the plant body stress response, fewer intronic structures reduce the time from transcription to translation in the defense against stress, lowering the cost of energy consumption and facilitating the plant’s rapid response to stimuli caused by adverse environmental conditions [34].
Through time-resolved research of LEA gene transcription and polypeptides in Arabidopsis, it was found that most LEA polypeptides appeared in the final stages of seed maturation, while their transcripts were detected at 10–20 days of maturation [35]. Here, we collected transcriptome and proteome data and found that ZmLEA gene expression was higher than ZmLEA peptide expression during the maize kernel development process, especially in the late stages of kernel maturation. This is consistent with the conclusion that the accumulation of LEA is limited when a plant acquires desiccation tolerance [35]. SbLEA3A-1 and SbLEA3A-2 in sorghum are highly homologous to ZmLEA20, and SbLEA3B-2 and ZmLEA2 are homologous genes belonging to the LEA_4 subfamily [36]. Interestingly, the SbLEA3A gene was expressed at a low level in developing seeds, with a significant increase in expression at maturity. SbLEA3B-2 was expressed only at maturity. This is largely consistent with the transcriptome results of ZmLEA2 expression during seed maturation. This unique expression pattern of the LEA_4 subfamily suggests that these genes may have specific roles at different stages of seed development. The transcriptome data of the kernels of two typical maize inbred lines at different developmental periods showed that 14 differentially expressed genes were identified among the 52 ZmLEA genes, of which 12 ZmLEA genes accumulated in large quantities in the late stage of maize kernel maturation, and two ZmLEA genes accumulated during kernel development. The expression tended first to increase and then to significantly decrease. In addition, the expression of the ZmLEA27 and ZmLEA34 genes showed an opposite trend compared to that of the other differentially expressed genes: in the slow-dehydrating type KB020 inbred line, it was significantly higher than in the fast-dehydrating KA105 inbred line. This difference in expression suggests that the expression of ZmLEA genes is highly related to the type of inbred line and might correspond to the rate of dehydration at the late stages of maize kernel development.
As for the function of the LEA proteins, it was demonstrated that they are involved in a variety of developmental processes and accumulate in large quantities when a plant faces drought, low temperature, salt stress, or is treated with the phytohormone ABA [37]. Under dehydration conditions, the LEA proteins can interact with phospholipids to help maintain the integrity of liposomes or with polypeptides as molecular chaperones that can inhibit the fusion of liposomes under desiccation stress and can protect proteins from dehydration-induced aggregation [38]. For example, proteins of the LEA_4 subfamily will form specific secondary structures to protect a variety of membrane structures and stabilize the cell under adverse environmental stresses, including dehydration stress and low-temperature stress [39,40,41]. In this study, the gene Zm00001eb238300, belonging to the ZmLEA_4 subfamily, was found to be located on chromosome 5 (chr5:145131834-145134200) and was defined as ZmLEA29, with gene ID Zm00001d016128 in the B73 reference genome version 4.0. It was associated with KMC and KDR by genome-wide association [7]. Moreover, its varying expression at the transcriptome and proteome level also showed it would respond to KMC and KDR at different kernel development stages. With the exception of ZmLEA29, the other four members of the ZmLEA_4 subfamily, i.e., ZmLEA2, ZmLEA12, ZmLEA20, and ZmLEA48, were also found to be expressed differently at the transcription or protein level (Figure 8). This suggests the ZmLEA_4 subfamily responds to dehydration in maize to some extent.
Given the inevitability of mechanized maize kernel harvesting development, we need to improve the plant density to achieve high yields, increase the kernel quality, and reduce the cost and labor requirements and breed new inbred lines with low KMC and fast KDR [42]. The application of molecular technology is becoming more and more effective in plant breeding; it can improve target traits directionally, such as herbicide resistance and insect resistance in maize [43,44]. However, few genes have been validated as affecting KMC or KDR by gene editing or mutants, besides GAR2 (GRMZM2G137211), CRY1-9 (GRMZM5G805627), and ZmHSP5 (Zm00001d047799) [7,45], which limits their application in breeding. More molecular genetic and biochemic work needs to be performed to uncover the regulation mechanisms of KMC and KDR. It will be important to identify the dominant alleles in natural populations, explore functional markers to characterize phenotypes, clearly identify the up- or down-regulation of genes to construct regulation networks, and so on. This study confirmed the relationship between ZmLEA and KMC and KDR, suggesting some candidate genes (especially, genes from the ZmLEA_4 subfamily) for optimizing the KMC and KDR in future maize breeding.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12213674/s1, Table S1: Transcriptome data of inbred lines KA105 and KB020; Table S2: Proteomic data of the inbred lines KA105.

Author Contributions

S.X. and J.X. conceived the research and designed the experiments. Y.Z., X.Z. (Xiaojun Zhang), L.Z., L.W., H.Z. and X.Z. (Xinghua Zhang) collected the data and performed the data analysis. Y.Z. and X.Z. (Xiaojun Zhang) wrote the manuscript. Y.Z., S.X. and J.X. modified the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Science and Technology Major Project of the Ministry of Agriculture and Rural Affairs of China (2022ZD0400505), the Shaanxi Province Research and Development Project (2021LLRH-07), and the Seed Industry Innovation of Yangling project (Ylzy-ym-03).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that supports the findings of this study are available in the supplementary material of this article.

Conflicts of Interest

The authors declare that they have no competing financial interest or personal relationships that could have influenced the work reported in this paper.

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Figure 1. Distribution of the 52 ZmLEA genes on chromosomes in maize.
Figure 1. Distribution of the 52 ZmLEA genes on chromosomes in maize.
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Figure 2. Evolutionary analysis of the LEA genes in maize, Arabidopsis, sorghum, and rice.
Figure 2. Evolutionary analysis of the LEA genes in maize, Arabidopsis, sorghum, and rice.
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Figure 3. Co-lineage and gene selection analysis of LEA family genes. (A) Co-lineage analysis of LEA family genes in maize, sorghum, rice, and A. thaliana. The gray line in the Figure refers to the covariate gene pairs among the four species, and the blue line represents the covariate gene pairs with maize LEA genes in the genomes of each species. (B) Maize LEA family gene selection pressure analysis, Ka stands for synonymous substitutions, and Ks for non-synonymous substitutions.
Figure 3. Co-lineage and gene selection analysis of LEA family genes. (A) Co-lineage analysis of LEA family genes in maize, sorghum, rice, and A. thaliana. The gray line in the Figure refers to the covariate gene pairs among the four species, and the blue line represents the covariate gene pairs with maize LEA genes in the genomes of each species. (B) Maize LEA family gene selection pressure analysis, Ka stands for synonymous substitutions, and Ks for non-synonymous substitutions.
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Figure 4. Covariate analysis of ZmLEA genes. The gray lines represent the covariates of the maize genome, and the red lines represent the ZmLEA covariate gene pairs.
Figure 4. Covariate analysis of ZmLEA genes. The gray lines represent the covariates of the maize genome, and the red lines represent the ZmLEA covariate gene pairs.
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Figure 5. Gene structure of the ZmLEA genes. The colored boxes represent exons, and the gray lines represent introns.
Figure 5. Gene structure of the ZmLEA genes. The colored boxes represent exons, and the gray lines represent introns.
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Figure 6. Conserved motif map of the ZmLEA genes.
Figure 6. Conserved motif map of the ZmLEA genes.
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Figure 7. (A) Promoter cis-element analysis and (B) functional annotation of the ZmLEA genes.
Figure 7. (A) Promoter cis-element analysis and (B) functional annotation of the ZmLEA genes.
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Figure 8. Expression heatmap of differentially expressed ZmLEA genes in KA105 and KB020.
Figure 8. Expression heatmap of differentially expressed ZmLEA genes in KA105 and KB020.
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Figure 9. Heatmap of ZmLEA protein expression in KA105.
Figure 9. Heatmap of ZmLEA protein expression in KA105.
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Table 1. Characteristics and physicochemical parameters of ZmLEA genes in the maize genome.
Table 1. Characteristics and physicochemical parameters of ZmLEA genes in the maize genome.
GeneSubfamilyGene IDLocationAmino AcidMW (KD)pIGRAVY
ZmLEA1SMPZm00001eb004470chr1:12338113-1233959929128,407.974.51−0.209
ZmLEA2LEA_4Zm00001eb005250chr1:14547169-1454860432233,788.066.03−0.94
ZmLEA3LEA_2Zm00001eb022900chr1:88936879-8893782321022,979.6710.50.16
ZmLEA4LEA_2Zm00001eb033830chr1:187537818-18753980726929,952.110.240.002
ZmLEA5LEA_2Zm00001eb044970chr1:234172861-23417432027429,476.1610.08−0.104
ZmLEA6SMPZm00001eb047600chr1:244450281-24445106723825,804.1110.64−0.634
ZmLEA7SMPZm00001eb057120chr1:282509952-28251116626627,177.355.41−0.367
ZmLEA8SMPZm00001eb057130chr1:282541392-28254237420521,194.355.14−0.53
ZmLEA9LEA_2Zm00001eb064120chr1:303982107-30398537631935,274.074.92−0.364
ZmLEA10LEA_2Zm00001eb065640chr2:765492-76674020421,790.278.90.334
ZmLEA11LEA_2Zm00001eb066210chr2:1899747-190085225126,926.1110.04−0.064
ZmLEA12LEA_4Zm00001eb071160chr2:11114272-1111559730532,277.88.39−0.887
ZmLEA13LEA_2Zm00001eb083540chr2:57422886-5742420422524,069.767.670.313
ZmLEA14LEA_2Zm00001eb106290chr2:210544418-21054527520322,724.019.1−0.179
ZmLEA15LEA_3Zm00001eb127770chr3:36494705-36495503959519.6710.36−0.001
ZmLEA16LEA_3Zm00001eb130570chr3:57423270-57423663919677.279.96−0.21
ZmLEA17LEA_3Zm00001eb130580chr3:57516852-5751768410110,625.259.76−0.22
ZmLEA18LEA_2Zm00001eb146910chr3:181095946-18109696223826,325.899.4−0.294
ZmLEA19LEA_2Zm00001eb150110chr3:192024649-19202619628630,406.8710.78−0.056
ZmLEA20LEA_4Zm00001eb155430chr3:210032549-21003386323123,542.868.96−0.763
ZmLEA21DehydrinZm00001eb155620chr3:210644563-21064576923624,810.075.99−0.893
ZmLEA22LEA_2Zm00001eb159430chr3:223080810-22308190518019,631.494.83−0.101
ZmLEA23LEA_2Zm00001eb160190chr3:225307642-22530891232032,924.7211.41−0.113
ZmLEA24DehydrinZm00001eb187010chr4:159677441-15967885928931,466.475.51−1.3
ZmLEA25LEA_2Zm00001eb198910chr4:202382893-20238563421323,251.769.230.162
ZmLEA26LEA_2Zm00001eb211120chr5:1888452-188941921723,291.757.950.226
ZmLEA27SMPZm00001eb213850chr5:5981874-598272121922,350.034.86−0.255
ZmLEA28LEA_2Zm00001eb221020chr5:26041536-2604359726528,327.9810.26−0.019
ZmLEA29LEA_4Zm00001eb238300chr5:145131834-14513420060461,763.467.26−0.814
ZmLEA30LEA_2Zm00001eb243040chr5:173362167-17336358320722,661.289.180.168
ZmLEA31LEA_2Zm00001eb247790chr5:190986503-19098801721323,198.8110.320.074
ZmLEA32DehydrinZm00001eb250120chr5:198232923-19823445529031,440.766.05−1.25
ZmLEA33LEA_2Zm00001eb259940chr6:5880193-588118521323,414.439.140.31
ZmLEA34LEA_2Zm00001eb259950chr6:5904344-590573321423,855.729.070.239
ZmLEA35DehydrinZm00001eb285360chr6:148134643-14813579516817,075.488.78−1.144
ZmLEA36LEA_3Zm00001eb285680chr6:149412498-1494133229310,139.526.29−0.432
ZmLEA37LEA_5Zm00001eb286150chr6:151445811-15144681311312,083.085.42−1.206
ZmLEA38LEA_5Zm00001eb286170chr6:151481816-151482230919669.496.61−1.266
ZmLEA39LEA_2Zm00001eb304760chr7:28700129-2870187932135,551.824.74−0.433
ZmLEA40LEA_1Zm00001eb308610chr7:78245306-7824613412512,633.179−0.477
ZmLEA41LEA_2Zm00001eb319570chr7:149210004-14921107421923,006.589.320.216
ZmLEA42LEA_2Zm00001eb323160chr7:163429460-16343034020322,824.468.69−0.046
ZmLEA43LEA_2Zm00001eb342130chr8:61665910-6166672415216,088.455.640.024
ZmLEA44LEA_2Zm00001eb344820chr8:76504841-7650572116617,984.545.3−0.093
ZmLEA45LEA_2Zm00001eb357160chr8:142815635-14282847431131,816.5911.2−0.048
ZmLEA46LEA_2Zm00001eb358640chr8:148997074-14899827218119,680.524.77−0.025
ZmLEA47DehydrinZm00001eb365110chr8:169507204-16950906347050,246.089.58−0.512
ZmLEA48LEA_4Zm00001eb395500chr9:139364531-13936610735437,909.356.6−1.036
ZmLEA49LEA_2Zm00001eb405330chr10:1744675-174624929031,093.0911.55−0.159
ZmLEA50LEA_2Zm00001eb407150chr10:6543035-654421321323,229.68.270.135
ZmLEA51LEA_2Zm00001eb422110chr10:117432618-11743377322424,042.78.830.247
ZmLEA52LEA_2Zm00001eb434170chr10:151062372-15106348022124,2798.620.113
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Zhang, Y.; Zhang, X.; Zhu, L.; Wang, L.; Zhang, H.; Zhang, X.; Xu, S.; Xue, J. Identification of the Maize LEA Gene Family and Its Relationship with Kernel Dehydration. Plants 2023, 12, 3674. https://doi.org/10.3390/plants12213674

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Zhang Y, Zhang X, Zhu L, Wang L, Zhang H, Zhang X, Xu S, Xue J. Identification of the Maize LEA Gene Family and Its Relationship with Kernel Dehydration. Plants. 2023; 12(21):3674. https://doi.org/10.3390/plants12213674

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Zhang, Yaping, Xiaojun Zhang, Liangjia Zhu, Lexin Wang, Hao Zhang, Xinghua Zhang, Shutu Xu, and Jiquan Xue. 2023. "Identification of the Maize LEA Gene Family and Its Relationship with Kernel Dehydration" Plants 12, no. 21: 3674. https://doi.org/10.3390/plants12213674

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