Genome-wide analysis of soybean MLPs reveals evolutionary and structural insights into drought, salt stress and ABA responses

Genome-wide analysis of soybean MLPs reveals evolutionary and structural insights into drought, salt stress and ABA responses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide analysis of soybean MLPs reveals evolutionary and structural insights into drought, salt stress and ABA responses Xiaocen Ma, Qi Wang, Qing Yu, Yixuan Li, Yongnuo Yuan, Heng Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7266563/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in BMC Plant Biology → Version 1 posted 12 You are reading this latest preprint version Abstract The study focuses on the Major Latex Protein ( MLP ) gene family, which encodes a class of proteins belonging to the Bet v 1 superfamily, renowned for their involvement in plant growth and development, as well as their responsiveness to both biotic and abiotic stresses, underscoring their pivotal role in stress adaptation mechanisms. However, the resilience-related functions of the GmMLPs in soybean have remained largely unexplored. This research identifies 17 genes within the soybean ( Glycine max ) genome, designated as GmMLPs , that harbor the Bet v 1 domain. Based on previous insights, these GmMLPs are categorized into three subgroups: Type I, Type II, and Type III. Chromosomal mapping analysis, leveraging genomic annotation files, reveals an uneven distribution of the GmMLPs across the 20 soybean chromosomes. Notably, these genes tend to cluster in the lower arm regions of the chromosomes, indicating a non-random pattern of chromosomal localization. Furthermore, an examination of their gene structures uncovers variations, with most GmMLPs featuring a single intron, whereas exceptions like GmMLP3 and GmMLP4 exhibit more than one intron, suggesting structural diversity within the gene family. GmMLPs were identified as ABA, drought and salt stress -responsive genes by qRT-PCR, suggesting that GmMLPs are involved in regulating soybean responses to drought, salt stress and ABA signaling. This study lays the foundation for future investigations into the specific roles of GmMLPs in soybean stress tolerance and adaptation strategies. Glycine max Genome-wide analysis MLP gene family Gene expression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Soybean (Glycine max) ranks as the fourth most significant crop worldwide, celebrated for its nitrogen-fixing abilities and as a vital source of protein- and oil-rich seeds [ 1 – 2 ]. Recent investigations have illuminated the pivotal role of MLPs in plant responses to biotic and abiotic stresses. These proteins possess unique functionalities that enable plants to withstand diverse environmental challenges [ 3 – 4 ]. MLPs have been identified and cloned across various plant species, with studies demonstrating their crucial involvement in plant growth, development, and stress resilience [ 5 ]. Understanding the physicochemical properties, structural characteristics, and functional roles of MLPs in plant growth and stress responses provides valuable insights for molecular breeding and crop improvement. Notably, MLP genes have been linked to enhanced stress tolerance, contributing to increased crop yields [ 6 – 8 ]. As temperature fluctuations, salinization, pests, and diseases pose significant constraints on plant growth and development [ 6 – 7 ], unraveling the mechanisms of stress tolerance has become a focal point in agricultural research. When exposed to adverse conditions, plants undergo intricate physiological and biochemical changes, often accompanied by the upregulation of stress-responsive genes. These genes encode proteins that function as sensors, transducers, and adaptors to stress signals [ 6 , 9 – 10 ]. Among these, MLPs represent a unique class of proteins specific to plants, noted for their efficacy against biotic stresses. Belonging to the Bet v 1 superfamily, MLPs exhibit low sequence similarity but high structural conservation, typically characterized by three α-helices and seven β-sheets [ 8 ]. Extensive research has demonstrated that MLP genes function in plant growth and respond to various stresses. [ 11 ]. For instance, low temperatures significantly affect the metabolism and gene expression of NtMLP423 in tobacco [ 12 ], while overexpression of GhMLP in Arabidopsis has been shown to reduce sensitivity to salt stress [ 13 ]. Following the initial isolation of MLPs from the latex of opium poppy ( Papaver somniferum ) in 1985 [ 14 ], advancements in whole-genome sequencing and bioinformatics have enabled the identification of MLPs in numerous plant species, including Arabidopsis, cotton and poplar [ 15 – 18 ]. Notably, multiple MLP gene members coexist within a single species, forming the MLP gene family (a subset of the Bet v 1 superfamily) [ 13 , 19 – 23 ]. For example, 26 MLP family members have been identified in Arabidopsis [ 13 ], 14 in grape [ 19 ], 59 in sugar beet [ 20 ], and 27 in tomato [ 21 ], highlighting their widespread distribution and diversity. Research indicates that plants upregulate MLP gene expression under stress conditions, resulting in the production of MLP proteins that facilitate survival in adverse environments [ 2 – 3 ]. Therefore, our research aims to elucidate the mechanisms by which GmMLPs regulate soybean responses to drought, salt stress, and ABA. This study will provide a theoretical foundation for screening drought, salt-tolerant soybean cultivars, contributing to improved agricultural resilience. Materials and Methods Identification of MLP genes in soybean The MLPs from soybean and Arabidopsis were sourced from previous studies [ 2 , 22 ]. Their protein sequences were downloaded from the Phytozome database ( https://phytozome-next.jgi.doe.gov ) and TAIR ( https://www.arabidopsis.org/ ). The soybean MLPs were identified by querying the legume plant database with AtMLPs and GmMLPs (E-value ≤ 10 − 5 ). Two online resources, the Conserved Domain Database (CDD) ( https://www.ncbi.nlm.nih.gov/cdd ) and SMART ( http://smart.embl.de/ ), were utilized to verify the presence of the Bet v 1 domain [ 19 ]. Chromosomal localization information was retrieved from the soybean genome database, while ProtParam was employed for analyzing the physicochemical properties [ 23 – 24 ]. Phylogenetic analysis of MLPs in Arabidopsis and soybean To explore the evolutionary relationships between AtMLPs and GmMLPs, multiple sequence alignments of protein sequences were performed using ClustalW [ 25 ]. The Neighbor-Joining (NJ) phylogenetic tree was subsequently generated in MEGA 11 and rendered through EvolView ( https://www.evolgenius.info/evolview/ ) [ 26 ]. The GmMLPs were grouped based on their classification. Analysis of intraspecific collinearity in soybean The Gene Structure Display Server (GSDS) ( http://gsds.gao-lab.org/index.php ) was utilized to conduct an intraspecific relationship analysis of the gene structure, focusing on the coding sequences (CDSs) and genomic sequences of the GmMLPs [ 27 – 28 ]. For collinearity analysis of the GmMLPs , the Circos plugin within TBtools was employed using default parameters [ 29 – 30 ]. Characterization of conserved motifs, protein domains, and gene structural features GmMLPs sequences were downloaded and analyzed for conserved motifs using MEME ( https://meme-suite.org/meme/ ) with default parameters [ 31 – 32 ]. The presence of the Bet v 1 domain was validated using CDD and SMART [ 33 – 34 ]. Images were modified using TBtools. Gene structures of GmMLPs were analyzed by comparing their coding sequences (CDSs) with corresponding genomic sequences using the Gene Structure Display Server (GSDS) ( http://gsds.gao-lab.org/index.php ) [ 32 ]. Chromosomal localization, 3D structure prediction, and collinearity analysis The annotated soybean genome files for 20 chromosomes were downloaded from Phytozome for chromosomal localization analysis, which was carried out using MapDraw software [ 35 ]. The 3D structures of the proteins were predicted using SWISS-MODEL software based on the protein sequences [ 36 ]. Furthermore, the collinearity relationships between soybean and three dicotyledonous plants (Arabidopsis, tomato, tobacco) and a monocotyledonous plant (rice) were determined using the MCScanX synteny plot in TBtools based on annotated and genomic files from different species [ 37 – 40 ]. Characterization of cis-acting elements (CAEs) in GmMLPs regulatory regions To analyze CAEs in GmMLPs , promoter sequences (2000 bp upstream of the start codon) were retrieved from Phytozome and submitted to PlantCARE ( http://bioinformatics.psb.ugent.be/webtools/plantcare/html/ ). Relevant CAEs were then imported into TBtools for analysis [ 38 , 40 ]. Abscisic acid, PEG and NaCl treatment To examine the expression level of GmMLPs in response to ABA, drought and salt, healthy and uniform-sized seeds of the Wm82 wild type were selected and grown hydroponically in distilled water for 14 days. The seedlings were then transferred to 100 µM ABA, 20% PEG and 200 mM NaCl or distilled water as a control, and treated for 4 h. Plants were dissected from the seedlings immediately after the treatments, frozen in liquid nitrogen for RNA extraction. Expression profile analysis - RNA extraction and qRT-PCR Total RNA was isolated with the Omega E.Z.N.A.® Kit following the manufacturer's protocol, and RNA integrity was verified by 1.2% agarose gel electrophoresis. After the DNase treatment, 1 or 2 mg total RNA was used to synthesize cDNA by oligo(dT)20-primed reverse transcription using the EasyScript One-Step gDNA Removal and cDNA Synthesis SuperMix (TransGen Biotech). The synthesized cDNA was used as the template for gene expression analysis. For qRT-PCR, each sample was amplified in three parallel reactions as technical replicates, and the GmEF-1a ( Glyma.17G186600 ) was amplified as a reference gene. The primers of GmMLPs and GmEF-1a used for expression analysis are listed in Table S1 . Results Identification and a nalysis of p hysicochemical p roperties of MLP s in s oybean In our study, we identified 17 members of the GmMLP s family in the Glycine max genome, each with distinct attributes. The genes are named sequentially from GmMLP1 to GmMLP17 based on their chromosomal locations and are associated with unique gene IDs starting with " Glyma ," followed by the chromosomal location and specific nucleotide coordinates. The CDS regions are indicated, highlighting the genomic segments encoding for these proteins. The genes are further categorized into three groups (I, II, and III), potentially reflecting their functional or evolutionary relationships. To gain further insights into the physicochemical properties of the GmMLPs , we analyzed their amino acid (aa) lengths, molecular weights (MW), and isoelectric points (pI). Among them, GmMLP11 exhibited the longest amino acid sequence (181 aa), while GmMLP4 had the shortest (126 aa). The MW ranged from 14.5 kDa ( GmMLP 4 ) to 20.8 kDa ( GmMLP11 ), and the pI ranged from 4.99 ( GmMLP1 ) to 8.25 ( GmMLP14 ) (Table 1). These results suggest differences in their biochemical properties and potential roles in cellular processes. Table 1 List of MLP genes in the Glycine max genome. Phylogenetic a nalysis of MLP s b etween s oybean and Arabidopsis Phylogenetic analysis of the MLP family across multiple species revealed a complex evolutionary history. Within the GmMLP s genome, we detected 17 genes containing the Bet v 1 domain and designated them as GmMLPs . To classify the GmMLPs , we constructed an NJ phylogenetic tree based on their protein sequences, including 17 GmMLPs and 25 AtMLPs from Arabidopsis. Following the classification of the Arabidopsis MLP genes family, we divided the GmMLP genes into three subgroups: Type I, Type II, and Type III (Fig. 1). Subgroup I comprises nine members: GmMLP12 , GmMLP6 , GmMLP7 , GmMLP13 , GmMLP5 , GmMLP1 , GmMLP2 , GmMLP17 , and GmMLP9 ; Subgroup II includes three members: GmMLP16 , GmMLP10 , and GmMLP3 ; and Subgroup III contains five members: GmMLP11 , GmMLP4 , GmMLP8 , GmMLP15 , and GmMLP14 . The phylogenetic tree revealed the presence of one orthologous pair of MLP members between soybean and Arabidopsis (GmMLP11 and At4G23680), although most GmMLP members are phylogenetically distant from AtMLP members (Fig. 1). Collinearity of GmMLP s w ithin s oybean s pecies We investigated the locations of the 17 GmMLPs across the 20 soybean chromosomes using genomic annotation files and found that their distribution was relatively uneven among these chromosomes (Fig. 2). Based on their chromosomal positions, the genes were named GmMLP1 to GmMLP17 from top to bottom. By comparing and analyzing the positions of different GmMLPs , we identified several collinear relationships: GmMLP1 and GmMLP2 exhibited collinearity on chromosomes 1 and 2, respectively; GmMLP4 and GmMLP17 showed collinearity on chromosomes 20 and 7, respectively; GmMLP3 on chromosome 2 and GmMLP10 on chromosome 10 also displayed collinearity; additionally, GmMLP12 and GmMLP13 on chromosome 15 and GmMLP5 on chromosome 8 exhibited collinear relationships. Gene s tructure, m otif a nalysis and c onserved s tructural d omain of GmMLP s To investigate the phylogenetic relationships of GmMLPs, we constructed an NJ phylogenetic tree of GmMLPs using MEGA 11 software. GmMLPs were also categorized into three subgroups (I, II, and III) (Fig. 3a), which is in agreement with the results of the evolutionary tree in Fig. 1. Next, we analyzed the structure, Coding sequences (CDSs), and genomic sequence of GmMLPs using GSDS 2.0. The analysis revealed that most GmMLPs have only one intron, while GmMLP3 and GmMLP4 have more introns (Fig. 3b). Using MEME to detect GmMLPs, most GmMLPs contain three motifs. All GmMLs in subgroup I contained all three motifs, whereas the GmMLP3 in subgroup II contained only two motifs (motif1 and motif3), and the GmMLP4 in subgroup III contained only motif 2. In addition, we found that the presence of motif1 and motif3 was prevalent in all GmMLPs except for GmMLP4 (Fig. 4a). And among the motif common sequences of GmMLPs, all three motifs contained amino acids such as glycine, glutamate and lysine. Among them, lysine is more abundant in the three motifs, and the position of lysine in the motifs is more uniform. Because lysine has the function of stabilizing the protein structure, it may help to maintain the conservatism of the motifs (Fig. 4b). Of course, the connection between the amino acid sequences in the motifs and the biological functions of MLPs needs to be further investigated. By comparing the images of motif analysis and Bet v 1 structural domain images, there was a one-to-one correspondence between motifs and Bet v 1 structural domains. That is, there are three motifs in most GmMLPs with similar positions (Fig. 4a), which is consistent with the distribution of Bet v 1 domains in GmMLPs (Fig. 5). For example, GmMLP4 has only motif 2 and is in the middle, and its Bet v 1 structural domain is only present in the middle, and GmMLP11 has three motifs but the position is shifted by a certain distance relative to the other GmMLPs with three motifs, which is also shown in Fig. 5. Chromosomal l ocalisation a nd i nterspecific c ovariance a nalysis When analysing the chromosomal localisation of the soybean GmMLPs , we found that its distribution was relatively uneven. Among them, the GmMLPs was only distributed on chromosomes 1, 2, 7, 8, 9, 10, 13, 15, 16 and 20, and the distribution position was uneven, mostly in the position of the chromosome off the lower arm position (Fig. 6). To explore the evolutionary relationships among MLPs , we constructed interspecific covariate relationships between soybean and three dicotyledons (Arabidopsis, tomato, and tobacco) and one monocotyledon (rice). Firstly, we observed that soybean and Arabidopsis had strong covariance, with four pairs having homology and all Arabidopsis and soybean covariance genes were located on chromosome 1. Secondly, tomato also had homology with soybean, with three genes having covariance with soybean existing on tomato chromosomes 4 and 5, respectively. Lastly, there was only one pair of genes having covariance with soybean and tobacco, which was located on tobacco chromosome 12. However, there were no genes having covariance between soybean and tobacco in the rice The presence of co-linear MLPs was not found in the gene co-linearity comparison for the time being (Fig. 7). Four members of the soybean MLP s exhibited collinearity with Arabidopsis MLPs . Therefore, soybean and Arabidopsis share strong homology, suggesting a common ancestor between the two species. Analysis of c is- a cting e lements (CAEs) of GmMLPs Because the stress response of GmMLPs is closely linked to their expression, it is crucial to study the CAEs of these genes. To analyse the CAEs of GmMLP s, we retrieved and submitted the promoter sequences (2000 bp upstream of the start codon) to PlantCARE and analyzed 17 CAEs associated with hormonal and abiotic stresses. It was shown that all GmMLPs , except GmMLP3 , contained anaerobic response elements and that the majority of GmMLPs (76%) contained ABA response elements (ABRE), nine of the 17 GmMLPs (52%) contained meja response elements (CGTCA and TGACG), three GmMLPs (17%) contained IAA response elements, and 10 of the GmMLPs (59%) contained SA response elements, and 9 GmMLPs (52%) contained MeJA response elements. In addition, six GmMLPs contained the drought-inducing element (MBS); the low-temperature-inducing element (LTR) was present in four GmMLPs ; nine GmMLPs contained the defence- and stress-responsive element TC-rich repeats; and only GmMLP1 contained the gibberellin-responsive elements GARE, TATC, and P-box. (Fig. 8) GmMLPs a re a biotic s tress a nd ABA r esponse g enes Given MLPs' established role in regulating plant salt stress responses and the prevalence of ABA-responsive/drought-inducing elements in GmMLP promoters, we hypothesize that GmMLPs mediate plant responses to ABA signaling, drought and salt stress. To investigate this, we analyzed the expression level of GmMLPs in soybean plants under ABA, drought and salt stress treatment using qRT-PCR. The results revealed that all GmMLPs , with the exception of GmMLP4 (which exhibited undetectably low expression levels), functioned as ABA, drought and salt stress--responsive genes. In the G1 subgroup, the expression of all GmMLPs was suppressed by ABA. Under drought stress, GmMLP1 , GmMLP2 and GmMLP6 expression were induced. Other members showed reduced expression levels. Under salt stress, GmMLP1 expression was induced, while GmMLP17 expression remained unchanged; the remaining GmMLPs in this subgroup showed reduced expression levels. In contrast, GmMLPs within the G2 subgroup exhibited drought and salt stress-induced expression but ABA-suppressed expression. For the G3 subgroup, only GmMLP11 expression was unaffected by ABA treatment, while GmMLP8 , GmMLP14 , and GmMLP15 were downregulated in response to ABA. Under drought and salt stress, GmMLP14 expression remained unchanged. GmMLP11 and GmMLP15 showed induced expression levels, whereas GmMLP8 expression was induced by drought stress and inhibited by salt stress (Fig. 9). Discussion MLPs represent a plant-specific protein family that plays crucial roles in plant growth and development [ 41 ], as well as responses to both biotic and abiotic stresses [ 5 , 13 , 42 – 43 ]. MLPs have been well-documented to participate in abiotic stress responses, biotic stress responses, and various developmental processes in plants [ 23 ]. Since the initial isolation of MLPs from opium poppy ( Papaver somniferum ) latex [ 44 ], the MLP gene family has been identified in multiple plant species including Arabidopsis, tomato, apple, and peach [ 5 , 13 , 19 , 42 , 45 – 46 ]. While several studies have investigated this gene family in various plants, the MLP genes in soybean ( Glycine max ) remain uncharacterized. Through genome-wide screening, we identified 17 MLP genes in soybean and characterized their physicochemical profiles, phylogeny, gene architectures, conserved motifs, and cis-regulatory elements. We further investigated the evolutionary patterns of this multigene family, including chromosomal localization and synteny relationships, as well as their expression profiles under ABA, drought, and salt stress treatments. Growing evidence supports a linear correlation between genome size and gene number in prokaryotes [ 47 – 48 ]. However, this relationship appears absent in eukaryotes, particularly plants. Among dicotyledonous plants, Arabidopsis (135 Mb genome) contains 25 MLP genes, tomato (827 Mb) has 34, apple (700 Mb) possesses 36, grape (490 Mb) contains 14, and peach (257 Mb) harbors 30 MLP genes [ 13 , 19 , 46 , 49 – 51 ]. In contrast, monocotyledonous plants show significantly fewer MLP members, with Brachypodium distachyon having only 2 [ 52 ], Zea mays (maize) containing 3, and Setaria italica (foxtail millet) possessing just 1 MLP gene [ 40 ]. This striking difference in MLP gene numbers between monocots and dicots suggests no direct correlation between MLP gene quantity and genome size in plants. The observed divergence likely occurred during plant evolution following the monocot-dicot differentiation, resulting in MLP gene loss in monocots and expansion in dicots. Notably, modern genetic studies confirm that monocots actually evolved from ancient dicots as a specialized branch, which may explain the distinct distribution patterns of MLP genes between these two plant groups. In this study, we identified 17 GmMLP genes in the soybean genome. Phylogenetic analysis classified these 17 GmMLP genes into three subgroups (Fig. 1 ), consistent with classifications reported for grape, Chinese cabbage, apple, and peach MLP genes [ 12 , 19 , 46 , 53 ]. Members within the same subgroup share similar gene structures and conserved motifs. The phylogenetic tree we constructed (Fig. 2 ) clearly shows that GmMLP3, GmMLP10, and AtMLP423 share the closest genetic relationship, belonging to the same subgroup II and clustering together on the same branch. Increasing studies indicate that genes belonging to the same functional group may possess similar expression patterns and regulatory mechanisms, suggesting that GmMLP genes within the same group may have similar functions. Subgroup I does not contain any Arabidopsis MLP members, indicating that gene duplication events occurred in this branch, which is a core driving force in plant evolution [ 54 ]. This result is consistent with reports on apple MLPs [ 50 ]. To explore the structural composition of MLP genes, we analyzed gene structures and conserved motifs. Gene structure analysis, similar to previous reports on grape MLP genes, showed that 15 out of 17 GmMLP genes contain only one intron, once again indicating that MLP genes are highly conserved during evolution [ 19 ] (Fig. 3 ). Motif analysis of the 17 soybean GmMLPs identified three relatively conserved amino acid motifs (Motif 1, 2, and 3) (Fig. 2 ). All GmMLPs contain the conserved Motif 1, which includes a glycine-rich loop, a structure crucial for the biological function of MLPs [ 55 – 56 ] (Fig. 4 ). These results demonstrate that GmMLPs are highly evolutionarily conserved. Critically, most GmMLPs incorporate glycine and lysine (mediating abiotic stress responses via photosynthesis), glutamate (functioning as stress signal transducer) and proline, which participates in signaling pathways critical for maintaining osmotic homeostasis. [ 57 – 59 ]. Recent research found that salt-tolerant rice varieties enhance osmotic adjustment ability through proline accumulation [ 60 ]. These results indicate that GmMLPs can respond to multiple abiotic stresses, especially drought and salt stress. It is noteworthy that plant-specific MLPs belong to the Bet v1 protein family [ 42 ]. Domain analysis revealed that all 17 MLP proteins contain a single Bet v 1 domain, which comprises the majority of each protein's length. Clearly, the majority of the coding regions of GmMLPs encode the Bet v 1 functional domain. This is highly consistent with MLPs identified in cucumber ( Cucumis sativus ) [ 22 ], apple ( Malus domestica ) [ 50 ], and Chinese cabbage ( Brassica rapa ) [ 53 ], while up to four Bet v1 domains were found in zucchini ( Cucurbita pepo ) [ 23 ]. These results indicate that although MLP proteins are evolutionarily conserved, they exhibit diversity among different species. Collinearity analysis of MLPs family revealed different numbers of MLP gene pairs between soybean and the dicot plants Arabidopsis, tomato, and tobacco (Fig. 5 ), suggesting these genes play important roles in MLP gene evolution. However, no gene pairs were found between soybean and the monocot plant rice (Fig. 5 ), indicating that MLP gene evolution is closely related to dicot plant divergence. Typically, cis-acting promoter elements determine gene expression patterns through interactions between promoter binding sites and transcription factors, playing a key role especially in stress response regulation [ 61 – 63 ]. In this study, the promoter regions of 14 GmMLP genes (82.4%) contained ABRE (abscisic acid-responsive element), a finding consistent with studies on MLP genes in other plants [ 19 , 42 ]. Promoters of peanut and apple MLP genes are also rich in ABRE motifs, indicating that MLP genes may be involved in ABA response processes [ 11 , 50 ]. However, unlike earlier studies, this study also identified the TGACG-motif (methyl jasmonate-responsive element) in the promoter regions of GmMLPs . This result aligns with the conclusion of cis-acting element analysis of poplar PtMLP , where MeJA (methyl jasmonate) is considered to be involved in plant growth and development processes, particularly playing an important role in responding to abiotic stresses such as low temperature stress and salt stress [ 18 , 64 ]. These results indicate that GmMLP genes play a key role in plant response to abiotic stress. Interestingly, we noted that GmMLP3 , GmMLP6 , and GmMLP17 are involved in ABA response, but no ABRE element was detected in their upstream regions (Fig. 6 ). Similarly, the promoter of GmMLP14 contains a drought stress-responsive element, but the expression level of this gene hardly changed under drought stress treatment (Fig. 9 ). Likewise, no salt stress-related cis-acting elements were detected in the upstream regions of all GmMLPs , all members responded to salt stress except GmMLP17 (Fig. 9 ). This finding indicates that the presence or absence of cis-acting elements in the promoter region is not directly correlated with the gene's stress response. Previous studies have reported MLP responses to various abiotic stresses. For example, VvMLP1/2/3/6/9 in grapevine showed significantly increased transcriptional levels under salt stress treatment [ 19 ], and MdMLP5/13 in apple also displayed significantly upregulated expression under salt stress treatment. The expression levels of MdMLP2/6/7/9/11 were upregulated under drought treatment [ 50 ]. We analyzed the ABA, drought, and salt stress responses of GmMLPs using qRT-PCR. In our study, soybean GmMLPs were also found to respond to abiotic stress (Fig. 9 ). Overall, drought treatment increased the transcriptional levels of 9 GmMLP genes, while decreasing 6 genes, with GmMLP14 expression showing almost no change. Salt stress induced 6 GmMLP genes and suppressed 7 members, while the expression of GmMLP14 and GmMLP17 hardly changed. Under salt stress conditions, the number of upregulated GmMLs was less than the number of downregulated members, suggesting that the GmMLPs may play a minor role in plant tolerance to salt stress. Specifically, all three genes in group 2 ( GmMLP3 , GmMLP10 and GmMLP16 ) exhibited significant upregulation under both drought and salt stress treatments (Fig. 9 ), implying they might be key regulatory genes for soybean drought and salt stress tolerance. These observations indicate that the MLP family is broadly involved in plant responses to drought and salt stress. The tertiary structure of MLPs is similar to ABA receptors, suggesting they likely possess similar ABA-binding functions or participate in ABA signaling transduction [ 65 – 66 ]. Previous studies have shown that MLPs can participate in ABA synthesis and signal transduction processes [ 49 , 67 ]. For example, AtMLP423 has been confirmed to possess ABA-binding ability and receptor activity [ 51 ]. Interesting, the expression of its homologous genes GmMLP3 and GmMLP10 were regulated by ABA, implying these genes may participate in the ABA signaling pathway. Therefore, we hypothesize that GmMLP3 and GmMLP10 regulate soybean tolerance to drought and salt stress through involvement in the ABA signaling pathways . Conclusion In conclusion, soybean remains an important economic crop in China, and enhancing its tolerance to abiotic stress continues to be a significant challenge. In this study, 17 GmMLP genes were identified in the soybean genome, which can be classified into three subgroups. Based on a series of bioinformatic analyses, the structural characteristics of GmMLP proteins were comprehensively predicted; these proteins contain cis-elements responsive to multiple hormones and stresses. Specifically, we propose that GmMLP3 , GmMLP10 , and GmMLP16 may play key roles in soybean responses to ABA signaling, drought, and salt stress. Functional characterization will be conducted subsequently. Overall, the findings of this study enhance our understanding of the soybean MLPs and provide candidate genes for research on soybean stress resistance. Declarations Supplementary Information The online version contains supplementary material available at https://doi.org/XXXXX/s1XXXXX, Table S1: Primers used in this study. Acknowledgments We thank all the lab members from both Beijing Forestry University and Linyi University for their helpful discussion and suggestions. Author s’ c ontribution Conceptualization, W.W. and S.C.; investigation, X.M., Q.W., Q.Y., Y.L., Y.Y., and H.L.; data curation, X.M., Q.W. and Q.Y.; writing—original draft preparation, W.W.; writing—review and editing, W.W. and S.C.; project administration, W.W.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by Shandong Provincial Natural Science Foundation (ZR2022QC102); the Special Fund for the Taishan Scholar Program of Shandong Province (TS20230726); and a startup fund from Linyi University (40621057). Data a vailability All data are presented in the manuscript. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Zhang M, Liu S, Wang Z, Yuan Y, Zhang Z, Liang Q, Yang X, Duan Z, Liu Y, Kong F, et al. Progress in soybean functional genomics over the past decade. Plant Biotechnol J. 2022;20(2):256-82. Shen Y, Liu J, Geng H, Zhang J, Liu Y, Zhang H, Xing S, Du J, Ma S, Tian Z. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7266563","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500121810,"identity":"bf59dca3-0d2d-4283-bfaa-f581772cd020","order_by":0,"name":"Xiaocen Ma","email":"","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xiaocen","middleName":"","lastName":"Ma","suffix":""},{"id":500121811,"identity":"1770107f-cb40-4d10-be80-fdca62cef8b2","order_by":1,"name":"Qi Wang","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Wang","suffix":""},{"id":500121812,"identity":"80813dfd-d99c-489f-b6d9-daa267addc5d","order_by":2,"name":"Qing Yu","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Yu","suffix":""},{"id":500121813,"identity":"d1fa5317-7fc4-4130-8ebd-f3cb971f9408","order_by":3,"name":"Yixuan Li","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Yixuan","middleName":"","lastName":"Li","suffix":""},{"id":500121814,"identity":"9678ecef-2258-4148-b6b4-c4d902d84132","order_by":4,"name":"Yongnuo Yuan","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Yongnuo","middleName":"","lastName":"Yuan","suffix":""},{"id":500121815,"identity":"457e119b-e7ba-4f57-aacb-12ac16cafdae","order_by":5,"name":"Heng Liu","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Liu","suffix":""},{"id":500121816,"identity":"c075b866-c47c-4b88-a87f-f1743d437cb3","order_by":6,"name":"Siyu Chen","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Chen","suffix":""},{"id":500121817,"identity":"20918c61-9a1c-4413-bc8c-5ba3b27586a1","order_by":7,"name":"Wei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYPACCR5+9uaDDx6AOQnEaZGR7DmWbJBAghYGG4MbOWoSRGkxOH728GueCgsehgM5bBWJbYcZ+NlzDBh+7sCj5UxemjXPGQkexoazx26AtEj2vDFg7D2DW4vZgRwz49w2CR5mxr40sBagCw2YGdvwaDn/BqjlnwQPGzOPWQFIiz1BLTdyjB/nNkjw8LDxmDGAbZEgoMX+xhsz5j/HJHiA9iRLJJxL55E486zgYC8eLZL9OcYfZ9TU2dvff3zww4cyazn+9uSND37i0QIEbBJwJiMbAw+IPoBXAwMD8wcE+w8BtaNgFIyCUTAiAQA7LFDjlUHpEAAAAABJRU5ErkJggg==","orcid":"","institution":"Linyi University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-01 02:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7266563/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7266563/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-07757-3","type":"published","date":"2025-11-25T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89093945,"identity":"e2316298-1a6e-4b6b-9590-7c0289c42c95","added_by":"auto","created_at":"2025-08-14 15:10:11","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73741,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree of MLPs in Arabidopsis and soybean. All \u003cem\u003eGmMLPs\u003c/em\u003e are classified into three subgroups: I, II, and III, represented by blue, green, and red colors, respectively. AtMLPs and GmMLPs are indicated in light blue and red, respectively. Initially, multiple sequence alignment of AtMLPs and GmMLPs protein sequences was performed using ClustalW. Subsequently, a Neighbor-Joining phylogenetic tree was constructed using MEGA 11 and visualized using EvolView.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/836dea10abc36a1ccb862ff4.jpeg"},{"id":89092844,"identity":"1a9abb49-cda5-4ba7-95ee-1168d9528c35","added_by":"auto","created_at":"2025-08-14 15:02:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130637,"visible":true,"origin":"","legend":"\u003cp\u003eCovariance analysis of MLPs in soybean intraspecies. Red lines represent \u003cem\u003eGmMLP\u003c/em\u003e gene pairs, while gray lines represent soybean genome.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/be5d0ecd931ee968b76b0f9b.png"},{"id":89092137,"identity":"752bdd66-8c8e-4e15-9d6f-8ccc9aaf4266","added_by":"auto","created_at":"2025-08-14 14:54:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":773912,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of \u003cem\u003eGmMLPs\u003c/em\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) Phylogenetic tree of GmMLPs. A neighbor-joining evolutionary tree was developed using MEGA 11 software and visualized using EvolView. (\u003cstrong\u003eb\u003c/strong\u003e) Gene structure of \u003cem\u003eGmMLPs\u003c/em\u003e. CDSs and genomic sequences were downloaded and gene structure analysis was performed using GSDA. Blue boxes represent UTR, yellow boxes represent CDS and thin black lines represent introns.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/556aa2c07f425abe50b6a1eb.png"},{"id":89094636,"identity":"50c5cd14-b19c-4a8b-a093-f5409b4d3cca","added_by":"auto","created_at":"2025-08-14 15:18:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":981362,"visible":true,"origin":"","legend":"\u003cp\u003eConserved motifs in GmMLPs. (\u003cstrong\u003ea\u003c/strong\u003e) Conserved structure analysis of GmMLPs. (\u003cstrong\u003eb\u003c/strong\u003e) Detailed information of motif shared sequences of GmMLPs. GmMLP protein sequences were retrieved from Phytozome and their conserved motifs were characterized using MEME Suite (Multiple EM for Motif Elicitation). Patterns of the three motifs are indicated in different colours. TBtools were used to visualize the images.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/e3c78648c178bd1502f6c9f1.png"},{"id":89092136,"identity":"bc665a23-36b9-496c-bacb-9d81e4c2bb2b","added_by":"auto","created_at":"2025-08-14 14:54:11","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":131519,"visible":true,"origin":"","legend":"\u003cp\u003eBet v 1 structural domain analysis of GmMLs. The Conserved Domain Database (CDD) website and the SMART website were used to validate the Bet v 1 structural domain. The long green bar represents the Bet v 1 structure.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/a06891dd4ce78d82a34e3903.jpeg"},{"id":89092141,"identity":"e223cf9f-e554-4b01-9ee7-921c8dd6a407","added_by":"auto","created_at":"2025-08-14 14:54:11","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":401300,"visible":true,"origin":"","legend":"\u003cp\u003eLocalization analysis of \u003cem\u003eGmMLPs\u003c/em\u003e on its chromosome. Chromosomes are shown in red and the default 0 end is the chromosome tessellation.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/17b9a95af7805484dac6c864.jpeg"},{"id":89092140,"identity":"9d3ab294-4e9f-40fc-a707-db32e538a16a","added_by":"auto","created_at":"2025-08-14 14:54:11","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1277711,"visible":true,"origin":"","legend":"\u003cp\u003eCo-linearity analysis of \u003cem\u003eMLPs\u003c/em\u003e between soybean and other plant species. Grey lines indicate covariate blocks and red lines indicate covariate \u003cem\u003eMLP\u003c/em\u003egene pairs between soybean and other species, including Arabidopsis, tomato, rice, and tobacco. (Color references in this legend are best viewed in the article's online version.)\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/836c57a581f2d25a0e22deae.jpeg"},{"id":89092852,"identity":"6d988ed0-53c7-44d0-a3c4-9121d8cfd756","added_by":"auto","created_at":"2025-08-14 15:02:12","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":736106,"visible":true,"origin":"","legend":"\u003cp\u003ePromoter cis-acting elements of the \u003cem\u003eGmMLPs\u003c/em\u003e. Different colours represent different cis-acting elements. TBtools were used to visualize the images.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/99f93b7fd690a0c21482bf26.jpeg"},{"id":89092851,"identity":"64071252-3018-4b8b-a566-8ffc484c3c55","added_by":"auto","created_at":"2025-08-14 15:02:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":170905,"visible":true,"origin":"","legend":"\u003cp\u003eTotal RNA was isolated from Wm82 wild type soybean, ABA, PEG and NaCl treatment plants. cDNA was synthesized and subjected to qRT-PCR to quantify \u003cem\u003eGmMLPs\u003c/em\u003e expression levels, using \u003cem\u003eGmEF-1α\u003c/em\u003e as the internal reference gene. \u003cem\u003eGmMLPs\u003c/em\u003e expression levels in wild-type plants were normalized to 1, and relative expression levels in ABA, PEG and NaCl treatment plants were calculated accordingly. The 2\u003csup\u003e-ΔΔCT \u003c/sup\u003emethod was used to calculate \u003cem\u003eGmMLPs\u003c/em\u003e gene expression. Data are presented as mean ± SD of three technical replicates\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/20c72f367563ba1ad7ebeb15.png"},{"id":97178773,"identity":"075e2e01-2557-4378-a512-c920b7d5663b","added_by":"auto","created_at":"2025-12-01 16:13:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5581045,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/686b2895-edaa-419d-9957-52dc89893c13.pdf"},{"id":89092132,"identity":"a6201245-9373-4a8b-a882-d20a1365a14d","added_by":"auto","created_at":"2025-08-14 14:54:11","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":51200,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.doc","url":"https://assets-eu.researchsquare.com/files/rs-7266563/v1/b7c48e1476e671e5d9bbc2c4.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide analysis of soybean MLPs reveals evolutionary and structural insights into drought, salt stress and ABA responses","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSoybean (Glycine max) ranks as the fourth most significant crop worldwide, celebrated for its nitrogen-fixing abilities and as a vital source of protein- and oil-rich seeds [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent investigations have illuminated the pivotal role of \u003cem\u003eMLPs\u003c/em\u003e in plant responses to biotic and abiotic stresses. These proteins possess unique functionalities that enable plants to withstand diverse environmental challenges [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. \u003cem\u003eMLPs\u003c/em\u003e have been identified and cloned across various plant species, with studies demonstrating their crucial involvement in plant growth, development, and stress resilience [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnderstanding the physicochemical properties, structural characteristics, and functional roles of \u003cem\u003eMLPs\u003c/em\u003e in plant growth and stress responses provides valuable insights for molecular breeding and crop improvement. Notably, \u003cem\u003eMLP\u003c/em\u003e genes have been linked to enhanced stress tolerance, contributing to increased crop yields [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As temperature fluctuations, salinization, pests, and diseases pose significant constraints on plant growth and development [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], unraveling the mechanisms of stress tolerance has become a focal point in agricultural research.\u003c/p\u003e\u003cp\u003eWhen exposed to adverse conditions, plants undergo intricate physiological and biochemical changes, often accompanied by the upregulation of stress-responsive genes. These genes encode proteins that function as sensors, transducers, and adaptors to stress signals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Among these, MLPs represent a unique class of proteins specific to plants, noted for their efficacy against biotic stresses. Belonging to the Bet v 1 superfamily, MLPs exhibit low sequence similarity but high structural conservation, typically characterized by three α-helices and seven β-sheets [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Extensive research has demonstrated that \u003cem\u003eMLP\u003c/em\u003e genes function in plant growth and respond to various stresses. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For instance, low temperatures significantly affect the metabolism and gene expression of \u003cem\u003eNtMLP423\u003c/em\u003e in tobacco [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while overexpression of \u003cem\u003eGhMLP\u003c/em\u003e in Arabidopsis has been shown to reduce sensitivity to salt stress [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFollowing the initial isolation of \u003cem\u003eMLPs\u003c/em\u003e from the latex of opium poppy (\u003cem\u003ePapaver somniferum\u003c/em\u003e) in 1985 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], advancements in whole-genome sequencing and bioinformatics have enabled the identification of \u003cem\u003eMLPs\u003c/em\u003e in numerous plant species, including Arabidopsis, cotton and poplar [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Notably, multiple \u003cem\u003eMLP\u003c/em\u003e gene members coexist within a single species, forming the \u003cem\u003eMLP\u003c/em\u003e gene family (a subset of the Bet v 1 superfamily) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For example, 26 \u003cem\u003eMLP\u003c/em\u003e family members have been identified in Arabidopsis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], 14 in grape [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], 59 in sugar beet [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and 27 in tomato [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], highlighting their widespread distribution and diversity.\u003c/p\u003e\u003cp\u003eResearch indicates that plants upregulate \u003cem\u003eMLP\u003c/em\u003e gene expression under stress conditions, resulting in the production of MLP proteins that facilitate survival in adverse environments [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, our research aims to elucidate the mechanisms by which \u003cem\u003eGmMLPs\u003c/em\u003e regulate soybean responses to drought, salt stress, and ABA. This study will provide a theoretical foundation for screening drought, salt-tolerant soybean cultivars, contributing to improved agricultural resilience.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eIdentification of\u003c/b\u003e \u003cb\u003eMLP\u003c/b\u003e \u003cb\u003egenes in soybean\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eMLPs\u003c/em\u003e from soybean and Arabidopsis were sourced from previous studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Their protein sequences were downloaded from the Phytozome database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phytozome-next.jgi.doe.gov\u003c/span\u003e\u003cspan address=\"https://phytozome-next.jgi.doe.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and TAIR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arabidopsis.org/\u003c/span\u003e\u003cspan address=\"https://www.arabidopsis.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The soybean \u003cem\u003eMLPs\u003c/em\u003e were identified by querying the legume plant database with \u003cem\u003eAtMLPs\u003c/em\u003e and \u003cem\u003eGmMLPs\u003c/em\u003e (E-value\u0026thinsp;\u0026le;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Two online resources, the Conserved Domain Database (CDD) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/cdd\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/cdd\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and SMART (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://smart.embl.de/\u003c/span\u003e\u003cspan address=\"http://smart.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), were utilized to verify the presence of the Bet v 1 domain [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Chromosomal localization information was retrieved from the soybean genome database, while ProtParam was employed for analyzing the physicochemical properties [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhylogenetic analysis of MLPs in Arabidopsis and soybean\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the evolutionary relationships between AtMLPs and GmMLPs, multiple sequence alignments of protein sequences were performed using ClustalW [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The Neighbor-Joining (NJ) phylogenetic tree was subsequently generated in MEGA 11 and rendered through EvolView (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.evolgenius.info/evolview/\u003c/span\u003e\u003cspan address=\"https://www.evolgenius.info/evolview/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The GmMLPs were grouped based on their classification.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of intraspecific collinearity in soybean\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Gene Structure Display Server (GSDS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gsds.gao-lab.org/index.php\u003c/span\u003e\u003cspan address=\"http://gsds.gao-lab.org/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to conduct an intraspecific relationship analysis of the gene structure, focusing on the coding sequences (CDSs) and genomic sequences of the \u003cem\u003eGmMLPs\u003c/em\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For collinearity analysis of the \u003cem\u003eGmMLPs\u003c/em\u003e, the Circos plugin within TBtools was employed using default parameters [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacterization of conserved motifs, protein domains, and gene structural features\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eGmMLPs\u003c/em\u003e sequences were downloaded and analyzed for conserved motifs using MEME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://meme-suite.org/meme/\u003c/span\u003e\u003cspan address=\"https://meme-suite.org/meme/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with default parameters [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The presence of the Bet v 1 domain was validated using CDD and SMART [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Images were modified using TBtools. Gene structures of \u003cem\u003eGmMLPs\u003c/em\u003e were analyzed by comparing their coding sequences (CDSs) with corresponding genomic sequences using the Gene Structure Display Server (GSDS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gsds.gao-lab.org/index.php\u003c/span\u003e\u003cspan address=\"http://gsds.gao-lab.org/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eChromosomal localization, 3D structure prediction, and collinearity analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe annotated soybean genome files for 20 chromosomes were downloaded from Phytozome for chromosomal localization analysis, which was carried out using MapDraw software [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The 3D structures of the proteins were predicted using SWISS-MODEL software based on the protein sequences [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, the collinearity relationships between soybean and three dicotyledonous plants (Arabidopsis, tomato, tobacco) and a monocotyledonous plant (rice) were determined using the MCScanX synteny plot in TBtools based on annotated and genomic files from different species [\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharacterization of cis-acting elements (CAEs) in\u003c/b\u003e \u003cb\u003eGmMLPs\u003c/b\u003e \u003cb\u003eregulatory regions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo analyze CAEs in \u003cem\u003eGmMLPs\u003c/em\u003e, promoter sequences (2000 bp upstream of the start codon) were retrieved from Phytozome and submitted to PlantCARE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/plantcare/html/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/plantcare/html/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Relevant CAEs were then imported into TBtools for analysis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eAbscisic acid, PEG and NaCl treatment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine the expression level of \u003cem\u003eGmMLPs\u003c/em\u003e in response to ABA, drought and salt, healthy and uniform-sized seeds of the Wm82 wild type were selected and grown hydroponically in distilled water for 14 days. The seedlings were then transferred to 100 \u0026micro;M ABA, 20% PEG and 200 mM NaCl or distilled water as a control, and treated for 4 h. Plants were dissected from the seedlings immediately after the treatments, frozen in liquid nitrogen for RNA extraction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExpression profile analysis - RNA extraction and qRT-PCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal RNA was isolated with the Omega E.Z.N.A.\u0026reg; Kit following the manufacturer's protocol, and RNA integrity was verified by 1.2% agarose gel electrophoresis. After the DNase treatment, 1 or 2 mg total RNA was used to synthesize cDNA by oligo(dT)20-primed reverse transcription using the EasyScript One-Step gDNA Removal and cDNA Synthesis SuperMix (TransGen Biotech). The synthesized cDNA was used as the template for gene expression analysis. For qRT-PCR, each sample was amplified in three parallel reactions as technical replicates, and the \u003cem\u003eGmEF-1a\u003c/em\u003e (\u003cem\u003eGlyma.17G186600\u003c/em\u003e) was amplified as a reference gene. The primers of \u003cem\u003eGmMLPs\u003c/em\u003e and \u003cem\u003eGmEF-1a\u003c/em\u003e used for expression analysis are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003enalysis of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003ehysicochemical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eroperties of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMLP\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ein\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003eoybean\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, we identified 17 members of the \u003cem\u003eGmMLP\u003c/em\u003e\u003cem\u003es\u003c/em\u003e family in the \u003cem\u003eGlycine max\u003c/em\u003e genome, each with distinct attributes. The genes are named sequentially from \u003cem\u003eGmMLP1\u003c/em\u003e to \u003cem\u003eGmMLP17\u003c/em\u003e based on their chromosomal locations and are associated with unique gene IDs starting with \u0026quot;\u003cem\u003eGlyma\u003c/em\u003e,\u0026quot; followed by the chromosomal location and specific nucleotide coordinates. The CDS regions are indicated, highlighting the genomic segments encoding for these proteins.\u003c/p\u003e\n\u003cp\u003eThe genes are further categorized into three groups (I, II, and III), potentially reflecting their functional or evolutionary relationships. To gain further insights into the physicochemical properties of the \u003cem\u003eGmMLPs\u003c/em\u003e, we analyzed their amino acid (aa) lengths, molecular weights (MW), and isoelectric points (pI). Among them, \u003cem\u003eGmMLP11\u003c/em\u003e exhibited the longest amino acid sequence (181 aa), while \u003cem\u003eGmMLP4\u003c/em\u003e had the shortest (126 aa). The MW ranged from 14.5 kDa (\u003cem\u003eGmMLP\u003c/em\u003e\u003cem\u003e4\u003c/em\u003e) to 20.8 kDa (\u003cem\u003eGmMLP11\u003c/em\u003e), and the pI ranged from 4.99 (\u003cem\u003eGmMLP1\u003c/em\u003e) to 8.25 (\u003cem\u003eGmMLP14\u003c/em\u003e) (Table 1). These results\u0026nbsp;suggest differences in their biochemical properties and potential roles in cellular processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e List of \u003cem\u003eMLP\u003c/em\u003e genes in the \u003cem\u003eGlycine max\u003c/em\u003e genome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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\" width=\"1144\" height=\"894\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003enalysis of MLP\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eetween\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003eoybean and Arabidopsis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhylogenetic analysis of the \u003cem\u003eMLP\u003c/em\u003e family across multiple species revealed a complex evolutionary history. Within the \u003cem\u003eGmMLP\u003c/em\u003es genome, we detected 17 genes containing the Bet v 1 domain and designated them as \u003cem\u003eGmMLPs\u003c/em\u003e. To classify the \u003cem\u003eGmMLPs\u003c/em\u003e, we constructed an NJ phylogenetic tree based on their protein sequences, including 17 GmMLPs and 25 AtMLPs from Arabidopsis. Following the classification of the Arabidopsis \u003cem\u003eMLP\u003c/em\u003e genes family, we divided the \u003cem\u003eGmMLP\u003c/em\u003e genes into three subgroups: Type I, Type II, and Type III (Fig. 1). Subgroup I comprises nine members: \u003cem\u003eGmMLP12\u003c/em\u003e, \u003cem\u003eGmMLP6\u003c/em\u003e, \u003cem\u003eGmMLP7\u003c/em\u003e, \u003cem\u003eGmMLP13\u003c/em\u003e, \u003cem\u003eGmMLP5\u003c/em\u003e, \u003cem\u003eGmMLP1\u003c/em\u003e, \u003cem\u003eGmMLP2\u003c/em\u003e, \u003cem\u003eGmMLP17\u003c/em\u003e, and \u003cem\u003eGmMLP9\u003c/em\u003e; Subgroup II includes three members: \u003cem\u003eGmMLP16\u003c/em\u003e, \u003cem\u003eGmMLP10\u003c/em\u003e, and \u003cem\u003eGmMLP3\u003c/em\u003e; and Subgroup III contains five members: \u003cem\u003eGmMLP11\u003c/em\u003e, \u003cem\u003eGmMLP4\u003c/em\u003e, \u003cem\u003eGmMLP8\u003c/em\u003e, \u003cem\u003eGmMLP15\u003c/em\u003e, and \u003cem\u003eGmMLP14\u003c/em\u003e. The phylogenetic tree revealed the presence of one orthologous pair of MLP members between soybean and Arabidopsis (GmMLP11 and At4G23680), although most GmMLP members are phylogenetically distant from AtMLP members (Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollinearity of GmMLP\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ew\u003c/strong\u003e\u003cstrong\u003eithin\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003eoybean\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003epecies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the locations of the 17 \u003cem\u003eGmMLPs\u003c/em\u003e across the 20 soybean chromosomes using genomic annotation files and found that their distribution was relatively uneven among these chromosomes (Fig. 2). Based on their chromosomal positions, the genes were named \u003cem\u003eGmMLP1\u003c/em\u003e to \u003cem\u003eGmMLP17\u003c/em\u003e from top to bottom. By comparing and analyzing the positions of different \u003cem\u003eGmMLPs\u003c/em\u003e, we identified several collinear relationships: \u003cem\u003eGmMLP1\u003c/em\u003e and \u003cem\u003eGmMLP2\u003c/em\u003e exhibited collinearity on chromosomes 1 and 2, respectively; \u003cem\u003eGmMLP4\u003c/em\u003e and \u003cem\u003eGmMLP17\u003c/em\u003e showed collinearity on chromosomes 20 and 7, respectively; \u003cem\u003eGmMLP3\u003c/em\u003e on chromosome 2 and \u003cem\u003eGmMLP10\u003c/em\u003e on chromosome 10 also displayed collinearity; additionally, \u003cem\u003eGmMLP12\u003c/em\u003e and \u003cem\u003eGmMLP13\u003c/em\u003e on chromosome 15 and \u003cem\u003eGmMLP5\u003c/em\u003e on chromosome 8 exhibited collinear relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etructure,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eotif\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003enalysis and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eonserved\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etructural\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eomain of GmMLP\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the phylogenetic relationships of GmMLPs, we constructed an NJ phylogenetic tree of GmMLPs using MEGA 11 software. GmMLPs were also categorized into three subgroups (I, II, and III) (Fig. 3a), which is in agreement with the results of the evolutionary tree in Fig. 1. Next, we analyzed the structure, Coding sequences (CDSs), and genomic sequence of \u003cem\u003eGmMLPs\u003c/em\u003e using GSDS 2.0. The analysis revealed that most \u003cem\u003eGmMLPs\u003c/em\u003e have only one intron, while \u003cem\u003eGmMLP3\u003c/em\u003e and \u003cem\u003eGmMLP4\u003c/em\u003e have more introns (Fig. 3b).\u003c/p\u003e\n\u003cp\u003eUsing MEME to detect GmMLPs, most GmMLPs contain three motifs. All GmMLs in subgroup I contained all three motifs, whereas the GmMLP3 in subgroup II contained only two motifs (motif1 and motif3), and the GmMLP4 in subgroup III contained only motif 2. In addition, we found that the presence of motif1 and motif3 was prevalent in all GmMLPs except for GmMLP4 (Fig. 4a). And among the motif common sequences of GmMLPs, all three motifs contained amino acids such as glycine, glutamate and lysine. Among them, lysine is more abundant in the three motifs, and the position of lysine in the motifs is more uniform. Because lysine has the function of stabilizing the protein structure, it may help to maintain the conservatism of the motifs (Fig. 4b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOf course, the connection between the amino acid sequences in the motifs and the biological functions of MLPs needs to be further investigated. By comparing the images of motif analysis and Bet v 1 structural domain images, there was a one-to-one correspondence between motifs and Bet v 1 structural domains. That is, there are three motifs in most GmMLPs with similar positions (Fig. 4a), which is consistent with the distribution of Bet v 1 domains in GmMLPs (Fig. 5). For example, GmMLP4 has only motif 2 and is in the middle, and its Bet v 1 structural domain is only present in the middle, and GmMLP11 has three motifs but the position is shifted by a certain distance relative to the other GmMLPs with three motifs, which is also shown in Fig. 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChromosomal\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003el\u003c/strong\u003e\u003cstrong\u003eocalisation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003end\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003enterspecific\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eovariance\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003enalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen analysing the chromosomal localisation of the soybean \u003cem\u003eGmMLPs\u003c/em\u003e, we found that its distribution was relatively uneven. Among them, the \u003cem\u003eGmMLPs\u003c/em\u003e was only distributed on chromosomes 1, 2, 7, 8, 9, 10, 13, 15, 16 and 20, and the distribution position was uneven, mostly in the position of the chromosome off the lower arm position (Fig. 6).\u003c/p\u003e\n\u003cp\u003eTo explore the evolutionary relationships among \u003cem\u003eMLPs\u003c/em\u003e, we constructed interspecific covariate relationships between soybean and three dicotyledons (Arabidopsis, tomato, and tobacco) and one monocotyledon (rice). Firstly, we observed that soybean and Arabidopsis had strong covariance, with four pairs having homology and all Arabidopsis and soybean covariance genes were located on chromosome 1. Secondly, tomato also had homology with soybean, with three genes having covariance with soybean existing on tomato chromosomes 4 and 5, respectively. Lastly, there was only one pair of genes having covariance with soybean and tobacco, which was located on tobacco chromosome 12. However, there were no genes having covariance between soybean and tobacco in the rice The presence of co-linear \u003cem\u003eMLPs\u003c/em\u003e was not found in the gene co-linearity comparison for the time being (Fig. 7). Four members of the soybean \u003cem\u003eMLP\u003c/em\u003e\u003cem\u003es\u003c/em\u003e exhibited collinearity with Arabidopsis \u003cem\u003eMLPs\u003c/em\u003e. Therefore, soybean and Arabidopsis share strong homology, suggesting a common ancestor between the two species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eis-\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003ecting\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003cstrong\u003elements (CAEs) of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGmMLPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause the stress response of \u003cem\u003eGmMLPs\u003c/em\u003e is closely linked to their expression, it is crucial to study the CAEs of these genes. To analyse the CAEs of \u003cem\u003eGmMLP\u003c/em\u003es, we retrieved and submitted the promoter sequences (2000 bp upstream of the start codon) to PlantCARE and analyzed 17 CAEs associated with hormonal and abiotic stresses. It was shown that all \u003cem\u003eGmMLPs\u003c/em\u003e, except \u003cem\u003eGmMLP3\u003c/em\u003e, contained anaerobic response elements and that the majority of \u003cem\u003eGmMLPs\u003c/em\u003e (76%) contained ABA response elements (ABRE), nine of the 17 \u003cem\u003eGmMLPs\u003c/em\u003e (52%) contained meja response elements (CGTCA and TGACG), three \u003cem\u003eGmMLPs\u003c/em\u003e (17%) contained IAA response elements, and 10 of the \u003cem\u003eGmMLPs\u003c/em\u003e (59%) contained SA response elements, and 9 \u003cem\u003eGmMLPs\u003c/em\u003e (52%) contained MeJA response elements. In addition, six \u003cem\u003eGmMLPs\u003c/em\u003e contained the drought-inducing element (MBS); the low-temperature-inducing element (LTR) was present in four \u003cem\u003eGmMLPs\u003c/em\u003e; nine \u003cem\u003eGmMLPs\u003c/em\u003e contained the defence- and stress-responsive element TC-rich repeats; and only \u003cem\u003eGmMLP1\u003c/em\u003e contained the gibberellin-responsive elements GARE, TATC, and P-box. (Fig. 8)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGmMLPs\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003ere\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003ebiotic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etress\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003end ABA\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003er\u003c/strong\u003e\u003cstrong\u003eesponse\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003cstrong\u003eenes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven \u003cem\u003eMLPs\u0026apos;\u003c/em\u003e established role in regulating plant salt stress responses and the prevalence of ABA-responsive/drought-inducing elements in \u003cem\u003eGmMLP\u003c/em\u003e promoters, we hypothesize that \u003cem\u003eGmMLPs\u003c/em\u003e mediate plant responses to ABA signaling, drought and salt stress. To investigate this, we analyzed the expression level of \u003cem\u003eGmMLPs\u003c/em\u003e in soybean plants under ABA, drought and salt stress treatment using qRT-PCR. The results revealed that all \u003cem\u003eGmMLPs\u003c/em\u003e, with the exception of \u003cem\u003eGmMLP4\u003c/em\u003e (which exhibited undetectably low expression levels), functioned as ABA, drought and salt stress--responsive genes.\u003c/p\u003e\n\u003cp\u003eIn the G1 subgroup, the expression of all \u003cem\u003eGmMLPs\u003c/em\u003e was suppressed by ABA. Under drought stress, \u003cem\u003eGmMLP1\u003c/em\u003e, \u003cem\u003eGmMLP2\u0026nbsp;\u003c/em\u003eand \u003cem\u003eGmMLP6\u003c/em\u003e expression were induced. Other members showed reduced expression levels. Under salt stress, \u003cem\u003eGmMLP1\u003c/em\u003e expression was induced, while \u003cem\u003eGmMLP17\u003c/em\u003e expression remained unchanged; the remaining \u003cem\u003eGmMLPs\u003c/em\u003e in this subgroup showed reduced expression levels. In contrast, \u003cem\u003eGmMLPs\u003c/em\u003e within the G2 subgroup exhibited drought and salt stress-induced expression but ABA-suppressed expression. For the G3 subgroup, only \u003cem\u003eGmMLP11\u003c/em\u003e expression was unaffected by ABA treatment, while \u003cem\u003eGmMLP8\u003c/em\u003e, \u003cem\u003eGmMLP14\u003c/em\u003e, and \u003cem\u003eGmMLP15\u003c/em\u003e were downregulated in response to ABA. Under drought and salt stress, \u003cem\u003eGmMLP14\u003c/em\u003e expression remained unchanged. \u003cem\u003eGmMLP11\u003c/em\u003e and \u003cem\u003eGmMLP15\u003c/em\u003e showed induced expression levels, whereas \u003cem\u003eGmMLP8\u003c/em\u003e expression was induced by drought stress and inhibited by salt stress (Fig. 9).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMLPs represent a plant-specific protein family that plays crucial roles in plant growth and development [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], as well as responses to both biotic and abiotic stresses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. MLPs have been well-documented to participate in abiotic stress responses, biotic stress responses, and various developmental processes in plants [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Since the initial isolation of MLPs from opium poppy (\u003cem\u003ePapaver somniferum\u003c/em\u003e) latex [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], the \u003cem\u003eMLP\u003c/em\u003e gene family has been identified in multiple plant species including Arabidopsis, tomato, apple, and peach [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. While several studies have investigated this gene family in various plants, the \u003cem\u003eMLP\u003c/em\u003e genes in soybean (\u003cem\u003eGlycine max\u003c/em\u003e) remain uncharacterized. Through genome-wide screening, we identified 17 \u003cem\u003eMLP\u003c/em\u003e genes in soybean and characterized their physicochemical profiles, phylogeny, gene architectures, conserved motifs, and cis-regulatory elements. We further investigated the evolutionary patterns of this multigene family, including chromosomal localization and synteny relationships, as well as their expression profiles under ABA, drought, and salt stress treatments.\u003c/p\u003e\u003cp\u003eGrowing evidence supports a linear correlation between genome size and gene number in prokaryotes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. However, this relationship appears absent in eukaryotes, particularly plants. Among dicotyledonous plants, Arabidopsis (135 Mb genome) contains 25 \u003cem\u003eMLP\u003c/em\u003e genes, tomato (827 Mb) has 34, apple (700 Mb) possesses 36, grape (490 Mb) contains 14, and peach (257 Mb) harbors 30 \u003cem\u003eMLP\u003c/em\u003e genes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In contrast, monocotyledonous plants show significantly fewer \u003cem\u003eMLP\u003c/em\u003e members, with Brachypodium distachyon having only 2 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], Zea mays (maize) containing 3, and Setaria italica (foxtail millet) possessing just 1 \u003cem\u003eMLP\u003c/em\u003e gene [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This striking difference in \u003cem\u003eMLP\u003c/em\u003e gene numbers between monocots and dicots suggests no direct correlation between \u003cem\u003eMLP\u003c/em\u003e gene quantity and genome size in plants. The observed divergence likely occurred during plant evolution following the monocot-dicot differentiation, resulting in \u003cem\u003eMLP\u003c/em\u003e gene loss in monocots and expansion in dicots. Notably, modern genetic studies confirm that monocots actually evolved from ancient dicots as a specialized branch, which may explain the distinct distribution patterns of \u003cem\u003eMLP\u003c/em\u003e genes between these two plant groups.\u003c/p\u003e\u003cp\u003eIn this study, we identified 17 GmMLP genes in the soybean genome. Phylogenetic analysis classified these 17 \u003cem\u003eGmMLP\u003c/em\u003e genes into three subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), consistent with classifications reported for grape, Chinese cabbage, apple, and peach \u003cem\u003eMLP\u003c/em\u003e genes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Members within the same subgroup share similar gene structures and conserved motifs. The phylogenetic tree we constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) clearly shows that GmMLP3, GmMLP10, and AtMLP423 share the closest genetic relationship, belonging to the same subgroup II and clustering together on the same branch. Increasing studies indicate that genes belonging to the same functional group may possess similar expression patterns and regulatory mechanisms, suggesting that \u003cem\u003eGmMLP\u003c/em\u003e genes within the same group may have similar functions. Subgroup I does not contain any Arabidopsis \u003cem\u003eMLP\u003c/em\u003e members, indicating that gene duplication events occurred in this branch, which is a core driving force in plant evolution [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This result is consistent with reports on apple \u003cem\u003eMLPs\u003c/em\u003e [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo explore the structural composition of \u003cem\u003eMLP\u003c/em\u003e genes, we analyzed gene structures and conserved motifs. Gene structure analysis, similar to previous reports on grape \u003cem\u003eMLP\u003c/em\u003e genes, showed that 15 out of 17 \u003cem\u003eGmMLP\u003c/em\u003e genes contain only one intron, once again indicating that \u003cem\u003eMLP\u003c/em\u003e genes are highly conserved during evolution [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Motif analysis of the 17 soybean GmMLPs identified three relatively conserved amino acid motifs (Motif 1, 2, and 3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All GmMLPs contain the conserved Motif 1, which includes a glycine-rich loop, a structure crucial for the biological function of MLPs [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results demonstrate that GmMLPs are highly evolutionarily conserved. Critically, most \u003cem\u003eGmMLPs\u003c/em\u003e incorporate glycine and lysine (mediating abiotic stress responses via photosynthesis), glutamate (functioning as stress signal transducer) and proline, which participates in signaling pathways critical for maintaining osmotic homeostasis. [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Recent research found that salt-tolerant rice varieties enhance osmotic adjustment ability through proline accumulation [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These results indicate that GmMLPs can respond to multiple abiotic stresses, especially drought and salt stress.\u003c/p\u003e\u003cp\u003eIt is noteworthy that plant-specific MLPs belong to the Bet v1 protein family [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Domain analysis revealed that all 17 MLP proteins contain a single Bet v 1 domain, which comprises the majority of each protein's length. Clearly, the majority of the coding regions of GmMLPs encode the Bet v 1 functional domain. This is highly consistent with MLPs identified in cucumber (\u003cem\u003eCucumis sativus\u003c/em\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], apple (\u003cem\u003eMalus domestica\u003c/em\u003e) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], and Chinese cabbage (\u003cem\u003eBrassica rapa\u003c/em\u003e) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], while up to four Bet v1 domains were found in zucchini (\u003cem\u003eCucurbita pepo\u003c/em\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These results indicate that although MLP proteins are evolutionarily conserved, they exhibit diversity among different species.\u003c/p\u003e\u003cp\u003eCollinearity analysis of \u003cem\u003eMLPs\u003c/em\u003e family revealed different numbers of \u003cem\u003eMLP\u003c/em\u003e gene pairs between soybean and the dicot plants Arabidopsis, tomato, and tobacco (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting these genes play important roles in \u003cem\u003eMLP\u003c/em\u003e gene evolution. However, no gene pairs were found between soybean and the monocot plant rice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicating that \u003cem\u003eMLP\u003c/em\u003e gene evolution is closely related to dicot plant divergence.\u003c/p\u003e\u003cp\u003eTypically, cis-acting promoter elements determine gene expression patterns through interactions between promoter binding sites and transcription factors, playing a key role especially in stress response regulation [\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. In this study, the promoter regions of 14 \u003cem\u003eGmMLP\u003c/em\u003e genes (82.4%) contained ABRE (abscisic acid-responsive element), a finding consistent with studies on \u003cem\u003eMLP\u003c/em\u003e genes in other plants [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Promoters of peanut and apple \u003cem\u003eMLP\u003c/em\u003e genes are also rich in ABRE motifs, indicating that \u003cem\u003eMLP\u003c/em\u003e genes may be involved in ABA response processes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. However, unlike earlier studies, this study also identified the TGACG-motif (methyl jasmonate-responsive element) in the promoter regions of \u003cem\u003eGmMLPs\u003c/em\u003e. This result aligns with the conclusion of cis-acting element analysis of poplar \u003cem\u003ePtMLP\u003c/em\u003e, where MeJA (methyl jasmonate) is considered to be involved in plant growth and development processes, particularly playing an important role in responding to abiotic stresses such as low temperature stress and salt stress [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. These results indicate that \u003cem\u003eGmMLP\u003c/em\u003e genes play a key role in plant response to abiotic stress. Interestingly, we noted that \u003cem\u003eGmMLP3\u003c/em\u003e, \u003cem\u003eGmMLP6\u003c/em\u003e, and \u003cem\u003eGmMLP17\u003c/em\u003e are involved in ABA response, but no ABRE element was detected in their upstream regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similarly, the promoter of \u003cem\u003eGmMLP14\u003c/em\u003e contains a drought stress-responsive element, but the expression level of this gene hardly changed under drought stress treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Likewise, no salt stress-related cis-acting elements were detected in the upstream regions of all \u003cem\u003eGmMLPs\u003c/em\u003e, all members responded to salt stress except \u003cem\u003eGmMLP17\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This finding indicates that the presence or absence of cis-acting elements in the promoter region is not directly correlated with the gene's stress response.\u003c/p\u003e\u003cp\u003ePrevious studies have reported \u003cem\u003eMLP\u003c/em\u003e responses to various abiotic stresses. For example, \u003cem\u003eVvMLP1/2/3/6/9\u003c/em\u003e in grapevine showed significantly increased transcriptional levels under salt stress treatment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and \u003cem\u003eMdMLP5/13\u003c/em\u003e in apple also displayed significantly upregulated expression under salt stress treatment. The expression levels of \u003cem\u003eMdMLP2/6/7/9/11\u003c/em\u003e were upregulated under drought treatment [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. We analyzed the ABA, drought, and salt stress responses of \u003cem\u003eGmMLPs\u003c/em\u003e using qRT-PCR. In our study, soybean \u003cem\u003eGmMLPs\u003c/em\u003e were also found to respond to abiotic stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Overall, drought treatment increased the transcriptional levels of 9 \u003cem\u003eGmMLP\u003c/em\u003e genes, while decreasing 6 genes, with \u003cem\u003eGmMLP14\u003c/em\u003e expression showing almost no change. Salt stress induced 6 \u003cem\u003eGmMLP\u003c/em\u003e genes and suppressed 7 members, while the expression of \u003cem\u003eGmMLP14\u003c/em\u003e and \u003cem\u003eGmMLP17\u003c/em\u003e hardly changed. Under salt stress conditions, the number of upregulated \u003cem\u003eGmMLs\u003c/em\u003e was less than the number of downregulated members, suggesting that the \u003cem\u003eGmMLPs\u003c/em\u003e may play a minor role in plant tolerance to salt stress. Specifically, all three genes in group 2 (\u003cem\u003eGmMLP3\u003c/em\u003e, \u003cem\u003eGmMLP10\u003c/em\u003e and \u003cem\u003eGmMLP16\u003c/em\u003e) exhibited significant upregulation under both drought and salt stress treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), implying they might be key regulatory genes for soybean drought and salt stress tolerance. These observations indicate that the \u003cem\u003eMLP\u003c/em\u003e family is broadly involved in plant responses to drought and salt stress. The tertiary structure of MLPs is similar to ABA receptors, suggesting they likely possess similar ABA-binding functions or participate in ABA signaling transduction [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Previous studies have shown that MLPs can participate in ABA synthesis and signal transduction processes [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. For example, AtMLP423 has been confirmed to possess ABA-binding ability and receptor activity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Interesting, the expression of its homologous genes \u003cem\u003eGmMLP3\u003c/em\u003e and \u003cem\u003eGmMLP10\u003c/em\u003e were regulated by ABA, implying these genes may participate in the ABA signaling pathway. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTherefore, we hypothesize that\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eGmMLP3\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand\u003c/span\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eGmMLP10\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eregulate soybean tolerance to drought and salt stress through involvement in the ABA signaling pathways\u003c/span\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn conclusion, soybean remains an important economic crop in China, and enhancing its tolerance to abiotic stress continues to be a significant challenge. In this study, 17 \u003cem\u003eGmMLP\u003c/em\u003e genes were identified in the soybean genome, which can be classified into three subgroups. Based on a series of bioinformatic analyses, the structural characteristics of GmMLP proteins were comprehensively predicted; these proteins contain cis-elements responsive to multiple hormones and stresses. Specifically, we propose that \u003cem\u003eGmMLP3\u003c/em\u003e, \u003cem\u003eGmMLP10\u003c/em\u003e, and \u003cem\u003eGmMLP16\u003c/em\u003e may play key roles in soybean responses to ABA signaling, drought, and salt stress. Functional characterization will be conducted subsequently. Overall, the findings of this study enhance our understanding of the soybean \u003cem\u003eMLPs\u003c/em\u003e and provide candidate genes for research on soybean stress resistance.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available at https://doi.org/XXXXX/s1XXXXX, Table S1: Primers used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the lab members from both Beijing Forestry University and Linyi University for their helpful discussion and suggestions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003cstrong\u003es\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eontribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, W.W. and S.C.; investigation, X.M., Q.W., Q.Y., Y.L., Y.Y., and H.L.; data curation, X.M., Q.W. and Q.Y.; writing\u0026mdash;original draft preparation, W.W.; writing\u0026mdash;review and editing, W.W. and S.C.; project administration, W.W.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Shandong Provincial Natural Science Foundation (ZR2022QC102); the Special Fund for the Taishan Scholar Program of Shandong Province (TS20230726); and a startup fund from Linyi University (40621057).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003evailability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are presented in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp dir=\"LTR\"\u003e\u003cstrong\u003ePublisher\u0026rsquo;s Note \u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhang M, Liu S, Wang Z, Yuan Y, Zhang Z, Liang Q, Yang X, Duan Z, Liu Y, Kong F, et al. 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BMC Plant Biol. 2020;20(1):475.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Glycine max, Genome-wide analysis, MLP gene family, Gene expression","lastPublishedDoi":"10.21203/rs.3.rs-7266563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7266563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study focuses on the \u003cem\u003eMajor Latex Protein\u003c/em\u003e (\u003cem\u003eMLP\u003c/em\u003e) gene family, which encodes a class of proteins belonging to the Bet v 1 superfamily, renowned for their involvement in plant growth and development, as well as their responsiveness to both biotic and abiotic stresses, underscoring their pivotal role in stress adaptation mechanisms. However, the resilience-related functions of the \u003cem\u003eGmMLPs\u003c/em\u003e in soybean have remained largely unexplored. This research identifies 17 genes within the soybean (\u003cem\u003eGlycine max\u003c/em\u003e) genome, designated as \u003cem\u003eGmMLPs\u003c/em\u003e, that harbor the Bet v 1 domain. Based on previous insights, these \u003cem\u003eGmMLPs\u003c/em\u003e are categorized into three subgroups: Type I, Type II, and Type III. Chromosomal mapping analysis, leveraging genomic annotation files, reveals an uneven distribution of the \u003cem\u003eGmMLPs\u003c/em\u003e across the 20 soybean chromosomes. Notably, these genes tend to cluster in the lower arm regions of the chromosomes, indicating a non-random pattern of chromosomal localization. Furthermore, an examination of their gene structures uncovers variations, with most \u003cem\u003eGmMLPs\u003c/em\u003e featuring a single intron, whereas exceptions like \u003cem\u003eGmMLP3\u003c/em\u003e and \u003cem\u003eGmMLP4\u003c/em\u003e exhibit more than one intron, suggesting structural diversity within the gene family. \u003cem\u003eGmMLPs\u003c/em\u003e were identified as ABA, drought and salt stress -responsive genes by qRT-PCR, suggesting that \u003cem\u003eGmMLPs\u003c/em\u003e are involved in regulating soybean responses to drought, salt stress and ABA signaling. This study lays the foundation for future investigations into the specific roles of \u003cem\u003eGmMLPs\u003c/em\u003e in soybean stress tolerance and adaptation strategies.\u003c/p\u003e","manuscriptTitle":"Genome-wide analysis of soybean MLPs reveals evolutionary and structural insights into drought, salt stress and ABA responses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 14:54:06","doi":"10.21203/rs.3.rs-7266563/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-01T20:22:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T19:52:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-26T15:27:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-13T17:52:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34440355495941130738144838607206576168","date":"2025-08-13T17:45:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121259635590059137679407699450390934531","date":"2025-08-11T05:42:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87931886210608153920482984096010852150","date":"2025-08-09T21:24:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-09T07:51:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-06T21:04:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T02:32:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T02:30:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-08-01T02:10:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f13b0779-12f4-4a4d-972e-4bd60c987939","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:06:22+00:00","versionOfRecord":{"articleIdentity":"rs-7266563","link":"https://doi.org/10.1186/s12870-025-07757-3","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-11-25 15:57:50","publishedOnDateReadable":"November 25th, 2025"},"versionCreatedAt":"2025-08-14 14:54:06","video":"","vorDoi":"10.1186/s12870-025-07757-3","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07757-3","workflowStages":[]},"version":"v1","identity":"rs-7266563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7266563","identity":"rs-7266563","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}