Skip to main content

Genome-wide screening of lectin putative genes from Sorghum bicolor L., distribution in QTLs and a probable implications of lectins in abiotic stress tolerance



Sorghum bicolor is one of the most important crops worldwide with the potential to provide resilience when other economic staples might fail against the continuous environmental changes. Many physiological, developmental and tolerance traits in plants are either controlled or influenced by lectins; carbohydrate binding proteins. Hence, we aimed at providing a comprehensive in silico account on sorghum’s lectins and study their possible implication on various desired agronomical traits.


We have searched sorghum’s genome from grain and sweet types for lectins putative genes that encode proteins with domains capable of differentially binding carbohydrate moieties and trigger various physiological responses. Of the 12 known plant lectin families, 8 were identified regarding their domain architectures, evolutionary relationships, physiochemical characteristics, and gene expansion mechanisms, and they were thoroughly addressed. Variations between grain and sweet sorghum lectin homologs in term of the presence/absence of certain other joint domains like dirigent and nucleotide-binding adaptor shared by APAF-1, R-proteins, and CED-4 (NB-ARC) indicate a possible neofunctionalization. Lectin sequences were found to be preferentially overrepresented in certain quantitative trait loci (QTLs) related to various traits under several subcategories such as cold, drought, salinity, panicle/grain composition, and leaf morphology. The co-localization and distribution of lectins among multiple QTLs provide insights into the pleiotropic effects that could be played by one lectin gene in numerous traits.


Our study offers a first-time inclusive details on sorghum lectins and their possible role in conferring tolerance against abiotic stresses and other economically important traits that can be informative for future functional analysis and breeding studies.

Peer Review reports


Sorghum (Sorghum bicolor L. Moench; family: Poaceae) is the second key food staple crop in Africa after corn, as well as the fifth produced grain worldwide after maize, rice, wheat, and barley [1]. Sorghum gains its importance from its many agronomical features; such as drought tolerance, waterlog, and low demands for fertilizers [2, 3]. And the fact that it is a gluten-free cereal, which makes it a favorable source of nutrition in gluten-intolerant populations [4]. The plant originated in Northeast Africa, where Sudan and Ethiopia are recognized as the major center of its diversity [5]. Both the wild and cultivated species of Sorghum are genetically very diverse with 22 known species. There are five interfertile races (bicolor, guinea, kafir, durra, and caudatum), though S. bicolor L. is considered the major contributor to a wide range of cultivated sources. The plant grows in semi-arid tropics and is adapted to lower rainfall and high temperature (average between 24 – 27 °C after germination), which makes it a viable choice for a sustainable future, and production under climate change and global temperature increase. Nonetheless, the current global production is hampered by the low investments, climate constraints in regions with a wetter environment, and possible low yield [1, 6, 7].

Over the years, the integrated approach that combines agronomic research and plant breeding had successfully increased plant productivity and tolerance. One of the most effective tools used by plant breeders to study and incorporate a specific genetic trait is the quantitative trait loci (QTL) mapping, which is deployed for marker-assisted selection to identify genes responsible for certain traits [8]. Since 1995, around 150 QTL and genome-wide association studies (QWAS) were published related to sorghum, which is yet to be fully utilized in sorghum genetic research and enhancement programs. This is mainly due to the large heterogenicity in sorghum QTL’s data in terms of locations, maps used, reliability of many QTL experiments, and the variable sorghum breeds used across studies. These obstacles were overcome by the availability of sorghum whole genome sequence, genetic linkage consensus maps, and the use and availability of genetic markers coordination information. These data are now integrated at the Sorghum QTL Atlas (, which provided an accessible source of information for plant breeding programs. The atlas incorporates over 6000 QTL and GWAS linked to 223 unique traits that are classified under 7 major trait categories (leaf, maturity, panicle, resistance abiotic, resistance biotic, stem composition, and stem morphology) [9, 10].

Identifying and linking genes to certain QTL can be sometimes a laborious process. However, there are several ways to achieve the desired outcome. For instance; using the fine-mapping approach that narrows the QTL to a specific genomic region and then to a specific gene or several genes. For instance, this approach successfully correlated the ZmCCoAOMT2 gene that encodes caffeoyl-CoA O-methyltransferase to the qMdr9.02 QTL which attributes to the resistance of grey leaf spot and southern leaf blight in maize [11]. The QTL mapping – expression eQTL analysis is also a method directed towards identifying candidate genes underlying or associated with multiple QTL, for example; the A10 QTL from Brassica napus contains the BnaA10g22080D gene that modulates the regulation of flowering time [12]. The availability of full genome sequences and assemblies made it possible for the use of bioinformatics tools to correlate QTLs with the occurrence of specific genes. it was used to study the distribution of the Receptor-like cytoplasmic kinase gene family (OsRLCKs) within the abiotic stress QTL in rice, and examine the distribution and overrepresentation of lectin families in morphological traits and stress responses [13, 14]. Many studies highlighted the association between lectins and QTL, for example, the MLOC_72613 gene encodes the L-type lectin domain-containing receptor kinase III which regulates water status under drought stress through the regulation of the brassinosteroid mediated pathway in barely [15]. In sorghum grown under different nitrogen levels, two lectin genes are upregulated (Sb01g033090, Mannose-binding lectin superfamily, and Sb01g045620, lectin protein kinase family protein). The mannose-binding lectin is associated with the QTLs related to the stover moisture content (qMC2-1a), the head moisture contents (qMC2-1), and the total biomass yield (qBY-1), whereas lectin protein kinase is correlated to the grain yield QTL (qGY-1b) [16].

Lectins are a heterogeneous group of proteins capable of binding reversibly to carbohydrate moieties of simple or complex glycans, with different affinity and avidity, and without altering their chemical structures [17]. Initially, lectins were believed to be localized in extracellular compartments and only expressed in response to a foreign organism as defense molecules. However, evidence showed that they can interact with endogenous glycan-receptors found in the nuclear/cytoplasmic cellular compartments. Hence, playing a huge number of physiological functions [18]. Due to restricted mobility, plants are unable to circumvent harsh environmental and unfavorable conditions. Consequently, they have to acclimatise to such situations by changing many of their physiological and molecular responses as well as regulating their metabolic pathways [19, 20]. Besides being involved in many plant physiological significances such as defense against predators [21], storage protein [22], in-cell sugar transport [23], etc., the role of plant lectin in different cellular locations as a stress adaptor has also been explored in different crops [24, 25]. For example, oil seed flax lectin was interestingly shown to possess different sugar specificities under different stress conditions, in addition to this, the lectin coefficient activity i.e., (1/lectin activity) was also noticed to vary considerably based on the type of stress and lectin organ of localization, authors have attributed these fluctuations in sugar specificity and lectin activity to stress modulating role of lectin [26]. At the transcriptomics level, rice (Oryza sativa) expresses 5 lectin genes (OsEULS2, OsEULS3, OsEUL1a, OsEUL1b, and OsEUL2D). Treatment of the plant with different stress types (biotic or abiotic) results in the detection of different EULs transcripts with variable levels of expression. Most of which were detected in the vascular tissues of roots and shoots, as well as the root tips and seeds [27]. The transgenic Arabidopsis thaliana plant harboring Nictaba-Like Lectin genes from Glycine max exhibits enhanced tolerability against bacterial infection compared to the wild-type plant. These outcomes indicated the involvement of G. max lectin in the biotic stress tolerance [25].

Numerous classification systems have been adopted for lectins, which included grouping them based on their sugar specificity, the structure of the mature protein, the primary sequence similarities, and their domain architectures. Genomic-wide screening and domains studies of lectin homologs showed that the majority of lectin putative genes are chimeric lectins, constructed by fusing a single or multiple lectins domains to another protein domain. This allows lectin homologs that belong to the same family to function in diverse physiological pathways [28,29,30]. Despite the wealth of information about plant lectins in terms of physicochemical, structural, expressional, and genomic studies, as well as their possible application [31,32,33,34], there had been no attempt to isolate and study sorghum lectins. To the best of our knowledge, only a single study was published in 1982; where seed lectins from 5 sorghum species were isolated and marginally characterized with respect to blood and sugar specificity only [35].

In this study, we investigated the presence, distribution, and evolution of lectin homologs predicted from the S. bicolor genomes (grain and sweet). Moreover, their occurrence in sorghum QTLs in relation to several traits such as biotic and abiotic stress and tolerance, morphological, composition, and maturity traits were also investigated. This work can provide valuable insights on sorghum’s QTL regions, and lectins’ physiological roles related to plant defense/tolerance, growth, and development.


Identification and distribution of lectin homologs from grain and sweet sorghum

Two genetically variable types of Sorghum bicolor genome assemblies, i.e., grain (BTx623) V3.1 (732.2 MB), and sweet (Rio) V2.1 (729.3 MB), deposited in the Phytozome V13 were screened for lectin gene homologs [36, 37]. A total of 119 and 113 genes, respectively, were extracted. Of the 12 known plant lectin families, 8 families were identified upon searching both sorghum genomes. They are Ricin-B, CRA, LysM, EUL, Hevein, Nictaba, JRL, and leg-B. Around 45% of the lectin homologs are from the legume family followed by the jacalin-related lectins (15%) and the Nictaba homologs (14%) (Table 1). The total number of chimeric putative lectins in grain sorghum is up to 83 genes (70%), and 81 genes (72%) in sweet sorghum. Sorghum bicolor has a haploid chromosomal number of 10 chromosomes. The distribution and abundance of each lectin family and its putative genes are heterogeneous with very minor variations between the two races. In general, lectin genes are clustered in both chromosomes 1 and 2 (36% of the total genes), while chromosomes 7 and 8 had the least number of genes, all of which are related to legume and LysM families (Additional file 1: Figure S1). 44% of the total lectin-related genes were subjected to different expansion events, dispersion and transposition events are the main cause of the genes duplication and contribute to up to 70% of the total lectin homologs’ expansion. However, 56% of the expanded Jacalin-related lectins were tandemly duplicated, while the only 2 Nictaba-related putative genes subjected to duplication were retained after a wide genome duplication event (WGD) (Fig. 1).

Table 1 Predicted lectin homologs from the grain and sweet Sorghum bicolor genome assemblies
Fig. 1
figure 1

Putative lectin gene homologs duplication from grain sorghum V3.1. A Segmental duplication map, B the evolutionary events responsible for each lectin family expansion

Domain architecture and characterization of lectin homologs

Sorghum putative lectin homologs are structurally organized into three groups; merolectins of a protein with a single lectin domain, hololectins where two or more lectin domains are linked, and chimerolectins that are constructed from a single or multiple lectin-domains fused to a single or multiple other protein domains [38] (Fig. 2, Fig. 3, and Additional files 2: Table S1, and Table S2). Ricin-B lectin homologs are glycosylated chimeric-proteins (~ 35 – 65 kDa) that belong to the type-II ribosomal inactivating proteins. The putative gene is built from a single ribosomal inactivating domain (RIP) that has RNA N-glycosidase activity (Pfam: PF00161), and forms the A-chain of the protein; fused by the C-terminal to either a single or two tandemly arrayed carbohydrate-binding domain Ricin-B that forms the B-chain. Only the double Ricin-B domains protein, which is constructed from a single exon, is predicted to be a secreted lectin with a signal peptide sequence attached N-terminally to the RIP domain. Sorghum’s class-V chitinase-related agglutinin genes (CRA) are exclusively merolectins that contain a short sequence of chitinase insertion domain (CID) of approximately 70 amino acids. All genes that are structured from a double exon and a single intron, have signal peptide sequences, devoid of the transmembrane domain, and are most likely to be targeted to the chloroplast, cytoplasm, or extracellularly. Like all monocots, the EUL homologs are either raised from a single or double lectin domain that belongs to type S3, which is proceeded by a long unrelated N-terminal sequence (~ 17 – 48 kDa). Most EUL lectins are synthesized without a signal peptide, hence they are considered nucleoplasmic proteins. However, one sequence from grain sorghum and two sequences from sweet sorghum have a signal peptide and are predicted to be targeted to the vacuoles and/or the chloroplast. Sorghum legume and Hevein families are both strictly chimeric lectins. Hevein is a monomer protein (~ 28 – 38 kDa) that binds a homopolymer of N-acetyl-D-glucosamine. A sole lectin domain is linked through the C-terminal to one or two domains of glycosyl hydrolase-19 (GH-19) that belong to the class-I chitinase group. 7 of the Hevein-like genes located at both chromosomes 6 and 9 contain a short intron sequence of approximately 88 bp. They are secreted proteins targeted either extracellularly or to the chloroplast [38]. Lectin-receptor kinases (Lec-RKs) are the most abundant type of lectins, which in sorghum encompass all putative genes from the legume lectin family, members of the LysM, and Jacalin-related families. Compared to legume homologs, LysM and Jacalin-RKs genes are structurally heterogenic in terms of their exon/intron length and number. In legumes and LysM Lec-RKs putative homologs, the protein kinase domain is proceeded either by the leg-B domain or the lysin motif, respectively. This is reverse to the architecture of the Jacalin-PKinase gene, where the tandemly arrayed Jacalin domains are proceeded by the Kinase domain. Unlike the LysM and Legume Lec-RKs homologs, the jacalin-PKinase is devoid of the signal peptide and the transmembrane domain, hence considered a nucleoplasmic protein. Other protein domains were reported to be linked to the JRL domains, such as the dirigent, NB-ARC, and Rx_N motifs. They are mainly found in proteins related to disease response in plants [39, 40]. The NB-ARC domain is also found fused to a member of the Nictaba family from only the grain sorghum race (179 kDa). Moreover, the sweet sorghum genome also lacks the jacalin-related lectins that contain the dirigent domain (Additional file 1: Figure S2).

Fig. 2
figure 2

Prediction of grain sorghum’s putative lectin genes subcellular localization

Fig. 3
figure 3

Representation of domain architecture and abundance in grain and sweet sorghum. LysM: Lysin motif, GH-19: glucosyl hydrolase-19 (PF00182), CBD-1: carbohydrate binding domain-1, PP2: Phloem protein 2, F-box (PF00646), NB-ARC (PF00954), Dirigent (PF03018), Rx_N (PF18052), PKinase: Protein kinase (PF00069, PF07714)

Distribution of lectin sequences within QTLs from grain sorghum

We screened the grain sorghum genome for lectins' wide distribution and association with QTL regions. Physical mapping of QTLs to sorghum genome’s (V3) 10-chromosomes and their subsequent intersections with lectins revealed that 96.6% of the total number of lectin sequences are located within one or more QTL regions of all major categories. Merging the QTLs of 223 unique traits into non-redundant unique QTLs under 9 sub-categories of 5 major categories (abiotic, biotic, maturity, composition, and morphology) resulted in stretching the QTL regions of several sub-categories all over certain chromosomes like 5, 8, and 10 (Fig. 4). Mainly, due to either the higher number of QTL in traits under question or the large size of these traits’ QTL. Only 34.3% of these QTLs contain lectin genes (Fig. 5). And unlike members of lectin families (Ricin-B, CRA, EUL, Hevein, Nictaba, and JRL) which are distributed in 6 out of 9 sub-categories, members of LysM and legume families are present in all of them (Additional files 3, and 4). Statistical analysis indicated that lectin sequences are only preferentially distributed in QTL regions related to root morphology under the major category morphology (z score = -1.8905, P0.05 = 0.029345). However, the analysis of all unique traits with QTL regions containing lectin genes (123 out of 223 traits), showed that several traits related to abiotic stress, panicle/grain composition, and morphology of the leaf contain significant lectin putative genes overrepresentation (Additional file 5).

Fig. 4
figure 4

Grain sorghum chromosomal physical map with lectin distribution and QTL regions. A Mapping QTLs for sub-categories related to maturity, composition, and morphology. B Mapping QTLs related to abiotic and biotic tolerance/resistance

Fig. 5
figure 5

Total number of QTLS for sub-categories and the number and percentage of QTLs with lectin genes

Lectin distribution in QTLs related to maturity, composition, and morphological traits

Lectin sequences are distributed in maturity quantitative trait loci with a percentage between 50 and 100%. However, they are primarily clustered in QTL regions associated with the days to a flowering trait with a total of 84 genes from all reported lectin families. A dirigent-JRL VER2 gene from Triticum aestivum which is homologous to Sobic.009G021600.1 and Sobic.005G183600.1 genes (sharing the identity of 40.66 and 39.19%, with both genes, respectively) located in the QTL-days to flowering was reported to mediate flowering upon inducing vernalization of seeds during germination. Knock out of the VER2 gene resulted in the delay of flowering for up to 6 weeks [41]. 83% of the total lectins are located in traits related to panicle/grain composition, with about 47 Lec-RK genes from the legume family. The significant overrepresentation of lectins was reported in composition category under QTLs related to grain element concentration and embryo size (z score = -2.064867, P0.025 = 0.019468, z score = -2.21837, P0.025 = 0.013252, respectively). Although there is no significant preferential representation in traits under the stem and leaf composition subcategories, lectin putative genes are concentrated in QTLs associated with stem cellulose and hemicellulose content, as well as leaf chlorophyll content (20, 9, and 12%, respectively). GRMZM2G402417 dirigent-JRL lectin identified in Zeya maize is closely related to other members of grass plants including sorghum, is upregulated and expressed up to 105-fold in grass-related cell-wall processes [42], its counter gene homolog in sorghum Sobic.009G021600.1 is located in a QTL related to cellulose content (stem composition). In morphological traits, 65 genes (58.6%) were found in leaf morphology QTL, whereas 30.6% and 32.4% were reported for panicle/grain and root morphology characteristics. Though, Tsaneva and colleagues reported that the QTLs for all morphological traits in Oryza sativa did not contain any lectin sequence from the families CRA and EUL [14, 43], in grain sorghum members of the EUL and CRA-related lectins can be found in QTLs of leaf and panicle morphological traits. The CRA gene Sobic.006G044200.1 located in QTLs associated with leaf width and angel, panicle length, and root brace was reported as one of the yieldin gene group responsible for the cell wall loosening associated with assembly and degradation [44]. (Additional files 3, 4, and 5).

Lectin distribution in QTLs related to abiotic and biotic tolerance/resistance traits

The biotic and abiotic QTLs which are associated with conferring resistance or tolerance during stress conditions comprise about 47.9% of the total QTLs found in the sorghum QTL atlas. All 36 traits under the cold and drought sub-categories contain at least one lectin gene from all families with significant overrepresentation in leaf growth rate (z score = -2.5704998, P0.025 = 0.005078) and germination index (z score = -1.7593058, P0.05 = 0.039263). Up to 48 lectin sequences are found in QTLs related to dry matter growth (cold tolerance sub-category) with 61% of the JRL members and 32% of Legume-like genes. Sobic.003G105700.1 gene is a member of the EUL family located at chromosome 3. Functional enrichment analysis using STRING predicted that its interacting partner is the embryo-specific protein (ATS3A). This lectin is a homolog to ArathEULS3 (At2g39050) from A. thaliana with a shared identity of 68%. ArathEULS3 has been linked to drought stress response and is believed to play a role with its partners ATS3A and ATS3B in ABA-induced stomatal closure [45]. Sobic.003G105700.1 is located in QTLs related to stay-green, and chlorophyll content traits, which are considered very important QTLs linked to drought and cold stresses [46]. Moreover, 39% and 47% of the total putative lectin genes contain a varying number of cis-acting elements that modulate gene expression during low temperature (LTR element) and drought (MBS element), respectively. Abscisic acid (ABA) is a plant hormone that coordinates different stress signals. Under low temperature and dehydration, the plant triggers ABA secretion, which results in the release of many transcriptional factors [47]. About 89% of sorghum lectin putative genes are either up- or down-regulated by ABA through the ABRE-cis-acting element (Additional file 6).

In biotic resistance traits, the highest number of lectin homologs were found in QTLs that connected to insect resistance (70 lectins putative genes), followed by fungal and parasitic resistance traits (64, 14 lectins, respectively). In fungal-related QTLs, lectins are concentrated in QTLs associated with rust resistance, and ergot resistance (% of infection and pollen quantity). While in insect-related QTLs, lectins are mainly located in green bug resistance, shot fly resistance, and head bug resistance-related loci. Although there was no significant overrepresentation of lectins in QTLs related to biotic resistance, many lectins’ promoters were reported to have cis-acting regulatory elements related to elicitation, wounding and pathogen responsiveness (20% box S and WUN-box, and 49% W-box) (Additional file 6). Furthermore, several protein domains are reported to be involved in plant immunity and defense against biological predators and parasites. For instance, NB-ARC [40], dirigent [48], and protein kinases [49] are domains that can be found fused to one or more lectin domains such as jacalin, Nictaba, LysM, and legume domains [21]. Interestingly, unlike the grain sorghum, the genome of sweet sorghum is devoid of the dirigent-JRL sequences compared to the 5 genes that are found in grain sorghum. However, these dirigent-JRLs have an evolutionary relationship with three single domain JRL putative genes from sweet sorghum (SbRio.02G096700.1, SbRio.02G096800.1, and SbRio.02G096600.1) (Fig. 3, Additional file 1: Figure S2). A dirigent-jacalin sorghum lectin (SL), which is a homolog to the β-glucosidase aggregating factor (BGAF) from maize, is suggested to mediate insects and pathogens interactions by lectin activity rather than modulating β-glucosidase [50] (Additional files 3, 4 and 5).


Sorghum is an important economic C4 crop with a simple genome, diverse phenotypes (i.e., grain, sweet, forage, and cellulosic sorghums), and water-use efficiency that allows it to produce under adverse conditions and low levels of required inputs [51]. Studies demonstrated that the phenotypical variation between grain and sweet sorghum is a reflection of the genetic differences which occur due to gene variation (~ 1,500 genes), SNPs, insertion/deletion (indels) segments, and the presence/absence variations (PAVs) in genetic regions. The majority of these genetic variations reside in genes with adverse biological functions and stress responses [52,53,54]. This can be noted in the variation observed in the simple sequence repeats (SSR) found in some of the lectins genomic sequences interms of numbers, length and sequence composition, and can be used for plant selection and identification of domesticated cultivors [55]. Moreover, there is a variation in lectin gene numbers observed for each family, especially the LysM family. Plant LysM-like lectins are chitin-elicitor proteins, which are involved in cell surface pattern recognition receptor signaling pathways leading to innate immunity against biotic (bacterial and fungal pathogens) [56], and abiotic stresses (tolerance to salinity, and heavy metal stresses) [57,58,59].

On the other hand, genetic duplication in sorghum is relatively low compared to other grass-member crops like maize and rice. Oryza sativa genome is bigger than sorghum’s genome and went through more gene expansion events than sorghum [60]. Although the number of lectin genes homologs in rice are triple the gene size of sorghum, the majority of rice lectin genes located in nonsegmental regions were expanded through tandem duplication events [14], whereas around 85.8% of sorghum’s lectin genes were expanded by various segmental events compared to the 16.6% tandemly duplicated lectin genes, mainly by dispersion or retrogene copies created by transposition event. The variation in genes number in each lectin family is also an indication that each family has evolved differently. Members of CRA and Ricin-B families had been retained in the sorghum genome without any duplication event, meanwhile, the rest of the lectin families evolved through various duplication mechanisms (either tandem or segmental or both) and were preserved through neofunctionalization or sub-functionalization events that offered various biological and physiological needs or adaptive benefits against environmental stresses. While there is conservation in the exon/intron organization between lectin gene homologs from the same family in general, many duplicates diverge from the common structure either by insertion (e.g. Sobic.001G386500.1-EUL and Sobic.002G420800.1-EUL duplicated by dispersion), or deletion (e.g. Sobic.003G313900.1-JRL and Sobic.003G314000.2-JRL tandemly duplicated) or both. The variation observed in intron number and length between gene members of the same family and between the different lectin families suggests functional roles related to splicing, enhancement of gene expression, controlling mRNA transport or chromatin assembly, and providing a source for new genes [61, 62]. On the other hand, the diversity observed in lectin putative genes architectures and rearrangement in terms of lectin domain number and/or fusion with other protein domains seen in lectin families and between members of the same family from sweet and grain sorghum, especially JRL, Nictaba, and EUL can be explained by key mechanisms related to a single-step terminal loss, fusion or fission [63].

Understanding the endogenous physiological role of plant lectins depends on several elements including domain arrangement, overall 3D structures, carbohydrate specificity, ultrastructural cellular location, interacting partners within pathways, and their differential expressional pattern during the life cycle of the plant. The presence and absence of signal peptide and/or transmembrane domains within members of sorghums’ same lectin family or between the different families indicate that putative lectins will end up in different cellular compartments, and eventually enters different pathways. Studies showed that plant lectins can physiologically function as plant innate immunity defense molecules, symbiotic receptors for microorganism attachment, pollen recognition, storage protein, and in various signal transduction pathways related to cellular organization, cell-wall elongation, embryogenesis, sugar transportation, wound healing, etc. [18, 21, 64, 65]. Although we’ve identified 119 and 113 lectin putative genes in grain and sweet sorghum respectively, there is no account for the isolation and extensive characterization of lectins from sorghum. The only paper published attempted to test the agglutination activity and blood specificity of sorghum whole grain crude extract [35]. However, several transcriptomic and expression analysis data that mentioned several sorghum lectins, as well as identified many lectin genes in QTLs of many traits are available [14, 16, 66, 67]. The statistically significant overrepresentation and clustering of sorghum’s lectin putative genes in QTLs related to many traits associated with drought, cold, composition, and morphology highlights the importance of such proteins, and predominantly associates them with plant developmental and growth aspects as well environmental adaptation.

Several lectins from grass members were functionally characterized in association with many traits. The endophytic colonization of Herbaspirillum seropedicae (a plant growth-promoting bacteria associated with crops like maize, sorghum, rice, and wheat [68]) to maize roots is mediated by jacalin-related lectins (MRL-1 and MRL-2) interaction with the diazotrophic betaproteobacterium lipopolysaccharides’ N-acetyl glucosamine residues [69]. A Ricin-B-like lectin TaRBL from wheat is conferring resistance to Fusarium head blight (FHB) fungal pathogen Fusarium graminearum by physically interacting with the pore-forming toxin-like protein TaPFT (its gene located in the quantitative trait locus Fhb1 associated with the Fusarium resistance). During the infection, the TaRBL expression is upregulated in resistant cultivars and downregulated in the susceptible ones. Furthermore, inducing TaRBL gene silencing in resistant cultivars using stripe mosaic virus resulted in an apparent reduction of FHB resistance [70].

Transcriptomic analysis of sweet sorghum stem expressed genes indicated that lectins genes are involved in the carbohydrate metabolism of stem sugars, where a mannose-binding lectin Sb10g022730.1 and LysM lectin Sb01g049890.1 with possible involvement in cell-wall degradation were twofold upregulated [71]. As stated in our results, the Sobic.006G044200.1 CRA putative gene located in major traits subcategories related to the morphology of leaf, panicle, and roots (leaf width and angle, panicle length, and brace roots) was reported to function in cell wall loosening [44].

Co-localization of QTLs between traits is often associated with differential gene expression. Several lectin genes are expressed under various nitrogen level stresses. Sb01g033090 sorghum gene belongs to the mannose-binding lectin superfamily is expressed with a fourfold increase in normal nitrogen levels under QTLs associated with the % stover moisture content (qMC2-1) and % head moisture content (qBY-1), whereas the expressional levels of a lectin with LysM domain (Sb01g048100) decreased up to 4.1-fold in the biomass yield QTL (qGY-1b) that partially overlaps with qMC2 and qBY-1 loci [16]. Since adaptation to environmental and climatic changes is often associated with physiological and morphological changes it was only natural to find the majority of the sorghum lectins located in QTLs of such traits to overlap with other QTLs related to biotic and abiotic stresses’ tolerance/resistance. In rice, OslecRK a receptor-kinase lectin provides the genetic evidence that links seed germination and plants innate immunity. Knocking out the OslecRK led to the down-expression of α-amylase genes which resulted in plummeting seed viability and subsequently decreasing the rate of germination, as well as reducing plant resistance to bacterial, fungal, and insect pathogens [72].


In conclusion, while there is a scarcity of knowledge on sorghum lectin studies in terms of biochemical and functional analysis compared to other grass members especially rice and maize, a glimpse of possible physiological roles was highlighted in other expressional studies among other genes. Lectins are at the center of plant developmental and adaptational events. Understanding their particular functions as well as possible pleiotropic effects within certain pathways, and how they contribute to various traits can offer tangible insights that help to produce more resilient cultivars with desired phenotypical traits. Our results provide a collective overview of sorghum lectins from grain and sweet types and the microvariations between members of lectin families from both types. The distribution of grain lectin putative genes homologs in various QTLs and their significant overrepresentation in certain traits related to cold and drought tolerances can provide valuable information that may contribute to the production of abiotic resilient plants in times that other crops like rice are extremely challenged with the global temperature increase.


Sorghum bicolor genome screening for putative lectin genes

The genomic assemblies of Sorghum bicolor (BTx623 V3.1.1; ID:454, and Rio V2.1; ID:468) [36, 37] found in the public plant genomic resources repository Phytozome V13 ( [73] were screened against the 12 known plant lectin families identified from Agaricus bisporus agglutinin (ABA – UniProtKB/SwissProt: Q00022.3, Pfam: PF07367), Amaranthus caudatus agglutinin (amaranthin – GenBank:AAL05954.1, Pfam: PF07468), Nostoc ellipsosporum agglutinin (cyanovirin—UniProtKB/SwissProt: P81180.2, Pfam: PF08881), Euonymus europaeus agglutinin (EUL – GenBank: ABW73993.1 Pfam: PF14200), Galanthus nivalis agglutinin (GNA – UniProt/SwissPRot: P30617.1, Pfam: PF01453), Artocarpus integer agglutinin (JRL – GenBank: AAA32680.1, Pfam: PF01419), Glycine max agglutinin (Legume – UniProt/SwissProt: P05046.1, Pfam: PF00139), Brassica juncea agglutinin (LysM – GenBank: BAN83772.1, Pfam: PF01476), Hevea brasiliensis agglutinin (Hevein – GenBank: ABW34946.1, Pfam: PF00187), Robinia pseudoacacia agglutinin (CRA – GenBank: ABL98074.1, Pfam: PF00704), Nicotiana tabacum agglutinin (Nictaba – GenBank: AAK84134.1, Pfam: PF14299), and Ricinus communis agglutinin (Ricin-B – GenBank: PDB2AAI_B, Pfam: PF00652). Each agglutinin sequence was aligned against S. bicolor genome using the Phytozome-BLAST tool (E value < 0.0001). Sequences with the highest identity match were used to perform a second BLAST search, then the generated candidate lectins for each family were retrieved using the BioMart tool and each protein sequence was individually checked for the presence of at least one lectin domain using the InterProScan 5 ( [74].

Putative gene structure and cis-acting elements analysis

The diversity and structure of lectin putative genes from each family (grain sorghum genome V3) were investigated by comparing the exon/intron organization of each gene coding sequence (CDS) and its corresponding genomic DNA sequence using Gene Structure Display Server GSDS ( [75]. Furthermore, the promoter regions (1500 bp upstream of each genomic sequence of each gene) were searched for the presence of cis-acting elements using the webtool PlantCARE [76] ( Seven categories were used to cluster the elements. Simple sequence repeats (SSR) markers found in putative lectins genomic sequences were also identified using Simple Sequence Repeat Identification Tool (SSIT) found in GRAMEME (Srrtool) [77] ( see (Additional file 7).

Putative lectin gene characterization

Each putative lectin homolog was analyzed for the presence of signal peptides and transmembrane domain using SignalP 5.0 server ( [78], and ( webtool transmembrane domains (TMHMM 2.0), respectively [79]. The subcellular localization for lectin homologs was predicted using WoLF PSORT ( [80]. Furthermore, the calculation of the molecular weight and isoelectric points (pI) for each sequence was done using Expasy: Compute pI/Mw ( [81], and the prediction of the N- and O-glycosylation were also performed using the following servers NetNGlyc 1.0 [82] ( and ( NetOGlyc 4.0 [83], respectively.

Gene duplication analysis

The Plant Duplicate Gene Database PlantDGD ( [84] was used to identify gene duplicates for putative lectin genes and determine the type of duplication (i.e.; tandem, proximal, dispersed, transposed, and wide genome duplication). This was followed by using the TBtools V1.0986853 Ka/Ks calculator to calculate the synonymous substitution (Ks) for lectin genes and their duplicates, and values higher than 1 were excluded [85].

Phylogenetic analysis of lectin homologs

Phylogenetic trees were constructed using only lectin domain sequences from each family. Sequences that contain more than one lectin domain were trimmed and each domain was used as a separate entry. Multiple sequence alignment Clustal W tool built on MEGA X software was used. The obtained files were used for the evolutionary analysis by the maximum likelihood method and the JTT matrix-based model of substitution. The final bootstrap consensus trees were inferred from 1000 replicates [86]. Genes related to each family from both Arabidopsis thaliana and Orzya sativa were used as orthologous groups for comparison.

Functional enrichment analysis

Putative lectin genes were analyzed to identify the interacting partners of each gene using the protein–protein interaction network STRING 11.5 server ( [87].

Mapping of lectin genes on chromosomes and QTL regions

QTL and their physical positions, linked groups, and description were retrieved from the OZ sorghum QTL Atlas database ( [10], and divided into 5 major categories (i.e.; biotic, abiotic, morphology, maturity, and composition QTL traits). All overlapped QTL were merged into a single non-redundant QTL, and the redundant QTL were excluded from further analysis. The number of total genes located in the newly merged QTL was manually calculated from the gene/transcript list of Sorghum bicolor V3.1 retrieved using the BioMart tool in Phytozome V13 [73]. Moreover, redundant transcripts were filtered out and unique gene IDs were used. Lectins and QTL were mapped in Sorghum chromosomes using MapChart 2.32 [43]. Each QTL was checked for the presence of lectin genes within the identified region based on their physical position.

Analysis of lectin overrepresentation on QTL

The preferential location of lectins on QTL was statistically analyzed using the Wald test developed by Tsaneva et al. [14]. The significance was determined at P values 0.05 and Bonferroni correction 0.025. The calculations were performed by considering the total number of genes (protein-coding transcripts) in the genome (N) to be 34,129 (, and the total number of lectin genes in Sorghum (s) is 119 gene.

Availability of data and materials

All data generated and analyzed during this study are included in the main article and its supplementary data provided (additional file documents 1–7). Genomic data of sorghum (grian and sweete) are freely accessible and all related UTR links were provided within the article under relevant mentions.


  1. Mundia CW, Secchi S, Akamani K, Wang G. A regional comparison of factors affecting global sorghum production: the case of North America, Asia and Africa’s Sahel. Sustainability. 2019;11(7):2135.

    Article  Google Scholar 

  2. Abdel-Ghany SE, Ullah F, Ben-Hur A, Reddy AS. Transcriptome analysis of drought-resistant and drought-sensitive sorghum (Sorghum bicolor) genotypes in response to PEG-induced drought stress. Int J Mol Sci. 2020;21(3):772.

    Article  CAS  PubMed Central  Google Scholar 

  3. Tucker BB, Bennett WF: Fertilizer use on grain sorghum. Changing patterns in fertilizer use 1968:189–220.

  4. Kulamarva AG, Sosle VR, Raghavan GV. Nutritional and rheological properties of sorghum. Int J Food Prop. 2009;12(1):55–69.

    Article  CAS  Google Scholar 

  5. Grenier C, Bramel P, Dahlberg J, El-Ahmadi A, Mahmoud M, Peterson G, Rosenow D, Ejeta G. Sorghums of the Sudan: analysis of regional diversity and distribution. Genet Resour Crop Evol. 2004;51(5):489–500.

    Article  CAS  Google Scholar 

  6. Venkateswaran K, Elangovan M, Sivaraj N: Origin, domestication and diffusion of Sorghum bicolor. In: Breeding Sorghum for diverse end uses. Elsevier; 2019: 15–31.

  7. Balakrishna D, Vinodh R, Madhu P, Avinash S, Rajappa P, Bhat BV: Tissue culture and genetic transformation in Sorghum bicolor. In: Breeding Sorghum for diverse end uses. Elsevier; 2019: 115–130.

  8. Tewodros M, Zelalem B. Advances in quantitative trait loci, mapping and importance of markers assisted selection in plant breeding research. International Journal of Plant Breeding and Genetics. 2016;10(2):58–68.

    Article  CAS  Google Scholar 

  9. Mace E, Jordan D. Integrating sorghum whole genome sequence information with a compendium of sorghum QTL studies reveals uneven distribution of QTL and of gene-rich regions with significant implications for crop improvement. Theor Appl Genet. 2011;123(1):169–91.

    Article  CAS  PubMed  Google Scholar 

  10. Mace E, Innes D, Hunt C, Wang X, Tao Y, Baxter J, Hassall M, Hathorn A, Jordan D. The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement. Theor Appl Genet. 2019;132(3):751–66.

    Article  PubMed  Google Scholar 

  11. Yang Q, He Y, Kabahuma M, Chaya T, Kelly A, Borrego E, Bian Y, El Kasmi F, Yang L, Teixeira P. A gene encoding maize caffeoyl-CoA O-methyltransferase confers quantitative resistance to multiple pathogens. Nat Genet. 2017;49(9):1364–72.

    Article  CAS  PubMed  Google Scholar 

  12. Li R, Jeong K, Davis JT, Kim S, Lee S, Michelmore RW, Kim S, Maloof JN. Integrated QTL and eQTL mapping provides insights and candidate genes for fatty acid composition, flowering time, and growth traits in a F2 population of a novel synthetic allopolyploid Brassica napus. Front Plant Sci. 2018;9:1632.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Vij S, Giri J, Dansana PK, Kapoor S, Tyagi AK. The receptor-like cytoplasmic kinase (OsRLCK) gene family in rice: organization, phylogenetic relationship, and expression during development and stress. Mol Plant. 2008;1(5):732–50.

    Article  CAS  PubMed  Google Scholar 

  14. Tsaneva M, De Schutter K, Verstraeten B, Van Damme EJ. Lectin sequence distribution in QTLs from rice (Oryza sativa) suggest a role in morphological traits and stress responses. Int J Mol Sci. 2019;20(2):437.

    Article  PubMed Central  CAS  Google Scholar 

  15. Gudys K, Guzy-Wrobelska J, Janiak A, Dziurka MA, Ostrowska A, Hura K, Jurczyk B, Żmuda K, Grzybkowska D, Śróbka J. Prioritization of candidate genes in QTL regions for physiological and biochemical traits underlying drought response in barley (Hordeum vulgare L.). Frontiers in plant science. 2018;9:769.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gelli M, Mitchell SE, Liu K, Clemente TE, Weeks DP, Zhang C, Holding DR, Dweikat IM. Mapping QTLs and association of differentially expressed gene transcripts for multiple agronomic traits under different nitrogen levels in sorghum. BMC Plant Biol. 2016;16(1):1–18.

    Article  CAS  Google Scholar 

  17. Nathan S, Halina L. Lectins as recognition molecules. Science. 1989;246:227–34.

    Article  Google Scholar 

  18. Moreira RdA. Ainouz IL, Oliveira JTAd, Cavada BS: Plant lectins, chemical and biological aspects. Mem Inst Oswaldo Cruz. 1991;86:211–8.

    Article  Google Scholar 

  19. Xu Y, Fu X. Reprogramming of Plant Central Metabolism in Response to Abiotic Stresses: A Metabolomics View. Int J Mol Sci. 2022;23(10):5716.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Zandalinas SI, Balfagón D, Gómez-Cadenas A, Mittler R. Plant responses to climate change: metabolic changes under combined abiotic stresses. J Exp Bot. 2022;73(11):3339–54.

    Article  PubMed  Google Scholar 

  21. Lannoo N, Van Damme EJ. Lectin domains at the frontiers of plant defense. Front Plant Sci. 2014;5:397.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Gaidamashvili M, Ohizumi Y, Iijima S, Takayama T, Ogawa T, Muramoto K. Characterization of the yam tuber storage proteins from Dioscorea batatas exhibiting unique lectin activities. J Biol Chem. 2004;279(25):26028–35.

    Article  CAS  PubMed  Google Scholar 

  23. Chettri D, Boro M, Sarkar L, Verma AK. Lectins: Biological significance to biotechnological application. Carbohyd Res. 2021;506: 108367.

    Article  CAS  Google Scholar 

  24. Sathyapriya P, Kalavani A, Arvinth S: APPLICATION OF PLANT LECTIN FOR BIOTIC STRESS CONTROL IN CROPS. Agricultural Reviews 2012, 33(3).

  25. Van Holle S, Smagghe G, Van Damme EJ. Overexpression of Nictaba-like lectin genes from Glycine max confers tolerance toward Pseudomonas syringae infection, aphid infestation and salt stress in transgenic Arabidopsis plants. Front Plant Sci. 2016;7:1590.

    PubMed  PubMed Central  Google Scholar 

  26. Levchuk A, Voitovich E, Lyakh V. Lectins of oil-seed flax plants exposed to abiotic stress. Russ J Plant Physiol. 2013;60(1):77–83.

    Article  CAS  Google Scholar 

  27. Lambin J, Demirel Asci S, Dubiel M, Tsaneva M, Verbeke I, Wytynck P, De Zaeytijd J, Smagghe G, Subramanyam K, Van Damme EJ. OsEUL lectin gene expression in rice: stress regulation, subcellular localization and tissue specificity. Front Plant Sci. 2020;11:185.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Van Holle S, Van Damme EJ. Distribution and evolution of the lectin family in soybean (Glycine max). Molecules. 2015;20(2):2868–91.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Eggermont L, Verstraeten B, Van Damme E: Genome-wide screening for lectin motifs in Arabidopsis thaliana. Plant Genome 2017, 10(2).

  30. Wytynck P, Rougé P, Van Damme EJ. Genome-wide screening of Oryza sativa ssp. japonica and indica reveals a complex family of proteins with ribosome-inactivating protein domains. Phytochemistry. 2017;143:87–97.

    Article  CAS  PubMed  Google Scholar 

  31. Awadallah AK, Osman MEM, Ibrahim MA, Bernardes ES, Dias-Baruffi M, Konozy EHE. Isolation and partial characterization of 3 nontoxic d-galactose–specific isolectins from seeds of Momordica balsamina. J Mol Recognit. 2017;30(2): e2582.

    Article  CAS  Google Scholar 

  32. Konozy EHE, Osman ME-fM, Dirar AI: Plant lectins as potent Anti-coronaviruses, Anti-inflammatory, antinociceptive and antiulcer agents. Saudi Journal of Biological Sciences 2022:103301.

  33. Osman M, Awadallah A, Konozy E. Isolation purification and partial characterization of three lectins from Tamarindus indica seeds with a novel sugar specificity. Int J Plant Res. 2016;6(1):13–9.

    Google Scholar 

  34. Osman MEM, Konozy EHE: Insight into Erythrina Lectins: Properties, Structure and Proposed Physiological Significance. The Open Bioactive Compounds Journal 2017, 5(1).

  35. Neucere JN. Lectins in grain sorghum [Sorghum bicolor (L.) Moench]. J Agric Food Chem. 1982;30(3):603–4.

    Article  CAS  PubMed  Google Scholar 

  36. McCormick RF, Truong SK, Sreedasyam A, Jenkins J, Shu S, Sims D, Kennedy M, Amirebrahimi M, Weers BD, McKinley B. The Sorghum bicolor reference genome: improved assembly, gene annotations, a transcriptome atlas, and signatures of genome organization. Plant J. 2018;93(2):338–54.

    Article  CAS  PubMed  Google Scholar 

  37. Cooper EA, Brenton ZW, Flinn BS, Jenkins J, Shu S, Flowers D, Luo F, Wang Y, Xia P, Barry K. A new reference genome for Sorghum bicolor reveals high levels of sequence similarity between sweet and grain genotypes: implications for the genetics of sugar metabolism. BMC Genomics. 2019;20(1):1–13.

    Article  CAS  Google Scholar 

  38. Tsaneva M, Van Damme EJ. 130 years of plant lectin research. Glycoconj J. 2020;37(5):533–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Esch L, Schaffrath U. An update on jacalin-like lectins and their role in plant defense. Int J Mol Sci. 2017;18(7):1592.

    Article  PubMed Central  CAS  Google Scholar 

  40. Van Ooijen G, Mayr G, Kasiem MM, Albrecht M, Cornelissen BJ, Takken FL. Structure–function analysis of the NB-ARC domain of plant disease resistance proteins. J Exp Bot. 2008;59(6):1383–97.

    Article  PubMed  CAS  Google Scholar 

  41. Yong W-d, Xu Y-y, Xu W-z. Wang X, Li N, Wu J-s, Liang T-b, Chong K, Xu Z-h, Tan K-h: Vernalization-induced flowering in wheat is mediated by a lectin-like gene VER2. Planta. 2003;217(2):261–70.

    Article  CAS  PubMed  Google Scholar 

  42. Bosch M, Mayer CD, Cookson A, Donnison IS. Identification of genes involved in cell wall biogenesis in grasses by differential gene expression profiling of elongating and non-elongating maize internodes. J Exp Bot. 2011;62(10):3545–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Voorrips R. MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered. 2002;93(1):77–8.

    Article  CAS  PubMed  Google Scholar 

  44. Rai KM, Thu SW, Balasubramanian VK, Cobos CJ, Disasa T, Mendu V. Identification, characterization, and expression analysis of cell wall related genes in Sorghum bicolor (L.) Moench, a food, fodder, and biofuel crop. Front Plant Sci. 2016;7:1287.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Van Hove J, De Jaeger G, De Winne N, Guisez Y, Van Damme EJ. The Arabidopsis lectin EULS3 is involved in stomatal closure. Plant Sci. 2015;238:312–22.

    Article  PubMed  CAS  Google Scholar 

  46. Johnson SM, Lim F-L, Finkler A, Fromm H, Slabas AR, Knight MR. Transcriptomic analysis of Sorghum bicolor responding to combined heat and drought stress. BMC Genomics. 2014;15(1):1–19.

    Article  CAS  Google Scholar 

  47. Yamaguchi-Shinozaki K, Shinozaki K. A novel cis-acting element in an Arabidopsis gene is involved in responsiveness to drought, low-temperature, or high-salt stress. Plant Cell. 1994;6(2):251–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Paniagua C, Bilkova A, Jackson P, Dabravolski S, Riber W, Didi V, Houser J, Gigli-Bisceglia N, Wimmerova M, Budínská E. Dirigent proteins in plants: modulating cell wall metabolism during abiotic and biotic stress exposure. J Exp Bot. 2017;68(13):3287–301.

    Article  CAS  PubMed  Google Scholar 

  49. Romeis T. Protein kinases in the plant defence response. Curr Opin Plant Biol. 2001;4(5):407–14.

    Article  CAS  PubMed  Google Scholar 

  50. Kittur FS, Yu HY, Bevan DR, Esen A. Homolog of the maize β-glucosidase aggregating factor from sorghum is a jacalin-related GalNAc-specific lectin but lacks protein aggregating activity. Glycobiology. 2009;19(3):277–87.

    Article  CAS  PubMed  Google Scholar 

  51. Boatwright JL, Brenton ZW, Boyles RE, Sapkota S, Myers MT, Jordan KE, Dale SM, Shakoor N, Cooper EA, Morris GP: Genetic characterization of a Sorghum bicolor multiparent mapping population emphasizing carbon-partitioning dynamics. G3 2021, 11(4):jkab060.

  52. Zheng L-Y, Guo X-S, He B, Sun L-J, Peng Y, Dong S-S, Liu T-F, Jiang S, Ramachandran S, Liu C-M. Genome-wide patterns of genetic variation in sweet and grain sorghum (Sorghum bicolor). Genome Biol. 2011;12(11):1–15.

    Article  Google Scholar 

  53. Hao H, Li Z, Leng C, Lu C, Luo H, Liu Y, Wu X, Liu Z, Shang L, Jing H-C. Sorghum breeding in the genomic era: opportunities and challenges. Theor Appl Genet. 2021;134(7):1899–924.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Kanbar A, Shakeri E, Alhajturki D, Riemann M, Bunzel M, Morgano MT, Stapf D, Nick P. Sweet versus grain sorghum: Differential sugar transport and accumulation are linked with vascular bundle architecture. Ind Crops Prod. 2021;167: 113550.

    Article  CAS  Google Scholar 

  55. Billot C, Ramu P, Bouchet S, Chantereau J, Deu M, Gardes L, Noyer JL, Rami JF, Rivallan R, Li Y, et al. Massive sorghum collection genotyped with SSR markers to enhance use of global genetic resources. PLoS ONE. 2013;8(4): e59714.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wan J, Zhang XC, Neece D, Ramonell KM, Clough S, Kim SY, Stacey MG, Stacey G. A LysM receptor-like kinase plays a critical role in chitin signaling and fungal resistance in Arabidopsis. Plant Cell. 2008;20(2):471–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Brotman Y, Landau U, Pnini S, Lisec J, Balazadeh S, Mueller-Roeber B, Zilberstein A, Willmitzer L, Chet I, Viterbo A. The LysM receptor-like kinase LysM RLK1 is required to activate defense and abiotic-stress responses induced by overexpression of fungal chitinases in Arabidopsis plants. Mol Plant. 2012;5(5):1113–24.

    Article  CAS  PubMed  Google Scholar 

  58. Buendia L, Girardin A, Wang T, Cottret L, Lefebvre B: LysM receptor-like kinase and LysM receptor-like protein families: an update on phylogeny and functional characterization. Frontiers in plant science 2018:1531.

  59. Ouelhadj A, Kaminski M, Mittag M, Humbeck K. Receptor-like protein kinase HvLysMR1 of barley (Hordeum vulgare L.) is induced during leaf senescence and heavy metal stress. J  Exp Bot. 2007;58(6):1381–96.

    Article  CAS  PubMed  Google Scholar 

  60. Rosas-Quijano R, Ontiveros-Cisneros A, Montes-García N, Díaz-Franco A, Vázquez-Ovando A, Gálvez-López D: A General Overview of Sweet Sorghum Genomics. Biotechnological Applications of Biomass 2021:113.

  61. Jo BS, Choi SS. Introns: The Functional Benefits of Introns in Genomes. Genomics Inform. 2015;13(4):112–8.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Boudet N, Aubourg S, Toffano-Nioche C, Kreis M, Lecharny A. Evolution of intron/exon structure of DEAD helicase family genes in Arabidopsis, Caenorhabditis, and Drosophila. Genome Res. 2001;11(12):2101–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kersting AR, Bornberg-Bauer E, Moore AD, Grath S. Dynamics and adaptive benefits of protein domain emergence and arrangements during plant genome evolution. Genome Biol Evol. 2012;4(3):316–29.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. De Hoff PL, Brill LM, Hirsch AM. Plant lectins: the ties that bind in root symbiosis and plant defense. Mol Genet Genomics. 2009;282(1):1–15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Van Damme EJ, Barre A, Rougé P, Peumans WJ. Cytoplasmic/nuclear plant lectins: a new story. Trends Plant Sci. 2004;9(10):484–9.

    Article  PubMed  CAS  Google Scholar 

  66. Adhikari P, Mideros SX, Jamann TM. Differential Regulation of Maize and Sorghum Orthologs in Response to the Fungal Pathogen Exserohilum turcicum. Front Plant Sci. 2021;12:930.

    Article  Google Scholar 

  67. Fu F, Girma G, Mengiste T. Global mRNA and microRNA expression dynamics in response to anthracnose infection in sorghum. BMC Genomics. 2020;21(1):1–16.

    Article  Google Scholar 

  68. Roncato-Maccari LD, Ramos HJ, Pedrosa FO, Alquini Y, Chubatsu LS, Yates MG, Rigo LU, Steffens MB, Souza EM. Endophytic Herbaspirillum seropedicae expresses nif genes in gramineous plants. FEMS Microbiol Ecol. 2003;45(1):39–47.

    Article  CAS  PubMed  Google Scholar 

  69. Balsanelli E, Tuleski TR, de Baura VA, Yates MG, Chubatsu LS, Pedrosa Fde O, de Souza EM, Monteiro RA. Maize root lectins mediate the interaction with Herbaspirillum seropedicae via N-acetyl glucosamine residues of lipopolysaccharides. PLoS ONE. 2013;8(10): e77001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Song P, Zhang L, Wu L, Hu H, Liu Q, Li D, Hu P, Zhou F, Bu R, Wei Q, et al. A Ricin B-Like Lectin Protein Physically Interacts with TaPFT and Is Involved in Resistance to Fusarium Head Blight in Wheat. Phytopathology. 2021;111(12):2309–16.

    Article  PubMed  Google Scholar 

  71. Calviño M, Bruggmann R, Messing J. Screen of genes linked to high-sugar content in stems by comparative genomics. Rice. 2008;1(2):166–76.

    Article  Google Scholar 

  72. Cheng X, Wu Y, Guo J, Du B, Chen R, Zhu L, He G. A rice lectin receptor-like kinase that is involved in innate immune responses also contributes to seed germination. Plant J. 2013;76(4):687–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, Mitros T, Dirks W, Hellsten U, Putnam N. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012;40(D1):D1178–86.

    Article  CAS  PubMed  Google Scholar 

  74. Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, McWilliam H, Maslen J, Mitchell A, Nuka G. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30(9):1236–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Guo A-Y, Zhu Q-H, Chen X, Luo J-C: GSDS: a gene structure display server. Yi chuan= Hereditas 2007, 29(8):1023–1026.

  76. Lescot M, Déhais P, Thijs G, Marchal K, Moreau Y, Van de Peer Y, Rouzé P, Rombauts S. PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res. 2002;30(1):325–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Temnykh S, DeClerck G, Lukashova A, Lipovich L, Cartinhour S, McCouch S. Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Res. 2001;11(8):1441–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, von Heijne G, Nielsen H: SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 2019, 37(4):420–423.

  79. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305(3):567–80.

    Article  CAS  PubMed  Google Scholar 

  80. Horton P, Park KJ, Obayashi T, Fujita N, Harada H, Adams-Collier CJ, Nakai K: WoLF PSORT: protein localization predictor. Nucleic Acids Res 2007, 35(Web Server issue):W585–587.

  81. Gasteiger E, Hoogland C, Gattiker A, Wilkins MR, Appel RD, Bairoch A: Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook 2005:571–607.

  82. Gupta R, Brunak S: Prediction of glycosylation across the human proteome and the correlation to protein function. In.; 2001.

  83. Steentoft C, Vakhrushev SY, Joshi HJ, Kong Y, Vester-Christensen MB, Schjoldager KTB, Lavrsen K, Dabelsteen S, Pedersen NB, Marcos-Silva L. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology. EMBO J. 2013;32(10):1478–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Qiao X, Li Q, Yin H, Qi K, Li L, Wang R, Zhang S, Paterson AH. Gene duplication and evolution in recurring polyploidization–diploidization cycles in plants. Genome Biol. 2019;20(1):1–23.

    Article  Google Scholar 

  85. Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, He Y, Xia R. TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data. Mol Plant. 2020;13(8):1194–202.

    Article  CAS  PubMed  Google Scholar 

  86. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35(6):1547.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–12.

    Article  CAS  PubMed  Google Scholar 

Download references


The authors acknowledge the valuable insights in QTL and statistical analysis provided by Dr Anuj Kumar, Department of Crop, Soil, & Environmental Sciences, University of Arkansas, Fayetteville, USA and Dr Mariya Tsaneva, Laboratory of Biochemistry and Glycobiology, Department of Biotechnology, Faculty of Bioscience Engineering, Ghent University, Belgium.


This research received no financial aid

Author information

Authors and Affiliations



M.E.M.O. assisted in drafting the idea and wrote the initial draft, retrieved data and performed the analysis and the final illustration. A.I.D. retrieved and analyzed the data relating to the grain sorghum lectin and assisted in the manuscript's final construction. E.H.E.K. conceptualized the main idea, supervised the study and validated the results. All authors participated in the final writing and editing of the final version of the manuscript. “The author(s) read and approved the final manuscript.” 

Corresponding author

Correspondence to Emadeldin Hassan E. Konozy.

Ethics declarations

Ethics approval and consent to participate

No specific permits were required and all repository data servers were freely accessable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Osman, M.Ef.M., Dirar, A.I. & Konozy, E.H.E. Genome-wide screening of lectin putative genes from Sorghum bicolor L., distribution in QTLs and a probable implications of lectins in abiotic stress tolerance. BMC Plant Biol 22, 397 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Sorghum
  • Lectins
  • QTLs
  • Domain architectures
  • Lectins expansion
  • Tolerance
  • Resistance