Skip to main content
NCBI home page
3 Biotech logoLink to 3 Biotech
. 2019 Sep 28;9(10):377. doi: 10.1007/s13205-019-1896-0

Field evaluation ratified by transcript and computational analyses unveils myco-protective role of SUGARWIN proteins in sugarcane

Aqsa Parvaiz 1, Ghulam Mustafa 1,✉, Hafiz Muhammad Walayat Ali Khan 2, Faiz Ahmad Joyia 1, Adnan Khan Niazi 1, Sultana Anwar 1, Muhammad Sarwar Khan 1
PMCID: PMC6765628  PMID: 31588401

Abstract

Nine elite sugarcane genotypes (SPF-234, CPF-246, CPF-247, CPF-248, HSF-240, CP-77-400, S-2006-US-658, S-2003-US-127 and S-2006-US-633) were assessed for field level tolerance against Colletotrichum falcatum followed by quantitative expression and computational analyses of mycoprotective proteins. Plug inoculation method was used to assess level of tolerance of aforementioned genotypes while growing in the field. Genotype S-2006-US-658 was categorized as resistant whereas genotypes CPF-246, CPF-248, HSF-240, S-2003-US-127, S-2006-US-633 and CP-77-400 were categorized as moderately resistant and genotypes SPF-234, CPF-247 as moderately susceptible. Quantitative transcript analyses also revealed that the expression of mycoprotective genes (SUGARWIN1 and SUGARWIN2) was maximum in genotype CPF-246 whereas lowest in genotype SPF-234. Hence these mycoprotective proteins play some critical role in fungal pathogen protection as genotypes with higher expression are more tolerant compared to the genotypes with lower expression of mycoprotective proteins. In-silico interaction of these mycoprotective proteins with chitin, glucan, chitosan and mannan (the core constituents of fungal cell wall) also validated their role in disease susceptibility or resistance. These studies will prove a step forward in understanding mycoprotective proteins and can be employed to develop molecular markers for the selection and screening of red rot resistant sugarcane varieties resulting in enhanced productivity of this valuable cash crop.

Electronic supplementary material

The online version of this article (10.1007/s13205-019-1896-0) contains supplementary material, which is available to authorized users.

Keywords: Field evaluation, Red rot, Mycoprotective proteins, Expression analysis, In silico analysis

Introduction

Sugarcane, a genetically complex Saccharum grass, is prone to several biotic and abiotic stresses which pose continuous threat to this valuable crop. Fungal pathogens are most notorious as they may cause the destruction of entire field. Major fungal pathogens include Colletotrichum falcatum (red rot disease), Fusarium spp., Ceratocystis paradoxa (pineapple disease) and Sporisorium scitamineum (sugarcane smut) (Souza et al. 2017). Among these, mere red rot may cause epidemic losses all over the world. The pathogen has proved to be destructive in sugarcane growing countries including India, USA, Bangladesh, Thailand, Pakistan, Taiwan and Australia. Red rot causes the tissue discoloration, sucrose inversion due to the production of pathogen-induced invertases. It also disturbs the development of cane stem, leaf midrib and damages the sugar quality (Viswanathan and Samiyappan 2002). Disease has resulted a considerable reduction in cane yield (30–100%) and sucrose recovery (25–75%). The only successful strategy, to avoid pathogen infection is to identify resistant varieties in breeding program. Sugarcane genotypes show a great degree of variation as far as susceptibility or resistance against Colletotrichum falcatum is concerned. Advancements in molecular and computational biology have helped researchers to understand and culminate these epidemic agents in a more successful manner. An essential cue in this on-going battle is the identification of fungicide targets, pathogenicity factors, plant defense response and pathogenesis related genes (Malathi and Viswanathan 2012; Yi et al. 2013). Plant defense response includes various physical and chemical barriers, up-regulated expression of pathogenesis related genes, production of antimicrobial compounds and localized programmed cell death (Bari and Jones 2009).

A two-layered innate immunity has been explored in plants i.e. primary and secondary innate immunity. Primary innate immunity encompasses recognition of conserved microbe-associated molecular patterns (MAMPs) which persuade the primary defense responses. This includes deposition of callose, amendments in cell wall structure and accretion of defense related proteins. Secondary innate immunity helps to recognize effector-induced agitations of host targets through pathogenesis-related proteins (PR) that arbitrates a strong local defense response resulting in the inhibition of plant growth. Plants produce a wide variety of PR proteins including chitinases, glucanases, chitosanases and metallothionine, NBS-LRR proteins, thaumatin, such as proteins, glycoproteins, catalases, peroxidases, ribosome-inactivating proteins and WRKY proteins, which recognize a wide variety of pathogens and modify physiological functions of the invaders (de Wit 2007; Souza et al. 2017).

Sugarcane expressed sequence tags (SUCESTs) have also helped in the identification of gene orthologous playing crucial role in plant defense mechanism. Likewise, two homologues of BARWIN proteins (SUGARWIN1 and SUGARWIN2) have been explored through SUCEST. These SUGARWIN proteins are toxic to red rot pathogens Colletotrichum falcatum and Ceratocystis paradoxa, and are induced in response to mechanical wounding, methyl jasmonate treatment, and sugarcane borer (Diatraea saccharalis) attack (Franco et al. 2014). These proteins play a major role in defense response against fungal pathogens by degrading the fungal cell wall (Medeiros et al. 2012) targeting its chitin, chitosan and β-1,3-glucan, mannans, polysaccharides and mucopolysaccharides. Computational biology has facilitated structural characterization of proteins and is a valuable tool to study protein–protein interaction, structure–function relationship, interaction with substrate and other physico-chemical properties. All this is dependent on the availability of some cutting-edge softwares and systems.

We tested genetic potential of indigenous sugarcane germplasm for fungal pathogen resistance in the field. Transcript and computational analyses were performed to assess the role of endogenous sugarcane Barwin-like proteins (SUGARWIN1 and SUGARWIN2) in fungal pathogen resistance as well as to explore their interaction with fungal cell wall components through in silico analyses. These findings not only helped to evaluate indigenous elite sugarcane genotypes against pathogen infection but will also prove a step forward in the screening of resistant genotypes through transcript analyses.

Materials and methods

Field evaluation of elite sugarcane genotypes for Colletotrichum falcatum infection

Establishment of plants in the field

Nine elite sugarcane genotypes SPF-234, CPF-246, CPF-247, CPF-248, HSF-240, CP-77-400, S-2006-US-658, S-2003-US-127 and S-2006-US-633 were grown at Sugarcane Research Institute, Ayub Agricultural Research Institute (AARI), Faisalabad. Two budded setts of the above mentioned genotypes were sown with 4 ft. row-to-row distance during 1st week of March. All of the genotypes were grown in same soil type under similar climatic conditions and same inputs were employed to get healthy plants at same growth stage. The experiment was repeated three times in three consecutive years.

Inoculation and data analysis

Fungal culture was established in PDA medium. Plants were inoculated with Colletotrichum falcatum in July–August. Twenty plants of each genotype were inoculated using fungal culture with 20–25 conidia per microscopic field in nodes of sugarcane plants. A standardized scale ranging from 0 to 9 was used for the screening of genotypes under investigation (Srinivasan and Bhat 1961), wherein 0–2 was taken as resistant, 2.1–4 as moderately resistant, and 5–6, 7–8 and 8–9 were categorized as moderately susceptible, susceptible and highly susceptible, respectively. After 1 month of inoculation, general symptoms were observed whereas after 2 months of inoculation split-cane analyses were performed to evaluate infection caused by the fungal pathogen. On the basis of retrieved data, genotypes were graded as resistant, moderately resistant, moderately susceptible, susceptible and highly susceptible.

Quantitative real time PCR analyses

Five to 6 months old field growing plants were used for the isolation of total cellular RNA. Trisolution plus reagent (GeneMark Cat. # TS100-plus) was used for the isolation of RNA using 200 mg of the inner leaf. Leaf samples were ground in liquid nitrogen, 1.0 mL of trisolution and 0.3 mL of chilled chloroform was added and was incubated at room temperature for 5 min. It was centrifuged for 15 min at 4 °C. Then took upper clear phase and added 0.8 volumes of isopropanol and incubated at room temperature for 10 min, followed by centrifugation for 5 min at 4 °C. Then the pellet was washed with 70% (v/v) ethanol. Genomic DNA was removed by DNase I treatment (ThermoFisher Scientifc, USA) following the manufacturer’s instructions. RevertAid First Strand cDNA synthesis kit (ThermoFisher Scientific, Cat. # K1621) was used for the synthesis of cDNA using 1 µg of total RNA following the manufacturer’s instructions. The resultant cDNA was used as template for the quantitative expression analyses of pathogenesis related sugarcane genes.

Normalization of reaction conditions by reference genes

For the normalization of reaction conditions three reference genes reported by de Andrade et al. (2017) namely, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), 25S ribosomal RNA (25S rRNA) and actin were used (Table 1). Primers flanking aforementioned genes were used for the optimization of conditions for expression analyses. qPCR reaction was performed using CFX96™ Real-Time PCR Detection System (Bio-Rad). A 12 µL of reaction mixture was used comprising of 6.0 μl 2xSYBR® Green PCR Supermix (BioRad, USA). The thermal cycler profile was set as initial denaturation at 95 °C for 3 min, followed by 40 cycles at 95 °C for 90 s, 51 °C for 90 s and 72 °C for 120 s. Specificity of primers was evaluated by melt curve. For each of the primer pair melt curve showed a unique peak of fluorescence, indicating amplification of specific fragment and the absence of non-specific fragments or dimers.

Table 1.

Sequences of the primers reference genes and endogenous sugarcane genes used for quantitative expression analysis

Gene Gene description Primer sequences (5′–3′) Size (bp) References
ACT Actin F: CTCAACCCCAAGGCTAACAG 195 de Andrade et al. (2017)
R: GGCATGAGGAAGGGCATAA
GAPDH Glyceraldehyde-3phosphate dehydrogenase F: TTGGTTTCCACTGACTTCGTT 122 de Andrade et al. (2017)
R: CTGTAGCCCCACTCGTTGT
25SrRNA 25S ribosomal RNA F: ATAACCGCATCAGGTCTCCAAG 110 de Andrade et al. (2017)
R: CCTCAGAGCCAATCCTTTTCC
SW1 Sugarwin 1 F: AGGATCGTGGACCAGTGCAG 120 Self developed
R: CGTTGAGGTGTCCCATCTG
SW2 Sugarwin 2 F: GTGAGGATCGTGGACCAGT 120 Self developed
R: CTGGTAGTTGACGGTGAGG
Open in a new tab

Comparative expression analysis of endogenous sugarcane genes in various genotypes

Quantitative expression analyses were carried out in the selected nine indigenous elite sugarcane genotypes. All primer sets were designed using Primer3 software (http://primer3.ut.ee) Primer sets of SUGARWIN1 and SUGARWIN2 genes were run against SPF-234, CPF-246, CPF-247, CPF-248, HSF-240, CP-77-400, S-2006-US-658, S-2003-US-127 and S-2006-US-633 genotypes. Comparative expression analysis of above mentioned genes was carried out against a negative control containing supermix with primers but no template DNA. Data were recorded as the threshold cycle (Ct) value of each sample depending upon the fluorescence produced for amplified target cDNA. All of the samples were run in triplicates to ensure validity of the results attained.

In-silico analyses of pathogenesis related genes for their functional characterization

Various online and desktop based tools were used in these studies including, Modeller v9.11 (Webb and Sali 2014), Phyre2, SWISS-MODEL, UCSF Chimera, Molecular Operating Environment (MOE 2005), PyMol, ExPASy tools, Clustalx2, Pfam and Patchdock server (bioinfo3d.cs.tau.ac.il/PatchDock/) for protein characterization and to study protein–protein interaction.

Sequence and structural characterization

We retrieved nucleotide sequences of SUGARWIN1 and SUGARWIN2 from NCBI with accession number CA145787, CA138924. The nucleotide sequences of SUGARWIN1 and SUGARWIN2 genes were modified and translated into protein sequence using Translation tool of JustBio online server (www.justbio.com/index.php?page=translator). The primary sequences of SUGARWIN1 and SUGARWIN2 were analyzed using online tools at ExPASy. The physico-chemical properties of SUGARWIN sequences were calculated by ProtParam tool available at ExPASy server (Gasteiger et al. 2003). Secondary structure analysis was performed using ESPript-3 (espript.ibcp.fr/ESPript/ESPript), SOPMA (Geourjon and Gilber 1995) and PSIPRED Server (McGuffin et al. 2000) and conserved domains were analyzed using Pfam (Finn et al. 2016).

Homology modeling was carried out to predict 3D structure of SUGARWIN proteins using Modeller (v9.11), Phyre2 and SWISS-MODEL. PSI-BLAST was run against PDB (protein data bank) to search out a potential template for homology modeling (Bernstein et al. 1978). The template with at least 25% sequence identity and 60% query coverage was selected as per selection criteria described by Fischer et al. (2001). After the selection of template (PDB ID: 1BW3) the alignment between template and SUGARWIN1 and SUGARWIN2 sequences was accomplished using Align2D script of Modeller 9.11 followed by the construction of 3D structures through Modellingscript. 3D structures of SUGARWIN proteins were also constructed by Phyre2 and SWISS-MODEL online servers.

Multiple models were built for each protein through modeler v9.11, Swiss model and Phyre2. The best model was selected depending upon the quality and stereochemical properties through VERIFY 3D (http://services.mbi. ucla.edu/Verify_3D/; Luthy et al. 1992), Ramachandran plot (services.mbi.ucla.edu/PROCHECK/; Laskowski et al. 1996), ERRAT (services.mbi.ucla.edu/ERRAT/; Colovos and Yeates 1993) and Qmean plot (swissmodel.expasy.org/qmean/cgi/index.cgi; Benkert et al. 2009). ProCheck server was used to calculate Ramachandran plot which is based on the distribution of backbone dihedral angles. Further UCSF Chimera was used for the superimposition of query and template structures.

Multiple sequence alignment and phylogenetic analysis

To find the conserved sequence, multiple sequence alignment of Chitin-binding domains of closely related species was performed using Clustalx2. The evolutionary relationship of SUGARWIN1 and SUGARWIN2 protein with chitin binding protein and wound inducible protein domain homologues was determined by constructing a phylogenetic tree using MEGA X (Kumar et al. 2018). Neighbour-joining method was used to infer the evolutionary history, while the Poisson correction method was used to figure out the evolutionary distances, the unit used was the number of amino acid substitutions per site.

Molecular docking studies

The proteins under study are PR proteins and possibly interaction with antifungal proteins i.e. chitin, chitosan, glucan and mannan of fungal cell wall to execute antifungal activity (Franco et al. 2018; Medeiros et al. 2012). Hence chitin, chitosan, glucan and mannan were used as ligand. The 3D structures of ligands (chitin, chitosan, glucan and mannan) were retrieved from PubChem (pubchem.ncbi.nlm.nih.gov). The computational molecular docking method was employed for the screening of these ligands against the SUGARWIN proteins. Patch dock (bioinfo3d.cs.tau.ac.il/PatchDock/) an automatic docking tool was used for molecular docking. The most stable confirmation of complex was selected based on highest score value. Furthermore, the binding was analyzed by PyMol, discovery studio, LigPlot+ and MOE (Molecular Operating Environment).

Results

Response of different genotypes to Colletotrichum falcatum inoculation under field conditions

The inoculated plants of indigenous elite sugarcane genotypes were analyzed for three consecutive years. Both, the internal and external symptoms were observed to see their response to fungal pathogen infection. External symptoms include drying and yellowing of leaves, drying and shrinking of stalks, death of entire tops, discoloration of rind, white lesions on the internodal part and presence of pink spore mass on the lower node. Internal symptoms were observed by split cane analysis method. One of the prominent internal symptoms was alcoholic or sour smell from the infected cane. Other symptoms include continuous or discontinuous red lesions throughout the stalk length, appearance of white patches on the red lesions, formation of pith cavities at advance stage of disease and appearance of grayish colonies in these cavities (Fig. 1). Based on the internal and external symptoms, these genotypes were categorized as resistant, moderately resistant, moderately susceptible, susceptible and highly susceptible. Data revealed that genotype S-2006-US-658 was resistant whereas genotypes CPF-246, CPF-248, HSF-240, S-2003-US-127 and S-2006-US-633 were moderately resistant. The genotype CP-77-400 was found to be moderately resistant (comparatively more susceptible) whereas SPF-234 and CPF-247 were evaluated as moderately susceptible varieties (Table 2).

Fig. 1.

Fig. 1

Open in a new tab

Analysis of field growing plants of indigenous sugarcane genotypes after inoculation with Colletotrichum falcatum. a Represent external symptoms appeared on the leaf midrib and flag leaf. b Split-cane analyses showing symptoms in the form of red lesions (variable fungal growth in susceptible and resistant genotypes)

Table 2.

Response of indigenous elite sugarcane genotypes to Colletotrichum falcatum infection

Sr. no. Variety names Response to red rot
1 SPF-234 MS
2 CPF-246 MR
3 CPF-247 MS
4 CPF-248 MR
5 HSF-240 MR
6 CP-77-400 MR*
7 S-2006-US-658 R
8 S-2003-US-127 MR
9 S-2006-US-633 MR
Open in a new tab

4–5 months old field growing plants were inoculated by plug inoculation method and data were recorded for the level of resistance or susceptibility

MS moderately susceptible, MR moderately resistant, R resistant genotypes

Expression analyses of endogenous sugarcane genes involved in fungal pathogen resistance

Comparative expression analysis of mycoprotective genes in indigenous sugarcane genotype

Quantitative expression analyses were carried out for SUGARWIN1 and SUGARWIN2 genes whereas reaction conditions were normalized using three reference genes (25SrRNA, GAPDH and actin). The cDNA generated from total RNA of field-grown sugarcane genotypes was used as template. Expression of SUGARWIN1 was highest in genotype CPF-246 followed by CPF-248, S-2006-US-633, S-2006-US-658, HSF-240, CPF-247, CP-77-400, S-2003-US-127 and SPF-234. Likewise, the expression of SUGARWIN2 was highest in genotype CPF-246 followed by CPF-248, S-2006-US-633, HSF-240, CP-77-400, S-2006-US-658, S-2003-US-127, CPF-247 and SPF-234. Hence, expression of both of the pathogenesis related genes (SUGARWIN1 and SUGARWIN2) was highest in genotype CPF-246 whereas, it was lowest in genotype SPF-234 (Fig. 2).

Fig. 2.

Fig. 2

Open in a new tab

Comparative expression analysis of endogenous SUGARWIN genes by real time qPCR. SWO represents SUGARWIN1, whereas SWT represents SUGARWIN2

In-silico analyses

Sequence characterization

Codon opitimized sequences of sugarwin proteins with similar amino acid sequences were characterized for physico-chemical properties i.e. GRAVY (Grand Average of Hydropathicity), instability index, isoelectric point, amino acid and atomic compositions using ProtParam tool of EXPASY (Supplementary Table 1). SUGARWIN1 protein appeared to be comprised of 152 amino acids where 120 and 32 amino acids are involved in the constitution of functional domain and signal peptide, respectively. It has seven negatively charged residues and eleven positively charged residues. SUGARWIN2 protein appeared to 121 amino acids for functional domain and 27 amino acids for signal peptide (Medeiros et al. 2012) with eight negatively charged and five positively charged residues. So, these in silico analyses revealed that SUGARWIN1 has a net positive charge whereas SUGARWIN2 has a net negative charge. The calculated isoelectric point (pI = 8.78) showed that SUGARWIN1 is a basic protein with molecular mass of 13251.79 Dalton while isoelectric point (pI = 4.54) of SUGARWIN2 indicated that it is an acidic protein with molecular mass of 12840.08 Dalton. The instability indices of SUGARWIN1 (9.72) and SUGARWIN2 (3.09) let them propose as stable proteins as any of the protein with less than 40 instability index is considered to be stable (Guruprasad et al. 1990). The aliphatic index values (70.25 and 61.40, respectively) also let them propose as thermostable proteins. Similarly, GRAVY values (− 0.485 and − 0.317) suggested these proteins as hydrophilic.

Structural characterization

In spite of advancements in next generation sequencing and molecular biology, only 1% proteins have experimentally been characterized to determine their structure and function. Bioinformatics tools are of central importance for the characterization as well as prediction of structure and function of unknown proteins. Secondary structure of both of the proteins under investigation predicted that SUGARWIN1 has 5.83% α helices, 25.83% β sheets, 68.34% loops while SUGARWIN2 has 37.2% β sheets and 62.8% loops. Domain analysis with Pfam tool suggested that both of the proteins have single domain structure, similar to the BARWIN domain. Multiple sequence alignment showed that this domain was conserved in several monocot and dicot species. 3D structure of proteins was predicted by homology modeling using Phyre2 and SWISS-MODEL and Modeller v9.11 (Fiser and Sali 2003; Eswar et al. 2008). A barley BARWIN domain (PDB ID: IBW3 A) was used as template for structure prediction. Models were predicted by Modeller v9.11, Phyre2 and Swiss model for each protein and the best model was selected after analysis of their quality and stereochemical properties through VERIFY 3D, Ramachandran plot, Z-score, ERRAT and Qmean plot. UCSF chimera and FATCAT were used for structural evaluation and superimposition of the model protein with template protein. The evaluation of SUGARWIN1 and SUGARWIN2 showed RMSD of 0.46 Å and 0.47 Å, respectively, and P value of 0.00e+00. Ramachandran plot results revealed that in case of SUGARWIN1, 72.8% residues were in most favorable region with 23.3% in additional allowed region. For SUGARWIN2, 73.5% residues were in most favorable region and 21.4% in additional allowed region. Verify3D analyses also revealed that 100.0% residues of SUGARWIN1 and 99.18% residues of SUGARWIN2 proteins have averaged 3D-1D score of > 0.2, estimating the matching of 3D model with sequence. ERRAT analysis showed 71.287 quality factor for SUGARWIN1 and 71.134 for SUGARWIN2 which represents good quality of predicted structures. Qmean plot also showed favorable results with -3.68 z-score for SUGARWIN1 and -3.43 for SUAGRWIN2. The predicted models were submitted to Protein Model DataBase (PMDB) with PMDB ID: PM0081631 and PMDB ID: PM0081632 for SUGARWIN1 and SUGARWIN2, respectively. The 3D models and their superimposition are shown in Figs. 3 and 4.

Fig. 3.

Fig. 3

Open in a new tab

Structure prediction of SUGARWIN proteins. a Predicted structure of SUGARWIN1 (PMDB ID: PM0081631). b Predicted structure of SUGARWIN2 (PMDB ID: PM0081632). c 3D structure of selected template (PDB ID: IBW3 A). d Ramachandran plot of predicted SUGARWIN1 showing 72.8% residues in most favorable region. e Ramachandran plot of predicted SUGARWIN2 showing 73.5% residues in most favorable region. f Ramachandran plot of template protein

Fig. 4.

Fig. 4

Open in a new tab

Superimposition of SUGARWIN protein with template. a superimposition of SUGARWIN1 (cyan) with template structure (pink). b superimposition of SUGARWIN2 (cyan) with template structure (pink)

Multiple sequence alignment and phylogenetic study

Multiple sequence alignment of BARWIN domains of SUGARWIN1 and SUGARWIN2 protein from sugarcane with HEVEIN-like, BARWIN-like and chitin-binding domains from other monocotyledonous and dicotyledonous plants indicated high level of similarity (Fig. 5). Phylogenetic tree revealed that SUGARWIN1 and SUGARWIN2 were closely related to chitin binding proteins of maize and sorghum, respectively (Fig. 6).

Fig. 5.

Fig. 5

Open in a new tab

Amino acid sequence alignment of chitin-binding and barley wound-inducible protein (BARWIN) domain homologues on mono- and dicotyledonous plants: Maize (NCBI accession: XP_008678014.1), Maize2 (PWZ29527.1), SUGARWIN1 (CA145787), SUGARWIN2 (CA138924), Wheatwin1 (O64392.1), Wheatwin2 (O64393.1), Sorghum (XP_002449770.1), Steria Italica (XP_004979597.1), Panicum hallii (PAN43211.1), Dichanthelium oligosanthes (OEL35676.1), Rice (EAY81551.1), Papaya (4JP7), Tobacco (CAA41438.1), Barley (IBW-A), Arabodopsis (AEE74125.1), Apple (XP_008370705.1), Grapes (XP_002264720), Rose (XP_024189759.1), Eucalyptus (KCW85278.1), Trifolium (PNX73452.1), Jatropha (XP_012089267.1), Tomato (NP_001234083.1), Potato (AFW90614.1) performed by ClustalX2.1

Fig. 6.

Fig. 6

Open in a new tab

Phylogenetic analysis of chitin-binding and barley wound-inducible protein (BARWIN) domain homologues on mono- and dicotyledonous plants: Maize (NCBI accession: XP_008678014.1), Maize2 (PWZ29527.1), SUGARWIN1 (CA145787), SUGARWIN2 (CA138924), Wheatwin1 (O64392.1), Wheatwin2 (O64393.1), Sorghum (XP_002449770.1), Steria Italica (XP_004979597.1), Panicum hallii (PAN43211.1), Dichanthelium oligosanthes (OEL35676.1), Rice (EAY81551.1), Papaya (4JP7), Tobacco (CAA41438.1), Barley (IBW-A), Arabidopsis (AEE74125.1), Apple (XP_008370705.1), Grapes (XP_002264720), Rose (XP_024189759.1), Eucalyptus (KCW85278.1), Trifolium (PNX73452.1), Jatropha (XP_012089267.1), Tomato (NP_001234083.1), Potato (AFW90614.1) constructed using neighbor-joining method based on multiple sequence alignment by MEGA-X

Molecular docking studies

Molecular docking results comprehended antifungal activity of the SUGARWIN proteins, when studied for interaction with chitin, glucan, chitosan and mannans. Interaction of sugarwin proteins with chitin revealed their chitinolytic activity, hence may be involved in degrading fungal cell wall by hydrolyzing β-1,4-linkage of N-acetylglucosamine and 1,3-β-D-glucosidic linkages in β-1,3-glucans (Leubner-Metzger and Meins 1999). In this study, chitin, glucan, mannan and chitosan were used as ligands. The 3D structure of all ligand molecules were retrieved from PubChem (pubchem.ncbi.nlm.nih.gov/) whereas Patch dock (bioinfo3d.cs.tau.ac.il/PatchDock/) was used for molecular docking. The most stable complex was confirmed and selected on the basis of maximum cocked score and minimum atomic contact energy (ACE). The interacting residues were analyzed by Ligplot+ and ligand bound with active site of proteins is shown by discovery studio. Both of the proteins under investigation (SUGARWIN1 and SUGARWIN2) appeared to have interaction with the core constituents of fungal cell wall. In case of SUGARWIN1, Tyr12, Ser84, Asn85 and Asp89 showed strong hydrogen bonding whereas Asn13, Asp81 and Gln82 showed hydrophobic interactions with chitin. For SUGARWIN2, Ala22, Asp79 and Ser82 showed hydrogen bonding while Tyr12, Thr24, Gln80, Asp87 and Leu88 showed hydrophobic interactions with chitin. Similarly, SUGARWIN1 appeared to form covalent bonds with Tyr11 and Asn13, hydrogen bonds with Asp81, Gln82 and Asn85 and hydrophobic interactions with Pro14, Val24, Ser25, Ala26, Ser84 and Asp89 residues of glucans whereas it appeared to form covalent bond with Tyr12 and Asn17, hydrogen bonds with Ala22, Asn83, Asp87 and hydrophobic interactions with Pro14, Thr24, Asp79, Gln80, Ser82 and Leu88 residues of SUGARWIN2. Chitosan also showed strong hydrogen bonding with Ser84 of SUGARWIN1 and with Asp79, Gln80 and Asn83 of SUGARWIN2. Moreover, it appeared to have hydrophobic interactions with Tyr12, Leu21, Val24, Ser25, Ala26, Asp81, Asp89 and Leu90 residues of SUGARWIN1 and Tyr12, Ala22, Thr24, Asp87 and Leu88 residues of SUGARWIN2. Mannan formed hydrogen bonding with Ser84 residues of SUGARWIN1 and Asn13 and Asn83 residues of SUGARWIN2. Mannan showed hydrophobic interactions with Tyr12, Leu21, Val24, Asp81, Asp89 and Leu90 residues of SUGARWIN1 and Tyr12, Ala22, Thr24, Asp79, Asp87 and Leu88 residues of SUGARWIN2. The retrieved information is of pivotal importance to explore the interaction of sugarwin proteins with the major constituents of fungal cell wall (glucan, chitin, chitosan and mannan) which revealed their antifungal activity. Further, this will be used to work out active sites of SUGARWIN1 and SUGARWIN2 proteins involved in antipathogenic activity which may be of great help to develop plants having resistance against fungal pathogens.

Discussion

Field assessment in combination with molecular and computational tools provides a sophisticated approach to understand and control disastrous sugarcane fungal pathogens including red rot. We extended the current understandings about the role of wound-inducible proteins (BARWINs) in fungal pathogen infection and impact of expression of these genes in mycoparasitism. Numerous BARWIN homologues have been explored in rice, wheat, barley, etc. to exhibit antipathogenic activity against disastrous phytopthogens. A wide range of accessions were retrieved from the NCBI and were used for phylogenetic analyses of defence-related proteins. SUGARWIN1 and SUGARWIN2 were found to be closely related to chitin binding proteins of maize and sorghum, respectively, hence revealing out their active role in mycoparasitism. Medeiros et al. (2012) also studied phylogenetic relatedness of these gene but different sequences were used. Sugarcane seems to recruit these sugarwin genes (SUGARWIN1 and SUGARWIN2) during the course of evolution, to protect itself from fungal pathogens. Recombinant SUGARWIN2 has also been characterized to promote morphological changes and apoptosis in causal agent of disastrous red rot. In-silico analyses explored SUGARWIN as a valuable protein for phytopathogenic fungi (Franco et al. 2018; Medeiros et al. 2012). We also evaluated that SUGARWINs are highly stable proteins. The values of aliphatic indices and instability complex revealed out stability of the proteins. GRAVY (Grand Average of Hydropathy) results showed − 0.485 GRAVY value for SUGARWIN1 and − 0.317 for SUAGRWIN2. Negative GRAVY value indicates that protein is hydrophilic in nature and vice versa (Kyte and Doolittle 1982) so, both of the proteins under investigation are hydrophilic. Protein–ligand interaction revealed that SUGARWIN proteins interact chitin, chitosan, mannan and glucan molecules (Fig. 7), the fundamental constituents of Colletotrichum falcatum (Wessels 1994). Hence, they have some role in antipathogenic activity.

Fig. 7.

Fig. 7

Open in a new tab

Protein–protein interaction studies of SUGARWIN with defense related plant proteins. Residues of SUGARWINs interacting with chitin, chitosan, glucan and mannan are shown. a SUGARWIN1 interaction with chitin. b SUGARWIN2 interaction with chitin. c SUGARWIN1 interaction with chitosan. d SUGARWIN2 interaction with chitosan. e SUGARWIN1 interaction with glucan. f SUGARWIN2 interaction with glucan. g SUGARWIN1 interaction with mannan. h SUGARWIN2 interaction with mannan

Sugarcane has long breeding cycle and convoluted genetic system. It flowers sparsely except in the tropical climatic conditions. Complexity of genome, variable chromosome number, rare flowering habit, narrow genetic base, poor fertility, long duration to breed elite cultivars, are major breeding challenges in this grass. In spite of enormous efforts, it may take more than 12 years for the release of a new variety after being tested for agronomic traits, genetic stability and performance of newly developed clones (Landell et al. 2010). Implication of innovative research to shorten this time period may prove a great milestone in the betterment of sugarcane crop. DNA-based molecular markers and expressed RNA sequences (Wei et al. 2010) have proved their worth in improving breeding program and for the screening of desired elite clones. Considering the importance of SUGARWIN proteins in antipathogenic activity and limitations in sugarcane breeding, we linked the expression of SUGARWIN1 and SUGARWIN2 genes with the level of fungal pathogen resistance or susceptibility in elite indigenous sugarcane genotypes growing in the field.

Nine elite indigenous sugarcane genotypes were challenged with Colletotrichum falactum, the causal agent of red rot by plug method (Viswanathan 2017; Malathi and Viswanathan 2012). Pathogen infection led to the development of red color in the cane-culm due to production of anthocyanin pigment by host tissues in response to C. falcatum infection. Genotypes with dark reddish lesions were taken as susceptible (SPF-234 and CPF-247) whereas genotypes with diffused light red lesions were taken as resistant genotypes (S-2006-US-658). Likewise, the expression of both SUGARWIN1 and SUGARWIN2 genes was comparatively higher in genotypes CPF-246, CPF-248, S-2006-US-633, S-2006-US-658 and was lowest in the genotypes SPF-234 (Fig. 2). Hence, we evaluated that genotypes with lowest expression of SUGARWIN genes is the most susceptible (among the tested genotypes) whereas genotypes with comparatively higher expression of these genes are comparatively resistant. The retrieved results are of critical importance in countries where sugarcane breeding is limited and the introduction of new varieties is dependent on imported fuzz. Expression of SUGARWIN genes is an indicator of fungal pathogen tolerance. The retrieved results may be helpful to find markers for the selection of disease resistant genotypes by quoting reference.

Conclusion

Field data in combination with transcriptomic and computational analyses of SUGARWIN1 and SUGARWIN2 genes and proteins revealed that these mycoprotective proteins have critical role in the control of red rot infection. These results not only helped to evaluate elite genotypes for fungal pathogen resistance but may also be helpful to find markers for the selection of disease resistant genotypes.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 17 kb) (18.9KB, docx)

Acknowledgements

The authors are highly thankful to Higher Education Commission (HEC), Pakistan for providing funds and Sugarcane Research Institute for providing plant material for this research.

Funding

Funding was provided by Higher Education Commission, NRPU Pakistan (Grant No. 4656).

Compliance with ethical standards

Conflict of interest

Authors have no conflict of interest regarding publication of this manuscript.

References

  1. Bari R, Jones JD. Role of plant hormones in plant defence responses. Plant Mol Biol. 2009;69:473–488. doi: 10.1007/s11103-008-9435-0. [DOI] [PubMed] [Google Scholar]
  2. Benkert P, Künzli M, Schwede T. QMEAN server for protein model quality estimation. Nucleic Acids Res. 2009;37:510–514. doi: 10.1093/nar/gkp322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bernstein FC, Koetzle TF, Williams GJ, Meyer EF, Jr, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M. The Protein Data Bank: a computer-based archival file for macromolecular structures. Arch Biochem Biophys. 1978;185:584–591. doi: 10.1016/0003-9861(78)90204-7. [DOI] [PubMed] [Google Scholar]
  4. Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993;2:1511–1519. doi: 10.1002/pro.5560020916. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. de Andrade LM, dos Santos Brito M, Junior RFP, Marchiori PER, Nóbile PM, Martins APB, Ribeiro RV, Creste S. Reference genes for normalization of qPCR assays in sugarcane plants under water deficit. Plant Methods. 2017;13:28. doi: 10.1186/s13007-017-0178-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. de Wit PJ. How plants recognize pathogens and defend themselves. Cell Mol Life Sci. 2007;64:2726–2732. doi: 10.1007/s00018-007-7284-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Eswar N, Eramian D, Webb B, Shen MY, Sali A. Protein structure modeling with MODELLER. Methods Mol Biol. 2008;426:145–159. doi: 10.1007/978-1-60327-058-8_8. [DOI] [PubMed] [Google Scholar]
  8. Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, Potter SC, Punta M, Qureshi M, Sangrador-Vegas A, Salazar GA, Tate J, Bateman A. The Pfam protein families’ database: towards a more sustainable future. Nucleic Acids Res. 2016;44:279–285. doi: 10.1093/nar/gkv1344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Fischer D, Elofsson A, Rychlewski L, Pazos F, Valencia A, Rost B, Ortiz AR, Dunbrack RL., Jr CAFASP2: the second critical assessment of fully automated structure prediction methods. Proteins Struct Funct Bioinform. 2001;45:171–183. doi: 10.1002/prot.10036. [DOI] [PubMed] [Google Scholar]
  10. Fiser A, Sali A. Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol. 2003;374:461–491. doi: 10.1016/S0076-6879(03)74020-8. [DOI] [PubMed] [Google Scholar]
  11. Franco FP, Santiago AC, Henrique-Silva F, de Castro PA, Goldman GH, Moura DS, Silva-Filha MC, Silva-Filho MC. The sugarcane defense protein SUGARWIN2 causes cell death in Colletotrichum falcatum but not in non-pathogenic fungi. PLoS One. 2014;9:e91159. doi: 10.1371/journal.pone.0091159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Franco FP, Dias RO, Toyama D, Henrique-Silva F, Moura DS, Silva-Filho MC. Structural and functional characterization of PR-4 SUGARWINs from sugarcane and their role in plant defense. Front Plant Sci. 2018;9:1916. doi: 10.3389/fpls.2018.01916. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gasteiger E, Gattiker A, Hoogland C, Ivanyi I, Appel RD, Bairoch A. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 2003;31:3784–3788. doi: 10.1093/nar/gkg563. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Geourjon C, Gilber D. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci. 1995;11:681–684. doi: 10.1093/bioinformatics/11.6.681. [DOI] [PubMed] [Google Scholar]
  15. Guruprasad K, Reddy BB, Pandit MW. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng Des Sel. 1990;4:155–161. doi: 10.1093/protein/4.2.155. [DOI] [PubMed] [Google Scholar]
  16. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–1549. doi: 10.1093/molbev/msy096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Mol Biol. 1982;157:105–132. doi: 10.1016/0022-2836(82)90515-0. [DOI] [PubMed] [Google Scholar]
  18. Landell MGA, Creste S, Pinto LR, Xavier MA, Bressiani JA (2010) The Strategy for regional selection for the development of energy sugarcane cultivar profile. In: Cortez LAB (ed) Sugar cane bioethanol: R&D for productivity and sustainability, Blucher, ISBN: 9788521205302, Sao Paulo, pp 345–52
  19. Laskowski RA, Rullmann JA, MacArthur MW, Kaptein R, Thornton JM. AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR. 1996;8:477–486. doi: 10.1007/BF00228148. [DOI] [PubMed] [Google Scholar]
  20. Leubner-Metzger G, Meins F., Jr . Functions and regulation of plant -1,3-glucanes (PR-2) In: Datta SK, Muthukrishnan S, editors. Pathogenesis-related proteins in plants. Boca Raton: CRC Press; 1999. pp. 49–76. [Google Scholar]
  21. Luthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature. 1992;356:83–85. doi: 10.1038/356083a0. [DOI] [PubMed] [Google Scholar]
  22. Malathi P, Viswanathan R. Identification of pathogenicity determinants in Colletotrichum falcatum using wild and mutant cultures. Sugar Tech. 2012;14:383–390. doi: 10.1007/s12355-012-0161-1. [DOI] [Google Scholar]
  23. McGuffin LJ, Bryson K, Jones DT. The PSIPRED protein structure prediction server. Bioinformatics. 2000;16:404–405. doi: 10.1093/bioinformatics/16.4.404. [DOI] [PubMed] [Google Scholar]
  24. Medeiros AH, Franco FP, Matos JL. Sugarwin: a sugarcane insect-induced gene with antipathogenic activity. Mol Plant-Microbe Interact. 2012;25:613–624. doi: 10.1094/MPMI-09-11-0254. [DOI] [PubMed] [Google Scholar]
  25. Souza TP, Dias RO, Silva-Filho MC. Defense-related proteins involved in sugarcane responses to biotic stress. Genet Mol Biol. 2017;40:360–372. doi: 10.1590/1678-4685-gmb-2016-0057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Srinivasan KV, Bhat NR. Criteria for grading resistance of sugarcane varieties to red rot (Glomerella tucumanensis) Curr Sci. 1961;30:425. [Google Scholar]
  27. Viswanathan R. Pathogen virulence in sugarcane red rot pathogen versus varieties in cultivation: classical case of loss in virulence in the pathotype CF06 (Cf671) Sugar Tech. 2017;19:293–299. doi: 10.1007/s12355-016-0458-6. [DOI] [Google Scholar]
  28. Viswanathan R, Samiyappan R. Induced systemic resistance by fluorescent pseudomonads against red rot disease of sugarcane caused by Colletotrichum falcatum. Crop Protec. 2002;21:1–10. doi: 10.1016/S0261-2194(01)00050-3. [DOI] [Google Scholar]
  29. Webb B, Sali A. Comparative protein structure modeling using MODELLER. Curr Protoc Bioinform. 2014;47:5–6. doi: 10.1002/0471250953.bi0506s47. [DOI] [PubMed] [Google Scholar]
  30. Wei X, Jackson PA, Hermann S, Kilian A, Heller-Uszynska K, Deomano E. Simultaneously accounting for population structure, genotype by environment interaction, and spatial variation in marker-trait associations in sugarcane. Genome. 2010;53:973–981. doi: 10.1139/G10-050. [DOI] [PubMed] [Google Scholar]
  31. Wessels JGH. Developmental regulation of fungal cell wall formation. Annu Rev Phytopathol. 1994;32:413–437. doi: 10.1146/annurev.py.32.090194.002213. [DOI] [Google Scholar]
  32. Yi Y, Li R, Xu H, Wu X, Li S, Zhang J, Yin Y. Identification of SRAP and RGA markers linked to powdery mildew (Blumeria graminis) resistance gene PmZB90 in common wheat. Aust J Crop Sci. 2013;7(3):454–459. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1 (DOCX 17 kb) (18.9KB, docx)

Articles from 3 Biotech are provided here courtesy of Springer

RESOURCES