INTRODUCTION

Rice is the major crop feeding more than 50% of the global population. It is also an important economic crop for farmers and landless labourers. Asian countries contribute more than 90% to the global rice production (Fukagawa and Ziska, 2019). India is the second largest producer of rice in the world producing 177.6 million tonnes (Kumar et al., 2021). In the year 2019-2020, India exported basmati rice to the tune of US $4.33 billion whereas the non-basmati export was of US$2.01 billion (Anonymous).

Currently, there is annual shortage of rice production and the gap is fulfilled by utilising the reserve stocks. If a similar situation continues without doubling the rice production, the annual shortage is estimated to reach 800,000 tons by 2030 (Asibi et al., 2019). Rice production can be enhanced by increasing the productivity or increasing the cultivated area. Increasing the area is highly difficult task due to unavailability of arable land (Samal and Babu, 2018). Several other challenges exist such as water and labour shortage, climate change, deteriorating soil health, etc. (Nawaz et al., 2022). Another important aspect is to avoid the losses due to environmental factors. It is estimated that the cumulative effect of the several environmental factors cause more than 50% losses in the yield (Anami et al., 2020). Rice blast disease is the major threat to rice production and may cause up to 100% yield loss in case of panicle blast (Khan et al., 2014).

Breeders have identified a large number of genes governing blast resistance in rice and several of them have been successfully utilized in breeding programmes (Ning et al., 2020). However, the success is limited by frequent breakdown of R gene mediated resistance (Mentlak et al., 2012) and few genes for panicle blast resistance. As the panicle blast is more severe in terms of yield losses, genes providing broad spectrum durable resistance against panicle blast are highly sought after (Du et al., 2021). Comparative transcriptome analysis of panicle tissues of blast resistant cultivar Tetep and a susceptible cultivar HP2216 at different time points of M. oryzae infection, revealed hundreds of differentially expressed genes including two von Willebrand factor domain A containing genes (Kumar et al., 2021). A thorough analysis of the vWA family genes by Karkute at el. (2022) identified OsvWA36 and OsvWA37 as the key genes for blast disease resistance among the 40 individual members of the family. OsvWA36 gene has also been reported to be associated with gall midge resistance in rice (Rawat et al., 2012). On the other hand, OsvWA37 gene was reported to be associated with resistance to the parasitic weed Striga hermonthica as it was induced at different stages of weed infection in rice (Swarbrick et al., 2008). Recently, both OsvWA36 and OsvWA37 genes were found to be present in a panicle blast resistance Pb-bd1 locus (Fang et al., 2019). Thus, all these reports suggest the significant role of OsvWA36 and OsvWA37 genes in rice blast disease response.

Considering the significant role of these genes in blast resistance, it is of utmost importance to analyse the molecular diversity of these genes in different species of rice. Therefore, the present study was carried out to analyse the allelic diversity of these genes in all the 11 species of rice, whose genome sequence data is available on Gramene database (Tello-Ruiz et al., 2021). The function of any protein depends on its 3 dimensional structure and therefore, we have also modelled the 3D structures of these proteins in cultivated germplasm cv. Tetep which is highly resistant to both leaf and panicle blast (Wang et al., 2019).

MATERIALS AND METHODS

A study was conducted at ICAR-National Institute for Plant Biotechnology, New Delhi Location/ Lab during 2021.

2.1.  Identification of OsvWA36 and OsvWA37 genes from Tetep genome

Complete genome sequence data available at ICAR-National Institute for Plant Biotechnology was utilized to retrieve the sequence of OsvWA36 and OsvWA37 genes using BLAST tool in BioEdit. Sequence of these genes in Nipponbare was used as a query for BLAST search. The retrieved sequences were subjected to FGENESH tool (Solovyev et al., 2006) to identify the open reading frame (ORF) and corresponding protein sequence.

2.2.  Retrieval of protein and promoter sequences of vWA genes in different rice species

The protein sequences of OsvWA36 and OsvWA37 genes in different rice species were retrieved from the Gramene database (Tello-Ruiz et al., 2021) by using the BLAST search tool. The sequences of OsvWA36 and OsvWA37 genes in Tetep cultivar were used as a query and searched with default parameters against different species of rice such as Oryza punctata, Oryza nivara, Oryza sativa Japonica, Oryza barthii, Oryza meridionalis, Oryza rufipogon, Oryza sativa Indica, Oryza glaberrima, Oryza brachyantha, Oryza glumaepatula, and Oryza longistaminata.

2.3.  Evolutionary relationship analysis of vWA genes in rice species

All the OsvWA36 protein sequences of rice species were aligned by using multiple alignment tool MUSCLE (Edgar, 2004) in MEGA-X (Kumar et al., 2018). The phylogenetic tree was constructed based on this alignment by using the maximum likelihood method and JTT matrix-based model with 1000 bootstrap replications. Likewise, phylogenetic tree for OsvWA37 proteins were generated from all 11 oryza species.

2.4.  Protein modelling, structure refinement and validation OsvWA36 and OsvWA37 proteins in Oryza sativa cv Tetep

The Three-Dimensional (3D) structures of OsvWA36 and OsvWA37 proteins were generated by utilizing the I-TASSER (Yang et al., 2015) web tool. This tool determines the C-score, TM-score value and root mean square deviation (RMSD) and provides with the best five predicted structure models of the given protein sequence (Roy et al., 2010). The modelled structure was chosen based on its C-score that range between -5 to 2. The model having higher C-score represents a better model. Additionally, the model structures were refined using the GalaxyRefine webserver (Heo et al., 2013). The structure refinement is the quality improvement process that provides a robust model by using the CASP10 based refinement method (Ko et al., 2012 Nugent et al., 2014). This webserver reconstructs side chains, re-builds unreliable loops, and then repacks them followed by structure relaxation using dynamic simulations (Heo et al., 2013). The refined structures were visualized using BIOVIA Discovery Studio 2020. Further, the Ramachandran plot was created for the refined protein structures by using RAMPAGE server to analyse the overall quality of the structure (Lovell et al., 2003).

RESULTS AND DISCUSSION

3.1.  Molecular diversity of OsvWA36 and OsvWA37 genes

The ORFs of OsvWA36 and OsvWA37 genes in Tetep cultivar encoded proteins of 633 and 598 amino acid residues respectively. Both these genes contain a single vWA domain which is important for their functioning through interaction with other proteins (Karkute et al., 2022). The protein sequence alignment revealed a significant diversity in OsvWA36 and OsvWA37 genes among the different species of rice (Table 1). Besides sequence variation, the number of amino acid residues also varied from 501 residues in O. longistaminatato 633 in O. sativa japonica group and Tetep cultivar in case of OsvWA36 protein. Even more diversity was observed in case of OsvWA37 protein. The protein encoded by OsvWA37 gene in O. longistaminatais only of 295 amino acid residues whereas it is of 1004 amino acid residues in O. meridionalis. The diversity in the protein sequences of these genes observed in different species reflects their genetic diversity. However, the vWA domain is highly conserved in vWA36 protein (Figure 1a) whereas it is comparatively less conserved in case of vWA37 protein (Figure 1b) among different species of rice. It is important to note that, there is also a significant variation in vWA domain sequence between OsvWA36 and OsvWA37 proteins.

Table 1: Protein level variation of OsvWA36 and OsvWA37 gene in different rice species

Figure 1: Alignment of vWA domain sequence of OsvWA36 (1a) and OsvWA37 (1b) proteins in different species of rice

Figure 1: Continue...

One of the interesting observations is that both these genes were not identified as separate genes in O. barthii, rather they are combined to form a single gene coding for 1488 amino acid residue protein. Normally, these two genes are located very closely to each other on chromosome 11 in O. sativa. This suggests that OsvWA36 and OsvWA37 genes might have been originated from a single large gene and got separated during the evolution. This also indicates that the O. barthii, a wild species of rice could be one of the oldest species and other species might have originated from it. O. barthii is reported to be the progenitor of African rice O. glaberrima (Linares, 2002).

3.2.  Evolutionary relationship of OsvWA36 gene in different rice species

Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the JTT model, and then selecting the topology with superior log likelihood value. Thus, the phylogenetic tree was generated showing relationship of OsvWA36 protein in different wild species of rice along with protein from Tetep cultivar (Figure 2). O. nivara, O. sativa indica, O. sativa japonica and O. sativa cv. Tetep were closely related to each other and thus, formed a single group (group I). The O. nivara is the wild progenitor of O. sativa present in Asia region (Haritha et al., 2018) and this has been reflected in the phylogenetic tree for OsvWA36 protein also. Another group (group II) was formed by four species O. glumaepatula, O. meridionalis, O. glaberrima, and O. longistaminata. This second subclade surprisingly consists of species from different geographical regions such as O. glaberrima, and O. longistaminata from Africa, O. glumaepatula from America and O. meridionalis from Australia (Mondal and Henry, 2018). These two groups formed a single clade including 8 species. OsvWA36 protein sequences in O. rufipogan and O. barthii were similar to each other but different from other species and were placed separately in phylogeny. vWA36 in O. brachyantha is extremely different from other species.

Figure 2: Phylogenetic tree of vWA36 protein in different rice species. The percentage of trees in which the associated taxa clustered together is shown next to the branches. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site

3.3.  Evolutionary relationship of OsvWA37 gene in different rice species

The evolutionary history of OsVWA37 gene was studied by generating the phylogenetic tree in the similar way as in case of OsvWA36 gene. Contrary to OsvWA36 gene, the evolutionary history of OsvWA37 formed two different clades (Figure 3). The clade I has 8 species including the cultivated species O. sativa and O. glaberrima. OsvWA37 protein in O. nivara, O. sativa cv. Tetep and O. sativa japonica group is completely conserved forming a single group with two more species O. meridionalis and O. glaberrima. The group 2 of clade I was formed by 3 species such as O. longistaminata, O rufipogan and surprisingly O. sativaindica group. On the other hand, clade II included most diverse species O. punctata, O. brachyantha, O. barthii, and O. glumaepatula.

Figure 3: Phylogenetic tree of vWA37 protein in different rice species. The percentage of trees in which the associated taxa clustered together is shown next to the branches. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site

3.4.  Tertiary structure prediction, refinement and validation analysis

The 3D structure of the proteins was modelled with the help of I-TASSER server. The server carries out 3D modelling based on the consequence of threading template alignment and ranks the confidence of models quantitatively on C-score. The obtained C-scores were -2.24, and -1.75, for the modelled 3D structure of OsvWA36, and OsvWA37 respectively (Figure 4). Further refinement of the models by GalaxyRefine significantly improved the quality parameters (Table 2). Finally, the best refined and polished models were subjected to generate the Ramachandran plot, which indicated the improved percentage of residues in the favored region (Figure 5). The refined models showed Ramachandran plot score of 92.407%, and 92.564%, for the modelled structure of OsvWA36, and OsvWA37 respectively. The vWA domain of the vWA proteins in involved in protein-protein interaction to carry out their cellular function (Yang et al., 2016). The high-quality structures of the proteins generated in the study will help to carry out the structural characterization and also can be confidently used for the interaction studies. This will be useful to understand and identify the genes interacting with these two key OsvWA proteins and subsequently use them in rice breeding for blast disease resistance.

Figure 4: Three dimensional models of the proteins showing prominent secondary arrangements (Red=Helix, Yellow= Sheet, Green=Loop)

Figure 5: Ramachandran plot statistics showing the % of highly preferred (green marks), preferred (orange marks), and questionable conformations (red marks). The black, dark grey, grey, and light grey represents the highly preferred conformations (Delta≥-2), white with black grid represents preferred conformation (-2>Delta≥-4), and white with grey grid represents questionable conformations (Delta<-4)

CONCLUSION

The blast disease responsive OsvWA36 and OsvWA37genes have conserved vWA domain among different cultivated and wild species of rice. The genes vary in size of the encoded protein in different rice species. OsvWA36 gene is comparatively more conserved than OsvWA37 gene. The vWA domain is involved in protein interactions and therefore to study the interaction of these proteins with other disease responsive proteins, high quality 3 dimensional structures have been developed that can be confidently used for docking studies.

FURTHER RESEARCH

The role of these novel OsvWA36 and OsvWA37 genes need to be characterized in detail to identify the mechanism of resistance and pathways in which the genes are involved in.

ACKNOWLEDGEMENT

The work was supported by ICAR-National Institute for Plant Biotechnology; Indian Council of Agricultural Research (ICAR)-Centre for Agricultural Bioinformatics, IASRI (CABin Scheme).

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