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Current Status of Conventional and Molecular Interventions for Blast Resistance in Rice Deepti SRIVASTAVA1, Md. SHAMIM2, Mahesh KUMAR2, Anurag MISHRA3, Pramila PANDEY4, Deepak KUMAR4, Prashant YADAV5, M. H. SIDDIQUI1, K. N. SINGH4 (1IIAST, Integral University, Kursi Road, Dashauli, Lucknow,U.P., 226021, India; 2Department of Molecular Biology and Genetic Engineering, Dr. Kalam Agricultural College, Bihar Agricultural University, Sabour, Bhagalpur, Bihar, 813210, India; 3Department of Agriculture Biotechnology, Sardar Vallabhbhai Patel University of Agriculture and Technology, Modipuram, Meerut, U.P., 250110, India; 4Department of Plant Molecular Biology and Genetic Engineering, N.D. University of Agriculture and Technology, Kumarganj, Faizabad, U.P., 224 229, India; 5ICAR-Directerate of Rapeseed Mustard Research, Bharatpur, Rajasthan, 321303, India)

Abstract: Pyricularia oryzae anamorph of Magnaporthe oryzae is one of the most notorious fungal pathogen of rice causes severe economic loss worldwide in rice production. Various methods, viz. cultural, biological and molecular approaches are utilized to counteract this pathogen. Moreover, some tolerant or resistant rice varieties have been developed with the help of breeding programmes. Isolation and molecular characterization of different blast resistance genes now open the gate for new possibilities to elucidate the actual allelic variants of these genes via various molecular breeding and transgenic approaches. However, the behavioral pattern of this fungus breakup the resistance barriers in the resistant or tolerant rice varieties. This host-pathogen barrier will be possibly countered in future research by comparative genomics data from available genome sequence data of rice and M. oryzae for durable resistance. Present review also emphasized fascinating recent updates, new molecular breeding approaches, transgenic and genomics approaches i.e. miRNA, genome editing for the management of blast disease in rice. The updated information will be helpful for the rice researcher’s for the durable, resistance breeding programme in rice against blast pathogen. Key words: backcross breeding; gene pyramiding; allele mining; transgenic

Rice (Oryza sativa) is one of the important cereal crops that constitute the staple diet all over the world and grown everywhere except Antarctica. There are up to 70 different diseases caused by various fungi, bacteria, viruses or nematodes have been accounted on rice (Zhang et al, 2009). Among them, rice blast is the most destructive disease, which largely widespread in rice fields that causes significant reduction in grain yield and quality. Rice blast is called Pyricularia oryzae and its anamorph is Magnaporthe oryzae. Infection of blast fungus can occur in all growth stages of rice and symptom of the disease can be found in any aerial part. Blast infection is initiated

when the conidia are deposited on leaves of young seedlings. The spores produced at later stages of growing season results in collar blast and neck blast (Wang et al, 2014a) and these causes about 30% of yield loss (Spence et al, 2014). In the present time, climate change may modify pathogen distribution and expansion charges, and adjust the resistance, growth, and metabolism of host plants. In comparison to the humid tropics and in warm, humid subtropical regions, such as Thailand, Southern China, and the Philippines, low temperatures increased the risk of blast epidemics (Bevitori and Ghini, 2014). To overcome the loss due to blast disease, many

Received: 21 May 2017; Accepted: 8 August 2017 Corresponding author: K. N. SINGH ([email protected]) Copyright © 2017, China National Rice Research Institute. Hosting by Elsevier B V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of China National Rice Research Institute http://dx.doi.org/

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management practices such as chemical control, biological control, disease forecasting, conventional breeding (recurrent selection, pedigree method and mutation breeding) were adopted. However, these methods were not sufficient to control the blast disease completely due to high labor cost and more time consumption. Conventional breeding also generally affected by linkage drag by which closely linked resistance gene undesired traits are also transferred. Analysis of rice germplasm with different races reveals complete resistance conferred by major blast R gene; however, the resistance may be broken down due to its single R gene locus having race specific characteristics. Progress in the different molecular marker development (SSR, InDel and SNPs) and functional genomics, blast resistance genetics in rice have been strengthened (Hayashi et al, 2006). Resistant germplasm carrying both major and minor R genes are an important genetic resource for rice breeders and by which blast resistance will be improved in elite rice varieties. In resistant varieties, most of the R genes were conserved with the point mutations and InDels. Hence, the identification of these R genes/alleles with the help of genomic tools will be help in modern plant breeding by the utilization of genetic and genomic resources (Kumari et al, 2013; Li et al, 2014). To date, 100 quantitative genes have been detected in rice, and about 22 R-genes have been successfully cloned and characterized (Sharma et al, 2012; Ashkani et al, 2016). Marker-assisted selection (MAS) and conventional breeding together have facilitated resistance genes to be combined (or ‘pyramided’) in elite rice varieties to improve blast resistance and its durability. QTLs, allele mining and association mapping improves molecular breeding against the blast resistance. Genetic engineering for blast disease resistance has been contributed to sustainable disease resistance (Coca et al, 2004). Like transgenic research, bioinformatics also plays an important role to understand the evolution of new pathotypes of Magnaporthe isolates by studying R and AVR genes in rice-Pyricularia oryzae ecosystems. This analysis is utilized for designing of better resistance breeding strategies. The available genome sequences of M. oryzae isolates and rice accelerate the study of host-pathogen interaction and possible strategies for resistance breeding in rice against the blast disease. In this connection, allele mining for resistance genes in

all sequenced rice genomes showed presence/absence of polymorphism and a large number of structural variations (Gowda et al, 2015; Mahesh et al, 2016). Similarly, genome editing is a powerful tool for creation of variation in gene pool either by inserting or deleting target gene which interrupts the functions of the gene. The CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPRassociated protein 9) genome editing method uses single sequence-specific nucleases by which targeted cleavage of genomic DNA occurs (Rani et al, 2016). Such techniques allow frequent introgression of single as well as multiple disease resistant genes into susceptible rice varieties. In the present review, we briefly discussed the several conventional, molecular, transgenic and bioinformatics approaches for the management of blast disease of rice that will certainly provide new insight into the blast disease control.

Rice blast disease management Earlier period, when no other method were known for controlling blast disease in rice, then cultural control practices were the only method for controlling blast disease in rice. The cultural control method includes management of nutrient, water, time of planting and spacing. In nutrient management, nitrogen (N) and silicon (Si) elements mostly affects the disease incidence and development. Silica application did not increase the yield but showed potential towards blast resistance (Siregar et al, 2016). Accumulated silicon in rice tissues enhances resistance against insects and diseases, increases erectness of leaves resulting in increased photosynthesis, improves water usage, and decreases toxicity of heavy metals and cuticular transpiration (Nakata et al, 2008). Application of fungicides is the most common method to control blast disease in rice. There are several important chemicals used to repress the blast disease in rice. These chemical fungicides are applied in different ways as seed treatment, soil drenching and foliar spray (Table 1). These chemicals includes copper fungicides, copper fungicide with mixture of phenylmercuric acetate (PMA), PMA and slaked lime organophosphorus fungicides, phosphorothiolate fungicides such as iprobenfos and edifenphos, dithiocarbamate and edifenphose, benomyl, carbendazim, systemic fungicides with different mode of action, melanin inhibitors, ergosterol biosynthesis inhibitor (EBI), like anti-mitotic compounds, bavistin,

Deepti SRIVASTAVA, et al. Current Status of Conventional and Molecular Interventions for Blast Resistance in Rice Table 1. Most common fungicides used against rice blast disease. Fungicide Probenazole Tricyclazole Azoxystrobin Isoprothiolane Propiconazole Benomyl

Nature Systemic Systemic Systemic Systemic Systemic Systemic

Mode of action

Activates plant defence response Melanin biosynthesis inhibitor-R (polyhydroxyl-naphthalene reductase) QOI complex 3 inhibitorg Phospholipid biosynthesis (methyltransferase) and/or choline biosynthesis inhibitor Sterol biosynthesis (14-demethylase) inhibitor Benomyl binds to microtubules, interfering with cell functions, such as meiosis and intracellular transportation Edifenphos Systemic Inhibitor of the biosynthesis of phosphatidylcholine Iprobenfos Systemic Inhibitor of the biosynthesis of phosphatidylcholine Pyroquilon Systemic Inhibitor of melanin biosynthesis inhibitors (MBIs) Diclocymet Systemic Inhibitor of melanin biosynthesis inhibitors (MBIs) Carpropamid Root-systemic Inhibits the enzyme scytalone dehydratase essential for the synthesis of the melanin biosynthesis and also induce resistance in plants Fenoxanil Systemic Inhibitor of melanin biosynthesis inhibitors (MBIs) Metominostrobin Systemic Inhibits respiratory electron transfer Zineb Contact When apply, it is converted to an isothiocyanate, and inactivates the sulphahydral (SH) groups enzymes of fungi Hexaconazole Systemic Belongs to group 3 and, inhibits ergosterol biosynthesis (steroid dimethylation inhibitor) thereby controlling growth and reproduction of plant fungal pathogens Carbendszim 12% Contact + Systemic Carbendazim works by inhibiting spindle formation at mitosis stage. Mancozeb affects + Mancozeb 63% combination the nervous system Eprobenfos Systemic Eprobenfos block the synthesis of phospholipid and alters the membrane structure of fungus by increasing the permeability with consequent loss of vital cellular component Kresoxim methyl Contact and Acts by binding to Qo site blocking electron transfer and respiration of the fungi Systemic (local) Tebuconazole Systemic Tebuconazole is dimethylase inhibitor (DMI)-interferes in process of building the structure of fungal cell wall

hinosan, tricyclazole, pyroquilon, carpropamid (melanin biosynthesis inhibitor), fenoxanil (melanin biosynthesis inhibitor), tiadinil (plant activator). In the field evaluation of commercial fungicide formulations nativo (tebuconazole + trifloxystobin) and rabicide (tetrachlorophthalide), score (difenoconazole) are found most effective. Tricyclazole 22% + Hexaconazole 3% SC fungicide can control rice blast disease effectively when applied at weekly intervals (Kumar et al, 2013). Some antibiotics are also used for rice blast such as cephalothecin is produced by a species of Cephalothecium, antimycin-A, antiblastin, blastmycin and blasticidin-A, Extracts of some plants such as Atalantia monophylla (Wild Lime), Plumbago rosea or Lal chitrak, Bidens pilosa, Ocimum sanctum (Tulasi), Aloe vera, Aegle marmelos (Bael), Azadirachta indica, Allium sativum, Annona muricata, Chrysanthemum coccineum, processed Coffee arabica, Camellia sinensis, Zingiber officinalis, Datura stramonium and Nicotiana tabacum were also effective in controlling blast disease. Disease forecasting is most effective and economical strategy against blast disease. An online SVM (support vector machines) is the first web-server for rice blast prediction. Disease forecast also helps

Active ingredient/ha 1000–2400 mL 225–300 mL 170 mL 300 mL 125 mL 120 mg 250–300 mg 300–500 mL 500 mL 300 mL 139 mL 120–150 mg 100 mg 1500 mg 50 mL 750 mg 240 mL 250 mg 187.5 mL

globally in plant science community as well as to the farmers (Kaundal et al, 2006). On the basis of computer simulation, forecast models LEAFBLAST (Choi et al, 1988), EPIBLAST (Kim and Kim, 1993) and EPIBLA (Manibhushanrao and Krishnan, 1991) are available. Disease forecasting have been reported by various methods viz. Weather variables (Calvero et al, 1996), machine learning algorithms (Kontargyri et al, 2008), SVM-based system using fuzzy directional features (Sadek et al, 2013), model validation for rice blast forecasting (Ramsey and Schafer, 1993), perceiving plant health condition (Liu et al, 2010) were successfully used in disease forecasting.

Traditional breeding approaches for blast resistance in rice Chemical control of blast disease is one of most effective practice, however, in developing countries, poor farmers cannot meet the expense of pesticides to control blast disease. Use of chemicals also causes serious environmental hazards. Continuous use of chemical fungicides also leads to the reappearance of resistant races of the pathogen. The conventional breeding method is the oldest breeding approach

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which is vital for the creation of new traits or novel genetic variants. The last 30 years, with the help of conventional breeding a wide range of genes for resistance to diseases in IRRI’s elite cultivars has been reported (Bonman et al, 1992). In conventional breeding program, different methods (pedigree method, backcrossing, recurrent selection and mutation breeding) have been used frequently in the last decades. The pedigree method is suitable when resistance is governed by major genes. It is most extensively used in rice improvement and highly appropriate to develop resistance to insects and diseases in short duration. Backcrossing is another widely used and most common technique in rice breeding for interrogation or substitution of the desired gene from donor parent to recipient (Allard, 1999). Backcrossing technique is mainly used to decline the donor genome content into the progenies (Xi et al, 2008). Besides backcross breeding, the recurrent selection is an additional established breeding method used in rice for disease control (Fujimaki et al, 1979). It allows shorter breeding cycles, more specific follow-up of genetic gains as well as provide an opportunity to develop a broad range of genetic diversity breeding lines. There were many examples of the blast resistant cultivars that were developed through recurrent selection such as upland cultivar CG-91 (Guimaraes et al, 2000). There are several major genes Pib, Pita, Pia, Pi1, Pikh, Pi2 and Pi4 have been identified and successfully introgressed into rice varieties for blast resistance through conventional breeding method (Korinsaka et al, 2011).

Mutation breeding Mutation breeding technique is very effective and helpful in development of new alleles that do not exist in germplasm pools. It is now possible to tag mutated genes and pyramid them into a single noble breeding line and follow up them in subsequent breeding programs (Shu, 2009). Many varieties developed by mutation breeding, including RD6, a radiationinduced glutinous mutant of the prominent nonglutinous variety Khao Dawk Mali 105 (KDML105). An attempt was made to induce blast resistance gene in the high yielding variety Ratna (IR8/TKm 6) with the help of chemo-mutagenesis with 0.1% and 0.2% EMS. Blast resistance mutant R917 was derived from the F1 progeny radiated by 10

krad 60Co c-ray (Zhang et al, 2003). In China, the mutant rice variety Zhefu 802 derived from the variety. Rice variety Simei No. 2 was also developed by Gamma ray irradiations has a high resistance to rice blast (Ahloowalia et al, 2004). The disadvantage of mutation breeding is limited influence in generating the dominant alleles including less efficiency in rice.

Biotechnological and molecular marker approaches for management of blast pathogen Before discussing the molecular approaches for the management of blast disease in detail it is important to know how the blast pathogen infects the rice plant and what are the mechanisms involved in resistance to blast disease in rice. The infection starts when blast fungus adheres on rice and within 24 h of infection, appressoria formation occurs that enters into the leaf cuticle and invades epidermal cells (Skamnioti and Gurr, 2009). After infection, M. Oryza Avr (avirulence) protein recognized by rice R (resistance) proteins then effector-triggered immunity (ETI) arises. These processes further lead to a hypersensitive response in the host and ultimately stop the fungal growth. In the other defence activation system, when fungal hyphae spread inside plant causing disease symptoms then fungal pathogen-associated molecular patterns (PAMP; e.g., chitins) is recognized by rice pattern-recognition receptors (PRR) and pathogen triggered immunity (PTI) is triggered (Chen and Ronald, 2011). The ETI defence mechanisms are stronger and faster than the PTI defense mechanism (Tao et al, 2013). Effectors are special protein molecules secreted by the pathogen that facilitate infection by altering the host cell structure and function and trigger the defence response (Dangl et al, 2013; Jain et al, 2016). In riceM. oryzae pathosystem, 16 effector proteins have been cloned and characterized including Avr effectors (Avr-Pita 1, PWL1, PWL2, ACE1, Avr-CO39, AvrPiz-t, AvrPia, AvrPii, AvrPik/km/kp, AvrPib), Secreted LysM protein (SLP1 and MC69), Biotrophyassociated secreted (BAS) proteins (BAS1, BAS2, BAS3 and BAS4) (Oliva et al, 2010; Selin et al, 2016). The majority of R gene works according to the gene for gene concept and race specific. But resistance governed by major R genes generally breakdown after some years because of the frequent variation in the blast fungus population, and due to the ability of blast fungus to adapt by mutating or deleting the

Deepti SRIVASTAVA, et al. Current Status of Conventional and Molecular Interventions for Blast Resistance in Rice

corresponding avirulence gene (McDonald and Linde, 2002). New molecular tools and available model plant-fungal interactions have improved the understanding of the mechanisms involves during fungal infections. The available annotated genome sequences of M. oryzae (Dean et al, 2005) and rice (Ohyanagi et al, 2006) help for the molecular mechanism and their interactions during the disease development. The studied host pathogen interaction and molecular marker information facilitate marker assisted selection (MAS) programme for the incorporation of blast resistance genes into rice breeding programmes. Molecular markers are widely used for the selection of desired traits and identification of genomic regions associated with different important disease including blast resistance against broad-spectrum isolates. Molecular markers are highly precise and reduce the selection time which is the major limitation of conventional breeding approach. In this section, we discussed some molecular methods for the management of blast disease but before molecular methods of disease management information about the identified resistance gene(s) will be more useful.

Identification of blast resistance gene(s) in rice through biotechnological tools Conventional genetic analysis identified donors with resistance, have been designated as Pi1, Pi2, Pi3, Pi4, Pi5, Pi6, Pi7, Pi9, Pi10, Pi11 (Causse et al, 1994), Pia, Pib, Pik, Pit, Pita, Pita2, Pi12, Pi17, Pi18, Pi19, Pi20, Pi23, Pi57, Pi62 (Nagato and Yoshimura, 1998), Pii, Pi15 (Pan et al, 2003), Pi21 (Fukuoka and Okuno, 2001), Pi25 (Yang et al, 2001), Pi27 (Zhou et al, 2004), Pi24, Pi25, Pi26, Pi27, Pi28, Pi29, Pi30, Pi31, Pi32 (Sallaud et al, 2003), Pi33 (Berruyer et al, 2003), Pish (Fukuta et al, 2004), Pi35 (Nguyen et al, 2006), Pi36 (Liu et al, 2005), Pi37 (Chen et al, 2006), Pi38 (Gowda et al, 2006), Pi39 (Lin et al, 2007) and Pi40 (Jeung et al, 2007). Out of 100, five blast resistance gene i.e. Pi-kh or Pi54 (Sharma et al, 2005), Pitp(t) (Barman et al, 2004), Pi42(t) (Kumar et al, 2010) and Pi-Wn(t) (Rathour et al, 2012) have been mapped in India. Nine blast resistant genes, Pi-b (Wu et al, 2004), Pi-ta (Bryan et al, 2000), Piz-5 and Piz-t (Zhou et al, 2005), Pi9 (Qu et al, 2006) and Pid2 (Chen et al, 2006) have been cloned. The position of different mapped R genes and linked markers are discussed (Fig. 1 and

Fig. 1. Location of the different mapped R genes on rice chromosomes. The red words on map indicate SSR markers and the numbers in the left column of the chromosomes are the gene position in centi-morgan (cM) based on Ashikini et al (2014).

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Table 2). Chromosome 11 of rice contains the maximum number of identified blast resistant genes followed by chromosome 12. Chromosome 7 of rice contains the minimum number of blast resistant genes i.e. 1% (Ashkani et al, 2014). On the short arm of chromosome 6, nine blast resistance genes [Pi26(t), Pigm(t), Piz(t), Pi9, Pi2, Piz-t, Pi40(t), Pi2-2 and Pi50(t)] are identified. Several studies proved that R genes from the Pi2/9 locus of chromosome 6 has widely used in the breeding program (Jiang et al, 2015; Khanna et al, 2015; Tian et al, 2016). Pi2, Piz-t and Pi9 gene have been cloned (Zhou et al, 2006). Pi8, Pi13(t), Pigm(t) are also mapped on chromosome 6

and has broad-spectrum resistance genes (Deng et al 2006). Recently, new blast resistant genes are also identified such as Pi-64 (Ma et al, 2015), Pi66(t) (recessive gene) identified in cultivar AS20-1 mapped on chromosome 3 (Liang et al, 2016), Pi65(t) fine-mapped using combination of bulk sergeant analysis and next generation sequencing (Zheng et al, 2016), and Pi-jnw1 characterized and fine-mapped from the japonica rice landrace Jiangnanwan (Wang et al, 2016a).

Marker assisted selection (MAS) for

Table 2. Blast resistance genes and tightly linked markers in rice for successful breeding. Chr 1

2

4

5

6

Gene

Tightly linked marker Type Marker name Pit SNP t311, t256, t8042 Pi27(t) SSR RM151, RN259 Pi24(t) – – Pitp(t) SSR RM246 Pi35(t) SSR RM1216, RM1003 Pi37 SSR RM302, RM212, FPSM1, FPSM2, FPSM4 STS S15628, FSTS1, FSTS2, FSTS3, FSTS4 Pi(t) – – Pid1(t) SSR RM262 Pig(t) SSR RM166, RM208 Pitq5 – – Piy1(t) SSR RM3248, RM20 Piy2(t) SSR RM3248, RM20 Pib SNP b213, b28, b2, b3989, Pibdom SSR RM138, RM166, RM208, RM266, RM138, RM166, RM208, RM266 Pi25(t) – – Pi14(t) – – Pi16(t) – – Pi21 STS P702D03-#79 Pikur1 – – Pi39(t) SSR RM3843, RM5473 Pi(t) – – Pi26(t) – – Pi23(t) – – Pi10 InDel OPF62700 Pi22(t) – – Pi26(t) – – Pi27(t) – – Pi40(t) SSR RM3330, RM527 CAPS S2539 Piz-5 – Piz InDel z4794 SNP Z60510, z5765, z56592, z565962 Piz-t InDel Z4794 Pi9 – – Pi25(t) – – Pid2 – – Pigm(t) CAPS C26348 Pitq1 – – Pi8 – – Pi13(t) – – Pi13 – – Based on Koide et al (2009).

Map position (cM) 12.2 28.4–38.8 64.4 114.1 132.0–136.6 136.1

Tjahaja Q14 Azucena Tetep Hokkai 188 St.No.1

Complete Complete – – Partial Japonica

– 87.5-89.9 142.0–154.1 150.5–157.9 153.2–154.1 153.2–154.1 154.1

Digu Guangchangzhan Teqing Yanxian 1 Yanxian 1 Tohoku IL9

– – – Complete –

157.9 – – 58.6 86 107.4–108.2 – 22.5–24.7 59.3–99.5 88.5–102.8 38.7–41.9 51.0–63.2 51.9 54.1–61.6

IR64 Maowang AUS373 Owarihatamochi Kuroka Chubu 111 (Haonaihuan) – Azucena Sweon 3655 Tongil Sweon 365 Gumei 2 IR64 IR65482-4-136-2-2

– Complete Complete Partial – – – – – Complete – – – –

Tadukan Zenith

Complete Complete

Torde1 75-1-127 (101141) Gumei 2 Digu Gumei 4 Teqing Kasalath Mawong Kasalath

Complete Complete – Complete – Complete Complete Complete –

58.7 58.7 58.7 58.7 63.2–64.6 65.8 65.8 103.0–124.4 – – –

Donor rice

Resistance type

Complete

Deepti SRIVASTAVA, et al. Current Status of Conventional and Molecular Interventions for Blast Resistance in Rice Table 2. Blast resistance genes and tightly linked markers in rice for successful breeding (continued). Chr 7 8

9

Gene

Tightly linked marker Type Marker name Pi17(t) – – Pi36 SSR RM5647 CAPS CRG2, CRG3, CRG4 Pi33 SSR RM72, RM44 Pizh– – Pi29(t) – – PiGD-1(t) – – Pii2(t) – – Pi5(t) CAPS 94A20r, 76B14f, 40 N23r

SNP JJ817 Pi3(t) Pa – – Pi15 – – Pii – – 10 Pi28(t) – – PiGD-2(t) – – 11 Pia CAPS Yca72 PiCO39(t) CAPS RGA8, RZ141, RGACO39 Pilm2 – – Pi30(t) – – Pi7(t) – – Pi34 – – Pi38 SSR RM206, RM21 PBR – – Pb1 – – Pi44(t) – – Pikh SSR RM206, TRS26, TRS33, RM144, RM224, RM1233 Pi54 – RM206, TRS26, TRS33, RM144, RM224, RM1233 Pi1 – – Pik-m InDel K6861, k2167 SNP K641, k6441, k473, k7237 Pi18(t) Pik InDel K6816, k2167 Pik-p SNP K641, k39575, k403, k3957 Pik-s SSR RM144, RM224, RM1233 Pik-g – – Pise1 – – Pif – – Mpiz – – Pikur2 – – Piisi – – 12 Pi24(t) SNP – Pi62(t) – – Pitq6 – – Pi6(t) – – Pi12(t) – – Pi31(t) – – Pi32(t) – – Pi12(t) – – Ipi(t) – – IPi3(t) – – Pi157 – – Pita SNP Ta642, ta801, ta3, ta577, ta5, Pita440, pi-ta1042, Pi-ta403 Pita2 SNP Ta642, ta801, ta3, ta577 Pi19(t) – – Pi39(t) CAPS 39M6, 39M7 Pi20(t) SSR RM1337, RM5364, RM7102 PiGD-3(t) – – Based on Koide et al (2009). Chr, Chromosome; SSR, CAPS,; SNP.

Map position Donor rice (cM) 94.0–104.0 DJ123 21.6–25.2 Q61 45.4 53.2–84.8 69.0

31.3–33.0

31.3–33.0 31.3–34.9 114.7 36 49.1 56.2–117.9 59.4–60.4 71.4–84.3 79.1–91.4 79.1–88.7 80.5–120.3 85.7–91.4 91.4–117.9 101.9 112.1–117.9 115.1–117 117.9 119.9–120.3 119.9–120.3 115.1–117.3 – – – – – – 10.3 12.2–26.0 29.2–47.5 32.6–63.2 42.8–53 44.3 47.5 47.6–48.2 47.6–58.3 47.6–58.3 49.5–62.2 50.4 50.4 50.4 51.5–51.8 55.8

Resistance type Complete –

IR64, Bala Zhai-Ye-Quing IR64 Sanhuangzan 2 Ishikari shiroke RIL125, RIL249, RIL260 (Moroberekan)

Complete Complete –

Kan-Tao GA25 Ishikari shiroke Azucena Sanhuangzhan 2 Aichi Asahi CO39 Lemont IR64 RIL29(Moroberekan) Chubu 32 Tadukan St No.1 Modan RIL29 (Moroberekan) Tetep Taipei 309 (TP) C101LAC (Lac23) Tsuyake

Complete Complete Complete – – Complete Complete Complete – Complete Partial – – Partial Complete Complete –

Sweon 365 Kusabue HR22 Shin 2 GA20 Sensho Chugoku 31-1(St.No.1) Zenith Kuroka Imochi shirazau Zhong 156 Yashiromochi Teqing Apura RIL10 (Moroberekan) IR64 IR64 K80 (Hong-jiaozhan)

Complete complete Complete Complete Complete – Partial – – – Complete – Complete Complete Complete – – – – – – Complete Complete Complete

Moroberecan Taducan Shimokita Aichi Asahi Q15 IR24 Sanhuangzhan 2

– Complete

Complete

Complete –

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blast resistance in rice programme Conventional breeding generally be contingent upon the phenotype of artificial identification and performance of field resistance, therefore, newly emerged virulent race of pathogen cannot be easily recognized and the release of improved varieties cannot be certain (Zhang et al, 2006). In contrast, MAS is very useful in blast resistance breeding because blast resistant phenotypes are encoded by single or few genes (Young, 1996). MAS have the useful in blast control by exploiting the specific interaction of R gene and avirulence (Avr) from host-pathogen interaction (Petit-Houdenot and Fudal, 2017). Molecular markers improve the efficiency of conventional breeding by selecting the molecular marker linked to the desired trait. A set of SSR markers (RM168, RM8225, RM1233, RM6836, RM5961 and RM413) linked to blast resistant trait have been reported and these could be used in MAS programs. For the development of durable blast-resistant variety, availability of molecular markers with MAS strategies is essential (Ashkani et al, 2012). High cost and low reliability of markers to determine phenotype are important factors hampering the effective application of molecular markers for varietal development. MAS has been successfully used for the development of blast resistant rice varieties (Table 3) which have several advantage in the identification of R-genes (Yan et al, 2017).

Marker-assisted backcross breeding (MABC) for blast resistance in rice improvement programme MABC is the process of using molecular markers to select the target loci, minimize the length of the donor segment that contain a target locus and enhance the recovery of the recurrent parent genome during each backcrossing (Hasan et al, 2015). The main principle of MABC is to transfer the targeted gene along with minimizing the donor segment and recovering the recurrent parent characters/or genes (Sundaram et al, 2009). Molecular markers which are tightly linked with important traits are used in MABC (Collard and Mackill, 2008; Hasan et al, 2015) and are superior to conventional backcrossing because it is highly precised and efficient as well as save time. This strategy was followed to transfer other resistance

genes in popular varieties worldwide (Koide et al, 2011; Gouda et al, 2013). The potential application of MAS and MABC for the improvement of different widely adopted rice varieties have been reviewed and described. Introgression of blast resistance genes Pi1, Pi2 and Pi33 into rice variety ADT43 through marker assisted backcross breeding has been conducted (Divya et al, 2014). Many studies and example of marker-assisted selection for development of the new rice varieties has been reported (Table 3).

Gene pyramiding Gene pyramiding is the assembling of desired genes into a single line or cultivar. These genes can be taken from single or multiple identified parents. The end product of a gene-pyramiding programme is a genotype having all of the desired genes. Gene pyramiding is one of the most effective and efficient strategies for achieving long-lasting resistance against rice blast (Koide et al, 2009) and have successfully used for accumulating various blast resistance genes in elite rice cultivars. Pi1, Pi5, Piz-5 and Pita (Liu et al, 2002; Lee et al, 2009), have been pyramided into various elite rice genotypes using marker-assisted selection. Pyramided lines with two resistance genes, Pish and Pib, for blast disease have been developed (Hittalmani et al, 2000). Three genes, Pi-d(t)1, Pi-b, and Pi-ta2, have been fixed into a donor rice line G46B (Chen et al, 2004), and two genes, Pi1 and Pi2, have been fixed into Zhenshan 97. Currently, Pusa 1602 and Pusa 1603 lines have been developed by incorporating the blast resistance genes Piz-5 and Pi54 through MAB (Singh et al, 2012). The leaf blast resistance D521 line and neck blast resistance line have been developed through introgression of Pi1 and Pi2 genes, respectively, using MAB programs (Fu et al, 2012). Two quantitative trait loci (QTLs) that confer resistance to blast disease have been successfully introgressed by Jao Hom Nin (JHN) into RD6 using MAS (Wongsaprom et al, 2010). Pi9 for blast resistance and Xa23 for bacterial blight resistance pyramided in Guangzhan 63S (GZ63S) (Ni et al, 2015) similarly Xa21, Xa13 and Pi54 were pyramided (Table 4) in Indian rice variety (Arunakumari et al, 2016).

Identification of blast resistance QTLs Both qualitative and quantitative types of blast

Deepti SRIVASTAVA, et al. Current Status of Conventional and Molecular Interventions for Blast Resistance in Rice Table 3. Successful examples of marker-assisted selection (MAS) and marker-assisted backcrossing (MABB) in rice for blast resistance breeding. Trait Blast resistance

Gene(s)/QTLs

Marker(s) used

Pi1, Piz-5, Pita RFLP

Technique MAS

Blast resistance Pi1 SSR and ISSR Bacterial blight resistance +Xa21, Piz SSR Blast resistance Blast resistance Pid1, Pib, Pita,SSR Pi2 Blast resistance Pi-z SSR

MAS MAS

Blast resistance + Xa13, Xa21, SSR Bacterial blight resistance + Pi54, Sheath blight resistance qSBR11 Blast resistance + BacterialPi-genes, Xa5 SSR blight resistance

MAS

Blast resistance

Pita

MAS MAS

MAS

Gene specific marker MAS

Submergence tolerance + chr9 QTL,SSR and STS Brown planthopper resistanceXa21, Bph and + Blast resistance + QTLs blast, and Bacterial blight resistance quality loci Blast resistance Pi1, Pi2, Pi33 SSR

MABB

Blast resistance + BacterialPi1, Pi2, Xa23 SSR blight Blast resistance Piz-5, Pi54 SSR

MABB

MABB

MABB

Introgressed into Jin23B cultivar through MABB

Chen et al (2008) Sucessfully applied for breeding variety Rongfeng B Fu et al (2012)

MABB

Introgressed into 07GY31 as the japonica

pB8

Blast resistance

Pi-1, Pi-z

SSR

MABB

Xa21,SSR

MABB

SSR

MABB

Blast resistance

Pi1, Pi2

SSR

MABB

Blast resistance

Pi46, Pita

SSR

MABB

Pizt, Pi2, Pigm,SSR Pi40, Pi9, Piz Blast resistance Pi9, Pizt, Pi54 SNP Based on Ashkani et al, (2015).

SSR MAS Near-isogeniclines (NILs) derived fromPinta et al (2013) two blast resistant crosses (RD6 × P0489 and RD6 × Jao Hom Nin) were pyramided with IR62266 (xa5), to transfer bacterial leaf blight resistance to RD6 lines Existence of the Pi-ta gene in 141 rice germplasm hasWang et al been successfully determined, but the results were (2007) more articulated when Pi-ta gene was introduced through advanced breeding lines MABB MABB confirmed the transfer of gene andToojinda et al QTL for into cultivar KDML105 (2005)

NIL

Pi-9(t)

Blast resistance

Reference

Combination of blast resistance gene from donor linesSingh et al (C101A51 and Tetep) into PRR78 to develop Pusa(2011) 1602 (PRR78 + Piz5) and Pusa 1603 (PRR78 + Pi54), respectively MABB applied to introgress the cultivar Luhui 17 Wen and Gao (2011) Pyramiding of Pi-1 and Piz-5 genes into introducedGouda et al PRR78 (2013) Introgressed into RPHR-1005 a stable restorese rice Kumar et al (2016) Introgressed into Turkish elite cultivars, Osmancik-97Beser et al and Halilbey (2016) Introgressed into Intan variety and BPT5204 used asHegde and donor parent Prashanthi (2016) Introgressed into Hang-Hui-179 (HH179) Wu-ming et al, (2016) Introgressed into Yangdao 6 Wu et al (2016)

Blast resistance

Blast reisstance+BacterialPi2, blight Xa33 Blast resistance Pi40

Application

Pyramiding of three near isogenic lines (C101LAC, Hittalmani et al C101A51 and C101PKT) for blast resistance in into a(2000) single cultivar Co-39, each carrying the major genes Pi1, Piz-5 and Pita, respectively Applied for backcross breeding of variety Liu et al (2002) MAS Functional for pyramiding of target traits Narayanan et al (2002) Pid1, Pib and Pita genes were introduced into G46BChen et al (2004) cultivar, while Pi2 Zhenshan 97B cultivars of rice Closely linked with Pi-z locus has been successfullyFjellstrom et al used for selection of blast resistance in a wide array of (2006) rice germplasm MAS-assisted transfer of genes conferring theSingh et al resistance toward three different diseases in rice (2012a)

resistance genes have been reported in rice germplasm (Ou, 1985). The majority of the QTLs are associated with qualitative genes. Blast resistance is divided into complete and partial resistance (Wang et al, 1994). Complete resistance (qualitative resistance and race specific) is governed by a major gene (R genes) while

Xiao et al (2017)

partial resistance (quantitative character and non-race specific) is governed by many genes known as QTLs (Young, 1996). Partial disease resistance shows durable resistance against a broad-range of pathogens, thus shows a potential approach for rice production in a sustainable way in future (Song and Goodman,

Rice Science, Vol. 24, No. 4, 2017 Table 4. Successful examples of gene pyramiding in important rice varieties against blast disease. Trait Blast resistance Blast resistance

Blast resistance Blast resistant

Parental line

Pyramided gene

C101LAC, C101A51 Pi1, Pi2, Pi33 IR5, IR8, IR20, IR22, IR24, IR26, IR28, Pib, Pita IR29, IR30, IR32, IR34, IR36, IR38, IR40, IR42, IR43, IR44, IR45, IR46, IR48, IR50 IR52, IR54, IR56, IR58, IR60, IR62, IR64, IR65, IR66, IR68, IR70, IR72, IR74 CO39 Pish, Pib IR64, JHN Multiple resistance QTLs

Marker SSR SSR

Reference Chen et al (2008) Fujita et al (2009)

Koide et al (2010) Sreewongchai et al (2010) Blast resistant Rongfeng B Pi1, Pi2, Xa23 Fu et al (2012) Blast resistant C101LAC, C101A51 Pi-1, Pi-2 RG64 and C481 Mahdian and Shahsavari (2013) Blast and bacterial leafRD6 × P0489, RD6 ×JHN Four QTLs for blast resistance andSSR Pinta et al (2013) blight resistance one gene (xa5) for bacterial leaf blight Blast resistant C101A51, Tetep Piz5, Pi54 SSR Singh et al (2011) Blast resistance Carnaroli, Baldo, Arborio Piz, Pi5 SSR Urso et al (2013) Leaf blast resistance Koshihikari Pi21, Pi34, Pi35 SSR Yasuda et al (2014) Blast resistance GZ63-4S Pi2 and Xa23 SSR (M-Xa23) Jiang et al (2015) Blast resistance Aichiasahi × Owarihatamochi pi21, Pi34, qBR4-2, qBR12-1 SSR Fukuoka et al (2015) Blast resistaance + bacterialSamba Mahsuri (possessing Xa21 and xa13)Pi54 + Xa21 + Xa13 SSR Arunakumari et al blight and NLR145 (possessing Pi54) introgressed (2016) into Indian rice variety MTU1010 Blast resistance RD6 × PO489, RD6 ×JHN 4 QTLs located on chromosomesSSR Suwannual et al 1, 2, 11 and 12, respectively (2017) Blast resistance + bacterialCBB23, HN88, Zhongzu 14, Shuhui 162,Xa23, Xa5, Pita, Pi1, Pi2, Bph3 SSR Juan et al (2016) blight + brown plant hopper Zhongzu 14 RH are donors Blast resistance + bacterialRPBio Patho-1, FBR1-15 are donors,Xa21, Xa33, Pi2, Rf3, Rf4w SSR Kumar et al (2016) blight RPHR-1005 was recurrent Based on Ashkani et al (2015).

2001). QTL detection is used to map major or minor genes responsible for the disease resistance (Wu et al, 2005). Mapping and tagging of QTLs associated with blast resistance could be helpful in the cloning of major disease resistance genes as well as marker assisted breeding program for development of resistant cultivars (Ashkani et al, 2015). Identification of multiple loci governing complete resistance as well as position and effect of the genomic region responsible for the partial disease is usually revealed by QTL mapping (Sallaud et al, 2003). Though, it is also possible that resistance governs by major genes could be highly partial and race specific. Many major genes for blast resistance are qualitative in nature and have been recognized and mapped in the rice genome (Ashkani et al, 2014). About 22 resistance-genes (R) have been successfully cloned and molecularly illustrated. For example, Pi-9 gene from indica rice line 75-1-127 (Liu et al, 2002), was transferred from wild species O. minuta (Amante et al, 1992). In O. rufipogon, O. nivara and in their hybrids with O. sativa, Pi-ta allele was identified (Jena and Khush, 2000). About 350 leaf blast

SSR Multiple QTLs

resistances QTLs have been mapped so far. These QTLs were identified in 15 diverse populations, most of which are derived from indica and japonica crosses (Sato et al, 2006) Pif [partial resistance] (Yunoki et al, 1970), Pi21 and Pib1 (Fujii et al, 1995). Assessment of genetic diversity, drought QTLs and blast resistance genes of 74 rice germplasm through molecular markers has been reported (Anupam et al, 2017). There are several reports for major blast QTL in rice (Fig. 2 and Table 5) were studied and used for gene pyramiding purpose (Sharma et al, 2012).

Association mapping Association mapping also termed as linkage disequilibrium (LD) mapping, does not require a biparental population having contrasting parents for the trait of interest. Instead, it exploits the historic linkage disequilibrium occurred during the course of evolution in a set of varieties/genotypes/species. Association mapping is performed by scanning entire genome to find out a significant association between a set of SNPs and a specific phenotype. Once any such

Deepti SRIVASTAVA, et al. Current Status of Conventional and Molecular Interventions for Blast Resistance in Rice

Fig. 2. Chromosomal location of 11 QTLs for blast resistance in rice. SSR markers are indicated on the right of the chromosomes. Genetic distance (cM) is shown on the left of the chromosomes. Black bars in each chromosome are the location intervals of QTLs for blast resistance with their names on the right (Zhang et al, 2012).

association is realized it must be independently verified for its contribution to the trait of interest directly or linked to a gene/QTL governing the trait (Yu et al, 2008). An association mapping of a panel of 1568 diverse inbred rice varieties data set has been successfully generated by using high density rice array (HDRA) that consist of 700 000 SNP. GWAS of resistance to rice blast was done using 150 accessions of japonica rice genotyped with 10 937 markers and indica rice genotyped with 14 187 markers (McCouch et al, 2016). In japonica rice panel quantitative resistance was higher than indica rice panel and two different loci associated with blast resistance identified in japonica rice (Raboin et al, 2016). An association study was conducted on 226 japonica rice varieties using 118 SSR markers. The phenotyping for blast resistance was performed by inoculating rice plants with two isolates DB22 and DB27 during 2013 and 2014. Based on the mixed linear model (MLM) analysis, a total of 31 associations with 17 different SSRs were found significantly associated with blast resistance. Results of various association studies demonstrated that association mapping can complement or enhance information about previously identified QTLs and thus helps in marker-assisted

selection in plant breeding (Guo et al, 2015). Similarly genotyping by sequencing (GBS) based diversity analysis of 190 African rice cultivars and association mapping of blast resistant genes and QTLs were done, where 190 cultivars divided into three groups on the basis of 184 K single nucleotide polymorphism developed by GBS. Association mapping results in the identification of 25 genomic regions associated with blast resistance (RABRs) in the rice genome (Mgonja et al, 2017).

Allele mining Allele mining is the frequently used approach to identify novel alleles or allelic variants of a gene of interest which has already been characterized from a large collection of germplasm. This method has been used in molecular plant breeding for crop improvement. Genetic material used for allele mining should be diverse and gene sequence information of particular crop species should be known. With the help of allele mining of genes superior alleles for blast resistance can be detected from both wild and cultivated rice species (Kumari et al, 2013). Now a

Rice Science, Vol. 24, No. 4, 2017 Table 5. Examples of QTL mapping for resistance to blast disease in rice. Mapping population

Parents

Total No. of QTLs Recombinant Inbred LineCT9993-5-10-1-m × KDML105 (F8); Zhenshan 97 × Minghui 63186 (RIL) (RILs); Moroberekan ×Co39 (F7); Lemont × Teqing (F8); Lemont × Teqing (F14); Bala × Azucena (F6); Zhong 156 × Gumei 2 (F8); OryzicaLlanos5 × Fanny (F5 and F6); SHZ-2 × Lijiangxin-tuan-heigu (LTH) (RILs); KDML105 × JHN (F6); Suweon 365 × Chucheong (RILs) Double Haploid (DH) IR64 ×Azucena; IR64 ×Azucena; ZYQ8 × JX17 146 Single-segment substitutionDeveloped by HXJ74 as recipient and 24 accessions as donors 11 lines (SSSLs) SSR Back cross population Back cross population WayRarem × OryzicaLlanos 5 (IRGC117017); MR219 × O.45 rufipogon IRGC105491; SHZ-2 × TXZ-13; Oryza rufipogon × IR64 F2, F3 and F4 Nipponbare × Owarihatamochi (F4 lines); Kahei × Koshihikari60 (F2:3); Tainung 69 × Koshihikari (F2); URN12 × Koshihikari (F2); Norin 29 × Chubu32 (F3); PongsuSeribu 2 × Mahsuri (F2:3); TAM × KHZ (F2:3); Junambyeo × O. minuta introgressionline IR71033-121-15 (F2:3); Danghang-Shali ×Hokkai 188 (F2:3) Based on Ashkani et al (2015).

days various approaches have been used to mine novel and superior alleles of blast resistance genes from diverse cultivated rice varieties and wild species. Allele mining helps in the identification of blast resistance alleles as well as development of functional markers (Ramkumar et al, 2010). Analysis of the sequence level similarity for Pikm alleles in 15 different rice cultivars (Table 6) has been conducted (Costanzo and Jia, 2010) and difference between the M. oryzae and M. grisea by using allele mining (Couch and Kohn, 2002) has been reported. The accomplishment of allele mining depends on the type of genetic materials used for screening and should be as diverse as possible. Wild and local landraces can be used because they are reservoirs of useful alleles hidden in their phenotype of the plants (Tanksley et al, 1996). But the challenge is to build the association between sequence polymorphisms and putative function of allele mining for R (resistant) genes. GWAS (genome-wide association study) analysis and high-throughput RNAi-like approaches, functional validation, could facilitate the identification of new R alleles. Allele mining has good potential to be applied in molecular plant breeding.

Transgenic approaches for blast resistance Presently, several methods are used to transfer a gene of interest into plant cells such as Agrobacterium

Marker

Reference

RFLPs, SSR,Cho et al (2008) RAPD, Isozymes, AFLPs, DR gene markers

RFLP, RAPD, isozymes SSR

Xu et al (2004)

SSR, SNP

Rahim et al (2012)

Zhang et al (2012)

RFLPs, SSR, STS Ashkani et al (2013a, b)

transformation and biolistic method. Among them, Agrobacterium mediated transformation ensures stable integration into the genome. Enhanced resistance to blast fungus in rice was developed by expression of rice chitinase gene [Rice class-I chitinase gene, Cht-2 or Cht-3] (Nishizawa et al, 1999). Enhancement of blast disease resistance by antifungal AFP protein expression from Aspergillus giganteus has been reported (Coca et al, 2004). The developed transgenic plants showed stable integration as well as inheritance of the transgene. In another study, development of improved basmati rice against fungal infection through gene transfer technology has been reported. Introduced RCC2 gene (for blast fungus resistance gene) by Agrobacterium mediated transformation shows significant level of resistance in basmati rice under the laboratory and in field condition (Asghar et al, 2007). Over expression of calcium-dependent protein kinase (OsCPK4) in transgenic rice shows resistance against the blast disease (Bundo and Coca, 2015). Transgenic rice plants having two elicitors genes (MoHrip1 and MoHrip2) from Magnaporthe oryzae show elevated resistance against rice blast and stronger tolerance to drought stress (Wang et al, 2017). Several important genes have been successfully transformed in the elite rice varieties for the tackling of blast disease in rice (Table 7).

miRNA in blast disease management

Deepti SRIVASTAVA, et al. Current Status of Conventional and Molecular Interventions for Blast Resistance in Rice Table 6. Example of Allele mining for blast resistance in rice. (Based on Ashikani et al, (2015)) R –Genes /locus Chromosome Pi-ta Pi-ta Pi-ta Pi-ta

12 12 12 12

Pi-ta Pi-ta Pi-ta

12 12 12

Pi-kh (Pi54) Pi-kh (Pi54) Pi-kh (Pi54) Pi-kh (Pi54) Pi-z(t) Pi-z(t) Pid3 Pid3-A4 Pi9

11 11 11 11 06 06 06 06

Pi9 AC134922

06 11

Rice germplasm

Reference

From wild rice species [O. rufipogon (Griff) and from O. rufipogon (ETOR)] Geng et al (2008) From O. rufipogon Huang et al (2008) From cultivated (AA) and wild species and invasive weedy rice Lee et al (2011) In 26 accessions, consisting of wild rice (O. rufipogon), cultivated rice(O. sativa) andYoshida and Miyashita (2009) related wild rice species (O. meridionalis and O. officinalis) collected from ten different countries of the world From land races and wild Oryza species Ramkumar et al (2010) In Indian land races of rice Sharma et al (2010) From Indian landraces of rice collected from different ecogeographical regionsThakur et al (2013a) including the northwestern Himalayan region of India From wild and cultivated species of rice Rai et al (2011) From the blast-resistant wild species of rice, O. rhizomatis Das et al (2012) From six cultivated rice lines and eight wild rice species Kumari et al (2013) In Indian land races of rice Sharma et al (2010) In Indian landraces of rice Sharma et al (2010) In 529 land races of rice collected at three different geographical locations of India Thakur et al (2013 b) From 36 accessions of wild rice O. rufipogon Xu et al (2014) From wild rice A4 (O. rufipogon) Lv et al (2013) In different rice species, five AA genome Oryza species including two cultivated riceLiu et al (2011) species (O. sativa and O. glaberrima) and three wild rice species (O. nivara, O. rufipogon, and O. barthii). From 338 rice landraces Imam et al (2016) Rice lines from various sources Wang et al (2014)

MicroRNAs (miRNAs) are essential regulators of development and defense in eukaryotes. Several evidences indicated that miRNAs are involved immunity against pathogen invasion in plants. A total of 28 000 miRNAs are listed in miRNA database (Katiyar-Agarwahat and Jin, 2010) could be used for the biological control. Identification of nine new miRNA from rice leaves after inoculation with M. oryzae elicitors has been reported (Baldrich et al, 2015), and expression of miRNAs also reported in rice upon M. oryzae infection (Li et al, 2014). Further, data indicated that miRNAs are involved in the immunity of rice against M. oryzae and the over expression of miR160a or miR398b can enhance resistance to the disease. Role of miR169 has been studied and suggested that it work as a negatively regulates rice immunity against the blast fungus (Li et al, 2017). Systematic silencing of Magnaporthe grisea, a causative organism of rice blast has been conducted and used for the enhancement of resistance (Kadotani et al, 2003). Rice accelerated cell death and resistance gene (OsACDR1) encodes a Raf-like putative mitogen-activated protein kinase kinase kinase (MAPKKK) and involve in eliciting defense-related pathways for upregulation of OsACDR1 transcript (Kim et al, 2009). Rice plants over expressing OsACDR1 showed spontaneous hypersensitive response (HR) and enhance accumulation of the phenolic compounds and secondary metabolites

(phytoalexins) and play a crucial role in the positive regulation of disease resistance in rice.

Genome editing With the advent of new technologies, sequencespecific nucleases (SSNs) have played an immense role in crop improvement, and among all the SSNs; CRISPER/Cas9 are most effective SSN used in rice (Li et al, 2012; Shan et al, 2015). Several other reports also showed that gene modification via CRISPER and TALEN is a useful approach for the management of several diseases (Malzahn et al, 2017). Development of blast disease resistant rice by using high-efficiency TALEN based gene editing has been conducted (Li et al, 2012) by the mutation in disease susceptibility and sucrose efflux transporter gene (OsSWEET14) is responsible for pathogen survival and virulence. Similarly, CRISPER/Cas9-targeted mutagenesis of the ethylene response transcription factor gene OsERF922 has also been investigated for the improvement of blast resistance (Wang et al, 2016). Plant ethylene responsive factors (ERF) are involved in the modulation of multiple stress tolerance. A total of 50 transgenic plants (T0) were developed, and among them, 21 C-ERF922 induced mutant plants (42%) were identified. Later, in T1 and T2 plants having the desired gene modification, no transferred DNA was

Rice Science, Vol. 24, No. 4, 2017 Table 7. Example of transgenic rice having blast resistance. Transferred gene

Transformation method

Class-I chitinase gene, Cht-2 or Cht-3 Agrobacterium mediated Rirlb (putative defense genes) Biolistic Barnase and Barstar Biolistic 6235-bp genomic sequence of Pid3 Agrobacterium mediated ACS2 (1-aminocyclopropane-1-carboxylic acidAgrobacterium mediated synthase, a key enzyme of ET biosynthesis) maize regulatory genes C1 (coloured-1), R (red)Bioloistic and the structural gene C2 (coloured-2, encoding chalcone synthase). Cecropins (antimicrobial peptides) cecropin A Agrobacterium-mediated Aspergillus giganteus antifungal protein (AFP) Agrobacterium mediated Ouroindoline genes pinA and/or pinB WRKY45 Agrobacterium mediated Thanatin (antimicrobial peptide) Agrobacterium mediated Aspergillus giganteus antifungal protein (AFP) Agrobacterium mediated Brassica juncea Non expressor ofAgrobacterium mediated pathogenesis-related genes 1 (BjNPR1) Overexpression of Pikh gene Agrobacterium mediated

obtained on segregation. In T2 generation, six homozygous mutant lines were obtained and analyzed for blast resistance by inoculating pathogen and analyzed for agronomic traits. Results showed that number of blast lesions was significantly decreased in all the six mutant lines compared with wild-type plants; however, there was no significant difference in agronomic traits between mutant lines and wild type plants.

CONCLUSION Blast disease is the most destructive disease of rice that causes significant reduction in yield. Conventional control measures are not effective at commercial level. The use of resistant cultivars is a most efficient method to reduce disease incidence and to overcome pesticide hazards. A number of blast resistant cultivars have been developed through classical plant breeding techniques but advancement in rice genomics has given a new opportunity to enhance the rice production system. The breeding techniques such as pyramiding of genes, multiline varieties and gene rotation have been found effective in resistance management. Additionally, cloning of R and Avr genes and their study of gene products will also enhance the knowledge of host-pathogen interaction. Rice cultivars have single R gene for a specific pathogen race often become susceptible over time due to the appearance of new virulent races. Understanding of a genetic identity of new M. oryzae race is important for accurate employment of rice

Variety in which gene transferred

Reference

Japonica varieties Nipponbare and Koshihikari Japonica variety Taipei 309 Japonica Zhonghua 8 and Taipei 309 Japonica variety TP309 Kitaake

Nishizawa et al (1999) Schaffrath et al (2000) Shengji Mao et al (2003) Shang et al (2009) Helliwell et al (2013)

Tp309

Gandikota et al (2001)

Mediterranean elite japonica rice cultivar Senia. Coca et al (2006) japonica rice Senia Coca et al (2004) M202 Krishnamurthy et al (2001) Oryza sativa cv Nipponbare Shimono et al (2007) Oryza sativa L. cv. Nipponbare Imamura et al (2010) japonica rice Seni Moreno et al (2005) Indica varieties Chaitanya and Samba MahsuriSadumpatia et al (2013) (SM) MR219 rice variety Azizi et al (2016)

cultivars with different R genes. Stacking of multiple R genes will provide long lasting resistance. In rice breeding programmes against blast disease, different combinations of resistance R genes in a single host plant should be considered. Some recent techniques, such as allele mining, association mapping and genome editing, will also play a vital role in controlling blast disease. The introduction of new sciences like nanotechnology in agricultural research and management could be proved very beneficial in future viz., NANO GREEN has been reported the control of rice blast using nano-molecules. However, due to the variable nature of pathogen, the need of regular research on the advancement of sustainable resistant cultivars will always be a never ending process due to co-evolution of pathogens.

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