Differential,Expression,of,Iron,Deficiency,Responsive,Rice,Genes,under,Low,Phosphorus,and,Iron,Toxicity,Conditions,and,Association,of,OsIRO3,with,Yield,in,Acidic,Soils

Ernieca Lyngdoh Nongbri, Sudip Das, Karma Landup Bhutia, Aleimo G. Momin, Mayank Rai, Wricha Tyagi

Research Paper

Differential Expression of Iron Deficiency Responsive Rice Genes under Low Phosphorus and Iron Toxicity Conditions and Association ofwith Yield in Acidic Soils

Ernieca Lyngdoh Nongbri, Sudip Das, Karma Landup Bhutia, Aleimo G. Momin, Mayank Rai, Wricha Tyagi

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With the hypothesis that iron (Fe) deficiency responsive genes may play a role in Fe toxicity conditions, we studied five such genes,,,andacross six contrasting rice genotypes for expression under high Fe and low phosphorus (P) conditions, and sequence polymorphism. Genotypes Sahbhagi Dhan, Chakhao Poirieton and Shasharang were high yielders with no bronzing symptom visible under Fe toxic field conditions, and BAM350 and BAM811 were low yielders but did not show bronzing symptoms. Hydroponic screening revealed that the number of crown roots and root length can be traits for consideration for identifying Fe toxicity tolerance in rice genotypes. Fe contents in rice roots and shoots of a high-yielding genotype KMR3 showing leaf bronzing were significantly high. In response to 24 h high Fe stress, the expression levels ofwere up-regulated in all genotypes except KMR3. In response to 48 h high Fe stress, the expression levels ofwere 3-fold higher in tolerant Shasharang, whereas in KMR3, it was significantly down-regulated. Even in response to 7 d excess Fe stress, the transcript abundances ofandwere contrasting in genotypes Shasharang and KMR3. This suggested that the reported Fe deficiency genes had a role in Fe toxicity and that in genotype KMR3 under excess Fe stress, there was disruption of metal homeostasis. Under the 48 h low P conditions,andwere significantly up-regulated in Fe tolerant genotype Shasharang and in low P tolerant genotype Chakhao Poirieton, respectively.sequence analysis across 3 024 rice genotypes revealed polymorphism for 4 genes. Sequencing acrossandrevealed nucleotide polymorphism between tolerant and susceptible genotypes for Fe toxicity. Non-synonymous single nucleotide polymorphisms and insertion/deletions (InDels) differing in tolerant and susceptible genotypes were identified. A marker targeting 25-bp InDel in, when run on a diverse panel of 43 rice genotypes and a biparental population, was associated with superior performance for yield under acidic lowland field conditions. This study highlights the potential of one of the vital genes involved in Fe homeostasis as a genic target for improving rice yield in acidic soils.

acidic soil; low phosphorus; iron toxicity;;; rice; yield

Iron (Fe) is one of the essential micronutrients for plants and is involved in many physiological processes. When present in excess, it can become toxic and the toxicity can cause tissue damage and disrupted cellular homeostasis. Fe toxicity affects millions of hectares of rice growing areas especially in Southeast Asia (Becker and Asch, 2005; Matthus et al, 2015). Flooded paddy fields exhibit low soil redox potential, resulting in occurrence of reduced and soluble Fe2+, which can give rise to higher Fe2+uptake by roots and transport to shoots when present in higher amounts in the soil (Matthus et al, 2015). The most prevalent symptom of Fe toxicity in rice is the formation of necrotic brown spots on the surface of leaves known as ‘leaf bronzing’ (Dufey et al, 2009). Fe toxicity can also lead to formation of plaques (Fe complex) that block the passage of other important nutrients through roots, giving rise to complex mineral and nutritional disorder (Audebert and Sahrawat, 2000).

At the cellular level, excess of Fe is detrimental to membrane lipids, proteins and nucleic acids (Becker and Asch, 2005). Moreover, acquisition of other nutrients such as phosphorus, zinc and magnesium is adversely affected by excess Fe, resulting in growth reduction, yield loss and even plant death (Sahrawat, 2004; Zhang et al, 2022). Plants in turn have evolved to tightly control cellular Fe levels and maintain Fe homeostasis (Li et al,2019). Three mechanisms of Fe toxicity tolerance have been proposed: (i) Exclusion of Fe2+: the formation of Fe plaques on the root surface creates a physical barrier which can prevent the excess of Fe2+uptake (Deng et al, 2010), thereby reducing translocation of Fe2+from roots to shoots and preventing oxidative stress in leaves; (ii) Storage of high levels of Fe2+: distribution of Fe2+into different subcellular compartments (like apoplasts and vacuoles) is crucial to reduce Fe2+toxicity (Moore et al, 2014); (iii) Inclusion and tolerance to reactive oxygen species: antioxidants like ascorbate, phenolics, glutathione or antioxidant enzymes like superoxide dismutase, catalase and ascorbate peroxidase can detoxify oxidative molecules (Fang et al, 2001; Wu et al, 2017).

In lowland conditions, rice roots take up Fe as Fe3+-phytosiderophore or in the form of Fe2+using iron-regulated transporters (IRT1 and IRT2) (Ishimaru et al, 2006). The role of genes involved in phytosiderophores like mugineic acid (MA) and 2′-deoxymugineic acid (DMA) is well worked out in rice (Bashir et al, 2010, 2014; Kobayashi et al, 2014). Trimerization of S-adenosylmethionine (SAM) (Bashir et al, 2017) into nicotianamine (NA) by nicotianamine synthase (NAS) is the first step of synthesis of MAs (Higuchi et al, 1999). NA amino transferase (NAAT) converts NA to 3′-keto intermediate (Takahashi et al, 1999).,andplay important roles in the trimerization of SAM. In response to Fe deficiency, expression levels ofandare highly induced in rice roots and yellow leaves, butexpression is mildly induced in roots and suppressed in leaves (Inoue et al, 2003).andare very closely located on rice chromosome 1, whereasis on chromosome 7 (Higuchi et al, 2001; Inoue et al, 2003). Genes involved in accumulation of Fe2+under Fe deficiency show similar expression changes asand(Kobayashi et al, 2014). Fe concentrations in leaves and polished seeds are increased by rice glutelin B1 promoter-driven(Zheng et al, 2010). Under excess Fe stress,synthesizes NA and DMA, which contributes to Fe detoxification in rice (Aung et al, 2019). Disruption ofleads to enhanced Fe translocation from roots to shoots, resulting in Fe toxicity.

DMA, formed by reduction of carbon-3 of the keto intermediate, binds to insoluble Fe in the soil, where it forms an Fe-DMA complex that is taken up by the roots via transportersof yellow stripe-like (YSL) family (Curie et al, 2001; Inoue et al, 2009). The YSL oligopeptide plays a very important role in distributing Fe to shoots. The Fe-DMA complex is first taken up byinto the roots (Inoue et al, 2009).is involved in the transports of Fe2+-NA and Mn2+-NA, and the translocations of Fe and Mn via phloem (Koike et al, 2004; Ishimaru et al, 2010).is responsible for the transfer of Fe3+-DMA via vascularbundles (Kakei et al, 2012).OsFRDL1 (Ferric reductasedefective like 1) is a plasma membrane localized citrate transporter required in efficient Fe distribution in rice (Yokosho et al, 2009). It mediates translocation of Fe from roots to shoots by release of citrate to the xylem (Inoue et al, 2004; Yokosho et al, 2009). Expression ofis less in roots with sufficient levels of Fe. Knockout of this gene results in decreased distribution of Fe to panicles, thereby decreasing pollen viability and grain fertility. However, in Fe deficient rice plants, expression of this gene is significantly decreased in shoots (Yokosho et al, 2016).

Fe-regulated bHLH transcription factor, OsIRO2,is reported to affect the expression levels of genes responsible for MA biosynthesis (,,and), methionine cycle and Fe3+- DMA transporter () (Ogo et al, 2007). Additionally, OsIRO2 is also a positive regulator (Kobayashi et al, 2007) and contains a sequence similar to Fe deficiency-acting element in its promoter region (Kobayashi et al, 2005; Ogo et al, 2006).is specifically induced by Fe deficiency and negatively regulates Fe uptake in rice, thus leading to susceptibility to Fe deficiency (Zheng et al, 2010).

Fe plaque accumulation on root layers brings to limited uptake of inorganic phosphorus (Pi) by the roots due to strong interactions of iron oxide with phosphorus (P) (Zhang et al, 1999; da Silveira et al, 2007). Therefore, Fe homeostasis is related to P levels in the soil (Rai et al, 2015; Tyagi and Rai, 2017). Information about the role of key genes in Fe uptake, translocation and loading has already known in rice. Most of these genes/transcription factors play important roles in Fe deficiency tolerance. However, whether these molecular components have any other roles in Fe toxicity and low Pi tolerance is unknown (Das et al, 2017a). In this study, we screened a set of contrasting rice genotypes under hydroponic conditions and analyzed the expression patterns of five rice genes with a known role in Fe deficiency tolerance under low Pi and high Fe conditions. We sequenced the selected rice genes across a diverse rice panel phenotyped under acidic lowland conditions (low available P and Fe toxicity during water logging conditions) and compared the information obtained with the already available single nucleotide polymorphism(SNP) information for 3 024 rice genotypes. Specific insertion-deletion(InDel) marker forwas also validated through marker trait association.

Fig. 1. Screening under acidic lowland soils and hydroponic conditions.

A, Leaf bronzing scores (0‒5, from left to right) given to rice genotypes grown in the lowland fields depending on the intensity of leaf bronzing symptoms on foliage.

B, Fe plaque accumulation in an experimental field.

C, Fe content evaluated on selected representative genotypes harvested from lowland fields. Data are Mean ± SD (= 20). Statistically significant differences between the root and shoot were determined by the Student’s-test (*,< 0.05).

D‒F, Root length (D), shoot length (E) and root fresh weight (F) under hydroponic conditions. The histograms represent average phenotypic values under control (0.0284 mmol/L Fe2+and 0.3500 mmol/L Pi) and treatment (3.6000 mmol/L Fe2+and 0.3500 mmol/L Pi) conditions. Data are Mean ± SD (= 10). Statistically significant differences between the control and treatment were determined by the Student’s-test (*,< 0.05).

G, Cross-section of roots after staining with Perl’s blue stain viewed under a Leica DM750 microscope. Scale bars, 1 mm.

SH, Shasharang; LR15, Priya; LR18, Paijong; CP, Chakhao Poirieton; Kas, Kasalath.

Upon evaluation of 43 diverse rice materials (Fig. S1 and Table S1) under acidic lowland field conditions, leaf bronzing and Fe plaque accumulation in the field were observed (Fig. 1-A and -B). The Fe uptake data in the contrasting rice genotypes revealed a significant increase in Fe uptake in the roots of Kasalath (Kas) and KMR3. In the case of SH, equal (but low) concentration of Fe was found in both roots and shoots (Fig. 1-C).

Fe toxicity screening under hydroponic conditions

Seven representative genotypes [Shasharang (SH), IR64, Priya (LR15), Paijong (LR18), Chakhao Poirieton (CP), Kas and KMR3] were selected for phenotypic screening under longduration Fe toxicity hydroponicconditions. A significant decrease in root length was observed in high Fe treated samples of SH, LR18, CP, Kas and KMR3 (Fig. 1-D). Significant differences in shoot length between the control and treatment conditions were observed in genotypes SH, LR18 and CP (Fig. 1-E). Differences were also observed in the root fresh weight of all the genotypes, though not always significant (Fig. 1-F). Root fresh weights of SH, IR64 and Kas grown in high Fe concentration were less than those grown under normal Fe conditions. Treated samples of LR15 and KMR3 showed higher root fresh weight than the control but the difference was not significant (Fig. 1-F). Root morphology changes and leaf bronzing were noted for genotypes when the amount of Fe was varied in presence or absence of EDTA (Fig. S2). Perl’s blue staining revealed that the roots grown under normal Fe conditions did not take up Fe2+(Fig. 1-G). The accumulation of Fe was very high in the roots of IR64 with more concentration in the vascular bundle. However, in SH, a very faint stain was distributed throughout the xylem, phloem and vascular bundle of the roots (Fig. 1-G).

Gene expression patterns of five Fe deficiency genes under low P and high Fe conditions in rice shoots

Fig. 2.Transcript levels of five rice genes involved in Fe deficiency responses in shoots of six rice genotypes after treatments of low P and high Fe for 24 h.

A‒E, Expression levels of(A),(B),(C),(D) and(E) under low P and high Fe conditions for 24 h.

Plants were grown using sand culture supplemented with Yoshida solution in control, low P and excess Fe treatments at pH 5.4. Shoots were harvested and used for qRT-PCR. Ricetranscript level was used for normalization and transcript abundance was expressed as a ratio relative to the levels in control (Mean ± SD,= 4).axes on the left and right represent normalized relative expression in the P and Fe experiments, respectively. CK, Control (0.3500 mmol/L Pi and 0.0284 mmol/L Fe2+); T, Treatment (low Pi: 0.0150 mmol/L Pi and 0.0284 mmol/L Fe2+; high Fe2+: 0.3500 mmol/L Pi and 3.6000 mmol/L Fe2+). SD, Sahbhagi Dhan; CP, Chakhao Poirieton; B350, BAM350; B811, BAM811; SH, Shasharang.

Expression levels of five rice genes with reported roles in Fe deficiency were evaluated for response to 24-h low Pi and high Fe2+treatments in a set of six rice genotypes. In response to low Pi (Figs. 2 and S2),was down-regulated in SH butdid not show any different expression in the other five genotypes.showeddifferent expression levels inthree genotypes [Sahbhagi Dhan(SD), CP and BAM350]. It was up-regulated in SD (8-fold) and BAM350 (12-fold) and down-regulated in CP (4-fold) (Fig. 2-B).was differentially expressed in SD, BAM350, SH and KMR3, with SD showing significant down-regulation (7-fold) (Fig. 2-C). Due to 24-h low Pi treatment,was up-regulated in genotypes SD, BAM350 and BAM811, but down-regulated in CP and SH (Fig. 2-D).was highly induced in genotypes CP, BAM350 and BAM811, whereas no significantly different expression levels were observed in SD, SH and KMR3 (Fig. 2-E). In response to high Fe2+,was down-regulated in SH (2-fold) and KMR3 (10-fold), while no significantly different expression levels were observed in the other four genotypes (Fig. 2-A).was significantlyup-regulated in BAM350 and BAM811, but did not show significantly different expression in SH and KMR3 (Fig. 2-B).maintained basal expression levels in genotypes SD, CP, BAM350 and KMR3, while significant up- regulation was observed in BAM811 (Fig. 2-C).was up-regulated in five genotypes (SD, CP, BAM350, BAM811 and SH) with 9-fold up-regulation in BAM811 (Fig. 2-D).was highly induced in SD, BAM811 and KMR3 and down-regulated in SH (Fig. 2-E).

Fig. 3.Transcript levels of five rice genes involved in Fe deficiency responses in shoots of six rice genotypes after treatments of low P and high Fe for 48 h.

A‒E, Expression levels of(A),(B),(C),(D) and(E) under low P and high Fe conditions for 48 h.

Plants were grown using sand culture supplemented with Yoshida solution in control, low P and excess Fe treatments at pH 5.4. Shoots were harvested and used for qRT-PCR. Ricetranscript level was used for normalization and transcript abundance was expressed as a ratio relative to the levels in control (Mean ± SD,= 4).axes on the left and right represent normalized relative expression in the P and Fe experiments, respectively. CK, Control (0.3500 mmol/L Pi and 0.0284 mmol/L Fe2+); T, Treatment (low Pi: 0.0150 mmol/L Pi and 0.0284 mmol/L Fe2+; high Fe2+: 0.3500 mmol/L Pi and 3.6000 mmol/L Fe2+). SD, Sahbhagi Dhan; CP, Chakhao Poirieton; B350, BAM350; B811, BAM811; SH, Shasharang.

In response to 48-h low Pi treatments,showeddifferent expression levels in two genotypes (BAM350 and SH) (Fig. 3-A).showed significantly different expression levels in genotypes SD, CP, BAM350, BAM811 and SH. It was up- regulated in tolerant genotype SD whereas down- regulated in genotypes CP, BAM350, BAM811 and SH (Fig. 3-B).was down-regulated in genotypes CP, BAM350 and BAM811 (Fig. 3-C)Different expression levels ofin P deficiency susceptible genotypes BAM350 and BAM811 along with SH and KMR3 were observed in response to 48-h low Pi treatments. The gene was up-regulated in genotypes BAM350, BAM811 and KMR3, but down- regulated in SH (Fig. 3-D).was differentially expressed in two genotypes CP and BAM811. The gene was highly up-regulated (5-fold) in CP and down-regulated in BAM811 (2-fold) (Fig. 3-E). In response to 48-h high Fe2+treatments,was significantly up-regulated in KMR3 and down-regulated in genotypes BAM350 and BAM811 (Fig. 3-A).was significantly up-regulated in CP, BAM350 and BAM811 (Fig. 3-B).was up-regulatedin BAM350, BAM811 and SH, and down-regulated in KMR3, while no significantly different expression was observed in P deficiency tolerant genotypes SD and CP (Fig. 3-C)was differentially expressed in all the genotypes except SH after 48 h exposure to excess Fe2+stress (Fig. 3-D).was up-regulated in BAM350 (Fig. 3-E).

Gene expression levels for the above mentioned five genes after 7 d exposure to excess Fe2+stress were also studied in genotypes SH (showed no leaf bronzing) and KMR3 (showed leaf bronzing) (Fig. 4). In response to 7-d high Fe2+stress,was down-regulated in KMR3, whereas no significantly different expression was observed in SH (Fig. 4-A). Under the similar stress conditions,was down-regulated in the both genotypes (Fig. 4-B).andwere down-regulated in the both genotypes (Fig. 4-C and -E). No significantly different expression ofwas observed in KMR3, but down-regulation was observed in SH (Fig. 4-D).

SNP analysis and sequencing alignment of selected genes

Sequence polymorphism was identified using the Sanger’s method of sequencing and by surveying available SNP and InDel data in the rice SNP-seek database. On rice chromosome 1,with the length of 6 961 kb spans from 41 971 444 to 41 978 404bp. Two non-synonymous SNPs at positions 41 972 116 and 41 972 720 bp in exon 4 along with a total of 420 InDels were identified in(Fig. S3). Genes,andwere located on rice chromosome3 spanning regions 6 131 845 to 6 142 785 bp, 10 929 483 to 10 930 895 bp and 15 000 062 to 15 002 205 bp, respectively. Nucleotide polymorphismsurvey across 3 024 rice genotypes forrevealed presence of 197 InDels and 2 non-synonymous SNPs at positions 6 134 135 and 6 136 209 bp in exons 5 and 2, respectively (Fig. S4). Sequencing data revealed SNPs at positions 381, 1 172, 2 241 and 3 411 bp of the gene. Forgene, 29 InDels and 2 non- synonymous SNPs were identified across 3 024 rice genotypes. However, only one non-synonymous SNP (at position 10 930 517 bp in exon 1) was identified in the smaller panel of the five selected rice genotypes. Our sequencing data also revealed the presence of an SNP in exon 1 at position 272 bp of the gene (Fig. S5). Totally, 30 InDels and 1 non-synonymous SNP at position 27 187 655 bp on rice chromosome 4 were identified in(Fig. S6).,with the gene length of 2.144 kb, contained 32 InDels and 2 non-synonymous SNPs across 3 024 rice genotypes, but in the smaller panel of the 5 selected rice genotypes, only 1 non- synonymous SNP at position 15 001 469 bp was found (Fig. S7). Sequencing data for SH revealed additional SNPs in exon 4 of(Fig. 5-A and -B). Additionally, a 25-bp insertion was identified in 3′- UTR (untranslated region) of the gene in SH, KMR3 and SD, which was developed as an InDel marker based on this polymorphism.

Fig. 4. Transcript levels of five rice genes involved in Fe deficiency responses in shoots of two genotypes after exposure to 7 d of high Fe treatment.

A‒E, Expression levels of(A),(B),(C),(D) and(E) under high Fe conditions for 7 d.

Plants were grown using sand culture supplemented with Yoshida solution in control (CK, 0.3500 mmol/L Pi and 0.0284 mmol/L Fe2+) and excess Fe (T, 0.3500 mmol/L Pi and 3.6000 mmol/L Fe2+) treatments at pH 5.4. Shoots were harvested and used for qRT-PCR. Ricetranscript level was used for normalization and transcript abundance was expressed as a ratio relative to the levels in control (Mean ± SD,= 4).axis on the left represents normalized relative expression under Fe experiment. SH, Shasharang.

Allele-specific marker and marker-trait association

The FR033-3 marker developed forwas validated in a panel of 43 rice genotypes and 358 SD × CP recombinant inbred lines (RILs, named ULRC34) phenotyped under the acidic lowland field conditions (Fig. 5-A and -B). Eleven traits. tiller number per plant at 30 d after transplanting (TN30), tiller number per plant at 60 d after transplanting (TN60), panicle length (PL), leaf area (LA), filled grain number per plant (FG), spikelet fertility rate (SF), grain yield per panicle (GYPP), spikelet number per panicle (SPP), 100-grain weight (HGW), biological yield (BY) and phosphorus use efficiency measured at harvest (PUE) were analyzed for phenotypic differences between the SD and CP type alleles for the FR033-3 marker. In the diverse 43 panel, associations of FR033-3 marker with PL (= 0.0736), LA (= 0.009), GYPP (= 0.0236), SPP (= 0.0256) and PUE (= 0.042) were found (Fig. 5-C), while in the ULRC34 population, one key trait, GYPP, showed association (Fig. 5-C). Allelic frequencies in contrasting genotypes were also compared for the associated traits. SD type allele distribution was significantly associated with higher GYPP, LA and SPP in the 43 panel (Fig. 5-C). In the case of SD × CP progeny, distribution was significant for traits like GYPP and FG (Fig. 5-C).

Fig. 5. Marker development and validation ofinsertion/deletion (InDel) marker.

A, A 25-bp InDel in 3¢-UTR (untranslated region) ofwas targeted for marker development. Schematic diagram ofgene along with primers designed to amplify the 3¢-UTR region is shown. Dark grey triangles and lines indicate introns and exons, respectively. The arrow and star in the top indicate transcription start and stop positions, respectively. The positions of forward and reverse primers (33-3F/R) are given below the gene diagram. Allelic polymorphisms acrossgene for five rice genotypes are given in the bottom. Dark grey and white colours indicate reference and novel alleles, respectively.

B, Representative polymorphisms observed in a panel of 43 diverse rice genotypes and ULRC34 recombinant inbred lines.

C, Associations ofInDel in two populations with major traits of interest in Fe toxic acidic lowland field. The difference in the phenotypic means of the two allelic classes was tested using the-test. Bars in the first two graphs represent significance of differences. Single and double asterisks on the top of bars indicate significance of phenotypic difference between the two allelic classes at 0.1 and 0.01 levels of significance. Bars in the third graph indicate phenotypic means of genotypes carrying SD and CP type alleles in the panel of 43 diverse rice genotypes. Error bars indicate confidence interval. TN30, Tiller number at 30 d after transplanting; TN60, Tiller number at 60 d after transplanting; PL, Panicle length (cm); LA, Leaf area (cm2); HGW, 100-grain weight (g); GYPP, Grain yield per panicle (g); SF, Spikelet fertility rate (%); SPP, Spikelet number per panicle; PUE, Phosphorus use efficiency at harvest (%); BY, Biological yield (g); FG, Filled grain number per plant.

M, Marker; SH, Shasharang; SD, Sahbhagi Dhan; CP, Chakhao Poirieton.

In the acid lowland soils where Fe2+toxicity is prevalent, leaf bronzing occurred in KMR3. Higher bronzing score is reported to be strongly correlated with yield loss (Audebert and Fofana, 2009). However, even with high bronzing score, KMR3 performed well in the acidic lowland fields.

Previous study had identified Kasalath as susceptible to Fe toxicity based on evaluation in hydroponic culture (Wu et al, 2014). The designation of genotype as susceptible/tolerant varies with the duration and intensity of Fe stress and pH of hydroponic solution (Wu et al, 2014; Dufey et al, 2015). Our previous screening data suggested that Kasalath performs as good as the tolerant genotype SH (Das et al, 2017b). Plaque precipitation in rice is attributed to secreting oxygen from rice roots, which oxidizes ferrous iron (Fe2+) into insoluble ferric iron (Fe3+) on the root surface (Yokosho et al, 2009). Cross section of the roots showed that the susceptible genotypes like IR64 accumulated high levels of Fe2+in the vascular cylinder. In contrast, tolerant genotypes like SH accumulated much lower Fe2+levels and picked up stain throughout the root tissues (Fig. 1-G). Genotype Kasalath showed a significant decrease in crown roots under the Fe toxic conditions at pH 4.2, whereas the tolerant genotypes maintained the crown root number (unpublished data). Previous reports suggested that crown root number is a heritable trait and a selectable target for plant breeding (Bayuelo-Jiménez et al, 2011). However, it has been shown that low crown root number improves nitrogen acquisition in maize by enhancing deep soil exploration in low nitrogen soils (Saengwilai et al, 2014).

Many Fe deficiency genes are reported, but information on rice genes involved in Fe2+toxicity tolerance is just emerging. Our 24-h Fe stress qRT-PCR results revealed that thetranscript levels in genotype SH, which showed no bronzing symptoms, were much higher compared with KMR3 (very low transcript levels in response to high levels of Fe). In response to 48-h high Fe2+stress, the expression levels ofwere 3-fold higher in tolerant SH, whereas in KMR3, it was significantly down-regulated, suggesting the role ofin leaf bronzing in response to Fe toxicity. The function of another rice NAS family member,,in Fe detoxification has already been reported (Aung et al, 2019). In this study, in response to 24-h high Fe2+stress,was up-regulated in all the genotypes except KMR3. The transcription factorsandwith a known regulatory role in Fe deficiency (Kobayashi et al, 2007; Zheng et al, 2010) also showed different expression patterns in response to low P and high Fe2+stress. In response to low P and high Fe2+conditions, the negative regulator,,was up- regulated inlow P susceptible rice genotypes BAM350 and BAM811, respectively. Even in response to 7-d excess Fe2+stress, the transcript abundances ofwere contrasting in SH and KMR3. Cross-talk between Fe and other nutrients has been reported (Müller et al, 2015; Das et al, 2017a; Tyagi and Rai, 2017; Zhang et al, 2022). Fe content and Fe uptake in flag leaves of SH are higher compared with KMR3 under acidic lowland field conditions, but the uptake of Fe from soil in SH is low (Das et al, 2017a, b). Based on the Fe content and expression data, it appeared that the distribution and uptake mechanism of Fe in SH was more efficient in maintaining Fe homeostasis even under low P conditions. The P uptake values in flag leaves of both genotypes KMR3 and SH are comparable. However, significant differences of PUE in the above mentioned genotypes were found (Yumnam et al, 2017).

Analysis of chemical forms of Fe in xylem and phloem saps of rice plants reveals distinct patterns (Álvarez-Fernández et al, 2014). In xylem sap, Fe is bound largely to citrate (around 65%) and slightly to DMA (around 5%), while in phloem sap, it is bound to DMA, citric acid and proteins (Yoneyama et al, 2015). It is now proposed that since Fe exists in a bound form in both xylem and phloem, its metal concentration remains constant after uptake. The flow of Fe from xylem to phloem at nodes is regulated by(Kakei et al, 2012), phloem loading by(Lee et al, 2009) and phloem unloading in reproductive organs by(Aoyama et al, 2009). Our data onshowed its down-regulation in Fe tolerant genotypes like SH which associated with better distribution of Fe in vascular bundles as shown by histochemical staining pattern.

The barley NAAT genes improve rice performance by increasing Fe availability in alkaline soils when overexpressed (Takahashi et al, 2001). The allelic variation for 3¢-UTR ofin contrasting rice genotypes also suggested that negative regulator of Fe deficiency played a role in Fe toxicity and low P deficiency tolerance. Thetranscript levels were up-regulated under the 48-h excess Fe2+stress in CP, BAM350 and BAM811, and the CP type allele was low yielding in field. Marker developed based on such allelic polymorphisms can be used for marker- assisted selection in Fe toxicity tolerance. Till date, 84 QTLs are reported for excess Fe tolerance in rice (Das et al, 2017a) with two QTLs lying on chromosome 3., located on rice chromosome 3, was differentially regulated in SD and CP. A QTLfor shoot length in response to Fe toxicity tolerance was reported by Meng et al (2017) using 55K SNP chip in the MAGIC population. The phenotypic variation in shoot length explained by this QTL is only 3.5% withreported as an underlying gene. However, data were based on hydroponics-based seedling stage screening. Ourspecific marker associated significantly with multiple yield contributing traits under the Fe toxic field conditions, indicating a more significant role ofin Fe toxicity tolerance.

In conclusion, the key genes involved in Fe uptake and translocation showed genotype specific expression differences in response to high Fe and low P stress. A released variety from North East India, SH, performed well under the excess Fe and low P conditions, most probably due to better cellular homeostasis. Our study also identified a genic marker forassociated with superior performance under the acidic lowland field conditions. According to our knowledge, this is the first study where a previously reported rice gene with a role in Fe deficiency tolerance has been targeted for marker-assisted selection for low P and high Fe tolerance under the acidic soil conditions.

Rice materials

The experimental materials comprised of seven diverse rice genotypes namely SH, Kas, KMR3, SD, CP, BAM350 and BAM811. SH is an Fe toxicity tolerant rice genotype performing well in acidic soils in North East India (Yumnam et al, 2017). Kas is an internationaltype variety sensitive to Fe toxicity (Engel et al, 2012) and tolerant to low P levels (Wissuwa and Ae, 2001). KMR3 is a released line from Kerela, a coastal state of India with acidic soils, showing bronzing symptoms (Das et al, 2017b). SD and CP are low P tolerant genotypes while BAM350 and BAM811 are P susceptible rice genotypes (Bhutia et al, 2020). In addition, 358 F5:6lines derived from SD × CP biparental population, evaluated for their performance in acidic lowland soils, were used for marker trait association analysis (Bhutia et al, 2021). We used 43 diverse genotypes including the 7 mentioned above to evaluate the allelic status of marker (Table S1). These lines included lowland and upland rice from North East India as well as BAM (Bioprospecting and allele mining) genotypes from Indian mini core collection (Tiwari et al, 2015). Eleven traits. TN30, TN60, PL, LA, FG, SF, GYPP, SPP, HGW, BY and PUE were recorded for all these lines in the acidic lowland fields.All these lines were maintainedat the College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University (CAU, Imphal), Umiam, India (Yumnam et al, 2017; Bhutia et al, 2020).

Estimation of P and Fe contents

Estimation of soluble Pi from flag leaf of each genotype was performed by the phosphomolybdate colorimetric method (Yumnam et al, 2017) and PUE calculated (Bhutia et al, 2021). Leaf bronzing symptoms were observed on 60-day-old rice leaves of some of the genotypes. The Fe contents of root and shoot samples were measured using a Perkin Elmer Analyst 200 Atomic Absorption Spectrometer facility at College of Fisheries, CAU (Imphal), Lembucherra, India.

Hydroponic screening

Six rice genotypes, SH, KMR3, SD, CP, BAM350 and BAM811, were used for expression study. The experiment was conducted in greenhouse (with natural light supplemented with sodium light, a day/night temperature of 30 ºC/22 ºC, a day/night photoperiod of 14/10 h, with approximately 80% relative humidity). Twenty to twenty-five surface sterilized seeds of each rice genotype were germinated on petri plates lined with moist germination paper and homogenous seedlings were selected for screening (both long and short durations) using Yoshida solution (Yoshida et al, 1976). Long duration screening was performed to evaluate physiological response to excess Fe2+and Perl’s blue staining (Roschzttardtz et al, 2010). Fe toxicity screening was slightly modified from Elec et al (2013). All seedlings were transferred from petri plates to Yoshida solution (1×) after 7 d and grown in Yoshida solution (pH 5.4) for another 7 d. Then, the seedlings (14-day-old) were divided into control (0.0284 mmol/L Fe2+), treatment 1 (3.6000 mmol/L Fe supplied as Fe2SO4and 1:2 molar ratio of Fe2+:EDTA) and treatment 2 (3.6000 mmol/L Fe2+supplied as Fe2SO4and no EDTA). pH of high Fe Yoshida solution was maintained at 4.2. Nutrient solution was replaced with a fresh batch at least once every 5 d. The plants were evaluated for degree of leaf bronzing and root morphology after 10 d. The roots were subjected to staining with Perl’s blue for approximately 15 min and rinsed with water. Free hand cross sections were made and the slides were studied under a microscope (DM750, Leica Microsystems, GmBH, Germany). Short duration screening was performed for gene expression study. First, 30‒35 seedlings at 5-day-old were transferred to plastic cups containing 300 mL Yoshida’s solution (pH 5.4) and grown for 7‒8 d. The solution was changed every 3 d. The seedlings were divided into sets of 10 plants each and transferred into sand culture supplemented with Yoshida solution. Two different experiments were conducted. For each experiment, a control was kept using Yoshida solution with optimal Pi (0.3500 mmol/L) and Fe2+(0.0284 mmol/L) supplied as FeCl2. The first set was treated with 0.0150 mmol/L of Pi and normal Fe2+(0.0284 mmol/L) while the second set was grown in optimal Pi (0.3500 mmol/L) and high Fe2+(3.6000 mmol/L). Shoots were harvested after 24 and 48 h (control, low Pi and high Fe2+), and 7 d (control and high Fe2+). The harvested shoots were dipped in liquid nitrogen and stored at -80 ºC.

Gene expression study

Total RNA was extracted from three biological replicates of each of the six genotypes grown in normal and treated conditions using the SpectrumTMPlant Total RNA kit (Sigma- Aldrich, Co., Missouri, USA) following the manufacturer’s protocol. Approximately 200 µg of the total RNA was subjected to first strand cDNA synthesis in a 50-µL final volume using 200 U of M-MLV reverse transcriptase, 10 µmol/L Oligo dT (GCC Biotech (I) Pvt. Ltd., India). cDNA was diluted to 25-fold and used as a template for qRT-PCR for 5 genes namely,,,and(primers listed in Table S2)with ricegeneas an internal reference. The qRT-PCR was performed in a 10-µL reaction system with 4 technical replicates using the Thermo Scientific PikoRealReal-Time PCR System as previously described (Bhutia et al, 2020).

DNA extraction, sequencing and SNP analysis

Genomic DNA was extracted by the CTAB (cetyltrimethyl- ammonium bromide) method (Murray and Thompson, 1980). Approximately 150 ng of genomic DNAs of SH, KMR3, SD, CP, BAM350 and BAM811 was used as templates for PCR with overlapping primers of,,,and(Table S2)as described previously (Tyagi et al, 2020). The genotype Kas was not sent for sequencing as the sequence has already availed in the Rice SNP-Seek Database (http://snp-seek.irri.org). The amplified PCR products were sent to Amnion Biosciences Pvt. Ltd., Bangalore, India for sequencing. Sequence alignment with Nipponbare as reference genome was done using BioEdit ver 7.2.5 (Hall, 1999).

Occurrences of SNPs and InDels across(),(),(),(),() and()were surveyed across 3 024 rice genotypes using the Rice SNP-Seek Database (http://snp-seek.irri.org). The data obtained were also sorted for a subset of five genotypes with known varying responses to high Fe2+treatment. Nipponbareis a temperate rice showing susceptibility to pulse-field stress treatment (Wu et al, 2014) and tolerance to 4-week treatment of high Fe2+(Dufey et al, 2015). Azucena, a tropicalrice, shows tolerance to the both types of treatment (Dufey et al, 2015). IR64 (1B type) is susceptible to both pulse-field and 4-week treatment of high Fe2+. Kasalath (-type) and BR IRGA 409 (1B type) are susceptible to high Fe2+under 4-week treatment and no response has been recorded for both genotypes in pulse-field treatment (Wu et al, 2014). Allele frequencies for the selected genes were plotted in Excel by considering both major and minor alleles of non-synonymous SNPs and InDels.

Marker trait association for InDel OsIRO3

The 25-bp InDel in the 3¢-UTR ofwas targeted for development of allelic marker. The sequences of SD and CP were aligned with reference genome sequence of Nipponbare. The genomic DNA was extracted from 43 diverse rice genotypes as well as 358 individual lines of F5:6progeny of SD × CP population (ULRC34). The PCR reaction was carried out using 50 ng of gDNA, 5× Promega PCR buffer, 10 pmol of each primer, 0.6 mmol/L of dNTPs, 1.75 mmol/L of MgCl2and 5 U Dreampolymerase (Thermo Fisher Scientific, India). PCR procedure consisted of a 5-min initial denaturation step at 95 ºC, followed by 34 cycles of 1 min denaturation at 95 ºC, 1 min annealing step at 55 ºC and 2 min at 72 ºC for extension, and final extension at 72 ºC for 5 min. The PCR products were analyzed using 1% agarose gel electrophoresis and visualized using the gel documentation system. The alleles were scored as SD (922 bp) and CP (897 bp) types, respectively.

Statistical analysis

Phenotypic data for various traits under acidic conditions were collected for mean and correlation analysis. The genotypic data generated from the diverse 43 genotypes and the ULRC34 population with the FR033-3 marker were used for studying marker-trait association using the-test and the-square test. Statistical analysis was carried out using the Microsoft Excel software. Relative expression levels of qRT-PCR for genes reported for Fe deficiency were compared between control and treatments for each rice genotype. For each set of comparison, the student’s-test for differences in mean was carried out based on the-test for equal variance.

ACKNOWLEDGEMENTS

This study was supported by the grants from Indian Council of Agricultural Research (Grant No. C30033/415101-036) and Department of Biotechnology, Government of India (Grant No. BT/566/NE/U-excel/2016/72). Karma Landup Bhutia and Sudip Das were supported by Rajiv Gandhi National Fellowship and National Fellowship for Higher Education of ST Students (Grant No. 201516-NFST-2015-17-ST-3514), respectively, from the Ministry of Tribal Affairs, University Grant Commission, Government of India. Dr. N. Sarla (Indian Institute of Rice Research, Hyderabad, India) is duly acknowledged for sharing seeds of KMR3. Ms. Julia S. Yumnam and Mr. Ebenazar Gympad are acknowledged for initial field phenotyping and extracting genomic DNA, respectively.

SUPPLEMENTAL DATA

The following materials are available in the online version of this article at http://www.sciencedirect.com/journal/rice-science; http://www.ricescience.org.

Fig. S1. Flow chart depicting schematic view of experimental plan.

Fig. S2. Root morphology and leaf bronzing of rice plants grown in normal and high Fe2+concentrations.

Fig. S3. Nucleotide polymorphism across

Fig. S4. Nucleotide polymorphism across

Fig. S5. Nucleotide polymorphism across

Fig. S6. Nucleotide polymorphism across

Fig. S7. Nucleotide polymorphism across

Table S1. List of rice genotypes used in this study.

Table S2. Primers used in this study.

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