Role of reactive oxygen species in lesion mimic formation, and  resistance to F. graminearum in barley lesion mimic mutant 5386

doi:10.21203/rs.3.rs-1642323/v1

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Role of reactive oxygen species in lesion mimic formation, and resistance to F. graminearum in barley lesion mimic mutant 5386

https://doi.org/10.21203/rs.3.rs-1642323/v1

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This study investigated barley lesion mimic mutant (LMM) 5386, it exhibited a leaf brown spot phenotype and conferred basal resistance to F. graminearum. RNA-seq analysis identified 1453 differentially expressed genes in LMM 5368 compared to those in the wild type. GO and KEGG functional annotations suggested that lesion mimic formation was mediated by pathways involving oxidation-reduction and glutathione metabolism. In addition, the accumulation of reactive oxygen species (ROS) in brown spots was substantially higher in LMM 5368. Antioxidant competence, as indicated by ROS accumulation, was significantly lower in LMM 5368. Further, the reduction of glycine in LMM 5386 inhibited glutathione biosynthesis. These results suggest that the decrease in antioxidant competence and glutathione caused the accumulation of large amounts of ROS and thus led to programmed cell death in leaves of LMM 5386, which eventually reduced the yield components in LMM 5386.

Lesion mimic

RNA-Seq

ROS accumulation

antioxidant competence

glutathione

Barley

LMM 5386 conferred basal resistance to F. graminearum and decreased antioxidant competence and GSH content cause ROS accumulation and subsequent PCD.

Lesion mimics (LM), also known as hypersensitive reaction-like traits, arise spontaneously in leaf tissue without attack by any plant pathogen (McGrann et al., 2015). Lesion mimic mutants (LMMs) spontaneously form necrotic plaques under normal growth conditions. Thus, LMMs are valuable genetic resources for the study of programmed cell death (PCD) signaling pathways and disease resistance in plants (Moeder and Yoshioka, 2008).

Over the past two decades, many LMMs have been identified and studied in a variety of plants including Arabidopsis (Serrano et al., 2010), barley (Hao et al., 2019), maize (Hurni et al., 2015), and rice (Shang et al., 2009). Previous studies of LMM formation have drawn the following conclusions: (1) Mutations or the abnormal expression of disease-resistance genes lead to hypersensitivity and subsequent PCD in plants, causing necrotic plaques similar to those caused by pathogen infection (Shirano et al., 2002); (2) the abnormal expression of genes controlling PCD leads to the loss of control of PCD, which can also lead to the formation of necrotic plaques (Dietrich et al., 1994); (3) plant metabolism disorders can induce necrotic plaques in plants (Hu et al., 1998); and (4) external environmental changes can also induce the appearance of plaques (Wang et al., 2015; Wang et al., 2016). These studies suggest that the pathogenesis of LM is diverse in plants. Therefore, the study of LMMs could elucidate the mechanisms of LM in plants.

To date, very few Triticeae-tribe LM genes have been cloned. Two LM genes have been cloned from barley, namely TIGRINA-D.12 (Khandal et al., 2009) and NEC1(Rostoks et al., 2006). TIGRINA-D.12 and NEC1 in barley are homologous to FLU (At3g14110) and HLM1 (At5g54250) in Arabidopsis thaliana, respectively. The FLU protein regulates chlorophyll synthesis in Arabidopsis, and the LMM FLU protein forms LM in mature leaves (Meskauskiene et al., 2011). In addition, two LM genes (lm1 and lm2) were mapped on 3BS and 4BL in wheat (Yao et al., 2009). A novel light-dependent LM gene (lm3) was mapped on 3BL and showed resistance to powdery mildew in wheat (Wang et al., 2016). However, the current understanding of LMMs is limited; therefore, more LM genes must be identified and studied to illustrate their functions.

LMMs are considered excellent materials for the study of the hypersensitive response (HR) and PCD in plants (Bruggeman et al., 2015). In addition, the level of reactive oxygen species (ROS) has also been identified as capable of producing LMMs (Sindhu et al., 2018). Hao et al. (2019) reported that autonomic lesions associated with lmm194 were often accompanied by excessive ROS, which occasionally leads to cell death. Plants containing lmm6 were more resistant to rice blast fungus and had a lower antioxidant competence and higher ROS accumulation (Xiao et al., 2015). Oxidative stress genes are also expressed in LMM individuals (Devadas et al., 2002; Torres et al., 2002), as are enzymes involved in antioxidant systems such as glutathione S-transferase (GST), peroxidase, and superoxide dismutase (SOD) (Klibenstein et al., 1999), suggesting that oxidative stress signals are activated by LMM genes.

Some LMMs have been isolated in plants, most of which display enhanced pathogen resistance. For instance, lls1 mutant exhibits enhanced resistance to fungal pathogens in maize (Simmons et al., 1998). spl mutations exhibited resistance to the blast fungus in rice (Yin et al. 2000). M66 mutant increased resistance to yellow rust and powdery mildew (Kinane and Jones, 2001). lm1 and lm2 enhanced resistance to leaf rust in wheat (Yao et al., 2009), etc.

Barley is both an ideal model of the Triticeae tribe and an economically important cereal. In the present study, we identified a novel LMM in barley, and then, we investigated (1) the resistance to F. graminearum. and (2) the formation of LM in LMM-containing plants. The results are expected to improve our understanding of the role of LMMs in barley.

Plant materials

The experiments were performed at Zaozhuang University, Zaozhuang, Shandong, China. Grains were sown at a density of 300 seeds m⁻². The plot size was 2 × 2 m with six rows (0.25 m between rows). Each plot was used for sample collection. The experiments were conducted at least in triplicates.

RNA-seq and data analysis

Flag leaves of WT and LMM 5386 lines were collected, and three biological replicates were used for RNA-seq. Total RNA was extracted from each sample using TRIzol reagent following the manufacturer’s specifications (Invitrogen). The quality and quantity of each RNA sample were measured using an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA).

The mRNAs were isolated from the total RNA using Dynabeads mRNA DIRECT Kit (Invitrogen) and were separated into short fragments using a fragmentation buffer. Using these short fragments as templates, random primers, and a SuperScript double-stranded cDNA synthesis kit (Invitrogen), double-stranded cDNA was synthesized. Ligated fragments were then generated through a series of reactions that included the purification of the PCR products, end repair, dA-tailing, and ligation of the Illumina adapters. After agarose gel electrophoresis, suitable fragments were selected for PCR amplification. The final library was evaluated using quantitative RT-PCR with a StepOne Plus Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). Sequencing reactions were performed on an Illumina HiSeq 2000 (Biomarker Technologies Corporation, Beijing, China).

GO and KEGG analyses were performed to identify DEGs enriched in GO terms and metabolic pathways, respectively. A corrected p-value (≤ 0.05) was set as the threshold for significantly enriched GO terms and KEGG pathways.

Determination of H₂O₂content and O₂•⁻ production rate

H₂O₂ content and O₂•⁻ production rates were measured according to previously reported methods (Hao et al., 2018). Accumulation ofO₂•⁻ and H₂O₂ in flag leaves was visually evaluated by staining with NBT (0.5 mg mL⁻¹, pH 7.6) for O₂•⁻ and DAB (1 mg mL⁻¹, pH 3.8) for H₂O₂.

Determination of GSH and Gly content

The GSH and Gly content was measured according to previously reported methods (Noctor et al., 1997).

Measurement of antioxidant enzyme activity

The activities of CAT, SOD, POD, APX, GR, and GST were detected based on previously described methods: SOD (Wang et al., 2020), CAT (Yang et al., 2020), APX (Wang et al., 2017), POD (Wang et al., 2016), GR (Yin et al., 2017), and GST (Li et al., 2018). All samples were analyzed using a Shimadzu UV-1900i spectrophotometer.

Quantitative reverse transcription PCR analysis

Total RNA was isolated from leaves using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA). The RNA was used to produce cDNA using a reverse transcription kit (Vazyme, Nanjing, China). Quantitative reverse transcription PCR was performed using the ChamQ Universal SYBR qPCR Master Mix Kit (Vazyme, China). The expression of a specific gene versus a control was determined using the formula 2⁻^△△^CT. Actin was evaluated as the control gene (Hao et al., 2018). Information on the genes analyzed is presented in Table 2.

Statistical analysis

All analyses were performed at least in triplicate. The IBM SPSS Statistics program was used to perform the statistical analyses. All comparisons were analyzed using factorial ANOVA. Differences between the means among the lines were compared using Duncan’s multiple range tests at 0.05 probability levels.

Phenotype analysis of WT and LMM 5368 lines

A barley LMM 5386 line was generated through the application of ethyl methanesulfonate (EMS) to the ‘Tamalpais’ wild type (WT) cultivar. We found that several brown spots were spontaneously produced in the leaves of LMM 5368 lines under field conditions (Fig. 1a–b). The brown spot area per leaf was quantified in WT and LMM 5368 lines, and that in LMM 5368 lines was significantly higher than in the WT (Fig. 1c).

We also compared the phenotypes of WT and LMM 5368 individuals (Fig. 2a). The tiller number of LMM 5368 was significantly lower (approximately 16.7%) than that of the WT (Fig. 2b), but there were no obvious differences in the plant height (Fig. 2c). Further, the number of leaf brown spots per plant was significantly higher in LMM 5368 plants than that in WT plants (Fig. 1d).

RNA-seq analysis of WT and LMM 5368 lines

To better understand the mechanism of LM formation in LMM lines, we performed an RNA-seq analysis using the flag leaves of WT and LMM 5368 lines. The RNA-seq analysis provided 65.80 Gb of clean bases. The percentage of Q30 in each sample was not less than 93.89%, 91.62–92.09% of reads could be accurately mapped to the reference genome, and 2.38–3.69% of reads could be mapped to multiple genome sequences (Table 1). The Pearson correlation coefficients among biological replicates were found to be higher than 0.95 (Fig. S1).

Compared with WT lines, 1453 differentially expressed genes (DEGs) were found in LMM 5368 lines, of which 1260 were upregulated and 193 were downregulated (Fig. 3). DEGs with the same or similar expression patterns were placed into 16 groups via hierarchical clustering analysis (Fig. S2). For example, the nine DEGs in group 1 were enriched in the glutathione (GSH) metabolic process, GSH transferase activity, anchored component of the plasma membrane, aleurone grain membrane, and cytokinin biosynthetic process (Fig. S3).

The functions of the 1453 DEGs were verified using the gene ontology (GO) database (http://www.geneontology.org), which provides annotations of biological processes, molecular functions, and cellular components. Specifically, we compared the biological process between the WT and LMM 5368 lines. Among the upregulated DEGs, those encoding the protein phosphorylation, oxidation-reduction process, and defense response were clearly over-represented (Fig. 4a). Among the downregulated DEGs, we found that many were significantly enriched in oxidation-reduction and redox homeostasis processes (Fig. 4b).

To compare the metabolic pathways of DEGs in WT and LMM 5368 lines, we analyzed the obtained DEGs using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.genome.jp/kegg/pathway.html). Among the upregulated DEGs, many were significantly enriched in GSH metabolism, plant-pathogen interactivity, and amino acid biosynthesis (Fig. 5a). Among the downregulated DEGs, many were significantly enriched in photosynthesis antenna proteins, glyoxylate and dicarboxylate metabolism, and carbon fixation in photosynthetic organisms (Fig. 5b).

ROS analysis of WT and LMM 5368 lines

GO analysis showed that many DEGs were enriched in the oxidation-reduction process (Fig. 4). Therefore, we compared the H₂O₂ content and O₂•⁻ production rate between WT and LMM 5368 lines (Fig. 6). The accumulation of O₂•⁻ was detected via nitroblue tetrazolium (NBT) staining, and O₂•⁻ accumulation in the brown spots of LMM 5368 lines was significantly higher than that in WT plants (Fig. 6a). Similarly, diaminobenzidine (DAB) staining suggested that H₂O₂ content in brown spots of LMM 5368 lines was significantly higher than that in the WT (Fig. 6c). Finally, we quantified the ROS accumulation. As shown in Fig. 6b and d, LMM 5368 lines showed a relatively higher O₂•⁻ production rate and H₂O₂ content than seen in the WT lines.

GSH and glycine (Gly) analysis of WT and LMM 5368 lines

KEGG analysis showed that many upregulated DEGs were significantly enriched in GSH metabolism (Fig. 5a). Therefore, the GSH and Gly content was compared between WT and LMM 5368 lines (Fig. 7a and b, respectively); both were significantly lower in LMM 5368 lines than in the WT.

Antioxidant competence analysis of WT and LMM 5368 lines

Antioxidant enzyme activities were also compared between WT and LMM 5368 lines (Fig. 8). We measured SOD (Fig. 8a), catalase (CAT) (Fig. 8b), ascorbate peroxidase (APX) (Fig. 8c), and peroxidase (POD) (Fig. 8d) activity levels, which were significant in LMM 5368 lines than in the WT. The activities of glutathione reductase (GR; Fig. 8e) and GST (Fig. 8f) were consistent with those of SOD, CAT, APX, and POD. Nevertheless, the downregulation of GR and GST was significantly greater than that of SOD, CAT, APX, and POD in LMM 5368 lines. Further, the antioxidant enzyme-encoding genes of WT and LMM 5368 were compared (Fig. 9). The expression of Cu/Zn-SOD (Fig. 9a), HvCAT1 (Fig. 9b), HvAPX1 (Fig. 9c), and HvGST6 (Fig. 9d) genes in LMM 5368 lines were significantly lower than those in the WT.

Resistance to F. graminearum analysis of WT and LMM 5368 lines

RNA-seq analysis indicated that a large number of genes associated with disease resistance were altered between WT and LMM 5368 lines. Therefore, we determined the expression of six disease resistance-related genes, namely, isochorismate synthase (HvICS) (Fig. 10a), ethylene response factor 1 (HvERF1) (Fig. 10b), HvWRKY38 (Fig. 10c), pathogenesis related protein-1a (HvPR1a) (Fig. 10d), ethylene-responsive transcription factor 3 (HvERFC3) (Fig. 10e), and flavonoid O-methyltransferase protein (HvFme) (Fig. 10f). Their expressions in the LMM 5368 lines were up-regulated compared to those in the WT (Fig. 10). We also compared the F. graminearum lesions on infected leaves between WT and LMM 5368 lines (Fig. 11). There were no differences in F. graminearum lesions at 3DAI between WT and LMM 5368 lines (Fig. 11a, b). After infected with F. graminearum at 7d, the length of lesions was significantly larger in all lines. But the lesions in the LMM 5368 were significantly smaller than those in WT (Fig. 11a, b).

The brown spots phenotype of LMM 5368 lines

Barley (Hordeum vulgare, 2n = 14), the fourth largest cereal crop in the world, offers high yields and good stress tolerance (Hao et al., 2019). Further, barley presents diverse morphological and genetic features and can be used as a model species for the Triticeae tribe. In this study, we identified the novel LMM 5386 in barley using EMS (Fig. 1a). LMMs have been classified as “initiation types” and “propagation types” (Landoni et al., 2013). In LMM 5386 plants, many brown spots were spontaneously produced at the four-leaf stage and then spread throughout the plant (Fig. 1b; Fig. 2a). Hence, we categorized LMM 5386 as the “propagation type.” However, the mechanism of LM formation remained to be elucidated.

LM caused by ROS accumulation in LMM 5386 lines

RNA-seq is a useful approach for analyzing DEGs at the transcriptome level and in clarifying their regulatory network, thus providing insight into the mechanism of LM formation (Wang et al., 2016). Through transcriptomic analysis, DEG responses were found to be mediated by various pathways (Li et al., 2017), and RNA-seq was used to analyze the 1453 DEGs involved in the formation of LM in LMM 5368 lines (Fig. 3).

PCD can be classified as autolytic and non-autolytic (Van Doorn et al., 2011). Autolytic PCD occurs mainly during plant growth and includes the formation of LM, whereas non-autolytic PCD occurs mainly when plants are subjected to external stress (Van Doorn et al., 2011). ROS such as H₂O₂ and O₂•⁻ are the main participants in the formation and regulation of the HR, which is the most obvious characteristic of PCD (Coll et al., 2011). GO analysis revealed that many DEGs were enriched in the oxidation-reduction process (Fig. 4), suggesting that this process plays an important role in LM formation. In the present study, the accumulation of ROS in brown spots was significantly higher in LMM 5368 lines than in the WT (Fig. 6). Wang et al. (2016) demonstrated that antioxidant enzymes can remove excess ROS to maintain better plant growth. However, the LMM 5368 lines demonstrated lower antioxidant competence, manifested as decreased SOD, CAT, APX, and POD enzyme activities (Fig. 8), which was consistent with the expression of the related antioxidant enzyme-encoding genes (Fig. 9). These results suggest that decreased antioxidant competence leads to the accumulation of ROS and subsequent PCD in LMM 5368 lines.

In rice, many signaling pathways and biological processes are involved in LM and PCD, including protein phosphorylation (Harkenrider et al., 2016), abscisic acid signaling (Wang et al., 2015), and protein ubiquitination (Liu et al., 2015). KEGG analysis showed that many upregulated DEGs were significantly enriched in GSH metabolism (Fig. 5a). H₂O₂ removal is mainly achieved by ascorbate/GSH cycles in higher plants (Noctor et al., 1998), and GSH exists as an intermediate recirculation product in this cycle. GSH is a special class of amino acid derivative consisting of glutamate, cysteine, and Gly. Gly produced by photorespiration plays an important role in GSH synthesis (Noctor et al., 1997). In the present study, we found that Gly content was significantly decreased in LMM 5368 lines, resulting in the inhibition of GSH biosynthesis (Fig. 7). In addition, the decreased activity of GR and GST also reduced the effective clearance of H₂O₂ (Fig. 8e–f), which caused the accumulation of large amounts of ROS and subsequent PCD in LMM 5368 lines. These results suggest that GSH metabolism plays an important role in LM formation.

LMM 5386 lines showed significant resistance to F. graminearum

Till now, more than 30 LM mutants exhibiting blast disease resistance have been identified (Zhu et al., 2016), However, there are still few studies on the mechanisms. It was reported that salicylic acid (SA), jasmonic (JA), and ethylene (ET) play an important role in the disease resistance of LM mutants (Kim et al., 2008). we examined HvICS, HvERF1, HvWRKY38, and HvERFC3, which encodes a regulator of disease resistance in SA/JA/ET pathways (Lorenzo et al., 2003; Vlot et al., 2009; Hao et al., 2019). In this study, their expressions were significantly increased in the LMM 5368 lines (Fig. 10a, b, c, e). HvPR1a and HvFme genes, directly involved in disease resistance (Qin et al., 2021), were also significantly up-regulated in the LMM 5368 lines (Fig. 10d, f). Using a leaf-based inoculated F. graminearum test, the lesions in the LMM 5368 lines were significantly smaller than those in WT at 7DAI (Fig. 11). The current study proposed that LMM 5368 conferred basal resistance to F. graminearum.

In conclusion, the barely LMM 5386, conferred basal resistance to F. graminearum in the present study. The results suggested potential mechanisms of LM formation in LMM 5386. The decreased antioxidant competence and GSH content cause ROS accumulation and subsequent PCD in LMM 5386, which eventually reduced the yield components in LMM 5386 (Table 3).

Author contribution statement The work presented here was carried out in collaboration among all authors. WW and WWQ defined the research theme. HQQ and ZJF designed most of the methods and experiments. HQQ, ZJF, GFX, WYH, LWK, and DYD carried out the laboratory experiments. WWQ, NF, and FDL wrote the paper. All authors discussed the results and approved the article.

Acknowledgements This work was supported by the Natural Science Foundation of Shandong (ZR2020QC113), the National Natural Science Foundation of China (32001536), the Science and Technology Plan Projects of Zaozhuang (2019NS01), the Doctoral Research Initiation Funds of Zaozhuang University (2018BS043, 2020BS001), the Opening Foundation of State Key Laboratory of Crop Biology (2020KF06).

Compliance with ethical standards

Conflict of interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Table 1

Summary of the sequence data from RNA sequencing
Samples	Clean reads	Clean bases	GC Content	%≥Q30	Mapped Reads	Multiple Map Reads
5368-1	37,047,360	10,979,042,458	57.65%	94.05%	68,044,432 (91.83%)	1,920,373 (2.59%)
5386-2	37,877,134	11,262,123,100	57.30%	93.89%	69,430,643 (91.65%)	2,093,998 (2.76%)
5386-3	35,801,059	10,557,653,852	57.24%	93.93%	65,598,544 (91.62%)	2,160,694 (3.02%)
WT-1	37,799,868	11,181,592,898	56.97%	94.08%	69,620,925 (92.09%)	2,833,217 (3.75%)
WT-2	37,517,418	11,105,141,892	57.06%	94.04%	69,011,360 (91.97%)	2,040,514 (2.72%)
WT-3	36,100,179	10,713,673,214	57.14%	93.98%	66,411,477 (91.98%)	2,642,342 (3.66%)

Table 2

Primers used in the current study
Target Genes		Primer sequence (5’ − 3’)	Application
HvActin	Forward	TCGCAACTTAGAAGCACTTCCG	qRT-PCR
	Reverse	AAGTACAGTGTCTGGATTGGAGGG	qRT-PCR
Cu/Zn SOD	Forward	CCCCTCACCAAGTCAGTCAT	qRT-PCR
	Reverse	ATTGCAAGTCGGTGTCCTTC	qRT-PCR
HvCAT1	Forward	TGGACGGATGGTACTGAACA	qRT-PCR
	Reverse	GTGCCTTTGGGTATCAGCAT	qRT-PCR
HvAPX1	Forward	CGCCCTCTTGTGGAGAAATA	qRT-PCR
	Reverse	CGCGCATAGTAGCAGCAGTA	qRT-PCR
HvGST6	Forward	ATCTCGTCAGAAACCCGTTC	qRT-PCR
	Reverse	CTTTCCACGACCACACATTG	qRT-PCR
HvICS	Forward	AGATTTACGATGGCGGTTTG	qRT-PCR
	Reverse	TTCAGTGAGCTCGAGGAGGG	qRT-PCR
HvERF1	Forward	GAGGAAGAGCAGAGCGACAC	qRT-PCR
	Reverse	GTCGCCACGAGTATGGTCTT	qRT-PCR
HvWRKY38	Forward	GTGAAGGACGGGTACCAATG	qRT-PCR
	Reverse	GTCGCCACGAGTATGGTCTT	qRT-PCR
HvPR1a	Forward	CACACCAAACCCAGAATGGAGA	qRT-PCR
	Reverse	CGTTGTGGGGTGAAAGGTAGT	qRT-PCR
HvERFC3	Forward	CGTGATGGAGCTTGAGGACCT	qRT-PCR
	Reverse	AGACCGGAGAATCAGATGGAGT	qRT-PCR
HvFme	Forward	TGCTGGTGACATGATGGAGTTC	qRT-PCR
	Reverse	TTACCAGCTTTCTCTCCGTGG	qRT-PCR

Table 3

Yield components of WT and LMM *5386* lines
Cultivar	1000-kernel weight (g)	Spike number	Grain length (mm)	Grain width (mm)
WT	39.5 ± 1.06a	65.0 ± 3.98a	7.03 ± 0.51a	3.07 ± 0.27a
LMM 5386	37.9 ± 1.13b	64.8 ± 2.45a	6.85 ± 0.47a	2.97 ± 0.22a

FigS1.tif
Fig. S1 Pearson correlation coefficients among three biological replicates of WT and LMM 5386 lines.
FigS2.tif
Fig. S2 Hierarchical clustering analysis of 1453 differentially expressed genes (DEGs) based on the log (FC) of gene expression. The color gradient from red to green represents relative levels of gene expression (from low to high, respectively). The numbers in the scale bar indicate the gene expression scores.
FigS3.tif
Fig. S3 Hierarchical clustering analysis of nine differentially expressed genes (DEGs) based on the log (FC) of gene expression in group 1. The color gradient from red to green represents relative levels of gene expression (from low to high, respectively). The numbers in the scale bar indicate the gene expression scores.

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Role of reactive oxygen species in lesion mimic formation, and resistance to F. graminearum in barley lesion mimic mutant 5386

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