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Fu et al. BMC Genomic Data (2023) 24: doi/10.1186/s12863-022-01098-y
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Open Access
BMC Genomic Data
Comparative transcriptome analysis
in peaberry and regular bean coffee to identify
bean quality associated genes
Xingfei Fu†, Guiping Li†, Faguang Hu†, Jiaxiong Huang, Yuqiang Lou, Yaqi Li, Yanan Li, Hongyan He,
YuLan Lv and Jinhuan Cheng*
Abstract Background The peaberry bean in Arabica coffee has exceptional quality compared to the regular coffee bean. Understanding the molecular mechanism of bean quality is imperative to introduce superior coffee quality traits. Despite high economic importance, the regulatory aspects of bean quality are yet largely unknown in peaberry. A transcriptome analysis was performed by using peaberry and regular coffee beans in this study. Results The result of phenotypic analysis stated a difference in the physical attributes of both coffee beans. In addi- tion, transcriptome analysis revealed low genetic differences. Only 139 differentially expressed genes were detected in which 54 genes exhibited up-regulation and 85 showed down-regulations in peaberry beans compared to regular beans. The majority of differentially expressed genes had functional annotation with cell wall modification, lipid binding, protein binding, oxidoreductase activity, and transmembrane transportation. Many fold lower expression of Ca25840-PMEs1, Ca30827-PMEs2, Ca30828-PMEs3, Ca25839-PMEs4, Ca36469-PGs. and Ca03656-Csl genes annotated with cell wall modification might play a critical role to develop different bean shape patterns in Arabica. The ERECTA family genes Ca15802-ERL1, Ca99619-ERL2, Ca07439-ERL3, Ca97226-ERL4, Ca89747-ERL5, Ca07056-ERL6, Ca01141-ERL7, and Ca32419-ERL8 along lipid metabolic pathway genes Ca06708-ACOX1, Ca29177-ACOX2, Ca01563-ACOX3, Ca34321-CPFA1, and Ca36201-CPFA2 are predicted to regulate different shaped bean development. In addition, flavonoid biosynthesis correlated genes Ca03809-F3H, Ca95013-CYP75A1, and Ca42029-CYP75A2 probably help to generate rarely formed peaberry beans. Conclusion Our results provide molecular insights into the formation of peaberry. The data resources will be impor- tant to identify candidate genes correlated with the different bean shape patterns in Arabica. Keywords Coffea arabica, Transcriptome analysis, Gene expression, Bean quality, Bean components
Background Coffee is one of the most popular beverages nowadays. Millions of people in the world consumed coffee to boost their concentration, productivity, and physical performance [ 1 ]. It becomes prime source of income in tropical regions of different countries, produced almost seven million tons every year worldwide, and is ranked among the top five most agricultural export commodi- ties of devolving countries [ 2 ]. e Brazil, Vietnam, and Colombia produced more than 50% of global coffee. e
†Xingfei Fu, Guiping Li and Faguang Hu contributed equally to this work.
*Correspondence: Jinhuan Cheng rjscjh@yaas.org Institute of Tropical and Subtropical Cash Crops, Yunnan Academy of Agricultural Sciences, Yunnan, Baoshan 678000, China
countries such as China, Ethiopia, Honduras, Indonesia, India, Malaysia, Nicaragua, and Peru are other major cof- fee growing countries in the world. Moreover, rigorous consumption of coffee beverages and their commerciali- zation ultimately caused wide development of the coffee industry in many non-tropical countries in recent years [ 3 – 5 ]. In China, the successful cultivation of coffee was first reported in Taiwan province followed by Yunnan province and the tropical area of Hainan province [ 6 ]. e genus of Coffea has 124 species with the addition of 20 closely related species from the genus Psilanthus [ 7 ]. However, Coffea arabica (Arabica) and C. canephora (Robusta) have more economic importance which gen- erates 70% and 30% of world coffee production, respec- tively [ 8 ]. e Arabica is allotetraploid species with 2 n = 4 × = 44, well adapted to highlands, and proved to have the best quality coffee beans than other species. In contrast, Robusta is diploid species with 2n = 2x = 22, better adaptation to warm or humid climatic conditions of lowlands, and regarded low quality coffee than Arabica due to higher caffeine concentration in beans [ 9 – 11 ]. Cli- mate change and insect pest resilient genotypes are criti- cal to mitigating the recent decline of coffee productivity worldwide [ 12 ]. With the increased knowledge of quality characteristics among consumers, the demand for high quality coffee beans has been increasing. e regular consumption of quality coffee usually not only improves physical perfor- mance but also reduces the risk of various disorders [ 13 ]. e Arabica coffee has aluminous dicots bean with vari- ous stored compounds in the mature endosperm [ 14 ]. e cell wall polysaccharides, sucrose, lipids, proteins, and chlorogenic acids are major storage compounds pre- sent in mature green coffee beans [ 15 – 18 ]. e precursor of these compounds determines the coffee final aroma, flavor, and taste [ 19 ]. e biochemical composition of storage compounds alters with environmental variables and genotypes [ 20 ]. A better understanding of the molec- ular mechanism of bean quality has critical importance to breed high quality coffee genotypes. e recent devel- opment in transcriptomic, proteomic, and metabolomics analytical techniques has identified the candidate genes related to bean storage components in different crops [ 21 – 23 ]. e high throughput research on coffee crops has gained attention with the recent free availability of the Robusta reference genome [ 24 ] and the draft genome of Arabica [ 25 ]. Different studies have already been per- formed to investigate the genetic control of various stress resistance [ 26 , 27 ] and the accumulation of major bean components in various species of coffee [ 28 , 29 ]. How- ever, a significant research gap still exists in Arabica. e genetic mechanism of bean quality traits is limited in Arabica. Large-scale high-throughput transcript data
resources can help to hybridize high quality bean geno- types in Arabica. The coffee plant produced fruit cherries and beans are the seeds inside ripened fruit. Usually coffee fruit cherry has two embryos, their fertilization generate two independent hemispherical shape beans. How- ever, sometimes only one embryo is further devel- oped to yield round shape thicker bean which is commonly known as a peaberry [ 30 ]. The probability of peaberries occurrence is extremely low under nor- mal conditions. Almost, 7% of mature green coffee crop is comprised of peaberries. The peaberries are rare in nature and can be formed at any pace in cof- fee planting areas [ 31 ]. The bean physical attributes is prime trait that not only disturbs market price but also significantly affects coffee roasting time [ 32 ]. To ensure high coffee quality, the customers com- monly separate peaberries from regular beans due to their higher market price and cup quality. Because of economic importance of peaberries, this study was designed to fulfill the research gap existing for pea- berry bean quality traits. The beans physical attrib- utes such as single bean size, length, and width were measured by using peaberry and regular coffee beans. Furthermore, a comparative transcriptome analysis was performed to reveal gene expression differences between both coffee beans. The results of this study further provide molecular insights into bean quality traits of peaberry coffee.
Results Phenotypic shape differences among peaberry and regular coffee beans The ripened fruit of Arabica generally contains two regular bean seeds. The probability of occurrence of peaberry coffee beans is extremely low. This study determined the phenotypic attributes of peaberry and regular coffee beans. Hereafter, these contrasting cof- fee beans were named CPB (peaberry coffee bean) and CB (regular coffee bean). Interestingly, mature fruit cherry of CPB has a different shape compared to CB (Fig. 1 a). The peeled bean of peaberry is round shaped whereas regular beans had hemispherical shape (Fig. 1 b). The average of 20 beans showed that CPB and CB had significant difference in bean length and width. However, the single bean weight had non- significant difference. The mean value of single bean weight was 0 g for CPB whereas CB had mean value of 0 g in this study (Fig. 1 c). The bean length and width for CB had a mean of 11 mm and 8 mm, respectively. However, the bean length and width of CPB were somehow lower with the observed mean
bases mean was 13,641,586,400 for CB with total reads and clean reads mean of 90, 943,909 and 90,873,708, respectively. Almost, the 93% of clean reads were mapped to reference the genome of C. arabica. Of which, nearly 75% of reads were uniquely mapped and only 18% were multiple mapped. e mean of Q30 was above 93% for each sequenced sample. Approximately, 88% of reads were mapped to the exon region in both coffee beans whereas intronic, intergenic, and splicing were almost 6%, 4%, and 1%, respectively (Figure S 1 ). All these results state high quality sequenced data suitable for down- stream analysis. e principal component analysis (PCA) revealed that the PC1 and PC2 described 58% of the total variation among all samples (Figure S 2 a). e statistics of correlation analysis stated undulant correlations among different samples of both coffee beans (Figure S 2 b).
Differentially expressed genes among peaberry and regular coffee beans e total number of expressed genes describes the overall view of the transcript landscape in the given sample. e expression level was measured with fragments per kilo- base per million reads (FPKM) value. Our results found a higher number of total expressed genes for CB than for CPB. For example, the total number of expressed genes was 38,543 for CB (Table S 1 ). However, 37,765 genes
were expressed in CPB. e higher ratio of genes had 0–3 FPKM expression followed by 3-15 FPKM in both coffee beans (Table S 1 ). However, the ratio was determined little higher for CB than CPB. e ratio of gene expression with > 15 FPKM value was 14% for CB and 14% for CPB. e FPKM scores were utilized to analyze the dynamic gene expression differences among CB and CPB. e differentially expressed genes (DEGs) between coffee beans were considered with p ≤ 0 and log2 (fold change) ≥ 1 or log2 (fold change) ≤-1. e total number of DEGs with the distribution of up or down reg- ulation is shown in Fig. 2. Comparative analysis among CB and CPB had shown 139 total DEGs (Fig. 2 a) with 85 genes up regulated in CB compared to CPB. In con- trast, 54 genes were down regulated in CB compared to CPB. Cluster analysis of the DEGs showed that genes had distinct expression clusters with contrasting expression trends between both coffee beans (Fig. 2 b). e lower number of DEGs demonstrated that both coffee beans had the same genetic background but small gene expres- sion profiles led to formation of peaberry coffee beans in Arabica. Functional enrichment analysis showed that most genes were annotated with pectinesterase activ- ity, enzyme inhibitor activity, manganese ion binding, ethylene-activated signaling pathway, and cell wall modi- fication (Figure S 3 ). erefore, our results presume that
Fig. 2 The total DEGs, their regulation, and expression profiles in comparison of peaberry and regular coffee beans a Total DEGs distribution b Expression profiles of total DEGs in clustered form
these gene dynamic expression changes and interactions influence bean quality traits of peaberry coffee.
Identi cation of bean quality traits associated genes in peaberry coffee e matured coffee bean endosperm is comprised of dif- ferent compositions of cell wall polysaccharides, sucrose, lipids, proteins, and chlorogenic acids [ 14 ]. ese stor- age compounds produce coffee color, aroma, and taste through a series of complex chemical reactions on roast- ing [ 8 ]. However, the roasting method in addition to total time had the least effect on the quality traits of cof- fee beans. erefore, the exploration of potential genes tightly correlated with quality attributes of matured green beans is essential to improving the quality aspects of coffee. Our targeted analysis identified several impor- tant genes associated with bean quality components of peaberry coffee beans. For instance, genes Ca25840- PMEs1, Ca30827-PMEs2, Ca30828-PMEs3, Ca25839- PMEs4, Ca03656-Csl, and Ca36469-PGs involved in cell wall modification had shown significantly altered expres- sion in the comparison of both coffee beans (Fig. 3 a). All these genes were annotated with pectin modifying enzymes such as pectin methylesterases (PMEs) and polygalacturonase (PGs) as well as cellulose synthase-like
(Csl). e pectin in addition to cellulose and hemicellu- lose are major constituents of the cell wall in plants. e degradation of pectin with pectinesterases or polygalac- turonase contributes to cell wall plasticity, morphogene- sis, intercellular communication, and pollen separation in plants [ 33 – 35 ]. Many fold lower expressions of Ca30827- PMEs2, Ca25839-PMEs4, and Ca36469-PGs in CPB anticipated their essential role in the modification of cell wall architecture. is modification of cell wall compo- nents might play a critical role to develop different bean shape patterns in peaberry coffee (Fig. 3 b). However, functional analysis is needed to quantify how these genes interact to induce the formation of peaberry and regular coffee beans. In addition, our analysis determined that eight DEGs involved in the biological function of protein phospho- rylation and belong to LRR receptor-like serine/threo- nine-protein kinase ERECTA family exhibited different expression profiles in both coffee beans. ese genes included Ca15802-ERL1, Ca99619-ERL2, Ca07439- ERL3, Ca97226-ERL4, Ca89747-ERL5, Ca07056-ERL6, Ca01141-ERL7, and Ca32419-ERL8 (Fig. 4 a). e ERL encoding transcripts have a diverse functional role in plant growth. eir defects produced irregular flower growth, petal polar expansion, carpel elongation, and
Fig. 3 The expression profiles of genes related to cell wall modification and how these regulate bean shape of peaberry a Expression profiles among CPB and CB b Simplest predicted mechanism of peaberry-shaped beans. The down-regulation of cell wall modification genes in CBP than CB might lead to lower pectin degradation and peaberry-shaped coffee beans. The red arrow represents the down-regulation of expression. PMEs: pectin methylesterases
in pollen growth and pollen tube formation [ 40 ]. e altered expression of genes involved in flavonoid bio- synthesis is probably involved in peaberry-shaped coffee beans in Arabica (Fig. 6 ). Because normal fertilization leads to the independent development of two embryos into regular coffee beans whereas the maturation of only a single embryo generates peaberry coffee bean [ 30 ]. e characterization of lipid metabolism along flavonoid biosynthesis-associated genes could help to reveal how abortion of a single embryo from two embryos leads to peaberry coffee beans instead of regular beans.
Quantitative real-time PCR (qRT-PCR) analysis Fourteen genes were selected to validate RNA-seq data by qRT-PCR. e selection was performed from genes associated with coffee bean quality that includes cell wall modification genes (Ca25840-PMEs1, Ca30827-PMEs2, Ca30828-PMEs3, and Ca25839-PMEs4), ERECTA pro- tein family genes (Ca99619-ERL2, Ca89747-ERL5, Ca07056-ERL6, and Ca01141-ERL7), lipid metabo- lism genes (Ca06708-ACOX1, Ca34321-CPFA1, and Ca36201-CPFA2), and flavonoid biosynthesis genes (Ca03809-F3H, Ca95013-CYP75A1, and Ca42029- CYP75A2). All selected genes showed significant down- regulation in peaberry coffee beans compared to normal
coffee beans in the qRT-PCR, which is consistent with RNA-seq data (Fig. 7 ). is result confirms the precision of the RNA-seq results in peaberry coffee beans.
Discussion In uence of bean physical attributes on quality of peaberry coffee Coffee is one of the most beverages consumed world- wide. Among all coffee species, Arabica is the most often used species due to its prime quality, taste, and flavor. It originated in Ethiopia and become a significant foreign exchange earning source for many tropical countries [ 41 ]. Plenty of research has revealed the biochemical compo- sition of quality coffee. Usually, the quantity of peaberry bean formation is mainly low in Arabica plants but their cup quality is superior to regular beans of the same cul- tivars. Despite its high economic value, the molecular mechanism of peaberry coffee bean quality is not yet fully revealed. is study through comparative transcriptome analysis explored the physical and transcript difference between peaberry and regular coffee beans, identified key regulatory genes, and finally discussed the molecular mechanism of bean quality characters in peaberry cof- fee beans. Our phenotypic analysis found that peaberry had diverse bean physical attributes compared to regular
Fig. 5 The expression profiles of lipid/fatty acid metabolic genes and how these influence the bean shape of peaberry a Expression profiles among CPB and CB b Simplest predicted mechanism of peaberry-shaped beans. The altered expression of lipid metabolic genes in CBP than CB might cause pollen degradation that results in peaberry-shaped coffee beans. The red down arrow and red up arrow represent the down-regulation and the up-regulation of expression. ACOX: acyl-CoA oxidase 3
coffee beans. e size, length, and width of a single bean were higher in regular coffee beans as compared to pea- berry. e phenotypic traits can be utilized to perform grading of coffee beans before marketing. In routine practice, the peaberries and regular coffee beans must be distinguished to yield high grade coffee. e market price of peaberries is much higher than regular beans because the majority of people desire to consume rarely produced
peaberry coffee beans. is result suggests that larger bean traits do not necessarily produce high quality coffee. In recent years, the international market demands supe- rior beans to generate the best quality beverages from coffee beans [ 42 ]. e superior quality in coffee is deter- mined by several factors that influence the final taste, aroma, and flavor of the coffee cup. ese factors include the physical attributes and biochemical composition of
Fig. 6 The expression profiles of flavonoid biosynthesis genes and how these influence the bean shape of peaberry a Expression profiles among CPB and CB b Simplest predicted mechanism of peaberry-shaped beans. The lower expression of flavonoid biosynthesis genes in CBP than CB may disturb pollen fertility that results in peaberry-shaped coffee beans. The red arrow represents the down-regulation of expression. F3H: flavanone-3-hydroxylase, CPY75A: cytochrome P450 family 75 subfamily A
Fig. 7 qRT-PCR analysis of 14 selected genes between CPB and CB. a gene expression based on the qRT-PCR approach, b a correlation analysis between qRT-PCR and RNA-seq expression profiles
are major secondary metabolites, belong to differ- ent types of flavones, flavanones, chalcones, flavonols, naringenin, and anthocyanins, and involved in several biological functions. In particular, sexual reproduction that includes pollen fertility, pollen growth, and pollen tube development is influenced with the abundance of flavonoids components in crops [ 40 , 53 ]. In this regard, the significant lower expression of flavonoids biosyn- thesis pathway genes in peaberry indicates low abun- dance of flavonoids components. e lower abundance of flavonoids may disturb energy balance, reduce pollen fertility, and ultimately contribute to hardly form pea- berry coffee beans. However, transgenic research can be useful to fully confirm the contribution of flavonoids components in formation of peaberry beans in Arabica. e genes involved in transmembrane transportation play a critical role in bean development. It mobilized the overall nutrient traffic, contribute to the deposi- tion of storage components, and eventually influenced the beverage quality of coffee [ 54 ]. e different expres- sions of transporter genes might result in different physical and biochemical quality traits of peaberry cof- fee beans. In concise, integration of our research with those previously reported, we presumed that genes correlated with biosynthesis, degradation, and storage of major bean components regulate the quality attrib- utes of peaberry coffee beans (Fig. 8 ). But, the potential mechanism of how these genes interact to influence the quality characters of peaberry coffee beans demands
further functional genomic research with combined metabolomics and transcriptomics analyses.
Conclusion is study detected dissimilarity in the physical attributes of peaberry and regular coffee beans. e comparative transcriptome analysis revealed a low number of gene expression differences among both coffee beans. Specifi- cally, the genes involved in the regulation of cell wall pol- ysaccharides, lipids, fatty acids, proteins, and Flavonoids had dynamic expression changes. ese genes most likely not only mediate different bean shape patterns but also influenced the bean composition in peaberry. Our results identified many putative candidate genes related to dif- ferent bean formations in Arabica. Furthermore, provide a platform to explore the genetic mechanism of rarely formed peaberry coffee beans.
Methods Plant material, phenotypic analysis, and RNA sequencing e plant material investigated in this study was a popu- lar C. Arabica variety introduced from Ethiopia (with- out a local name). is variety is able to provide 20% of peaberry coffee beans (CPB). e fresh fruit cherries of CPB and regular coffee beans (CB) were harvested from planting areas of Baoshan city of Yunnan province in China. e formal identification of the plant material has been conducted by Prof: Jinhuan Chen. No permission is needed to collect/study this material and a voucher
Fig. 8 The proposed molecular mechanism for the formation of peaberry coffee beans in Arabica
specimen can be obtained at Institute of Tropical and Subtropical Cash Crops under the accession number: ITSCC4296100X. After sample harvesting, 20 beans were randomly selected each for CBP and CB. The fruit was peeled before the determination of single grain weight (g), bean length (mm), and bean width (mm). The high quality RNA was extracted in three biological repli- cates for each coffee bean by using TRIZOL® reagent (Life Technologies, Carlsbad, CA, USA). The RNase- free DNase I (TaKaRa, Kyoto, Japan) was mixed to remove genomic DNA contamination. The RNA con- centration and purity were later confirmed with Nan- oDrop ND-1000 (NanoDrop, Wilmington, DE, USA). The accurate detection of RNA integrity was accessed with Bioanalyzer 2100 (Agilent Technologies, Califor- nia, USA). After preliminary quality measurements, the poly (A) RNA was fragmented into small pieces using Magnesium RNA Fragmentation Module (NEB, cat, USA). The cleaved RNA fragments were then reverse transcribed to synthesize six individual final cDNA libraries according to the protocol for the mRNA-Seq sample preparation kit (Illumina, San Diego, USA). The agarose gel electrophoresis was used for final fragment size selection and then PCR ampli- fication was performed with standard protocol. After final libraries were constructed with standard quality, pair-end RNA sequencing was performed on Illumina HiSeq 4000 platform with recommended protocol at Wuhan Baiyi Huineng Biotechnology Co., Ltd China.
Transcriptome data analysis The raw sequenced data were acquired from RNA sequencing platform. The high quality clean reads were produced from raw reads by filtering low qual- ity reads, adaptors, and ambiguous bases with FASTQ software [ 55 ]. Clean reads were aligned with the coffee reference genome using HISAT2 [ 56 ]. Only mapped reads without mismatches were retained for tran- scriptome downstream analysis. The expression abun- dance of each gene in FPKM (fragments per kilobase of exon per million mapped fragments) form was measured with StringTie [ 57 ]. The FPKM of 0 was considered the threshold criteria for gene expression. The total number of differentially expressed genes (DEGs) was detected with DESeq2 [ 58 ]. The crite- ria log2 (fold change) ≥ 1 or ≤ -1 and p-value ≤ 0. was applied to identify DEGs between CPB and CB. Principal component analysis was performed with ggfortify package in R by using FPKM values. Pearson
correlation coefficient was used to measure the cor- relation between samples. All DEGs were subjected to functional enrichment analysis with ClusterProfiler [ 59 ] with a p-value ≤ 0 is used as the threshold for screening significant enrichment results.
qRT-PCR analysis e TransScript One-Step gDNA Removal kit long with cDNA SynthesisSuperMix (TransGen, China) for used to synthesize cDNA for qRT-PCR of selected genes. e gene specific primers were designed with the Oligo 7 (Table S 2 ). e reaction mixture was prepared with QIA- GEN SYBR Green PCR Kit in three biological and tech- nical repeats for each target gene. e running protocol for qRT-PCR was followed as detailed in the previous study [ 60 ]. Actin7 was the reference gene and the rela- tive expression of target genes was determined with the 2 −ΔΔCt data analysis method.
Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s12863- 022- 01098-y.
Additional le 1: Figure S1. The mapped region’s statistics for among peaberry and regular coffee beans (a) Mapped regions for peaberry coffee beans (b) Mapped regions for regular coffee beans. Figure S2. Principal component analysis and correlation among peaberry and regular coffee beans (a) Principal component analysis (b) Correlation analysis among different coffee beans. Figure S3. Functional enrichment terms of DEGs detected among peaberry and regular coffee beans. Additional le 2: Table S1. Statistics of total expressed genes with their expression ratios in peaberry and regular coffee beans. Additional le 3: Table S2. Primer sequences of the selected genes for qRT-PCR.
Acknowledgements Not applicable. Authors’ contributions Conceptualization, G L; Data curation, F H; Formal analysis, Y L and J C; Funding acquisition, J H and J C; Investigation, X F, Y L, H H and Y L; Methodology, X F, F H and J C; Software, F H, Y L, Y L and J C; Supervision, G L and J H; Validation, X F and Y L; Visualization, J C; Writing – original draft, X F and J C; Writing – review & editing, J C. All authors have read and approved final version of the manuscript. Funding This work was funded by the Coffee and cocoa industrial chain integrated demonstration project (No. 2020YFD1001202), the major scientific special project plan in Yunnan-The research and development, demonsteation of criti- cal technology to improve quality and increase efficiency in coffee industry (202202AE090002), and Yunnan Coffee Sci & Tech Mission to Longyang County (No. 202004BI090136). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Availability of data and materials The raw RNA-seq data has been submitted to NCBI SRA under the project number PRJNA743796 (https:// http://www. ncbi. nlm. nih. gov/ bioproject/? term= PRJNA 743796). The analyzed data is presented in this article.
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Comparative-transcriptome-analysis
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