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106 results for “Sorghum bicolor”
Sorghum bicolor (L.) Moench (BR0000011616856)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000012615384)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000005698028)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000011615651)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000012354511)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000014444913)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000011616191)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000011614968)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000012354610)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000012389469)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000011615293)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000014444999)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Sorghum bicolor (L.) Moench (BR0000024508445)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor
<p>Metabolomics data from the study: Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor. Files are raw chromatograms.</p> <p>Metabolite profiling analysis<br>Metabolite Extraction and Quantification. The whole metabolite extraction and quantification pipeline followed the guidelines as described in Giavalisco et al. (2011) and Salem et al. (2020). Briefly, 10-25 mg of fresh plant tissue was harvested and immediately frozen in N2, and ground using zirconia beads with the help of Tissue Lyser Mixer-Mill (Qiagen) 3 min at 25 Hz. After samples were extracted in 100% cold methanol, they were centrifuged for 10 min at 14000 rpm (Room temperature) and 3:1 chloroform was added. After vortexing thoroughly, 1:1 volume of water was added and the samples were subsequently vortexed and centrifuged. The semi-polar phase was used for analysis of semi-polar secondary and primary metabolites. For secondary semi-polar metabolites, the dried polar aliquots were resuspended in water:methanol (1:1 v/v).<br>3 Analysis of semi-polar metabolites was performed using a Thermo Q Exactive Focus coupled to a reverse-phase C18 column. The column was maintained at 40°C with a flow rate of 400 μl/min, and the eluent system consisted of water (eluent A) and acetonitrile (eluent B), both containing 0.1% formic acid. Mass spectra were acquired in full scan mode over a range of 100-1500 m/z using data-independent acquisition (DIA) with high-energy collisional dissociation (HCD) energy set at 30 eV in positive mode.<br>9 For primary metabolite metabolites, the dried polar was derivatized as described in Lisec et al. (2006). Derivatization was carried out at 37°C for 120 min using 40 μl of 20 mg/ml methoxyamine hydrochloride in pyridine, followed by a 30-min treatment at 37°C with 70 μl of trimethylsilyl-N-methyl trifluoroacetamide (MSTFA). The derivatized samples (1 μl) were injected in splitless mode into a gas chromatograph coupled to a time-of-flight mass spectrometer (Pegasus HT TOF-MS). Gas chromatography was performed on a 30-m DB-35 column using helium as the carrier gas. The initial temperature of the oven was 85°C, and it was ramped up at a rate of 15°C/min to a final temperature of 360°C. Mass spectra were recorded in the range of 70-600 m/z at a rate of 20 scans/s. Data Processing and Compound Annotation. LC-MS full scan data were processed using MS Refiner (Expressionist 14.0). Processing of chromatograms, peak detection, and integration were performed using RefinerMS (version 5.3; GeneData). Metabolite identification and annotation were performed using in-house reference compound library, tandem MS (MS/MS) 22 fragmentation, and metabolomics databases (Alseekh et al., 2021). For the annotation of metabolites measured by GC-MS the Golm Metabolome Database was used (Kopka et al., 2005).<br><br></p> <div> <div> <p><span><span>#heatmap of fold changes</span></span><span> </span></p> </div> <div> <p><span><span>ts</span><span> <</span><span>-read.csv(</span><span>"all_log2fc_heat_all.csv", </span><span>sep</span><span>=";", </span><span>fileEncoding</span><span>='latin1</span><span>',check</span><span>.names=F, header=TRUE)</span></span><span> </span></p> </div> <div> <p><span><span>ts1 <- </span><span>ts</span></span><span> </span></p> </div> <div> <p><span><span>ts1$class <- NULL </span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>row.names</span><span>(ts1) <- ts1$compound</span></span><span> </span></p> </div> <div> <p><span><span>ts1$compound <- NULL</span></span><span> </span></p> </div> <div> <p><span><span>m <- </span><span>as.matrix</span><span>(ts1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>ann</span><span> <- </span><span>ts</span><span>[1:2]</span></span><span> </span></p> </div> <div> <p><span><span>row.names</span><span>(</span><span>ann</span><span>) <- </span><span>ann$compound</span></span><span> </span></p> </div> <div> <p><span><span>ann$compound</span><span> <- NULL</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>pheatmap</span><span>")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>library("</span><span>pheatmap</span><span>")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>m1 <- m[</span><span>rownames</span><span>(</span><span>ann</span><span>)</span><span>, ]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>pheatmap</span><span>(m1, </span><span>annotation_row</span><span> = </span><span>ann</span><span>, </span><span>cluster_rows</span><span> = </span><span>F,cluster_cols</span><span> = F, </span><span>cellheight</span><span> = 10, </span></span><span> </span></p> </div> <div> <p><span><span> </span><span>cellwidth</span><span> = 10, </span><span>gaps_row</span><span> = </span><span>cumsum</span><span>(</span><span>c(</span><span>3,105,30,11,21,4,32,5,17)), </span><span>gaps_col</span><span> = </span><span>cumsum</span><span>(</span><span>c(</span><span>2,2,2)), scale = 'none')</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#boxplots</span></span><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("ggplot2")</span></span><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>readr</span><span>")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>library(ggplot2)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>readr</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>tidyverse</span><span>")</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>tidyverse</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>grid <- </span><span>read.csv(</span><span>"sec_exp1_logfc_20.csv", </span><span>sep</span><span>=";", header=TRUE)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>grid_g</span><span> <- grid%>% </span><span>gather(</span><span>"compound", "value", 6:25)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>d=</span><span>ggplot</span><span>(data = </span><span>grid_g</span><span>, </span><span>aes</span><span>(x=compound, y=value, fill=factor(group)</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_boxplot</span><span>(</span><span>outlier.shape</span><span> = </span><span>NA)+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_point</span><span>(position = </span><span>position_jitterdodge</span><span>(0.1))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>d+theme</span><span>(text = </span><span>element_text</span><span>(size = 15</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>scale_fill_manual</span><span>(values = </span><span>c(</span><span>"#ca5826","#e4ab92", "#008080", "#99cccc"</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>labs(</span><span>y="log2</span><span>fc(</span><span>metabolite content)</span><span>",fill</span><span>="</span><span>gtype</span><span>")+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme(</span><span>axis.text.x</span><span> = </span><span>element_text</span><span>(angle = 60, </span><span>hjust</span><span> = 1, size = 5)) + </span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme_classic</span><span>() +</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>coord_flip</span><span>() +</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>facet_grid</span><span>(condition </span><span>~ .</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>ggsave</span><span>("logfcsum_exp1_15_bri1.png", width = 10, height = 10)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#boxplots all data</span></span><span> </span></p> </div> <div> <p><span><span>library(ggplot2)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>readr</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>viridis</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>tidyverse</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>dplyr</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA</span><span> <</span><span>-read.csv(</span><span>"GCMS_polar.csv", </span><span>sep</span><span>=",", </span><span>fileEncoding</span><span>='latin1', </span><span>check.names</span><span> = F, header=TRUE)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>row.names</span><span>(</span><span>sPCA</span><span>) <- </span><span>sPCA$Sample</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_D</span><span> <- </span><span>sPCA</span><span>[6:155]</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- log(</span><span>sPCA_D</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog10 <- log10(</span><span>sPCA_D</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- </span><span>cbind</span><span>(</span><span>sPCA_Dlog</span><span>, group = </span><span>sPCA$Sample_group</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- </span><span>sPCA_Dlog</span><span>%>% </span><span>select(</span><span>group, </span><span>everything(</span><span>))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- </span><span>cbind</span><span>(</span><span>sPCA_Dlog</span><span>, sample = </span><span>sPCA$Sample</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog</span><span> <- </span><span>sPCA_Dlog</span><span>%>% </span><span>select(</span><span>sample, </span><span>everything(</span><span>))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>write.csv(</span><span>sPCA_Dlog10, "GCMS_polar_lg.csv")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA_Dlog_g</span><span> <- </span><span>sPCA_Dlog</span><span>%>% </span><span>gather(</span><span>"compound", "value", 3:152)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>d <- </span><span>ggplot</span><span>(</span><span>sPCA_Dlog_g</span><span>, </span><span>aes</span><span>(x=sample, y=value, fill=factor(group)</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_boxplot</span><span>(</span><span>outlier.shape</span><span> = </span><span>NA)+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_point</span><span>(position = </span><span>position_jitterdodge</span><span>(0.1))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>d + </span><span>theme(</span><span>text = </span><span>element_text</span><span>(size = 15</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>scale_fill_manual</span><span>(values = </span><span>c(</span><span>"#CC6677","#882255","#44AA99", "#117733"</span><span>))+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>labs(</span><span>y="</span><span>lg</span><span>(normalized metabolite content)", x= "sample</span><span>" ,</span><span> fill="group</span><span>")+</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme(</span><span>axis.text.x</span><span> = </span><span>element_text</span><span>(angle = 60, </span><span>hjust</span><span> = 1)) + </span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme_classic</span><span>() +</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_hline</span><span>(</span><span>yintercept</span><span>=0, </span><span>linetype</span><span>="dashed", </span><span>color</span><span> = "black")</span></span><span> </span></p> </div> <div> <p><span><span>#PCA</span></span><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>plotly</span><span>")</span></span><span> </span></p> </div> <div> <p><span><span>install.packages</span><span>("</span><span>ggfortify</span><span>")</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>plotly</span><span>)</span></span><span> </span></p> </div> <div> <p><span><span>library(</span><span>ggfortify</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA</span><span> <</span><span>-read.csv(</span><span>"prim.csv", </span><span>sep</span><span>=";", header=TRUE)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCA</span><span> <- </span><span>subset(</span><span>sPCA</span><span>, </span><span>experiment!=</span><span>"2")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sPCAtd</span><span> <- </span><span>all_d</span><span>[7:240]</span></span><span> </span></p> </div> <div> <p><span><span>sPCAtdn</span><span> <- </span><span>sPCAtd</span><span>[-</span><span>c(</span><span>24)</span><span>, ]</span></span><span> </span></p> </div> <div> <p><span><span>sPCAn</span><span> <- </span><span>sPCA</span><span>[-</span><span>c(</span><span>24)</span><span>, ]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#normal</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec.pcat</span><span> <- </span><span>prcomp</span><span>(</span><span>sPCAtd</span><span>, </span><span>center</span><span> = </span><span>TRUE,scale</span><span>. = TRUE)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>summary(</span><span>sec.pcat</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>p <- </span><span>autoplot</span><span>(</span><span>sec.pcat</span><span>, data = </span><span>all_d</span><span>, colour = 'condition', shape = 'Genotype') + </span></span><span> </span></p> </div> <div> <p><span><span> </span><span>geom_text</span><span>(</span><span>aes</span><span>(label = Replicate), </span><span>nudge_y</span><span> = 0.02) +</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>theme_classic</span><span>() +</span></span><span> </span></p> </div> <div> <p><span><span> </span></span><span><span>scale_color_manual</span><span>(</span><span>values</span><span>=</span><span>c(</span><span>"#56B4E9", "#E69F00"))</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>ggplotly</span><span>(p)</span></span><span> </span></p> </div> <div> <p><span><span>#</span><span>experiment</span><span> wise fold changes</span></span><span> </span></p> </div> <div> <p><span><span>write.csv(</span><span>sec_fc_mean</span><span>, "sec_foldchange_mean.csv")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1 <- </span><span>sect[</span><span>1]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.drought <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.drought</span><span>, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.drought1 <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.drought</span><span>.1, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.drought2 <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.drought</span><span>.2, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.control <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.control</span><span>, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.control1 <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.control</span><span>.1, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$bri1.control2 <- </span><span>foldchange(</span><span>sect$X72.bri</span><span>1.control</span><span>.2, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.drought <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..drought</span><span>, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.drought1 <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..drought</span><span>.1, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.drought2 <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..drought</span><span>.2, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.control <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..control</span><span>, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.control1 <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..control</span><span>.1, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span><span>sec_fc_exp1$WT.control2 <- </span><span>foldchange(</span><span>sect$X</span><span>72.WT..control</span><span>.2, sect$median.WT.c1)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>write.csv(</span><span>sec_fc_exp1, "sec_fc_exp1.csv")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#</span><span>all</span><span> compounds for </span><span>RNAsq</span> <span>comarision</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>all_R</span><span> <</span><span>-read.csv(</span><span>"all_RNAsq.csv", </span><span>sep</span><span>=";", </span><span>fileEncoding</span><span>='latin1</span><span>',check</span><span>.names=F, header=T)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>all_fc_exp2 <- </span><span>all_R</span><span>[1]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>all_fc_exp2$drought <- </span><span>foldchange(</span><span>all_R$bri1_drought, </span><span>all_R$WT_drought</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>all_fc_exp2$control <- </span><span>foldchange(</span><span>all_R$bri1_control, </span><span>all_R$WT_control</span><span>)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>write.csv(</span><span>all_fc_exp2, "all_foldchange_exp2.csv")</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>#students </span><span>t</span><span> test</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>secfc_2_t <</span><span>-read.csv(</span><span>"sec_exp1_logfc.csv", </span><span>sep</span><span>=";", </span><span>fileEncoding</span><span>='latin1</span><span>',check</span><span>.names=F, header=T)</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>secfc_2_t <- secfc_2_</span><span>t[</span><span>-2]</span></span><span> </span></p> </div> <div> <p><span><span>secfc_2_t <- secfc_2_</span><span>t[</span><span>-(14:25)]</span></span><span> </span></p> </div> <div> <p><span> </span></p> </div> <div> <p><span><span>st2<- secfc_2_t %>% </span><span>gather(</span><span>"compound", "value", 6:171)</span></span><span> </span></p> </div> <div> <p><span><span>stat.test1 <- st2 %>%</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>group_by</span><span>(compound) %>%</span></span><span> </span></p> </div> <div> <p><span><span> </span><span>t_test</span><span>(value ~ group)</span></span><span> </span></p> </div> <div> <p><span><span>add_significance</span><span>()</span></span><span> </span></p> </div> <div> <p><span><span>stat.test1</span></span><span> </span></p> </div> </div> <p> </p>
GRAS Family Transcription Factor Binding Behaviors in Sorghum bicolor, Oryza, and Maize
<p>Supplemental Data Files for the manuscript entitled "GRAS Family Transcription Factor Binding Behaviors in Sorghum bicolor, Oyrza, and Maize".</p>
Data from: Sorghum bicolor TX08001 nodal root tissue development gene expression profiling
<div class="page"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>Bioenergy sorghum's large nodal root system enables deposition of soil organic carbon deep in soil profiles aiding production of low carbon intensity biofuels from this crop. During bioenergy sorghum's long growing season, plants produce ~175 nodal roots In review bearing lateral roots that take up water and nutrients from >2 m deep in soil profiles, and aerial roots that support a complex phyllosphere. In the current study, nodal root bud development, a slow process spanning ~40 days, was characterized using microscopy and transcriptome analysis. A first ring of 10-15 nodal root buds was initiated in the stem pulvinus of phytomer 7 near sub-epidermal vascular bundles. A second ring of buds formed above the first ring much later in phytomer development. Nascent nodal root buds from phytomer 7 exhibited relatively high expression of pericycle marker genes (PFA) and genes involved in auxin transport (ABCB19, PIN4, LAX2), cytokinin signaling (TSO, MYB3R1), and cell proliferation (CYCB2;4, CDKB2;1, REM1).</p> <p>Following initiation, expression of genes involved in cell proliferation and cytokinin-signaling decreased while expression of genes involved in proliferative arrest, ABA-signaling, dormancy and stress tolerance increased. Further bud development was correlated with increased expression of WOX11 and PLT5 followed by PLT2, PLT4 and genes encoding RGF peptides that regulate PLT-expression and bud development. Expression of the ARF7-regulated LBD29, a gene required for nodal root formation, increased in parallel with increasing bud size to a maximum late in NRB development. Appearance of the nodal root bud cap late in development coincided with expression of SMB and FEZ, whereas genes such as WOX5 and two MYB36 family members were expressed at higher levels in outgrowing aerial roots. Genes involved in gibberellin, brassinosteroid, strigolactone, ethylene, jasmonate, salicyclic acid, and eATP signaling showed complex patterns of expression during nodal root bud formation. Overall, this study provides a detailed description of bioenergy sorghum nodal root bud development and transcriptome information useful for molecular analysis of networks that regulate nodal root development.</p> </div> </div> </div> </div>
Data from: Sorghum bicolor TX08001 nodal root tissue development gene expression profiling
Open the record for dataset details and reuse information.
Sorghum bicolor SNP data
<div> <div> <p>Sorghum diversity set was utilized for the present study.</p> </div> </div> <div> </div>
Fig. 3 in Stabilization of dhurrin biosynthetic enzymes from Sorghum bicolor using a natural deep eutectic solvent
Fig. 3. NADES-based stabilization of the dhurrin biosynthetic enzymes. A) Illustration of proteoliposomes comprising the POR2B, CYP79A1, CYP71E1 and UGT85B1 reconstituted in liposomes composed of phospholipids extracted from etiolated sorghum seedlings (Metabolon). B) Recovery of activity upon storage of enzymes in NADES and glycerol compared to buffer upon dilution displayed as relative conversion of tyrosine for the Metabolon samples and conversion of cyanohydrin to dhurrin for the UGT85B1 samples. Values are mean of three technical replicates± SD. C) Stability of dhurrin biosynthetic enzymes stored at room temperature in aqueous buffer, NADES and glycerol. Samples were diluted in buffer prior to activity assay. Values are mean of three technical replicates ±SD and fitted to a double exponential decay. D) Bar plot showing relative activity of the enzymes following incubation at various temperatures for 30 min in aqueous buffer, NADES and glycerol. Samples were diluted in buffer prior to activity assay. All values are mean of three independent technical replicates ± SD.
Fig. 2 in Stabilization of dhurrin biosynthetic enzymes from Sorghum bicolor using a natural deep eutectic solvent
Fig. 2. Dhurrin biosynthesis in the presence of different NADESs. A) Etiolated sorghum seedlings used for preparation of microsomes. B) Tyrosine conversion assay in microsomes at different NADES concentrations indicates an optimum at 5% NADES for both glucose:tartrate and glucose:malate. Values are mean of three technical replicates ± SD.
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