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202 results for “mutagenesis”

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zenodo44/100

A saturation-mutagenesis analysis of the interplay between stability and activation in Ras

Dataset for the Hidalgo et al. eLife paper DOI: <a href="https://doi.org/10.7554/eLife.76595">https://doi.org/10.7554/eLife.76595</a>

opencc-by-4.0Feb 2022View details →

zenodo44/100

APARENT2 Genome-wide In-silico Saturation Mutagenesis

In-silico saturation mutagenesis predictions for all polyadenylation signals found in PolyADB V3 using the APARENT2 model (transcript-wide). The file 'aparent2_ism_scores_polyadb_v3.csv.gz' contains all data. The file 'aparent2_ism_scores_polyadb_v3_cutoff.csv.gz' contains only variants with more than 1.25-fold increase or decrease in isoform odds. The data columns 'delta_logodds' and 'delta_usage' contain variant isoform log odds ratios and isoform proportion differences (wrt. PolyADB measurements) for polyadenylation occurring anywhere +/- 100bp of the canonical cleavage site. The columns 'delta_logodds_narrow' and 'delta_usage_narrow' contains log odds ratios and proportion differences for cleaveage that occurs +0bp to +50bp immediately downstream of the canonical core hexamer motif. The data columns 'pas_position_hg19' and 'pas_position_hg38' indicate the start coordinate of the core hexamer.

opencc-by-4.0Nov 2022View details →

zenodo40/100

UV mutagenesis conjugated to high throughput screening as a tool to generate new phenotypic diversity in wine yeast

The current global changes, societal and climatic, strongly challenge the wine industry. Multiple methods are applied in the development of new strains for the industry, but many are based on the existing phenotypic and genetic diversities. UV mutagenesis, as an untargeted strategy, has been successfully used for years, with significant examples on wine. Here we developed and validated a UV-mutant generation strategy coupled with a high throughput screening in wine-like conditions. This strategy led to the production of a 502 mutant’s library for which concentrations of eight primary metabolites after fermentation were assessed. This data paper presents the resulting data.

opencc-by-4.0Oct 2024View details →

dryad40/100

Supplementary data for: Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression

Supplementary Note 1: <ul> <li>S1 Text: Oncogenomic comparisons between SB candidate Trunk driver genes and their direct orthologs in human Cancer Gene Census; Pyrosequencing analysis of SB-driven keratinocyte cancer models; References.</li> </ul> Supplementary Figures 1-11: <ul> <li>S1 Fig: Overview of genetic crosses to generate SB|Trp53|Onc3 mouse model.</li> <li>S2 Fig: SB insertion patterns in activated and inactivated drivers.</li> <li>S3 Fig: Evaluating the reproducibility of SBCapSeq results from bulk cuSCC and normal skin specimens.</li> <li>S4 Fig. Hierarchical two-dimensional clustering of recurrent events in cuKA and cuSCC.</li> <li>S5 Fig. Curated biological pathways and processes enriched within SB-induced cuSCC.</li> <li>S7 Fig: ZMIZ1 metagene within the TCGA Head & Neck Squamous Cell Carcinoma (hnSCC) RNA-seq dataset.</li> <li>S8 Fig: Clonally selected SB insertions affect trunk driver proto-oncogene expression in SB-cuSCC genomes.</li> <li>S9 Fig: Clonally selected SB insertions affect trunk driver genes by inactivating expression in SB-cuSCC genomes.</li> <li>S10 Fig: CREBBP knockdown does not alter proliferation rate in cuSCC cell lines.</li> <li>S11 Fig: Gross photographs of cuSCC xenograft masses collected at necropsy showing robust TurboGFP expression.</li> <li>S12 Fig: SB T2/Onc3 TG.12740 allele donor position mapping and exclusion for SB Driver Analysis.</li> </ul> Supplementary Tables 1-20: <ul> <li>S1 Table: Tumor incidence and subgroup classifications by cohort.</li> <li>S2 Table: Specimen metafile data for projects sequenced using SBCapSeq protocol with Ion Torrent Proton sequencer.</li> <li>S3 Table: Discovery and progression SB Driver Analysis for cuSCC60_SBC.</li> <li>S4 Table: Trunk SB Driver Analysis for cuSCC60_SBC.</li> <li>S5 Table: Discovery and progression SB Driver Analysis for cuKA11_SBC.</li> <li>S6 Table: Trunk SB Driver Analysis for cuKA11_SBC.</li> <li>S7 Table: Discovery and progression SB Driver Analysis for cuSK32_SBC.</li> <li>S8 Table: SBCapSeq read depth and analysis for 4 cuSCC genomes selected for multi-region resequencing because they had intermixing of cuSCC and cuKA histologies.</li> <li>S9 Table: Enrichr gene set pathway enrichment analysis of cuSCC drivers.</li> <li>S10 Table: Summary of 7 cuSCC transcriptomes selected for whole transcriptome RNAseq analysis.</li> <li>S11 Table: BED file of SBfusion insertions in 7 cuSCC genomes by whole transcriptome RNAseq analysis.</li> <li>S12 Table: Venn diagram for overlap of genes with SBfusion reads detected by whole transcriptome RNAseq analysis and cuSCC60_SBC discovery driver.</li> <li>S13 Table: Venn diagram for overlap of genes with SBfusion reads detected by whole transcriptome RNAseq analysis and all cuSCC drivers.</li> <li>S14 Table: Transcripts per million (TPM) normalized whole transcriptome RNAseq values per gene from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S15 Table: Fragments Per Kilobase of Transcripts per Million (FPKM) normalized whole transcriptome RNAseq values per gene transcript from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S16 Table: Normalized microarray values per gene from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S17 Table: Normalized microarray values per probe from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S18 Table: All 289 genes with differential expression analysis from microarray data from RNA isolated from cuSCC genomes with and without Zmiz1 insertions with P<0.0001 and q<0.05.</li> <li>S19 Table: Lentiviral vectors containing shRNAs used in this study.</li> <li>S20 Table: TaqMan probes used in this study.</li> </ul> Supplementary Datasets 1-5: <ul> <li>S1 Data: BED file of SB insertions for cuSCC60_SBC.</li> <li>S2 Data: BED file of SB insertions for cuKA11_SBC.</li> <li>S3 Data: BED file of SB insertions for cuSK32_SBC</li> <li>S4 Data: BED file of SB insertions for 4 cuSCC genomes selected for multi-region resequencing because they had intermixing of cuSCC and cuKA histologies.</li> <li>S5 Data: Numerical data for graphs pertaining to Figure Panels Fig1A; Fig5A–E; Fig6A–B,D; Fig7C–G; Fig8A–B,D–F; Fig9A–I in the paper on the publicly availble PLOS Genetics Web site.</li> </ul>

opencc-zeroAug 2021View details →

zenodo40/100

Code repository for: Base editing mutagenesis maps functional alleles to tune human T cell activity

Jupyter notebook and supplemental datasets required to created critical figures for the publication.

opencc-by-4.0Oct 2023View details →

dryad40/100

Supplementary data for: Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression

Open the record for dataset details and reuse information.

publicAug 2021View details →

zenodo36/100

Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor

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. Metabolite profiling analysis 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). 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. 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). <div> <div> #heatmap of fold changes  </div> <div> ts <-read.csv("all_log2fc_heat_all.csv", sep=";", fileEncoding='latin1',check.names=F, header=TRUE)  </div> <div> ts1 <- ts  </div> <div> ts1$class <- NULL   </div> <div>   </div> <div> row.names(ts1) <- ts1$compound  </div> <div> ts1$compound <- NULL  </div> <div> m <- as.matrix(ts1)  </div> <div>   </div> <div> ann <- ts[1:2]  </div> <div> row.names(ann) <- ann$compound  </div> <div> ann$compound <- NULL  </div> <div>   </div> <div> install.packages("pheatmap")  </div> <div>   </div> <div> library("pheatmap")  </div> <div>   </div> <div> m1 <- m[rownames(ann), ]  </div> <div>   </div> <div> pheatmap(m1, annotation_row = ann, cluster_rows = F,cluster_cols = F, cellheight = 10,   </div> <div>          cellwidth = 10, gaps_row = cumsum(c(3,105,30,11,21,4,32,5,17)), gaps_col = cumsum(c(2,2,2)), scale = 'none')  </div> <div>   </div> <div> #boxplots  </div> <div> install.packages("ggplot2")  </div> <div> install.packages("readr")  </div> <div>   </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div>   </div> <div> install.packages("tidyverse")  </div> <div> library(tidyverse)  </div> <div> grid <- read.csv("sec_exp1_logfc_20.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> grid_g <- grid%>% gather("compound", "value", 6:25)  </div> <div>   </div> <div> d=ggplot(data = grid_g, aes(x=compound, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d+theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#ca5826","#e4ab92", "#008080", "#99cccc"))+  </div> <div>   labs(y="log2fc(metabolite content)",fill="gtype")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 5)) +   </div> <div>   theme_classic() +  </div> <div>   coord_flip() +  </div> <div>   facet_grid(condition ~ .)  </div> <div>   </div> <div> ggsave("logfcsum_exp1_15_bri1.png", width = 10, height = 10)  </div> <div>   </div> <div> #boxplots all data  </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div> library(viridis)  </div> <div> library(tidyverse)  </div> <div> library(dplyr)  </div> <div>   </div> <div> sPCA <-read.csv("GCMS_polar.csv", sep=",", fileEncoding='latin1', check.names = F, header=TRUE)  </div> <div>   </div> <div> row.names(sPCA) <- sPCA$Sample  </div> <div> sPCA_D <- sPCA[6:155]  </div> <div> sPCA_Dlog <- log(sPCA_D)  </div> <div> sPCA_Dlog10 <- log10(sPCA_D)  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, group = sPCA$Sample_group)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(group, everything())  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, sample = sPCA$Sample)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(sample, everything())  </div> <div>   </div> <div> write.csv(sPCA_Dlog10, "GCMS_polar_lg.csv")  </div> <div>   </div> <div> sPCA_Dlog_g <- sPCA_Dlog%>% gather("compound", "value", 3:152)  </div> <div>   </div> <div> d <- ggplot(sPCA_Dlog_g, aes(x=sample, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d + theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#CC6677","#882255","#44AA99", "#117733"))+  </div> <div>   labs(y="lg(normalized metabolite content)", x= "sample" , fill="group")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1)) +   </div> <div>   theme_classic() +  </div> <div>   geom_hline(yintercept=0, linetype="dashed", color = "black")  </div> <div> #PCA  </div> <div> install.packages("plotly")  </div> <div> install.packages("ggfortify")  </div> <div> library(plotly)  </div> <div> library(ggfortify)  </div> <div>   </div> <div> sPCA <-read.csv("prim.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> sPCA <- subset(sPCA, experiment!="2")  </div> <div>   </div> <div> sPCAtd <- all_d[7:240]  </div> <div> sPCAtdn <- sPCAtd[-c(24), ]  </div> <div> sPCAn <- sPCA[-c(24), ]  </div> <div>   </div> <div> #normal  </div> <div>   </div> <div> sec.pcat <- prcomp(sPCAtd, center = TRUE,scale. = TRUE)  </div> <div>   </div> <div> summary(sec.pcat)  </div> <div>   </div> <div> p <- autoplot(sec.pcat, data = all_d, colour = 'condition', shape = 'Genotype') +   </div> <div>   geom_text(aes(label = Replicate), nudge_y = 0.02) +  </div> <div>   theme_classic() +  </div> <div>   scale_color_manual(values=c("#56B4E9", "#E69F00"))  </div> <div>   </div> <div> ggplotly(p)  </div> <div> #experiment wise fold changes  </div> <div> write.csv(sec_fc_mean, "sec_foldchange_mean.csv")  </div> <div>   </div> <div> sec_fc_exp1 <- sect[1]  </div> <div>   </div> <div> sec_fc_exp1$bri1.drought <- foldchange(sect$X72.bri1.drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought1 <- foldchange(sect$X72.bri1.drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought2 <- foldchange(sect$X72.bri1.drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$bri1.control <- foldchange(sect$X72.bri1.control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control1 <- foldchange(sect$X72.bri1.control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control2 <- foldchange(sect$X72.bri1.control.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.drought <- foldchange(sect$X72.WT..drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought1 <- foldchange(sect$X72.WT..drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought2 <- foldchange(sect$X72.WT..drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.control <- foldchange(sect$X72.WT..control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control1 <- foldchange(sect$X72.WT..control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control2 <- foldchange(sect$X72.WT..control.2, sect$median.WT.c1)  </div> <div>   </div> <div>   </div> <div> write.csv(sec_fc_exp1, "sec_fc_exp1.csv")  </div> <div>   </div> <div> #all compounds for RNAsq comarision  </div> <div>   </div> <div> all_R <-read.csv("all_RNAsq.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> all_fc_exp2 <- all_R[1]  </div> <div>   </div> <div> all_fc_exp2$drought <- foldchange(all_R$bri1_drought, all_R$WT_drought)  </div> <div>   </div> <div> all_fc_exp2$control <- foldchange(all_R$bri1_control, all_R$WT_control)  </div> <div>   </div> <div> write.csv(all_fc_exp2, "all_foldchange_exp2.csv")  </div> <div>   </div> <div> #students t test  </div> <div>   </div> <div> secfc_2_t <-read.csv("sec_exp1_logfc.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> secfc_2_t <- secfc_2_t[-2]  </div> <div> secfc_2_t <- secfc_2_t[-(14:25)]  </div> <div>   </div> <div> st2<- secfc_2_t %>% gather("compound", "value", 6:171)  </div> <div> stat.test1 <- st2 %>%  </div> <div>   group_by(compound) %>%  </div> <div>   t_test(value ~ group)  </div> <div> add_significance()  </div> <div> stat.test1  </div> </div>

opencc-by-4.0Jul 2024View details →

dryad36/100

Data from: Tissue-specific mutagenesis from endogenous guanine damage is suppressed by PolK and DNA repair

Open the record for dataset details and reuse information.

publicNov 2025View details →

dryad32/100

Characterization and mutagenesis of Chinese hamster ovary cells endogenous retroviruses to inactivate viral particle release

The Chinese hamster ovary (CHO) cells used to produce biopharmaceutical proteins are known to contain type‐C endogenous retrovirus (ERV) sequences in their genome and to release retroviral‐like particles. Although evidence for their infectivity is missing, this has raised safety concerns. As the genomic origin of these particles remained unclear, we characterized type‐C ERV elements at the genome, transcriptome, and viral particle RNA levels. We identified 173 type‐C ERV sequences clustering into three functionally conserved groups. Transcripts from one type‐C ERV group were full‐ length, with intact open reading frames, and cognate viral genome RNA was loaded into retroviral‐like particles, suggesting that this ERV group may produce functional viruses. CRISPR‐Cas9 genome editing was used to disrupt the gag gene of the expressed type‐C ERV group. Comparison of CRISPR‐derived mutations at the DNA and RNA level led to the identification of a single ERV as the main source of the release of RNA‐loaded viral particles. Clones bearing a Gag loss‐of‐function mutation in this ERV showed a reduction of RNA‐containing viral particle release down to detection limits, without compromising cell growth or therapeutic protein production. Overall, our study provides a strategy to mitigate potential viral particle contaminations resulting from ERVs during biopharmaceutical manufacturing.

opencc-zeroDec 2019View details →

zenodo32/100

Mutagenesis of RRL-NSD3-Short-3xFLAG Lentiviral Vector

SGC Open Notebook Project to Characterize the HMTase NSD3 Exp013 Objective: The NSD3’s PWWP1 domain is present in both long and short isoforms. It has been shown to bind H3K36me2 (Sankaran et al.(2016) - PMID:26912663) and be required for the maintenance of AML (Chen et al.(2015 - PMID: 26912663). However, it is still unclear how this domain contributes to NSD3’s function at enhancers. To study this aspect of NSD3’s biology, I will mutate W284 to alanine in the RRLNSD3-3xFLAG-IRES-Puro plasmids I described earlier (exp010) by site-directed mutagenesis. This is the second tryptophan within the PWWP1 motif and is critical for substrate recognition (Qin, S & Min, J(2014) - PMID:25277115). This construct will be useful for understanding how NSD3 recognition of methylated histones influences its putative activity in cancer.

opencc-by-4.0Feb 2018View details →

zenodo32/100

Mutagenesis to Replace IRES with T2A in NSD3-Short-3xFLAG Lentiviral Expression Vector

SGC Open Notebook Project to Characterize the HMTase NSD3 Exp016 Objective: Bicistronic elements are common features in lentiviral expression systems that allow expression of two protein products from a single promoter. The two most common bicistronic elements are the internal ribosome entry site (IRES), which allows cap-independent translation of a second open reading frame within an mRNA, and the 2A system, which introduces a self-cleaving peptide between two desired protein products. There are several advantages to the the 2A system over an IRES, which include matched expression levels of your two protein products of interest as well as having a smaller footprint (~1/10 size in kb), which may improve viral titre. Therefore, I will use site-directed mutagenesis to alter the NSD3short lentiviral expression plasmid to replace the IRES sequence with a T2A sequence.

opencc-by-4.0Mar 2018View details →

zenodo32/100

In silico saturation mutagenesis of cancer genes

Source data and code needed to reproduce figures and pipeline associated to this study: In silico saturation mutagenesis of cancer genes Ferran Muiños, Francisco Martinez-Jimenez, Oriol Pich, Abel Gonzalez-Perez, Nuria Lopez-Bigas DOI: 10.1038/s41586-021-03771-1 URL: https://www.nature.com/articles/s41586-021-03771-1 Repo to reproduce all the figures of the paper: https://github.com/bbglab/boostdm-analyses Repo to reproduce the training and prediction pipeline of the paper: https://github.com/bbglab/boostdm-pipeline

opencc-by-nc-4.0May 2021View details →

zenodo32/100

Precancerous liver diseases do not cause increased mutagenesis in liver stem cells

VCF files after variant calling using the HMF pipeline (v4.8) on bam files downloaded from PCAWG as well as from whole genome sequencing of intrahepatic cholangiocyte organoids.

opencc-by-4.0Oct 2021View details →

zenodo32/100

Fig. 4 in Site directed mutagenesis of Catharanthus roseus (+)-vincadifformine 19-hydroxylase (CYP71BY3) results in two distinct enzymatic functions

Fig. 4. GOLD's highest rated poses for (¡)-tabersonine and (þ)-vincadifformine into the V19H model generated from the 3ruk template with and without increased flexibility in the binding site residues. ()-tabersonine (B) and (+)-vincadifformine (A) best poses are shown in a rigid binding site (top) and a flexible binding site (bottom). The 19C is labelled in both ligands (arrows), and neither ligand is docked in the correct orientation for oxidation in a rigid binding site (top. However, in a flexible binding site, (+)-vincadifformine is in the correct orientation for 19C oxidation (bottom; B), whereas ()-tabersonine is not (bottom; A). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opennotspecifiedSep 2022View details →

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Fig. 5 in Site directed mutagenesis of Catharanthus roseus (+)-vincadifformine 19-hydroxylase (CYP71BY3) results in two distinct enzymatic functions

Fig. 5. The four residues in the V19H (left) and T3O (right) binding sites that were predicted to affect the opposite enantioselectivity of these highly homologous enzymes using models created on the 3ruk template. Residues shown in the V19H pocket are K106, S312, A376, and F377. Residues shown in the T3O pocket are R105, T311, P375, and L377. The model generated for T19H (3ruk template) looks identical to the T3O pocket, and the correlating residues are R101, T310, P374, and L375.

opennotspecifiedSep 2022View details →

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Fig. 3 in Site directed mutagenesis of Catharanthus roseus (+)-vincadifformine 19-hydroxylase (CYP71BY3) results in two distinct enzymatic functions

Fig. 3. Saturation kinetics of V19H: comparative (þ)-vincadifformine saturation curve in the absence or presence of (¡)-vincadifformine. The rate of (+)-vincadifformine consumption by V19H was reduced in the presence of 3 μM of ()-vincadifformine within the range of 15–20 μM of substrate yet remains unchanged at low and saturated concentrations. Error bars indicate the standard deviation from three technical replicate assays.

opennotspecifiedSep 2022View details →

zenodo32/100

Fig. 7. The V19H four-point mutant coupled with T3R in Site directed mutagenesis of Catharanthus roseus (+)-vincadifformine 19-hydroxylase (CYP71BY3) results in two distinct enzymatic functions

Fig. 7. The V19H four-point mutant coupled with T3R converts (¡)-tabersonine to 2,3-dihydro-3-hydroxytabersonine. Traces from in vitro experiments with ()-tabersonine (1) and V19H four-point mutant microsomes or T3O microsomes in the absence or presence of purified recombinant T3R are shown. Yeast microsomes containing the V19H 4-point mutant (red) (V19HL106R–S312T-A376P–F377L) convert ()-tabersonine to tabersonine 2,3-epoxides (5) in the absence of T3R, and to 2,3-dihydro-3-hydroxytabersonine in the presence of T3R. Yeast microsomes containing wild-type T3O (blue) were used as positive controls for tabersonine-2,3-epoxide (5) and 2,3-dihydro-3-hydroxytabersonine biosynthesis in the absence and presence of T3R, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opennotspecifiedSep 2022View details →

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Fig. 2 in Site directed mutagenesis of Catharanthus roseus (+)-vincadifformine 19-hydroxylase (CYP71BY3) results in two distinct enzymatic functions

Fig. 2. Representation of the 3ruk V19H model superimposed onto the 3ruk template and the location of the catalytic heme. V19H model (purple) is shown in purple, and the 3ruk template is shown in pink with the heme binding site in yellow. The H-bond interaction (green) between PHE443 and CYS450, the first and eighth residues of the heme binding site, which is responsible for the beta bulge (yellow) around the heme cofactor (black). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opennotspecifiedSep 2022View details →

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Fig. 1 in Site directed mutagenesis of Catharanthus roseus (+)-vincadifformine 19-hydroxylase (CYP71BY3) results in two distinct enzymatic functions

Fig. 1. The formation of (þ)- and (¡)-aspidosperma MIAs in Catharanthus roseus. In multiple C. roseus tissues, the ()-aspidosperma pathways begin with the biosynthesis of ()-tabersonine (1) from O-acetylstemmadenine (18) through acetylstemmadenine oxygenase (ASO), geissoschizine synthase (GS), and hydrolase 1 (HL1), whereas the (+)-aspidosperma pathway requires hydrolase 3 or 4 (HL3/4) to produce (+)-vincadifformine (15). Leaf-specific enzymes (black) convert ()-tabersonine (1) to vindoline (6), which accumulates in the leaves, while root-specific enzymes (purple) are responsible for the formation of two major root alkaloids, lochnericine (7) and h¨orhammericine (8). A third major root alkaloid, (+)-echitovenine (17) is derived from (+)-vincadifformine (15) via (+)-minovincinine (16) by separate root-specific (blue) enzymes. The remaining ()-aspidosperma alkaloids shown are derived chemically (grey box) from ()-tabersonine (1) to generate ()-vincadifformine (12) or enzymatic conversion by T19H to form 19-hydroxytabersonine (10) or its 19-O acetyltabersonine (11) by the action of TAT. These MIAs [()-vincadifformine (12), 19-hydroxytabersonine (10) and 19-O acetyltabersonine (11)] do not naturally accumulate in C. roseus. T16H: tabersonine 16-hydroxylase [CYP71D12 (T16H1) or CYP71D351 (T16H2); 16OMT: tabersonine 16-O-methyltransferase; T3O: tabersonine 3-oxygenase (CYP71D1V2); T3R: tabersonine 3-reductase; NMT: N-methyltransferase; D4H: desacetoxyvindoline 4-hydroxylase; DAT: deacetylvindoline O-acetyltransferase; TEX: tabersonine epoxidase [CYP71D521 (TEX1) or CYP71D347 (TEX2)]; T19H: tabersonine 19-hydroxylase (CYP71BJ1); TAT: tabersonine 19-acetyltranferase; V19H: (+)-vincadifformine 19-hydroxylase (CYP71BY3); MAT: minovincinine 19-O-acetyltransferase. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

opennotspecifiedSep 2022View details →

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Fig. 8 in Mutagenesis of a Lotus japonicus GSK3β/Shaggy-like kinase reveals functionally conserved regulatory residues

Fig. 8. Effect of overexpression of different LjSK1 variants in L. japonicus hairy roots. Physiological parameters of L. japonicus wild type hairy roots transformed with LjSK1 variants: LjSK1 90–467 lacking the 89 N-terminal residues, LjSK1_K167A, LjSK1_Y298A, and native LjSK1 at 21 days post inoculation with rhizobium. Control roots were transformed with the empty T-DNA vector. Values shown are the average ± SEM of n = 6–16 individual hairy roots per construct. Letters above each graph indicate statistically significant differences (p <0.05) between samples using One-way ANOVA followed by Fischer's LSD post-hoc comparison.

opennotspecifiedJun 2021View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record