Arabidopsis thaliana Ler developmental series
Ontology highlight
ABSTRACT:
ORGANISM(S): Arabidopsis thaliana
PROVIDER: GSE12676 | GEO | 2008/09/17
SECONDARY ACCESSION(S): PRJNA112751
REPOSITORIES: GEO
Similar Datasets
Project description:Leaf development has been monitored chiefly by following anatomical markers. Analysis of transcriptome dynamics during leaf maturation revealed multiple expression patterns that rise or fall with age or that display age specific peaks. These were used to formulate a digital differentiation index (DDI), based on a set of selected markers with informative expression during leaf ontogeny. The leaf-based DDI reliably predicted the developmental state of leaf samples from diverse sources and was independent of mitotic cell division transcripts or propensity of the specific cell type. To calibrate and test the DDI a series of Arabidopsis shoot development was used (Efroni et al, 2008) Experiment Overall Design: Four samples from different stages of shoot and leaf development taken from plants grown at short days (10 hours light at 20C), starting from 5 days after sowing (DAS) apices to 35 DAS fully expanded leaves. RNA was hybridized to affymatrix ATH1 arrays and done in duplicates.
2008-10-25 | E-GEOD-12676 | biostudies-arrayexpress
Project description:Leaf development has been monitored chiefly by following anatomical markers. Analysis of transcriptome dynamics during leaf maturation revealed multiple expression patterns that rise or fall with age or that display age specific peaks. These were used to formulate a digital differentiation index (DDI), based on a set of selected markers with informative expression during leaf ontogeny. The leaf-based DDI reliably predicted the developmental state of leaf samples from diverse sources and was independent of mitotic cell division transcripts or propensity of the specific cell type. When calibrated by informative root markers, the same algorithm accurately diagnosed dissected root samples. We used the DDI to characterize plants with reduced activities of multiple CINCINNATA (CIN)-TCP growth regulators. These plants had giant curled leaves made up of small cells with abnormal shape, low DDI scores and low expression of mitosis markers, depicting the primary role of CIN-TCPs as promoters of differentiation. Delayed activity of several CIN-TCPs resulted in abnormally large but flat leaves with regular cells. The application of DDI has therefore portrayed the CIN-TCPs as heterochronic regulators that permit the development of a flexible and robust leaf form through an ordered and protracted maturation schedule.
2008-09-17 | GSE12691 | GEO
Project description:Leaf development has been monitored chiefly by following anatomical markers. Analysis of transcriptome dynamics during leaf maturation revealed multiple expression patterns that rise or fall with age or that display age specific peaks. These were used to formulate a digital differentiation index (DDI), based on a set of selected markers with informative expression during leaf ontogeny. The leaf-based DDI reliably predicted the developmental state of leaf samples from diverse sources and was independent of mitotic cell division transcripts or propensity of the specific cell type. When calibrated by informative root markers, the same algorithm accurately diagnosed dissected root samples. We used the DDI to characterize plants with reduced activities of multiple CINCINNATA (CIN)-TCP growth regulators. These plants had giant curled leaves made up of small cells with abnormal shape, low DDI scores and low expression of mitosis markers, depicting the primary role of CIN-TCPs as promoters of differentiation. Delayed activity of several CIN-TCPs resulted in abnormally large but flat leaves with regular cells. The application of DDI has therefore portrayed the CIN-TCPs as heterochronic regulators that permit the development of a flexible and robust leaf form through an ordered and protracted maturation schedule. Experiment Overall Design: Vegetative apices of arabidopsis plants from modified CIN-TCP activity were collected. Experiment Overall Design: 1. Overexpression of an artificial microRNA targeting TCP5, TCP13 and TCP17 Experiment Overall Design: 2. Overexpression of the endogenous microRNA 319b targeting TCP2, TCP3, TCP4, TCP10 and TCP24 Experiment Overall Design: 3. the combination of the two microRNA, knocking down the eight CIN-TCP Experiment Overall Design: 4. Overexpression of TCP4 under the leaf specific BLS promoter
2008-10-25 | E-GEOD-12691 | biostudies-arrayexpress
Project description:Gene expression biomarkers are now available for application in the identification of genotoxic hazards. The TGx-DDI transcriptomic biomarker can accurately distinguish DDI from non-DDI exposures based on changes in the expression of 64 biomarker genes. The bioamarker was originally derived from DNA microarray gene expression profiles of TK6 human lymphoblastoid cells post 4-hour exposure to 28 reference DDI and non-DDI agents [Li et al. 2015]. To broaden the applicability of TGx-DDI, the biomarker was tested using quantitative RT-PCR (qPCR), which is accessible to most molecular biology laboratories. To assess the classification capability of the biomarker using qPCR, a custom 96-well TaqMan qPCR array (TGx-DDI qPCR array) was constructed using the 64 biomarker genes. TK6 cells were exposed to each of the 28 reference agents and their vehicle controls for 4 hours and the expression level of the TGx-DDI genes were profiled using the TaqMan arrays. This study provides reference qPCR expression profiles of the TGx-DDI biomarker for DDI chemical classification using qPCR.
2018-12-21 | GSE121532 | GEO
Project description:Introduction: Higher-throughput mode-of-action-based assays provide a valuable approach to expedite chemical evaluation for human health risk assessment. In this study, we combined the high-throughput alkaline CometChip® assay with the TGx-DDI transcriptomic biomarker (DDI = DNA damage-inducing) using high-throughput TempO-Seq® as an integrated genotoxicity testing approach. Objective: To determine if the high-throughput CometChip® assay and TGx-DDI biomarker analysis can be combined using human HepaRG™ cells to provide an efficient next-generation genotoxicity screening approach to identify DNA damage-inducing (DDI) chemicals. Methods: Metabolically competent human HepaRG™ cell cultures were exposed to increasing concentrations of 12 chemicals (9 DDI and 3 non-DDI) using a repeat exposure design (0 hr, 24 hr, 48 hr) in 96-well plates to measure and classify chemicals based on their ability to damage DNA. After a 4 hr recovery period, exposed cells were used to perform the Trevigen CometChip assay to assess DNA damage or used for TempO-Seq® S1500+ Targeted Transcriptome Sequencing for TGx-DDI classification purposes. Bioinformatics and statistical tools were used to classify chemicals as DDI or non-DDI using three separate analyses. Benchmark concentration analysis was also conducted for both assays for the purposes of potency ranking. Results: In combination, the CometChip® assay and the TGx-DDI were 100% accurate in identifying chemicals that induce DNA damage. Quantitative benchmark concentration (BMC) modeling was applied to evaluate chemical potencies for both assays. The BMCs for the CometChip® assay and the TGx-DDI for all 12 chemicals were highly concordant (within four-fold) and resulted in identical potency rankings. Conclusions: These results suggest that these two assays can be integrated for efficient, high-throughput identification of DNA damage in HepaRG™ cells. This study provides evidence for the complementarity of the high-throughput CometChip® assay with TGx-DDI biomarker analysis. Integration of these two tests provides an effective and high-throughput approach to identify DDI agents. This work is a first step in accomplishing a more integrated genotoxicity testing strategy to derive mechanistic information to better inform human health risk assessment in a high-throughput manner.
2021-07-21 | GSE171360 | GEO
Project description:Background: Modern testing paradigms seek to apply human-relevant cell culture models and integrate data from multiple test systems to accurately inform potential hazards and modes of action for chemical toxicology. In genetic toxicology, the use of metabolically competent human hepatocyte cell culture models provides clear advantages over other more commonly used cell lines that require the use of external metabolic activation systems, such as rat liver S9. HepaRG™ cells are metabolically competent cells that express Phase I and II metabolic enzymes and differentiate into mature hepatocyte-like cells, making them ideal for toxicity testing. We assessed the performance of the flow cytometry in vitro micronucleus (MN) test and the TGx-DDI transcriptomic biomarker to detect DNA damage-inducing (DDI) chemicals in human HepaRG™ cells after a 3-day repeat exposure. The biomarker, developed for use in human TK6 cells, is a panel of 64 genes that accurately classifies chemicals as DDI or non-DDI. Herein, the TGx-DDI biomarker was analyzed by Ion AmpliSeq whole transcriptome sequencing to assess its classification accuracy using this more modern gene expression technology as a secondary objective. Methods: HepaRG™ cells were exposed to increasing concentrations of 10 test chemicals (six genotoxic chemicals, including one aneugen, and four non-genotoxic chemicals). Cytotoxicity and genotoxicity were measured using the In Vitro MicroFlow® kit, which was run in parallel with the TGx-DDI biomarker. Results: A concentration-related decrease in relative survival and a concomitant increase in MN frequency were observed for genotoxic chemicals in HepaRG™ cells. All five DDI and five non-DDI agents were correctly classified (as genotoxic/non-genotoxic and DDI/non-DDI) by pairing the test methods. The aneugenic agent (colchicine) yielded the expected positive result in the MN test and negative (non-DDI) result by TGx-DDI. Conclusions: This next generation genotoxicity testing strategy is aligned with the paradigm shift occurring in the field of genetic toxicology. It provides mechanistic insight in a human-relevant cell-model, paired with measurement of a conventional endpoint, to inform the potential for adverse health effects. This work provides support for combining these assays in an integrated test strategy for accurate, higher throughput genetic toxicology testing in this metabolically competent human progenitor cell line.
2020-02-18 | GSE136009 | GEO
Project description:The TGx-DDI is a transcriptomic biomarker used to predict the DNA damage-inducing (DDI) capability of chemicals by providing mechanistic information to aid in the interpretation of positive genotoxicity data. The biomarker was developed within the Health and Environmental Sciences Institute (HESI) and measures transcriptional changes in a set of primarily p53-responsive genes. We investigated three antibiotic drugs, nitrofurantoin (NIT), novobiocin sodium salt (NOV) and metronidazole (MTZ), with a long history of clinical use in human and/or veterinary applications. While all three drugs are considered safe, there is some evidence of chromosome damage with NIT and NOV. We conducted high-throughput CometChip and TGx-DDI assays to evaluate the drugs in a parallel test strategy to explore their toxicity using these newer mechanism-based tests. An extended concentration-response analysis was conducted in human TK6 cells (from 0 to 1 mM). The alkaline CometChip assay was used to measure DNA damage, while TGx-DDI was applied using TempO-Seq and NanoString technologies (in two different laboratories) to confirm reproducibility of the results. The CometChip was negative for all three drugs except at overtly cytotoxic concentrations, which were eliminated from the gene expression analysis. NIT and MTZ were classified as non-DDI by TGx-DDI at all concentrations tested. NOV classified as DDI at the highest concentrations tested. NOV is bacterial DNA-gyrase inhibitor that also acts as a topoisomerase II inhibitor at high concentrations. The TGx-DDI classification results were identical across laboratories/technologies. This case study demonstrates the utility of TGx-DDI to de-risk NIT and NOV, and the reproducibility of the biomarker.
2022-08-02 | GSE196369 | GEO
Project description:The TGx-DDI is a transcriptomic biomarker used to predict the DNA damage-inducing (DDI) capability of chemicals by providing mechanistic information to aid in the interpretation of positive genotoxicity data. The biomarker was developed within the Health and Environmental Sciences Institute (HESI) and measures transcriptional changes in a set of primarily p53-responsive genes. We investigated three antibiotic drugs, nitrofurantoin (NIT), novobiocin sodium salt (NOV) and metronidazole (MTZ), with a long history of clinical use in human and/or veterinary applications. While all three drugs are considered safe, there is some evidence of chromosome damage with NIT and NOV. We conducted high-throughput CometChip and TGx-DDI assays to evaluate the drugs in a parallel test strategy to explore their toxicity using these newer mechanism-based tests. An extended concentration-response analysis was conducted in human TK6 cells (from 0 to 1 mM). The alkaline CometChip assay was used to measure DNA damage, while TGx-DDI was applied using TempO-Seq and NanoString technologies (in two different laboratories) to confirm reproducibility of the results. The CometChip was negative for all three drugs except at overtly cytotoxic concentrations, which were eliminated from the gene expression analysis. NIT and MTZ were classified as non-DDI by TGx-DDI at all concentrations tested. NOV classified as DDI at the highest concentrations tested. NOV is bacterial DNA-gyrase inhibitor that also acts as a topoisomerase II inhibitor at high concentrations. The TGx-DDI classification results were identical across laboratories/technologies. This case study demonstrates the utility of TGx-DDI to de-risk NIT and NOV, and the reproducibility of the biomarker.
2022-08-02 | GSE196372 | GEO
Project description:Transcriptomic signatures, or biomarkers, of toxicity can facilitate rapid mechanistic analysis of chemicals using high-content transcriptomic data and identification of potential hazards to human health. We developed an 81-gene transcriptomic biomarker, named TGx-HDACi, to detect histone deacetylase inhibitors (HDACi) in TK6 human lymphoblastoid cells after 4 hours of chemical exposure. This work leveraged an established transcriptomic biomarker of DNA damage, TGx-DDI, which was developed by Heng Hong Li et al. (2015); TGx-DDI contains 63 genes and can distinguish between DNA damage-inducing (DDI) and non-DDI agents after 4 hours of exposure in TK6 cells. TGx-HDACi was derived from Templated Oligo-Sequencing (TempO-Seq) whole transcriptome gene expression profiles of 20 reference compounds, consisting of 10 HDACi and 10 non-HDACi compounds. Fourteen of the TGx-HDACi reference compounds are also part of the 28-compound reference set for TGx-DDI. The nearest shrunken centroid (NSC) method was applied to the gene expression profiles and 81 genes were identified that accurately identified HDACi compounds in the NSC probability analysis. The classification performance of TGx-HDACi was validated using an additional set of 11 test compounds (4 HDACi and 7 non-HDACi). Thus far, TGx-HDACi has demonstrated 100% accuracy. The availability of TGx-HDACi increases the diversity of tools that can facilitate mode of action analysis of toxicants using gene expression profiling.
2021-04-14 | GSE164478 | GEO
Project description:A developmental series of wind-treated Populus leaf tissue was subjected to array analyses in order to address the issue of age-dependent responsiveness to environmental changes. The following developmental stages were defined for the experiment: Y – “youngest leaf” including the shoot tip = smallest fully enrolled leaf; E – “expanded leaf” = oldest leaf that had not reached full leaf thickness; M – “mature leaf” = 5th leaf below E = has reached full leaf expansion and full leaf thickness; O – “old leaf” = 5th leaf below E. Keywords: transcription profiling
2008-04-03 | GSE11026 | GEO