IVIGET (In Vitro Gene Expression Testing)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA664095 | ENA |
REPOSITORIES: ENA
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Project description:Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states.
2019-08-23 | MTBLS1044 | MetaboLights
Project description:Primary outcome(s): Gene expression of targeted genes
| 2625389 | ecrin-mdr-crc
Project description:ABSTRACT: The number of patients affected by chronic diseases with special vaccination needs is burgeoning. In this scenario, predictive markers of immunogenicity, as well as signatures of immune responses are typically missing even though it would especially improve the identification of personalized immunization practices in these populations. We aimed to develop a predictive score of immunogenicity to Influenza Trivalent Inactivated Vaccination (TIV) by applying deep machine learning algorithms using transcriptional data from sort-purified lymphocyte subsets after in vitro stimulation. Peripheral blood mononuclear cells (PBMCs) collected before TIV from 23 vertically HIV infected children under ART and virally controlled were stimulated in vitro with p09/H1N1 peptides (stim) or left unstimulated (med). A multiplexed-qPCR for 96 genes (Fluidigm, Biomark) was made on fixed numbers of 3 B cell subsets, 3 T cell subsets and total PBMCs. The ability to respond to TIV was assessed through hemagglutination Inhibition Assay (HIV) and ELIspot and patients were classified as Responders (R) and Non Responders (NR). A predictive modeling framework was applied to the data set in order to define genes and conditions with the higher predicted probability able to inform the final score. Twelve NR and 11 R were analyzed for gene expression differences in all subsets and 3 conditions (med, stim or Δ (stim-med). Differentially expressed genes between R and NR were selected and tested with the Adaptive Boosting Model to build a prediction score. The score obtained from subsets revealed the best prediction score from 46 genes from 5 different subsets and conditions. Calculating a combined score based on these 5 categories, we achieved a model accuracy of 95.6% and only one misclassified patient. In vitro stimulation, Cell sorting and RNA extraction. PBMC were thawed and cells were counted with Countess Automated Cell counter (Life technology). Cells were resuspended in complete RPMI medium at a concentration of 5 x 106 PBMCs/mL and left at 37°C for 16 hours in the presence or absence of of H1N1 A/California /09 HA peptides in a final concentration 20uL/mL. PMBCs were stained for surface markers, Vivid (Pacific Blue), CD10 (PECy7), CD20 (PE), CD27 (APC), IgD (FITC), CD21 (PECy5) for the B cell panel for 15 minutes and for CD3 (AmCyan), CD4 (PerCP Cy5.5), CD45RO (ECD), CCR7 (Alexa Fluor 700), and CXCR5 (Alexa Fluor 647) and a live/dead marker (ViViD; Molecular Probes) for the T cell panel for 15 minutes. Subsequently, stained PBMCs were washed twice in PBS, finally filtered with a 40 uM mesh and sorted by FACSAriaII (BD Biosciences). The purity of the sorted cell populations were typically >99%. All antibodies were previously titrated. Viable lymphocytes were identified as live dead amine dye negative (ViViD-) cells (Invitrogen). 500 live cells per B and T cell subset were sorted into tubes previously loaded with 9uL of PCR buffer (see also Figure 1 for gating strategy). After sorting, cells were immediately centrifuged (3000RPM for 3 minutes) and kept on ice. Samples were subsequently transferred in PCR tubes and 18 PCR cycles were performed on a C1000 Thermal Cycler (Bio Rad) with the following scheme (50°C for 20’, 95°C for 2’, 95°C 15’’, 60°C for 4’. Last step repeated 18 times). Cells were finally kept at -20 until further analysis. PCR buffer premix for cell sorting contained the following: Cells Direct Reaction mix 5uL, DEPC water 1,4, Superscript III + Taq 1ul, 0.2x diluted assay (96 primer mix) 2,5uL, Superasein 0,1uL. Multiplexed RT-PCR Previously amplified samples were loaded on a Fluidigm 96.96 standard chip following manufacturer’s instructions. Briefly, assay pre-mix was prepared 1:1 20X TaqMan Gene Expression Assay (Applied Biosystems) and Assay Loading Reagent (Fluidigm). The sample pre-mix was prepared with TaqMan Universal PCR Master Mix (2X)(Applied Biosystems), 20XGE Sample Loading Reagent (Fluidigm), and cDNA. Full list of the two panels of gene probes (B subsets and T subsets is shown in supplemental table 1 and 2). 5ul of Assay and Sample mix were loaded into the chip according to manufacturers instructions. Genes’ selection has been made according to previous analysis on RNA Sequencing on HIV infected children from a different cohort (data not shown), the literature and online gene banks and biological queries. Cycle threshold value (Ct) deriving from exported files was corrected according to number of cells sorted if lower than 500. Calculations were made following the expression 67,5 / 500 = Y / X where X is the number of cells sorted and Y is the cells equivalent cDNA of cell sorted. The dilution factor (n) was calculated as n= 67,5/Y, and base 2 log of n was subsequently subtracted to Ct value in order to get Corrected Cycle Threshold (c-Ct). Expression threshold (Et), which was used for the main analysis was finally obtained with 40-cCT. Once exported and corrected, data were analyzed through Fluidigm SingluaR (SingulaR analysis toolset 3.0) package, loaded on R (software R 3.0.2 GUI 1.62). As previously described (De Armas LR, 2017) gene expression differences between different groups within same subset and condition were used to identify Differentially Expressed Genes (DEGs). Alternatively, paired gene expression differences between stimulated (stim) and unstimulated samples (med) (stim-med) within the same subset were used to define Differentially Induced Genes (DIGs).
2020-10-29 | GSE155730 | GEO
Project description:There is a current interest in reducing the in vivo toxicity testing of nanomaterials in animals by increasing toxicity testing using in vitro cellular assays; however, toxicological results are seldom concordant between in vivo and in vitro models. This study compared global multi-walled carbon nanotube (MWCNT)-induced gene expression from human lung epithelial and microvascular endothelial cells in monoculture and coculture with gene expression from mouse lungs exposed to MWCNT. Using a cutoff of 10% false discovery rate and 1.5 fold change, we determined that there were more concordant genes (gene expression both up- or downregulated in vivo and in vitro) expressed in both cell types in coculture than in monoculture. When reduced to only those genes involved in inflammation and fibrosis, known outcomes of in vivo MWCNT exposure, there were more disease-related concordant genes expressed in coculture than monoculture. Additionally, different cellular signaling pathways are activated in response to MWCNT dependent upon culturing conditions. As coculture gene expression better correlated with in vivo gene expression, we suggest that cellular cocultures may offer enhanced in vitro models for nanoparticle risk assessment and the reduction of in vivo toxicological testing.
2019-04-12 | GSE129640 | GEO
Project description:In vitro fibroblast gene expression - tissue comparison
| PRJNA734657 | ENA
Project description:Biomaterial properties can be tailored for specific applications via systematic and high-throughput screening of biomaterial–cell interactions. However, progress in material development is often hampered by the lack of adequate in vitro testing methods, frequently due to incomplete understanding of involved in vivo mechanisms. In line with drug discovery in pharmacology, a crucial step in assay development for biomaterial screening is the identification of a target to direct the screen against. Herein, the cell type to be used for screening is of essential importance and has to be carefully chosen. So far, few attention has been put on selecting a cell type specifically suitable for in vitro testing of materials for predefined applications. In this manuscript, we describe an approach to define a suitable cell type for screening assays, for which biomaterials for bone regeneration served as example. Using a bioinformatics methodology, different cell lines are compared on three well-characterized model materials. The transcriptional profiles of MG63, iMSC, SV-HFO, hPPCT, hBPCT and SW480 cells are assessed on 3 calcium phosphate-based materials to evaluate their potential application in a screening assay. We show that MG63 is the most suitable cell line to evaluate biomaterials for bone regeneration applications, evidenced by their robust gene expression differences between the 3 model materials. The gene expression differences between the cell lines were assessed based on the overall gene expression profiles and specific subsets of genes and pathways related to osteogenesis and bone homeostasis in response to the 3 materials tested. In the next phase, this cell line will be used to identify a target correlating with in vivo biomaterial performance and henceforth to develop an in vitro screening system.
2018-08-12 | E-MTAB-7057 | biostudies-arrayexpress
Project description:Interventions: Group 1: Quantitative Expression Analysis of the proteom and gene Expression of Primary Tumor, normal tissue, and metastases
Primary outcome(s): Disease associated Proteins and Genes
Study Design: Allocation: ; Masking: ; Control: ; Assignment: ; Study design purpose: basic science
| 2431957 | ecrin-mdr-crc
Project description:Microglial gene expression after TGFb1 treatment in vitro
| PRJNA475680 | ENA
Project description:Canine adipose-derived mesenchymal stem cells (cAD-MSCs) show therapeutic promise for their regenerative potential, particularly in their secretome. However, concerns arise regarding the impact of in vitro cultivation need for storing therapeutic doses, prompting this study to comprehensively explore the impact of in vitro aging on gene expression and secretome composition. The study involved collecting abdominal adipose tissue samples from nine healthy female dogs, from which cAD-MSCs were extracted and cultured. Stem cells were validated through trilineage differentiation assays and flow cytometry immunophenotyping. Gene expression profiling, using RT-qPCR array and cAD-MSCs secretome LC-MS/MS analysis, were conducted at passages 3 and 6 to reveal gene expression and protein composition alterations during in vitro culture. Gene expression profiling of in vitro aged cAD-MSCs revealed expression alterations, while significant downregulation was observed in two MSC-associated genes. Proteomic analysis revealed 10% distinctively expressed proteins, and several up- and downregulations. Grouping these proteins with biologically significant ones, Gene Ontology Panther Pathway analysis revealed that P3 proteins were significantly associated with cytoskeletal regulation, nicotinic acetylcholine receptor signaling, inflammation mediated by chemokine and cytokine signaling, Wnt signaling, and CCKR signaling map. In contrast, P6 proteins were linked to the xanthine and guanine salvage pathway, adenine and hypoxanthine salvage pathway, and blood coagulation pathway. To the best of our knowledge, this study presents the first original perspective on the changes in secretome composition that occur when cAD-MSCs age in vitro. Our findings highlight significant changes that, in conclusion, indicate the regenerative potential of cAD-MSCs and that their secretome may be compromised due to in vitro aging. Consequently, our study suggests a preference for earlier passages when considering these cells for therapeutic applications.
2024-03-13 | PXD049324 | Pride