Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

The mouse microarray M22K_08_26 with B16F10 Cells untreated and treated with the compound 6,to observe over and down expression of genes involved in biological processes related to this treatment.

ABSTRACT: The mouse microarray M22K_08_26 with B16F10 Cells untreated and treated with the compound 6 [N- (p-methoxy-phenyl) -2- (p-methyl-phenyl) -3-phenoxy-azetidin-2-one], was approached to observe over and down expression of genes involved in biological processes. This experiment gives us information on its possible mechanism of action as anticancer compound. GenArise analysis results showed the over-expressed and inhibited genes by compound 6 on mouse melanoma B16F10 cells. In the range of 1.5 standard deviations of z-score, 676 genes were found down regulated and 1524 genes were overexpressed. In order to trace the biological impact of altered genes, an enrichment analysis by using online program Database for the Annotation, Visualization and Integrated Discovery (DAVID) was performed, the results showed a group of biological pathways involved to cell death, synthesis of the cytoskeleton and cell division processes.

ORGANISM(S): Mus musculus

SUBMITTER: Fabian Olazaran

PROVIDER: E-MTAB-4351 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:we aimed to provide an objective roadmap for histone sample preparation wherein the most notable findings and most important metrics were highlighted. This roadmap included a Histone Coverage Tool that allows researchers to make a snapshot of public or in-house data in search of a protocol that suits their specific needs. However, despite the fact that the coverage provided by a sample preparation workflow is pivotal to answer specific research questions, it should be noted that other metrics such as enzyme specificity and workflow variability can be equally important. Moreover, when it comes to histone sample preparation there are multiple additional challenges and pitfalls compared to regular bottom-up proteomics workflows. First there is the abundance and unequal distribution of arginine and lysine cleavage sites, which makes it virtually impossible to cover the entire histone code with a single bottom-up workflow. The most widely spread workaround for the abundance of cleavage sites is derivatization of lysine residues used to block tryptic digest and create longer peptides. However, this also introduces chemical noise, which obscures biological PTMs, while lowering sensitivity due to diluted signal. The most obvious alternative is an arginine specific digest. However, on the one hand, derivatization of lysine residues not only increases peptide length but also increases retention on the LC-system, while decreasing charge state distribution of the precursor ions. The latter presumably reduces instrumental variation of the workflow, which is remarkable since this source of variation is generally assumed to be constant in the field of LC-MS/MS based proteomics. On the other hand, lysine derivatization also increases the singly charged precursor fraction, which is usually not fragmented in regular data dependent acquisitions, thus increasing the risk of losing annotations. It is clear that not one histone sample preparation workflow will fit all research questions. However, we hope that this manuscript will guide researchers in making informed decisions about the many pitfalls and trade-offs involved in optimizing or assessing histone sample preparation workflows.

Dataset Information

The mouse microarray M22K_08_26 with B16F10 Cells untreated and treated with the compound 6,to observe over and down expression of genes involved in biological processes related to this treatment.

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