Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Upregulation of B-cell CLL/lymphoma (BCL)2 expression following lithium treatment is seemingly well established and has been related to the neuroprotective property of the drug. However, while demonstrated by some (but not all) studies based on low-throughput techniques (e.g. qPCR) this effect is not reflected in high-throughput studies, such as microarrays and RNAseq. This manuscript presents a systematic review of currently available reports of lithium's effect on BCL2 expression. To our surprise, we found that the majority of the literature does not support the effect of lithium on BCL2 transcript or protein levels. Moreover, among the positive reports, several used therapeutically irrelevant lithium doses while others lack statistical power. We also noticed that numerous low-throughput studies normalized the signal using genes/proteins affected by lithium, imposing possible bias. Using wet bench experiments and reanalysis of publicly available microarray data, here we show that the reference gene chosen for normalization critically impacts the outcome of qPCR analyses of lithium's effect on BCL2 expression. Our findings suggest that experimental results might be severely affected by the choice of normalizing genes, and emphasize the need to re-evaluate stability of these genes in the context of the specific experimental conditions.
Pubmed ID: 29686228
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
A high performance search engine for gene expression that integrates thousands of manually curated public microarray and RNAseq experiments and nicely visualizes gene expression across different biological contexts (diseases, drugs, tissues, cancers, genotypes, etc.). There are two basic analysis approaches: # for a gene of interest, identify which conditions affect its expression. # for condition(s) of interest, identify which genes are specifically expressed in this/these conditions. Genevestigator builds on the deep integration of data, both at the level of data normalization and on the level of sample annotations. This deep integration allows scientists to ask new types of questions that cannot be addressed using conventional tools.
View all literature mentionsSoftware repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.
View all literature mentionsCommercial vendor and service provider of laboratory reagents and antibodies. Supplier of scientific instrumentation, reagents and consumables, and software services.
View all literature mentionsTool for the identification of reliable and condition specific reference genes for RT-qPCR data normalization. RefGenes is available within Genevestigator.
View all literature mentionsResource for reuse, sharing and meta-analysis of expression profiling data. Database and set of tools for meta analysis, reuse and sharing of genomics data. Targeted at analysis of gene expression profiles. Users can search, access and visualize coexpression and differential expression results.
View all literature mentions