Effect of sample size and P-value filtering techniques on the detection of transcriptional changes induced in rat neuroblastoma (NG108) cells by mefloquine

BackgroundThere is no known biochemical basis for the adverse neurological events attributed to mefloquine. Identification of genes modulated by toxic agents using microarrays may provide sufficient information to generate hypotheses regarding their mode of action. However, this utility may be compromised if sample sizes are too low or the filtering methods used to identify differentially expressed genes are inappropriate.MethodsThe transcriptional changes induced in rat neuroblastoma cells by a physiological dose of mefloquine (10 micro-molar) were investigated using Affymetrix arrays. A large sample size was used (total of 16 arrays). Genes were ranked by P-value (t-test). RT-PCR was used to confirm (or reject) the expression changes of several of the genes with the lowest P-values. Different P-value filtering methods were compared in terms of their ability to detect these differentially expressed genes. A retrospective power analysis was then performed to determine whether the use of lower sample sizes might also have detected those genes with altered transcription.ResultsBased on RT-PCR, mefloquine upregulated cJun, IkappaB and GADD153. Reverse Holm-Bonferroni P-value filtering was superior to other methods in terms of maximizing detection of differentially expressed genes but not those with unaltered expression. Reduction of total microarray sample size (< 10) impaired the capacity to detect differentially expressed genes.ConclusionsAdequate sample sizes and appropriate selection of P-value filtering methods are essential for the reliable detection of differentially expressed genes. The changes in gene expression induced by mefloquine suggest that the ER might be a neuronal target of the drug.

[1]  Trey Ideker,et al.  Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data , 2000, J. Comput. Biol..

[2]  R. Price,et al.  Population pharmacokinetics of mefloquine in patients with acute falciparum malaria , 1999, Clinical pharmacology and therapeutics.

[3]  Peter J. Murray,et al.  Shaping Gene Expression in Activated and Resting Primary Macrophages by IL-101 , 2002, The Journal of Immunology.

[4]  J. Thomas,et al.  An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. , 2001, Genome research.

[5]  R. Kaufman,et al.  Stress signaling from the lumen of the endoplasmic reticulum: coordination of gene transcriptional and translational controls. , 1999, Genes & development.

[6]  P. Schlagenhauf.  Mefloquine for malaria chemoprophylaxis 1992-1998: a review. , 1999, Journal of travel medicine.

[7]  S. Baudry,et al.  Stereoselective Passage of Mefloquine Through the Blood‐Brain Barrier in the Rat , 1997, The Journal of pharmacy and pharmacology.

[8]  シェパード，ロビン,et al.  The use of (+) mefloquine for the treatment of malaria , 1998 .

[9]  W. Dupont,et al.  Power and sample size calculations. A review and computer program. , 1990, Controlled clinical trials.

[10]  Response of neurons to an irreversible inhibition of endoplasmic reticulum Ca(2+)-ATPase: relationship between global protein synthesis and expression and translation of individual genes. , 2001 .

[11]  J. Taylor‐Papadimitriou,et al.  Culture of Animal Cells. A manual for basic techniques , 2000 .

[12]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[13]  M. Go,et al.  Effects of mefloquine on Ca2+ uptake and release by dog brain microsomes. , 1996, Archives internationales de pharmacodynamie et de therapie.

[14]  S. Dudoit,et al.  Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. , 2000, Genome research.

[15]  N. White,et al.  Cerebral uptake of mefloquine enantiomers in fatal cerebral malaria. , 1999, International journal of clinical pharmacology and therapeutics.

[16]  P. Brown,et al.  DNA arrays for analysis of gene expression. , 1999, Methods in enzymology.

[17]  Nelson L Michael,et al.  Impact of viral infection on the gene expression profiles of proliferating normal human peripheral blood mononuclear cells infected with HIV type 1 RF. , 2002, AIDS research and human retroviruses.

[18]  R. Ian Freshney,et al.  Culture of Animal Cells: A Manual of Basic Technique and Specialized Applications , 2010 .

[19]  D. Botstein,et al.  The transcriptional program in the response of human fibroblasts to serum. , 1999, Science.

[20]  R. Letz,et al.  Mefloquine tolerability during chemoprophylaxis: focus on adverse event assessments, stereochemistry and compliance , 1996, Tropical medicine & international health : TM & IH.

[21]  W. Pan,et al.  How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach , 2002, Genome Biology.

[22]  P. Phillips-Howard,et al.  CNS Adverse Events Associated With Antimalarial Agents , 1995, Drug safety.

[23]  H. Pahl,et al.  The ER-overload response: activation of NF-κB , 1997 .

[24]  S. Dudoit,et al.  Multiple Hypothesis Testing in Microarray Experiments , 2003 .

[25]  T. Herdegen,et al.  AP-1 proteins in the adult brain: facts and fiction about effectors of neuroprotection and neurodegeneration , 2001, Oncogene.

[26]  A. Goffeau,et al.  Genome microarray analysis of transcriptional activation in multidrug resistance yeast mutants , 2000, FEBS letters.

[27]  Christina Kendziorski,et al.  On Differential Variability of Expression Ratios: Improving Statistical Inference about Gene Expression Changes from Microarray Data , 2001, J. Comput. Biol..

[28]  W. Paschen,et al.  Response of neurons to an irreversible inhibition of endoplasmic reticulum Ca(2+)-ATPase: relationship between global protein synthesis and expression and translation of individual genes. , 2001, The Biochemical journal.