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Transcriptome profiling and pathway analysis of genes expressed differentially in participants with or without a positive response to topiramate treatment for methamphetamine addiction.

Li MD, Wang J, Niu T, Ma JZ, Seneviratne C, Ait-Daoud N, Saadvandi J, Morris R, Weiss D, Campbell J, Haning W, Mawhinney DJ, Weis D, McCann M, Stock C, Kahn R, Iturriaga E, Yu E, Elkashef A, Johnson BA - BMC Med Genomics (2014)

Bottom Line: The molecular mechanisms underlying its effects are largely unknown.Pathway analyses based on nominally significant genes revealed 27 enriched pathways shared by the Weeks 8 and 12 TPM groups.Topiramate treatment of methamphetamine addicts significantly modulates the expression of genes involved in multiple biological processes underlying addiction behavior and other physiological functions.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, USA. Ming_Li@virginia.edu.

ABSTRACT

Background: Developing efficacious medications to treat methamphetamine dependence is a global challenge in public health. Topiramate (TPM) is undergoing evaluation for this indication. The molecular mechanisms underlying its effects are largely unknown. Examining the effects of TPM on genome-wide gene expression in methamphetamine addicts is a clinically and scientifically important component of understanding its therapeutic profile.

Methods: In this double-blind, placebo-controlled clinical trial, 140 individuals who met the DSM-IV criteria for methamphetamine dependence were randomized to receive either TPM or placebo, of whom 99 consented to participate in our genome-wide expression study. The RNA samples were collected from whole blood for 50 TPM- and 49 placebo-treated participants at three time points: baseline and the ends of weeks 8 and 12. Genome-wide expression profiles and pathways of the two groups were compared for the responders and non-responders at Weeks 8 and 12. To minimize individual variations, expression of all examined genes at Weeks 8 and 12 were normalized to the values at baseline prior to identification of differentially expressed genes and pathways.

Results: At the single-gene level, we identified 1054, 502, 204, and 404 genes at nominal P values < 0.01 in the responders vs. non-responders at Weeks 8 and 12 for the TPM and placebo groups, respectively. Among them, expression of 159, 38, 2, and 21 genes was still significantly different after Bonferroni corrections for multiple testing. Many of these genes, such as GRINA, PRKACA, PRKCI, SNAP23, and TRAK2, which are involved in glutamate receptor and GABA receptor signaling, are direct targets for TPM. In contrast, no TPM drug targets were identified in the 38 significant genes for the Week 8 placebo group. Pathway analyses based on nominally significant genes revealed 27 enriched pathways shared by the Weeks 8 and 12 TPM groups. These pathways are involved in relevant physiological functions such as neuronal function/synaptic plasticity, signal transduction, cardiovascular function, and inflammation/immune function.

Conclusion: Topiramate treatment of methamphetamine addicts significantly modulates the expression of genes involved in multiple biological processes underlying addiction behavior and other physiological functions.

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Related in: MedlinePlus

Schematic diagram of study workflow,including probe set filtering steps and statistical test strategies for detecting significant single genes and pathways. The probe intensities measured in 209 hybridized Affymetrix HG-U133 plus 2.0 arrays were normalized by Robust Multichip Average followed by a baseline correction step. Probes marked ‘Presence’ in fewer than four arrays in each group (because for Week 12 placebo group, only two positive responders were included, probes with two valid measurements were kept) were removed. Probes corresponding to control or less well-defined genes, and duplicated probes were removed. Genes with low FCs; i.e., within 1 standard deviation (denoted by σ) for a total of L (~7500) genes also were removed, as most of them were not likely to be differentially expressed to a statistically significant extent. The remaining genes were tested by the ordinary Student’s t-test, and genes with P values < 0.05 were used for pathway analysis. In total, 3698, 3532, 3328, and 3405 genes were tested for the Week 8 TPM, Week 8 placebo, Week 12 TPM, and Week 12 placebo groups, respectively.
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Fig3: Schematic diagram of study workflow,including probe set filtering steps and statistical test strategies for detecting significant single genes and pathways. The probe intensities measured in 209 hybridized Affymetrix HG-U133 plus 2.0 arrays were normalized by Robust Multichip Average followed by a baseline correction step. Probes marked ‘Presence’ in fewer than four arrays in each group (because for Week 12 placebo group, only two positive responders were included, probes with two valid measurements were kept) were removed. Probes corresponding to control or less well-defined genes, and duplicated probes were removed. Genes with low FCs; i.e., within 1 standard deviation (denoted by σ) for a total of L (~7500) genes also were removed, as most of them were not likely to be differentially expressed to a statistically significant extent. The remaining genes were tested by the ordinary Student’s t-test, and genes with P values < 0.05 were used for pathway analysis. In total, 3698, 3532, 3328, and 3405 genes were tested for the Week 8 TPM, Week 8 placebo, Week 12 TPM, and Week 12 placebo groups, respectively.

Mentions: Probe set filtering: The HG-U133plus2.0 array contains 54,675 oligonucleotide-based probe sets. However, not all of these sets correspond to well-defined genes. By using the latest Affymetrix annotation file (dated November 30, 2008), we found that a total of 33,752 (61.73%) probe sets correspond to unique genes, whereas the remaining probe sets do not and were thus excluded from our statistical analysis. Furthermore, we implemented a series of filtering procedures to reduce the number of probe sets to be tested, which is summarized as follows: (i) Filtering “Absence call” probe sets: We applied a Bioconductor package called “Presence-Absence Calls with Negative Probesets” (PANP) that uses Affymetrix-reported probe sets with no known hybridization partners. PANP uses a simple empirically derived approach to generate P values for thresholds to define “presence/absence” calls. The “presence/absence” calls and P values are returned as two matrices: “Pcalls” and “Pvals,” respectively. Probe sets with < 50% present calls among all arrays within each group were removed, which is considered restrictive [57,58], leaving ~15,000 probe sets for further analysis. (ii) Filtering biologically irrelevant genes and duplicate probe set(s) for each selected gene: Among the ~15,000 probe sets, control sets of various housekeeping genes (e.g., GAPDH) and spiked-in controls (e.g., Ec-bioB, Ec-bioC, Ec-bioD), as well as those genes that are not well defined or have unknown functions were removed. After removing duplicate probe set(s) for the same gene, such that only the probe set with the smallest test statistic was kept for each gene [59], about 7,500 genes remained. (iii) Filtering out genes with low fold changes (FCs): Genes with log2(FC) < 0.67 × standard deviation (SD) away from the group mean (i.e., between the first and the third quartile assuming that log2(FC) follows a normal distribution) were removed. After these sequential steps of filtering, about 3,500 genes were left for downstream statistical analyses for each group. A schematic diagram of the detailed data mining and analysis plan is shown in Figure 3.Figure 3


Transcriptome profiling and pathway analysis of genes expressed differentially in participants with or without a positive response to topiramate treatment for methamphetamine addiction.

Li MD, Wang J, Niu T, Ma JZ, Seneviratne C, Ait-Daoud N, Saadvandi J, Morris R, Weiss D, Campbell J, Haning W, Mawhinney DJ, Weis D, McCann M, Stock C, Kahn R, Iturriaga E, Yu E, Elkashef A, Johnson BA - BMC Med Genomics (2014)

Schematic diagram of study workflow,including probe set filtering steps and statistical test strategies for detecting significant single genes and pathways. The probe intensities measured in 209 hybridized Affymetrix HG-U133 plus 2.0 arrays were normalized by Robust Multichip Average followed by a baseline correction step. Probes marked ‘Presence’ in fewer than four arrays in each group (because for Week 12 placebo group, only two positive responders were included, probes with two valid measurements were kept) were removed. Probes corresponding to control or less well-defined genes, and duplicated probes were removed. Genes with low FCs; i.e., within 1 standard deviation (denoted by σ) for a total of L (~7500) genes also were removed, as most of them were not likely to be differentially expressed to a statistically significant extent. The remaining genes were tested by the ordinary Student’s t-test, and genes with P values < 0.05 were used for pathway analysis. In total, 3698, 3532, 3328, and 3405 genes were tested for the Week 8 TPM, Week 8 placebo, Week 12 TPM, and Week 12 placebo groups, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4279796&req=5

Fig3: Schematic diagram of study workflow,including probe set filtering steps and statistical test strategies for detecting significant single genes and pathways. The probe intensities measured in 209 hybridized Affymetrix HG-U133 plus 2.0 arrays were normalized by Robust Multichip Average followed by a baseline correction step. Probes marked ‘Presence’ in fewer than four arrays in each group (because for Week 12 placebo group, only two positive responders were included, probes with two valid measurements were kept) were removed. Probes corresponding to control or less well-defined genes, and duplicated probes were removed. Genes with low FCs; i.e., within 1 standard deviation (denoted by σ) for a total of L (~7500) genes also were removed, as most of them were not likely to be differentially expressed to a statistically significant extent. The remaining genes were tested by the ordinary Student’s t-test, and genes with P values < 0.05 were used for pathway analysis. In total, 3698, 3532, 3328, and 3405 genes were tested for the Week 8 TPM, Week 8 placebo, Week 12 TPM, and Week 12 placebo groups, respectively.
Mentions: Probe set filtering: The HG-U133plus2.0 array contains 54,675 oligonucleotide-based probe sets. However, not all of these sets correspond to well-defined genes. By using the latest Affymetrix annotation file (dated November 30, 2008), we found that a total of 33,752 (61.73%) probe sets correspond to unique genes, whereas the remaining probe sets do not and were thus excluded from our statistical analysis. Furthermore, we implemented a series of filtering procedures to reduce the number of probe sets to be tested, which is summarized as follows: (i) Filtering “Absence call” probe sets: We applied a Bioconductor package called “Presence-Absence Calls with Negative Probesets” (PANP) that uses Affymetrix-reported probe sets with no known hybridization partners. PANP uses a simple empirically derived approach to generate P values for thresholds to define “presence/absence” calls. The “presence/absence” calls and P values are returned as two matrices: “Pcalls” and “Pvals,” respectively. Probe sets with < 50% present calls among all arrays within each group were removed, which is considered restrictive [57,58], leaving ~15,000 probe sets for further analysis. (ii) Filtering biologically irrelevant genes and duplicate probe set(s) for each selected gene: Among the ~15,000 probe sets, control sets of various housekeeping genes (e.g., GAPDH) and spiked-in controls (e.g., Ec-bioB, Ec-bioC, Ec-bioD), as well as those genes that are not well defined or have unknown functions were removed. After removing duplicate probe set(s) for the same gene, such that only the probe set with the smallest test statistic was kept for each gene [59], about 7,500 genes remained. (iii) Filtering out genes with low fold changes (FCs): Genes with log2(FC) < 0.67 × standard deviation (SD) away from the group mean (i.e., between the first and the third quartile assuming that log2(FC) follows a normal distribution) were removed. After these sequential steps of filtering, about 3,500 genes were left for downstream statistical analyses for each group. A schematic diagram of the detailed data mining and analysis plan is shown in Figure 3.Figure 3

Bottom Line: The molecular mechanisms underlying its effects are largely unknown.Pathway analyses based on nominally significant genes revealed 27 enriched pathways shared by the Weeks 8 and 12 TPM groups.Topiramate treatment of methamphetamine addicts significantly modulates the expression of genes involved in multiple biological processes underlying addiction behavior and other physiological functions.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, USA. Ming_Li@virginia.edu.

ABSTRACT

Background: Developing efficacious medications to treat methamphetamine dependence is a global challenge in public health. Topiramate (TPM) is undergoing evaluation for this indication. The molecular mechanisms underlying its effects are largely unknown. Examining the effects of TPM on genome-wide gene expression in methamphetamine addicts is a clinically and scientifically important component of understanding its therapeutic profile.

Methods: In this double-blind, placebo-controlled clinical trial, 140 individuals who met the DSM-IV criteria for methamphetamine dependence were randomized to receive either TPM or placebo, of whom 99 consented to participate in our genome-wide expression study. The RNA samples were collected from whole blood for 50 TPM- and 49 placebo-treated participants at three time points: baseline and the ends of weeks 8 and 12. Genome-wide expression profiles and pathways of the two groups were compared for the responders and non-responders at Weeks 8 and 12. To minimize individual variations, expression of all examined genes at Weeks 8 and 12 were normalized to the values at baseline prior to identification of differentially expressed genes and pathways.

Results: At the single-gene level, we identified 1054, 502, 204, and 404 genes at nominal P values < 0.01 in the responders vs. non-responders at Weeks 8 and 12 for the TPM and placebo groups, respectively. Among them, expression of 159, 38, 2, and 21 genes was still significantly different after Bonferroni corrections for multiple testing. Many of these genes, such as GRINA, PRKACA, PRKCI, SNAP23, and TRAK2, which are involved in glutamate receptor and GABA receptor signaling, are direct targets for TPM. In contrast, no TPM drug targets were identified in the 38 significant genes for the Week 8 placebo group. Pathway analyses based on nominally significant genes revealed 27 enriched pathways shared by the Weeks 8 and 12 TPM groups. These pathways are involved in relevant physiological functions such as neuronal function/synaptic plasticity, signal transduction, cardiovascular function, and inflammation/immune function.

Conclusion: Topiramate treatment of methamphetamine addicts significantly modulates the expression of genes involved in multiple biological processes underlying addiction behavior and other physiological functions.

Show MeSH
Related in: MedlinePlus