TOF-SIMS analysis of an isocitrate dehydrogenase 1 mutation-associated oncometabolite in cancer cells.

The development of analytical tools for accurate and sensitive detection of intracellular metabolites associated with mutated metabolic enzymes is important in cancer diagnosis and staging. The gene encoding the metabolic enzyme isocitrate dehydrogenase 1 (IDH1) is mutated in various cancers, and mutant IDH1 could represent a good biomarker and potent target for cancer therapy. Owing to a mutation in an important arginine residue in the catalytic pocket, mutant IDH1 catalyzes the production of 2-hydroxyglutarate (2-HG) instead of its wild type product α-ketoglutarate (α-KG), which is involved in multiple cellular pathways involving the hydroxylation of proteins, ribonucleic acid, and deoxyribose nucleic acid (DNA). Since 2-HG is an α-KG antagonist, inhibiting normal α-KG-dependent metabolism, high intracellular levels of 2-HG result in abnormal histone and DNA methylation. Therefore, accurate and sensitive analytical tools for the direct detection of 2-HG in cancer cells expressing mutant IDH1 would benefit this field, as it would minimize the need both for complicated experimental procedures and for large amounts of biological samples. Here, the authors describe a useful analytical method for the direct detection of 2-HG in lysates from a mutant IDH1-expressing cell line by time-of-flight secondary ion mass spectrometry (TOF-SIMS) analysis, a powerful surface analysis tool. In addition, the authors verified the efficacy of the specific mutant IDH1 inhibitor AGI-5198 by tracking the intracellular 2-HG concentration, which decreased in a dose-dependent manner. Our results demonstrate the large potential of TOF-SIMS as an analytical tool for the simple, direct detection of oncometabolites during cancer diagnosis, and for verifying the efficiency of the targeted cancer drugs.

Identification of epistasis in ischemic stroke using multifactor dimensionality reduction and entropy decomposition.

We investigated the genetic associations of ischemic stroke by identifying epistasis of its heterogeneous subtypes such as small vessel occlusion (SVO) and large artery atherosclerosis (LAA). Epistasis was analyzed with 24 genes in 207 controls and 271 patients (SVO = 110, LAA = 95) using multifactor dimensionality reduction and entropy decomposition. The multifactor dimensionality reduction analysis with any of 1- to 4-locus models showed no significant association with LAA (P > 0.05). The analysis of SVO, however, revealed a significant association in the best 3-locus model with P10L of TGF-beta1, C1013T of SPP1, and R485K of F5 (testing balanced accuracy = 63.17%, P < 0.05). Subsequent entropy analysis also revealed that such heterogeneity was present and quite a large entropy was estimated among the 3 loci for SVO (5.43%), but only a relatively small entropy was estimated for LAA (1.81%). This suggests that the synergistic epistasis model might contribute specifically to the pathogenetsis of SVO, which implies a different etiopathogenesis of the ischemic stroke subtypes. [BMB reports 2009; 42(9): 617-622].

Multilocus genotypic association with vascular dementia by multifactor dimensionality reduction and entropy-based estimation.

OBJECTIVE: We conducted a simultaneous analysis of candidate genetic loci for their genotypic association with the susceptibility to vascular dementia (VaD) to put forth the best model for predicting genetic susceptibility to VaD. METHODS: Individual-locus effects and their epistatic effects on susceptibility to VaD were simultaneously assessed by multifactor dimensionality reduction and entropy-based method. The 23 loci in 12 genes were studied in 207 VaD patients and age-matched and sex-matched 207 controls. RESULTS: The multifactor dimensionality reduction analysis revealed that the best single-locus candidate model included angiotensinogen (AGT) Thr235Met with testing accuracy (TA) of 58.31%, the best two-locus candidate model included AGT Thr235Met and transforming growth factor-beta1 Pro10Leu with TA of 58.06%, the best three-locus candidate model was not significant (P>0.05), and the best four-locus candidate model included transforming growth factor-beta1 Pro10Leu, AGT Thr235Met, sterol regulatory element binding protein 2 G34995T, and leukemia inhibitory factor T4524G with TA of 57.13% (P<0.05). The best four-locus model was, however, still in question because of the inconsistent best model selection by cross-validation. Synergistic epistatic effect of the best two-locus model was proven by entropy-based estimation. CONCLUSION: The best predictor for genetic susceptibility to VaD was the single-locus model of AGT. The best two-locus model reflecting epistasis would be also employed for predicting its susceptibility. Further studies on the epistasis are to elucidate their underlying mechanisms.

Estimation of epistasis among finite polygenic loci for complex traits with a mixed model using Gibbs sampling.

Epistasis among loci is important factor behind the expression of many complex traits, but many analyses have ruled out its possibility. A method to estimate epistasis was introduced with a mixed model using Gibbs sampling (MMGS). The posterior mean estimate for every possible genotype combined from multiple loci was calculated as the mean of the conditional expected values of the parameters in post warming-up rounds from Gibbs sampling. A simulation study was performed to compare MMGS with restricted partition method (RPM). Mean square prediction error (MSPE) using MMGS was smaller than that using RPM (P<0.05), which might be due to information loss introduced by grouping of genotypes in RPM. This was also supported by the result that MSPE increased as the number of merged groups decreased. The simulation study implied that MMGS was more plausible in estimating epistatic effects than the RPM.