Alterations in coenzyme Q10 status in a cybrid line harboring the 3243A>G mutation of mitochondrial DNA is associated with abnormal mitochondrial bioenergetics and dysregulated mitochondrial biogenesis.

Mitochondrial DNA (mtDNA) mutations, including the m.3243A>G mutation that causes mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS), are associated with secondary coenzyme Q10 (CoQ10) deficiency. We previously demonstrated that PPARGC1A knockdown repressed the expression of PDSS2 and several COQ genes. In the present study, we compared the mitochondrial function, CoQ10 status, and levels of PDSS and COQ proteins and genes between mutant cybrids harboring the m.3243A>G mutation and wild-type cybrids. Decreased mitochondrial energy production, defective respiratory function, and reduced CoQ10 levels were observed in the mutant cybrids. The ubiquinol-10:ubiquinone-10 ratio was lower in the mutant cybrids, indicating blockage of the electron transfer upstream of CoQ, as evident from the reduced ratio upon rotenone treatment and increased ratio upon antimycin A treatment in 143B cells. The mutant cybrids exhibited downregulation of PDSS2 and several COQ genes and upregulation of COQ8A. In these cybrids, the levels of PDSS2, COQ3-a isoform, COQ4, and COQ9 were reduced, whereas those of COQ3-b and COQ8A were elevated. The mutant cybrids had repressed PPARGC1A expression, elevated ATP5A levels, and reduced levels of mtDNA-encoded proteins, nuclear DNA-encoded subunits of respiratory enzyme complexes, MNRR1, cytochrome c, and DHODH, but no change in TFAM, TOM20, and VDAC1 levels. Alterations in the CoQ10 level in MELAS may be associated with mitochondrial energy deficiency and abnormal gene regulation. The finding of a reduction in the ubiquinol-10:ubiquinone-10 ratio in the MELAS mutant cybrids differs from our previous discovery that cybrids harboring the m.8344A>G mutation exhibit a high ubiquinol-10:ubiquinone-10 ratio.

Curtailed two-stage design for comparing two arms in randomized phase II clinical trials.

In phase II clinical trials, patients are recruited sequentially and consequently the time required to complete the clinical trial will become long if the accrual rate is low. To speed up the drug development process and account for ethical issues, stochastically and non-stochastically curtailed two-stage designs have been proposed in single-arm phase II clinical trials. More recently, randomized phase II clinical trials are being increasingly recommended to avoid biased evaluation of the treatment effect when compared with a historical control. The current patient population and the historical one may be quite heterogeneous. Moreover, it is impossible to randomly assign patients for treatments. Consequently, various two-stage designs have been presented for comparing two arms. Since the sample size required in a randomized phase II trial is usually larger than that required in a single-arm phase II trial, we introduce the concept of curtailed sampling procedure to develop curtailed two-stage design for two-armed, randomized phase II clinical trials. The proposed design does not require pairwise patient response comparison, yet it allows a trial to be stopped early as soon as the difference in therapeutic effect of the experimental therapy and the standard at the end of a trial is foreknown.

A spatial scan statistic for compound Poisson data.

The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits.