Growth Mixture Modeling With Nonnormal Distributions: Implications for Data Transformation.

This study investigated the extent to which class-specific parameter estimates are biased by the within-class normality assumption in nonnormal growth mixture modeling (GMM). Monte Carlo simulations for nonnormal GMM were conducted to analyze and compare two strategies for obtaining unbiased parameter estimates: relaxing the within-class normality assumption and using data transformation on repeated measures. Based on unconditional GMM with two latent trajectories, data were generated under different sample sizes (300, 800, and 1500), skewness (0.7, 1.2, and 1.6) and kurtosis (2 and 4) of outcomes, numbers of time points (4 and 8), and class proportions (0.5:0.5 and 0.25:0.75). Of the four distributions, it was found that skew-t GMM had the highest accuracy in terms of parameter estimation. In GMM based on data transformations, the adjusted logarithmic method was more effective in obtaining unbiased parameter estimates than the use of van der Waerden quantile normal scores. Even though adjusted logarithmic transformation in nonnormal GMM reduced computation time, skew-t GMM produced much more accurate estimation and was more robust over a range of simulation conditions. This study is significant in that it considers different levels of kurtosis and class proportions, which has not been investigated in depth in previous studies. The present study is also meaningful in that investigated the applicability of data transformation to nonnormal GMM.

LONP1 and ClpP cooperatively regulate mitochondrial proteostasis for cancer cell survival.

Mitochondrial proteases are key components in mitochondrial stress responses that maintain proteostasis and mitochondrial integrity in harsh environmental conditions, which leads to the acquisition of aggressive phenotypes, including chemoresistance and metastasis. However, the molecular mechanisms and exact role of mitochondrial proteases in cancer remain largely unexplored. Here, we identified functional crosstalk between LONP1 and ClpP, which are two mitochondrial matrix proteases that cooperate to attenuate proteotoxic stress and protect mitochondrial functions for cancer cell survival. LONP1 and ClpP genes closely localized on chromosome 19 and were co-expressed at high levels in most human cancers. Depletion of both genes synergistically attenuated cancer cell growth and induced cell death due to impaired mitochondrial functions and increased oxidative stress. Using mitochondrial matrix proteomic analysis with an engineered peroxidase (APEX)-mediated proximity biotinylation method, we identified the specific target substrates of these proteases, which were crucial components of mitochondrial functions, including oxidative phosphorylation, the TCA cycle, and amino acid and lipid metabolism. Furthermore, we found that LONP1 and ClpP shared many substrates, including serine hydroxymethyltransferase 2 (SHMT2). Inhibition of both LONP1 and ClpP additively increased the amount of unfolded SHMT2 protein and enhanced sensitivity to SHMT2 inhibitor, resulting in significantly reduced cell growth and increased cell death under metabolic stress. Additionally, prostate cancer patients with higher LONP1 and ClpP expression exhibited poorer survival. These results suggest that interventions targeting the mitochondrial proteostasis network via LONP1 and ClpP could be potential therapeutic strategies for cancer.

Androgen-induced expression of DRP1 regulates mitochondrial metabolic reprogramming in prostate cancer.

Androgen receptor (AR) signaling plays a central role in metabolic reprogramming for prostate cancer (PCa) growth and progression. Mitochondria are metabolic powerhouses of the cell and support several hallmarks of cancer. However, the molecular links between AR signaling and the mitochondria that support the metabolic demands of PCa cells are poorly understood. Here, we demonstrate increased levels of dynamin-related protein 1 (DRP1), a mitochondrial fission mediator, in androgen-sensitive and castration-resistant AR-driven PCa. AR signaling upregulates DRP1 to form the VDAC-MPC2 complex, increases pyruvate transport into mitochondria, and supports mitochondrial metabolism, including oxidative phosphorylation and lipogenesis. DRP1 inhibition activates the cellular metabolic stress response, which involves AMPK phosphorylation, induction of autophagy, and the ER unfolded protein response, and attenuates androgen-induced proliferation. Additionally, DRP1 expression facilitates PCa cell survival under diverse metabolic stress conditions, including hypoxia and oxidative stress. Moreover, we found that increased DRP1 expression was indicative of poor prognosis in patients with castration-resistant PCa. Collectively, our findings link androgen signaling-mediated mitochondrial dynamics to metabolic reprogramming; moreover, they have important implications for understanding PCa progression.