TRIM2 promotes metabolic adaptation to glutamine deprivation via enhancement of CPT1A activity.

Cancer cells undergo metabolic adaptation to promote their survival and growth under energy stress conditions, yet the underlying mechanisms remain largely unclear. Here, we report that tripartite motif-containing protein 2 (TRIM2) is upregulated in response to glutamine deprivation by the transcription factor cyclic AMP-dependent transcription factor (ATF4). TRIM2 is shown to specifically interact with carnitine O-palmitoyltransferase 1 (CPT1A), a rate-limiting enzyme of fatty acid oxidation. Via this interaction, TRIM2 enhances the enzymatic activity of CPT1A, thereby regulating intracellular lipid levels and protecting cells from glutamine deprivation-induced apoptosis. Furthermore, TRIM2 is able to promote both in vitro cell proliferation and in vivo xenograft tumor growth via CPT1A. Together, these findings establish TRIM2 as an important regulator of the metabolic adaptation of cancer cells to glutamine deprivation and implicate TRIM2 as a potential therapeutic target for cancer.

Probabilistic Modeling for Image Registration Using Radial Basis Functions: Application to Cardiac Motion Estimation.

Cardiovascular diseases (CVDs) are the leading cause of death, affecting the cardiac dynamics over the cardiac cycle. Estimation of cardiac motion plays an essential role in many medical clinical tasks. This article proposes a probabilistic framework for image registration using compact support radial basis functions (CSRBFs) to estimate cardiac motion. A variational inference-based generative model with convolutional neural networks (CNNs) is proposed to learn the probabilistic coefficients of CSRBFs used in image deformation. We designed two networks to estimate the deformation coefficients of CSRBFs: the first one solves the spatial transformation using given control points, and the second one models the transformation using drifting control points. The given-point-based network estimates the probabilistic coefficients of control points. In contrast, the drifting-point-based model predicts the probabilistic coefficients and spatial distribution of control points simultaneously. To regularize these coefficients, we derive the bending energy (BE) in the variational bound by defining the covariance of coefficients. The proposed framework has been evaluated on the cardiac motion estimation and the calculation of the myocardial strain. In the experiments, 1409 slice pairs of end-diastolic (ED) and end-systolic (ES) phase in 4-D cardiac magnetic resonance (MR) images selected from three public datasets are employed to evaluate our networks. The experimental results show that our framework outperforms the state-of-the-art registration methods concerning the deformation smoothness and registration accuracy.