Up-regulation of NMRK2 mediated by TFE3 fusions is the key for energy metabolism adaption of Xp11.2 translocation renal cell carcinoma.

Due to the inadequate awareness of Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC), its metabolic features have not been described. Here, by using nontargeted LC-MS-based metabolomics, we found that the chimeric TFE3 protein, the major oncogenic driver in Xp11.2 tRCC, regulated the metabolic pathways in Xp11.2 tRCC, including glycerophospholipid metabolism, purine metabolism, amino acid metabolism, fatty acid metabolism and energy metabolism. Combined with our present metabolomic data and previous studies, it was found that Xp11.2 tRCC preferred mitochondrial respiration, which was obviously different from renal clear cell carcinoma (ccRCC). Furthermore, by using bioinformatics and data mining, NMRK2, an important target for energy metabolism adaptation of Xp11.2 tRCC, was identified. Additionally, we confirmed that chimeric TFE3 could transcriptionally activate the expression of NMRK2, but the NONO-TFE3 fusion, which lacks the activation domain encoded by exons 4-5 of the TFE3 gene, functioned as a transcription factor by recruiting TFEB. When NMRK2 was knocked down, the mitochondrial respiration of Xp11.2 tRCC, rather than glycolysis, was significantly weakened. Therefore, the present study revealed the mechanism of the energy metabolism adaptation by which the TFE3 fusion promotes mitochondrial respiration by upregulating NMRK2 in Xp11.2 tRCC.

Robust feature-preserving mesh denoising based on consistent subneighborhoods.

In this paper, we introduce a feature-preserving denoising algorithm. It is built on the premise that the underlying surface of a noisy mesh is piecewise smooth, and a sharp feature lies on the intersection of multiple smooth surface regions. A vertex close to a sharp feature is likely to have a neighborhood that includes distinct smooth segments. By defining the consistent subneighborhood as the segment whose geometry and normal orientation most consistent with those of the vertex, we can completely remove the influence from neighbors lying on other segments during denoising. Our method identifies piecewise smooth subneighborhoods using a robust density-based clustering algorithm based on shared nearest neighbors. In our method, we obtain an initial estimate of vertex normals and curvature tensors by robustly fitting a local quadric model. An anisotropic filter based on optimal estimation theory is further applied to smooth the normal field and the curvature tensor field. This is followed by second-order bilateral filtering, which better preserves curvature details and alleviates volume shrinkage during denoising. The support of these filters is defined by the consistent subneighborhood of a vertex. We have applied this algorithm to both generic and CAD models, and sharp features, such as edges and corners, are very well preserved.