LncRNA-Encoded Micropeptide ACLY-BP Drives Lipid Deposition and Cell Proliferation in Clear Cell Renal Cell Carcinoma via Maintenance of ACLY Acetylation.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of lethal kidney cancer. Reprogramming of fatty acid and glucose metabolism resulting in the accumulation of lipids and glycogen in the cytoplasm is a hallmark of ccRCC. Here, we identified a micropeptide ACLY-BP encoded by the GATA3-suppressed LINC00887, which regulated lipid metabolism and promoted cell proliferation and tumor growth in ccRCC. Mechanistically, the ACLY-BP stabilizes the ATP citrate lyase (ACLY) by maintaining ACLY acetylation and preventing ACLY from ubiquitylation and degradation, thereby leading to lipid deposition in ccRCC and promoting cell proliferation. Our results may offer a new clue for the therapeutic approaches and the diagnostic assessment for ccRCC. IMPLICATIONS: This study identifies ACLY-BP encoded by LINC00887 as a lipid-related micropeptide that stabilizes ACLY to generate acetyl-CoA, driving lipid deposition and promoting cell proliferation in ccRCC.

Extremely Sparse Networks via Binary Augmented Pruning for Fast Image Classification.

Network pruning and binarization have been demonstrated to be effective in neural network accelerator design for high speed and energy efficiency. However, most existing pruning approaches achieve a poor tradeoff between accuracy and efficiency, which on the other hand, has limited the progress of neural network accelerators. At the same time, binary networks are highly efficient, however, a large accuracy gap exists between binary networks and their full-precision counterparts. In this article, we investigate the merits of extremely sparse networks with binary connections for image classification through software-hardware codesign. More specifically, we first propose a binary augmented extremely pruning method that can achieve ~98% sparsity with small accuracy degradation. Then we design the hardware architecture based on the resulting sparse and binary networks, which extensively explores the benefits of extreme sparsity with negligible resource consumption introduced by binary branch. Experiments on large-scale ImageNet classification and field-programmable gate array (FPGA) demonstrate that the proposed software-hardware architecture can achieve a prominent tradeoff between accuracy and efficiency.