Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and its dismal prognosis indicates the urgent need to elucidate the potential oncogenic mechanisms. SIRT7 is a classic NAD+-dependent deacetylase that stabilizes the transformed state of cancer cells. However, its functional roles in PDAC are still unclear. Here, we found that SIRT7 expression is upregulated and predicts poor prognosis in PDAC. Then we screened the new interacting proteins of SIRT7 by mass spectrometry and the results showed that SIRT7 can interact with O-GlcNAc transferase (OGT). O-GlcNAcylation stabilizes the SIRT7 protein by inhibiting its interaction with REGγ to prevent degradation, and hyper-O-GlcNAcylation in pancreatic cancer cells leads to hypoacetylation of H3K18 via SIRT7, which promotes transcriptional repression of several tumour suppressor genes. In addition, SIRT7 O-GlcNAcylation at the serine 136 residue (S136) is required to maintain its protein stability and deacetylation ability. In vivo and in vitro experiments showed that blocking SIRT7 O-GlcNAcylation at S136 attenuates tumour progression. Collectively, we demonstrate that O-GlcNAcylation is an important post-translational modification of SIRT7 in pancreatic cancer cells, and elucidating this mechanism of SIRT7 is expected to pave the way for the development of novel therapeutic methods in the future.

The diagnosis algorithm of time-varying failures is considered for a class of nonlinear systems that are affected by external disturbances. By combining the adaptive control theory and the approach of state observer, an anti-disturbance fault diagnosis algorithm has been proposed. When the external disturbances and the internal failures exist simultaneously, the designed fault diagnosis algorithm is able to give specific estimated values of states and failures, respectively, rather than just give a fault warning. The asymptotical stability of the state observer in diagnosis algorithm is guaranteed by setting the adaptive adjusting law of the time-varying failure vector. In addition, a theoretically rigorous proof based on Lyapunov's stability theory has been given. Three experiments have been implemented to evaluate the effectiveness of fault diagnosis algorithm. The experimental results have demonstrated that the performance is satisfactory in both estimation accuracy and convergence speed.

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