P62/SQSTM1 mediates the autophagy-lysosome degradation of CDK2 protein undergoing PI3Kα/AKT T308 inhibition.

CDK2 forms a complex with cyclin A and cyclin E to promote the progress of cell cycle, but when cyclin A and cyclin E are dissociated from the complex and degraded by the ubiquitin proteasome pathway, the fate of the inactive CDK2 is unclear. In this study, we found that the inactive CDK2 protein was degraded by autophagy-lysosome pathway. In the classic model of G0/G1 phase arrest induced by serum starvation, we found that the mRNA level in CDK2 did not change but the protein level decreased. Subsequently, using PI3K and AKT inhibitors and gene knockout methods, it was found that CDK2 degradation was mediated by the inhibition of PI3Kα/AKTT308. In addition, P62/SQSTM1 was found to bind to the inactivated CDK2 protein to help it enter autophagy-lysosome degradation in a CTSB-dependent manner. Taken together, these results confirm that the PI3Kα/AKTT308 inhibition leads to degradation of CDK2 protein in the autophagy-lysosome pathway. These data reveal a new molecular mechanism of CDK2 protein degradation and provide a new strategy and method for regulating CDK2 protein.

Poly(ADP-Ribose) Polymerase Activity and Coronary Artery Disease in Type 2 Diabetes Mellitus: An Observational and Bidirectional Mendelian Randomization Study.

OBJECTIVE: Experimental evidence suggests a close link between PARP (poly[ADP-ribose] polymerase) activation and diabetic endothelial dysfunction. Here, we tested whether PARP activity in circulating leukocytes was associated with coronary artery disease (CAD) among patients with type 2 diabetes mellitus (T2DM). Approach and Results: We performed observational and bidirectional Mendelian randomization studies of 3149 Chinese individuals with T2DM who underwent coronary angiography, with leukocyte PARP activity, 16 tag single-nucleotide polymorphisms in PARP1 and PARP2, and 17 CAD risk single-nucleotide polymorphisms analyzed. Of 3149 participants, 1180 who further received percutaneous coronary intervention were prospectively followed for 1 year to track major adverse cardiovascular and cerebrovascular events. Overall, greater PARP activity was cross-sectionally associated with an odds ratio of 1.23 for obstructive CAD, and prospectively with a hazard ratio of 1.34 for 1-year major adverse cardiovascular and cerebrovascular events after percutaneous coronary intervention (both P<0.001). Using a genetic score of 5 screened single-nucleotide polymorphisms in PARP1 and PARP2 as the instrumental variable, genetically predicted elevation in PARP activity showed a causal association with obstructive CAD (odds ratio=1.35, P<0.001). In contrast, the genetic risk of CAD had no significant effect on PARP activity. Ex vivo and in vitro cultures of human monocytes showed that rs747657, as the lead single-nucleotide polymorphism strongly associated with PARP activity, caused the differential binding of transcription factor GATA2 (GATA-binding protein 2) to an intronic regulatory region in PARP1, thus modulating PARP1 expression and PARP activity. CONCLUSIONS: Greater PARP activity may have causal roles in the development of obstructive CAD among patients with diabetes mellitus.

MicroRNA-92b acts as an oncogene by targeting PTEN/AKT in NSCLC.

MicroRNAs can act as tumour suppressors or oncogenes by regulating cellular differentiation, proliferation and apoptosis, and the dysregulation of miRNA is involved in the occurrence and development of NSCLC. Here, we provided evidence that miR-92b as an oncogene in NSCLC by targeting PTEN/AKT. We found that miR-92b was up-regulated in human NSCLC tissues and cell lines. MiR-92b knockdown suppressed the NSCLC cells proliferation and migration in both in vivo and in vitro models. Conversely, miR-92b overexpression induced an aggressive phenotype. Moreover, miR-92b-mediated regulation of NSCLC cell proliferation and migration depended on binding to PTEN mRNA, which then led to the degradation of PTEN and activation of the downstream AKT signalling pathway. Overall, this study revealed the oncogenic roles of miR-92b in NSCLC by targeting PTEN/AKT, and provided novel insights for future treatments of NSCLC patients. SIGNIFICANCE OF THE STUDY: MiR-92b was up-regulated in human NSCLC tissues and cell lines. Our study demonstrated that miR-92b as an oncogene in NSCLC by targeting PTEN/AKT in both in vivo and in vitro models and provided novel insights for future treatments of NSCLC patients.

Oridonin induces Mdm2-p60 to promote p53-mediated apoptosis and cell cycle arrest in neuroblastoma.

Oridonin could induce NB (neuroblastoma) cells growth inhibition by inducing apoptosis and cell cycle arrest, and the molecular mechanisms behind the effects deserve to be further explored. Here, oridonin was confirmed to cause the reactivation of p53 (cellular tumor antigen p53) to promote the expression of a series of apoptosis- and cell cycle arrest-related proteins for the biological effects. During the process, oridonin relied on the caspase activation to cleave p53-induced Mdm2 (E3 ubiquitin-protein ligase Mdm2) to generate Mdm2-p60. The generation of Mdm2-p60 stabilized p53, and resulted in p53 accumulation for p53 continuous activation. In our research, it was also found that the reactivation of p53 induced by oridonin was closely related with the generation of ROS (reactive oxygen species). Taken together, these findings explain that oridonin exerts its anticancer activity partially by targeting the Mdm2-p53 axis in NB cells, which lay an experimental base for future research of exploring the effects and molecular mechanisms of oridonin.

Application of commercial cytomegalovirus IgG avidity diagnostic kit.

BACKGROUND: In the absence of acute clinical manifestation, the diagnosis of cytomegalovirus (CMV) infection is usually done by serology, which has been an important tool in the clinical medicine. METHODS: Many commercial CMV IgG avidity kits are available for identification. We decided to evaluate the Autobio Diagnostics Co., Ltd cytomegalovirus IgG avidity diagnostic kit for recent infection by comparing the performances of different CMV IgG avidity diagnostic kits on three commercially available seroconversion panels. RESULTS: In this study, we described the application of some commercial CMV IgG avidity diagnostic kits in the diagnostics of CMV recent infection. The results of three commercially available seroconversion panels RP-003, RP-019, and PTC901 with a total of 37 samples showed that 99 days after seroconversion, the values of Autobio CMV IgG avidity higher than 50% indicated a high avidity, the high avidity can exclude recent 3 months infection. A total of 130 specimens were tested and compared with the RADIM CMV IgG avidity assay. Seven gray zone results were excluded. The low avidity sample agreement was 100% (22/22), high avidity sample agreement was 95.0% (96/101). The specificity of the Autobio CMV IgG avidity assay was evaluated by testing a total of 744 specimens which were reactive with the Autobio CMV IgG assay and nonreactive with the Autobio CMV IgM assay. Seventeen gray-zone specimens were excluded, the clinical specificity of the Autibio CMV IgG avidity assay was 97.4% (708/727). CONCLUSIONS: We concluded that the Autobio CMV IgG avidity assay is a comparatively reliable tool to identify CMV recent infection according to these performance evaluation data.

Time-frequency approach to underdetermined blind source separation.

This paper presents a new time-frequency (TF) underdetermined blind source separation approach based on Wigner-Ville distribution (WVD) and Khatri-Rao product to separate N non-stationary sources from M(M <; N) mixtures. First, an improved method is proposed for estimating the mixing matrix, where the negative value of the auto WVD of the sources is fully considered. Then after extracting all the auto-term TF points, the auto WVD value of the sources at every auto-term TF point can be found out exactly with the proposed approach no matter how many active sources there are as long as N ≤ 2M-1. Further discussion about the extraction of auto-term TF points is made and finally the numerical simulation results are presented to show the superiority of the proposed algorithm by comparing it with the existing ones.

Minimum-volume-constrained nonnegative matrix factorization: enhanced ability of learning parts.

Nonnegative matrix factorization (NMF) with minimum-volume-constraint (MVC) is exploited in this paper. Our results show that MVC can actually improve the sparseness of the results of NMF. This sparseness is L(0)-norm oriented and can give desirable results even in very weak sparseness situations, thereby leading to the significantly enhanced ability of learning parts of NMF. The close relation between NMF, sparse NMF, and the MVC_NMF is discussed first. Then two algorithms are proposed to solve the MVC_NMF model. One is called quadratic programming_MVC_NMF (QP_MVC_NMF) which is based on quadratic programming and the other is called negative glow_MVC_NMF (NG_MVC_NMF) because it uses multiplicative updates incorporating natural gradient ingeniously. The QP_MVC_NMF algorithm is quite efficient for small-scale problems and the NG_MVC_NMF algorithm is more suitable for large-scale problems. Simulations show the efficiency and validity of the proposed methods in applications of blind source separation and human face images analysis.

Online blind source separation using incremental nonnegative matrix factorization with volume constraint.

Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.

Mixing matrix estimation from sparse mixtures with unknown number of sources.

In blind source separation, many methods have been proposed to estimate the mixing matrix by exploiting sparsity. However, they often need to know the source number a priori, which is very inconvenient in practice. In this paper, a new method, namely nonlinear projection and column masking (NPCM), is proposed to estimate the mixing matrix. A major advantage of NPCM is that it does not need any knowledge of the source number. In NPCM, the objective function is based on a nonlinear projection and its maxima just correspond to the columns of the mixing matrix. Thus a column can be estimated first by locating a maximum and then deflated by a masking operation. This procedure is repeated until the evaluation of the objective function decreases to zero dramatically. Thus the mixing matrix and the number of sources are estimated simultaneously. Because the masking procedure may result in some small and useless local maxima, particle swarm optimization (PSO) is introduced to optimize the objective function. Feasibility and efficiency of PSO are also discussed. Comparative experimental results show the efficiency of NPCM, especially in the cases where the number of sources is unknown and the sources are relatively less sparse.

Blind spectral unmixing based on sparse nonnegative matrix factorization.

Nonnegative matrix factorization (NMF) is a widely used method for blind spectral unmixing (SU), which aims at obtaining the endmembers and corresponding fractional abundances, knowing only the collected mixing spectral data. It is noted that the abundance may be sparse (i.e., the endmembers may be with sparse distributions) and sparse NMF tends to lead to a unique result, so it is intuitive and meaningful to constrain NMF with sparseness for solving SU. However, due to the abundance sum-to-one constraint in SU, the traditional sparseness measured by L0/L1-norm is not an effective constraint any more. A novel measure (termed as S-measure) of sparseness using higher order norms of the signal vector is proposed in this paper. It features the physical significance. By using the S-measure constraint (SMC), a gradient-based sparse NMF algorithm (termed as NMF-SMC) is proposed for solving the SU problem, where the learning rate is adaptively selected, and the endmembers and abundances are simultaneously estimated. In the proposed NMF-SMC, there is no pure index assumption and no need to know the exact sparseness degree of the abundance in prior. Yet, it does not require the preprocessing of dimension reduction in which some useful information may be lost. Experiments based on synthetic mixtures and real-world images collected by AVIRIS and HYDICE sensors are performed to evaluate the validity of the proposed method.