The success of bortezomib therapy for treatment of multiple myeloma (MM) led to the development of structurally and pharmacologically distinct novel proteasome inhibitors. In the present study, we evaluated the efficacy of one such novel orally bioactive proteasome inhibitor MLN9708/MLN2238 in MM using well-established in vitro and in vivo models.MM cell lines, primary patient cells, and the human MM xenograft animal model were used to study the antitumor activity of MN2238.Treatment of MM cells with MLN2238 predominantly inhibits chymotrypsin-like activity of the proteasome and induces accumulation of ubiquitinated proteins. MLN2238 inhibits growth and induces apoptosis in MM cells resistant to conventional and bortezomib therapies without affecting the viability of normal cells. In animal tumor model studies, MLN2238 is well tolerated and inhibits tumor growth with significantly reduced tumor recurrence. A head-to-head analysis of MLN2238 versus bortezomib showed a significantly longer survival time in mice treated with MLN2238 than mice receiving bortezomib. Immununostaining of MM tumors from MLN2238-treated mice showed growth inhibition, apoptosis, and a decrease in associated angiogenesis. Mechanistic studies showed that MLN2238-triggered apoptosis is associated with activation of caspase-3, caspase-8, and caspase-9; increase in p53, p21, NOXA, PUMA, and E2F; induction of endoplasmic reticulum (ER) stress response proteins Bip, phospho-eIF2-α, and CHOP; and inhibition of nuclear factor kappa B. Finally, combining MLN2238 with lenalidomide, histone deacetylase inhibitor suberoylanilide hydroxamic acid, or dexamethasone triggers synergistic anti-MM activity.Our preclinical study supports clinical evaluation of MLN9708, alone or in combination, as a potential MM therapy.
Ascorbic acid (AsA) is an important antioxidant for human health. The concept of “oil-vegetable-duel-purpose” can significantly enhance the economic benefits of the rapeseed industry. Rapeseed, when utilized as a vegetable, serves as a valuable food source of AsA. In this study, we integrated transcriptome and metabolome analyses, along with substrate feeding, to identify the L-galactose pathway as the primary source for AsA production, which is primarily regulated by light. Through seven different photoperiod treatments from 12 h/12 h (light/dark) to 24 h/0 h, we found that AsA content increased with longer photoperiods, as well as chlorophyll, carotenoids, and soluble sugars. However, an excessively long photoperiod led to photooxidative stress, which negatively affected biomass accumulation in rapeseed seedlings and subsequently impacted the total accumulation of AsA. Furthermore, different enzymes respond differently to different photoperiods. Analysis of the correlation between the expression levels of AsA biosynthesis-related genes and AsA content highlighted a dynamic balancing mechanism of AsA metabolism in response to different photoperiods. The study revealed that the 16 h/8 h photoperiod is optimal for long-term AsA accumulation in rapeseed seedlings. However, extending the photoperiod before harvest can enhance AsA content without compromising yield. These findings offer novel insights into an effective strategy for the biofortification of AsA in rapeseed.
The cost term Ψ(w = wt) is removed from Φ(f, w = wt) since it is a constant in the above optimization problem. Let L = I − D −1/2 v HWD −1 e H D −1/2 v . In the cost term, we can prove Ω(f, w = wt) = fLf (see next section). L is positive semi-definite given Ω(f, w = wt) ≥ 0 for any f , which also implies that Ω(f, w = wt) is convex in f . Therefore, we can simply take derivative with respect to f to get the optimal solution f∗ = (1 − α)((1 − α)I + αL)−1y, where α = μ 1+μ (Zhou et al., 2006). This is equivalent to solving the linear system (1− α)((1− α)I + αL)f = y. In the second step in each iteration, the HyperPrior algorithm fixes f = ft learned in the previous step to learn the optimal weighting of hyperedges w by solving the quadratic programming problem:
The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call 'synergistic outcome determination' (SOD), a concept similar to 'Synthetic Lethality'. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.
Abstract The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.
Abstract COP9 signalosome subunit 5 (CSN5) plays a key role in carcinogenesis of multiple cancers and contributes to the stabilization of target proteins through deubiquitylation. However, the underlying role of CSN5 in thyroid carcinoma has not been reported. In this research, our data showed that CSN5 was overexpressed in thyroid carcinoma tissues compared with paracancerous tissues. Furthermore, a series of gain/loss functional assays were performed to demonstrate the role of CSN5 in facilitating thyroid carcinoma cell proliferation and metastasis. Additionally, we found there was a positive correlation between CSN5 and angiopoietin-like protein 2 (ANGPTL2) protein levels in thyroid carcinoma tissues and that CSN5 promoted thyroid carcinoma cell proliferation and metastasis through ANGPTL2. We also identified the underlying mechanism that CSN5 elevated ANGPTL2 protein level by directly binding it, decreasing its ubiquitination and degradation. Overall, our results highlight the significance of CSN5 in promoting thyroid carcinoma carcinogenesis and implicate CSN5 as a promising candidate for thyroid carcinoma treatment.
China is currently in a strategic opportunity period for green and high-quality development, and developing the digital economy is an important choice to achieve environmental pollution control, improve regional ecological efficiency, and enhance social welfare. In this context, the impact of the digital economy on ecological well-being performance and the role of environmental regulation need to be examined. In this study, the super-efficiency SBM-DEA model was used to measure the level of ecological well-being performance in 30 provinces of China from 2011 to 2019. On this basis, the mediating effect model and spatial Durbin model were adopted to explore the transmission mechanism and regional heterogeneity of the impact of the digital economy on ecological well-being performance. The empirical results show that the digital economy significantly contributes to regional ecological well-being performance in China, and there is significant spatial spillover as well. Moreover, the findings still hold under robustness tests. The results also show that environmental regulation is an important transmission path for the digital economy to enhance regional ecological well-being performance, and the impact of environmental regulation on ecological well-being performance varies by region; specifically, the impact in eastern China is positive but not significant. However, the digital economy plays a significant positive role in promoting ecological well-being performance in the central and western regions, and is more obvious in the central region. Finally, suggestions are put forward to enhance the role of the digital economy in regional ecological well-being performance, which is of great significance for promoting green economic growth and high-quality development.
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