Regulation of c-Myc Expression by Ahnak Promotes Induced Pluripotent Stem Cell Generation.

We have previously reported that Ahnak-mediated TGFß signaling leads to down-regulation of c-Myc expression. Here, we show that inhibition of Ahnak can promote generation of induced pluripotent stem cells (iPSC) via up-regulation of endogenous c-Myc. Consistent with the c-Myc inhibitory role of Ahnak, mouse embryonic fibroblasts from Ahnak-deficient mouse (Ahnak(-/-) MEF) show an increased level of c-Myc expression compared with wild type MEF. Generation of iPSC with just three of the four Yamanaka factors, Oct4, Sox2, and Klf4 (hereafter 3F), was significantly enhanced in Ahnak(-/-) MEF. Similar results were obtained when Ahnak-specific shRNA was applied to wild type MEF. Of note, expressionof Ahnak was significantly induced during the formation of embryoid bodies from embryonic stem cells, suggesting that Ahnak-mediated c-Myc inhibition is involved in embryoid body formation and the initial differentiation of pluripotent stem cells. The iPSC from 3F-infected Ahnak(-/-) MEF cells (Ahnak(-/-)-iPSC-3F) showed expression of all stem cell markers examined and the capability to form three primary germ layers. Moreover, injection of Ahnak(-/-)-iPSC-3F into athymic nude mice led to development of teratoma containing tissues from all three primary germ layers, indicating that iPSC from Ahnak(-/-) MEF are bona fide pluripotent stem cells. Taken together, these data provide evidence for a new role for Ahnak in cell fate determination during development and suggest that manipulation of Ahnak and the associated signaling pathway may provide a means to regulate iPSC generation.

Improvement of the recombinant phytase expression by intermittent feeding of glucose during the induction phase of methylotrophic yeast Pichia pastoris.

PURPOSE: For growth of methylotrophic yeast, glycerol is usually used as a carbon source. Glucose is used in some cases, but not widely consumed due to strong repressive effect on AOX1 promoter. However, glucose is still considered as a carbon source of choice since it has low production cost and guarantees growth rate comparable to glycerol. RESULTS: In flask cultivation of the recombinant yeast, Pichia pastoris GS115(pPIC9K-appA38M), while methanol induction point(OD600) and methanol concentration significantly affected the phytase expression, glucose addition in induction phase could enhance phytase expression. The optimal flask cultivation conditions illustrated by Response Surface Methodology were 10.37 OD600 induction point, 2.02 h before methanol feeding, 1.16% methanol concentration and 40.36µL glucose feeding amount(for 20 mL culture volume) in which the expressed phytase activity was 613.4 ± 10.2U/mL, the highest activity in flask cultivation. In bioreactor fermentation, the intermittent glucose feeding showed several advantageous results such as 68 h longer activity increment, 149.2% higher cell density and 200.1% higher activity compared to the sole methanol feeding method. These results implied that remaining glucose at induction point might exhibit a positive effect on the phytase expression. CONCLUSION: Glucose intermittent feeding could be exploited for economic phytase production and the other recombinant protein expression by P. pastoris GS115.

Spatiotemporal informer: A new approach based on spatiotemporal embedding and attention for air quality forecasting.

Accurate prediction of air pollution is essential for public health protection. Air quality, however, is difficult to predict due to the complex dynamics, and its accurate forecast still remains a challenge. This study suggests a spatiotemporal Informer model, which uses a new spatiotemporal embedding and spatiotemporal attention, to improve AQI forecast accuracy. In the first phase of the proposed forecast mechanism, the input data is transformed by the spatiotemporal embedding. Next, the spatiotemporal attention is applied to extract spatiotemporal features from the embedded data. The final forecast is obtained based on the attention tensors. In the proposed forecast model, the input is a 3-dimensional data that consists of air quality data (AQI, PM2.5, O3, SO2, NO2, CO) and geographic information, and the output is a multi-positional, multi-temporal data that shows the AQI forecast result of all the monitoring stations in the study area. The proposed forecast model was evaluated by air quality data of 34 monitoring stations in Beijing, China. Experiments showed that the proposed forecast model could provide highly accurate AQI forecast: the average of MAPE values for from 1 h to 20 h ahead forecast was 11.61%, and it was much smaller than other models. Moreover, the proposed model provided a highly accurate and stable forecast even at the extreme points. These results demonstrated that the proposed spatiotemporal embedding and attention techniques could sufficiently capture the spatiotemporal correlation characteristics of air quality data, and that the proposed spatiotemporal Informer could be successfully applied for air quality forecasting.

Nox4-IGF2 Axis Promotes Differentiation of Embryoid Body Cells Into Derivatives of the Three Embryonic Germ Layers.

Reactive oxygen species (ROS) play important roles as second messengers in a wide array of cellular processes including differentiation of stem cells. We identified Nox4 as the major ROS-generating enzyme whose expression is induced during differentiation of embryoid body (EB) into cells of all three germ layers. The role of Nox4 was examined using induced pluripotent stem cells (iPSCs) generated from Nox4 knockout (Nox4-/-) mouse. Differentiation markers showed significantly reduced expression levels consistent with the importance of Nox4-generated ROS during this process. From transcriptomic analyses, we found insulin-like growth factor 2 (IGF2), a member of a gene family extensively involved in embryonic development, as one of the most down-regulated genes in Nox4-/- cells. Indeed, addition of IGF2 to culture partly restored the differentiation competence of Nox4-/- iPSCs. Our results reveal an important signaling axis mediated by ROS in control of crucial events during differentiation of pluripotent stem cells.

Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network.

Water quality forecasting can provide useful information for public health protection and support water resources management. In order to forecast water quality more accurately, this paper proposes a novel hybrid model by combining data decomposition, fuzzy C-means clustering and bidirectional gated recurrent unit. Firstly, the original water quality data is decomposed into several subseries by empirical wavelet transform, and then, the decomposed subseries are recombined by fuzzy C-means clustering. Next, for each clustered series, bidirectional gated recurrent unit is applied to develop prediction model. Finally, the forecast result is obtained by the summation of the predictions for the subseries. The proposed forecast model is evaluated by the water quality data of Poyang Lake, China. Results show that the proposed forecast model provides highly accurate forecast result for all of the six water quality data: the average of MAPE of the forecast results for the six water quality datasets is 4.59% for 7 day ahead prediction. Furthermore, our model shows better forecast performance than the other models. Particularly, compared with the single BiGRU model, MAPE decreased by 32.86% in average. Results demonstrate that the proposed forecast model can be used effectively for water quality forecasting.

Forecasting air pollutant concentration using a novel spatiotemporal deep learning model based on clustering, feature selection and empirical wavelet transform.

Accurate forecasting of air pollutant concentration is of great importance since it is an essential part of the early warning system. However, it still remains a challenge due to the limited information of emission source and high uncertainties of the dynamic processes. In order to improve the accuracy of air pollutant concentration forecast, this study proposes a novel hybrid model using clustering, feature selection, real-time decomposition by empirical wavelet transform, and deep learning neural network. First, all air pollutant time series are decomposed by empirical wavelet transform based on real-time decomposition, and subsets of output data are constructed by combining corresponding decomposed components. Second, each subset of output data is classified into several clusters by clustering algorithm, and then appropriate inputs are selected by feature selection method. Third, a deep learning-based predictor, which uses three dimensional convolutional neural network and bidirectional long short-term memory neural network, is applied to predict decomposition components of each cluster. Last, air pollutant concentration forecast for each monitoring station is obtained by reconstructing predicted values of all the decomposition components. PM2.5 concentration data of Beijing, China is used to validate and test our model. Results show that the proposed model outperforms other models used in this study. In our model, mean absolute percentage error for 1, 6, 10 h ahead PM2.5 concentration prediction is 4.03%, 6.87%, and 8.98%, respectively. These outcomes demonstrate that the proposed hybrid model is a powerful tool to provide highly accurate forecast for air pollutant concentration.

A Novel Kinase Inhibitor AX-0085 Inhibits Interferon-γ-Mediated Induction of PD-L1 Expression and Promotes Immune Reaction to Lung Adenocarcinoma Cells.

In this study, we describe a novel kinase inhibitor AX-0085 which can suppress the induction of PD-L1 expression by Interferon-γ (IFN-γ) in lung adenocarcinoma (LUAD) cells. AX-0085 effectively blocks JAK2/STAT1 signaling initiated by IFN-γ treatment and prevents nuclear localization of STAT1. Importantly, we demonstrate that AX-0085 reverses the IFN-γ-mediated repression of T cell activation in vitro and enhances the anti-tumor activity of anti-PD-1 antibody in vivo when used in combination. Finally, transcriptomic analyses indicated that AX-0085 is highly specific in targeting the IFN-γ-pathway, thereby raising the possibility of applying this reagent in combination therapy with checkpoint inhibitor antibodies. It may be particularly relevant in cases in which PD-L1-mediated T cell exhaustion leads to immunoevasive phenotypes.

2-(trimethylammonium)ethyl (R)-3-methoxy-3-oxo-2-stearamidopropyl phosphate enhances thrombopoietin-induced megakaryocytic differentiation and plateletogenesis.

We have previously reported the effects of 2-(trimethylammonium) ethyl (R)-3-methoxy-3-oxo-2-stearamidopropyl phosphate [(R)-TEMOSPho], a synthetic phospholipid, on megakaryocytic differentiation of myeloid leukemia cells. Here, we demonstrate that (R)-TEMOSPho enhances megakaryopoiesis and plateletogenesis from primary hematopoietic stem cells (HSCs) induced by thrombopoietin (TPO). Specifically, we demonstrate at sub-saturation levels of TPO, the addition of (R)-TEMOSPho enhances differentiation and maturation of megakaryocytes (MKs) from murine HSCs derived from fetal liver. Furthermore, we show that production of platelets with (R)-TEMOSPho in combination with TPO is also more efficient than TPO alone and that platelets generated in vitro with these two agents are as functional as those from TPO alone. TPO can thus be partly replaced by or supplemented with (R)-TEMOSPho, and this in turn implies that (R)-TEMOSPho can be useful in efficient platelet production in vitro and potentially be a valuable option in designing cell-based therapy. [BMB Reports 2019; 52(7): 434-438].

Phytosphingosine promotes megakaryocytic differentiation of myeloid leukemia cells.

We report that phytosphingosine, a sphingolipid found in many organisms and implicated in cellular signaling, promotes megakaryocytic differentiation of myeloid leukemia cells. Specifically, phytosphingosine induced several hallmark changes associated with megakaryopoiesis from K562 and HEL cells including cell cycle arrest, cell size increase and polyploidization. We also confirmed that cell type specific markers of megakaryocytes, CD41a and CD42b are induced by phytosphingosine. Phospholipids with highly similar structures were unable to induce similar changes, indicating that the activity of phytosphingosine is highly specific. Although phytosphingosine is known to activate p38 MAPK-mediated apoptosis, the signaling mechanisms involved in megakaryopoiesis appear to be distinct. In sum, we present another model for dissecting molecular details of megakaryocytic differentiation which in large part remains obscure.