TGF-Beta suppresses VEGFA-mediated angiogenesis in colon cancer metastasis.

The FET cell line, derived from an early stage colon carcinoma, is non-tumorigenic in athymic nude mice. Engineered FET cells that express TGF-α (FETα) display constitutively active EGFR/ErbB signaling. These cells readily formed xenograft tumors in athymic nude mice. Importantly, FETα cells retained their response to TGF-beta-mediated growth inhibition, and, like the parental FET cells, expression of a dominant negative TGF-beta type II receptor (DNRII) in FETα cells (FETα/DNRII) abrogated responsiveness to TGF-beta-induced growth inhibition and apoptosis under stress conditions in vitro and increased metastatic potential in an orthotopic model in vivo, which indicates metastasis suppressor activity of TGF-beta signaling in this model. Cancer angiogenesis is widely regarded as a key attribute for tumor formation and progression. Here we show that TGF-beta signaling inhibits expression of vascular endothelial growth factor A (VEGFA) and that loss of autocrine TGF-beta in FETα/DNRII cells resulted in increased expression of VEGFA. Regulation of VEGFA expression by TGF-beta is not at the transcriptional level but at the post-transcriptional level. Our results indicate that TGF-beta decreases VEGFA protein stability through ubiquitination and degradation in a PKA- and Smad3-dependent and Smad2-independent pathway. Immunohistochemical (IHC) analyses of orthotopic tumors showed significantly reduced TGF-beta signaling, increased CD31 and VEGFA staining in tumors of FETα/DNRII cells as compared to those of vector control cells. These results indicate that inhibition of TGF-beta signaling increases VEGFA expression and angiogenesis, which could potentially contribute to enhanced metastasis of those cells in vivo. IHC studies performed on human colon adenocarcinoma specimens showed that TGF-beta signaling is inversely correlated with VEGFA expression, indicating that TGF-beta-mediated suppression of VEGFA expression exists in colon cancer patients

Inhibition of phosphorylated c-Met in rhabdomyosarcoma cell lines by a small molecule inhibitor SU11274

<p>Abstract</p> <p>Background</p> <p>c-Met is a receptor tyrosine kinase (RTK) that is over-expressed in a variety of cancers and involved in cell growth, invasion, metastasis and angiogenesis. In this study, we investigated the role of c-Met in rhabdomyosarcoma (RMS) using its small molecule inhibitor SU11274, which has been hypothesized to be a potential therapeutic target for RMS.</p> <p>Methods</p> <p>The expression level of phosphorylated c-Met in RMS cell lines (RD, CW9019 and RH30) and tumor tissues was assessed by phospho-RTK array and immunohistochemistry, respectively. The inhibition effects of SU11274 on RMS cells were studied with regard to intracellular signaling, cell proliferation, cell cycle and cell migration.</p> <p>Results</p> <p>A high level of phosphorylated c-Met was detected in 2 alveolar RMS cell lines (CW9019 and RH30) and 14 out of 24 RMS tissue samples, whereas relatively low levels of phospho-c-Met were observed in the embryonic RMS cell line (RD). The small molecule SU11274 could significantly reduce the phosphorylation of c-Met, resulting in inhibition of cell proliferation, G1 phase arrest of cell cycle and blocking of cell migration in CW9019 and RH30 cell lines.</p> <p>Conclusion</p> <p>These results might support the role of c-Met in the development and progression of RMS. Furthermore, the inhibitor of c-Met, SU11274, could be an effective targeting therapy reagent for RMS, especially alveolar RMS.</p

Functional proteomic analysis reveals the involvement of KIAA1199 in breast cancer growth, motility and invasiveness.

BACKGROUND: KIAA1199 is a recently identified novel gene that is up-regulated in human cancer with poor survival. Our proteomic study on signaling polarity in chemotactic cells revealed KIAA1199 as a novel protein target that may be involved in cellular chemotaxis and motility. In the present study, we examined the functional significance of KIAA1199 expression in breast cancer growth, motility and invasiveness. METHODS: We validated the previous microarray observation by tissue microarray immunohistochemistry using a TMA slide containing 12 breast tumor tissue cores and 12 corresponding normal tissues. We performed the shRNA-mediated knockdown of KIAA1199 in MDA-MB-231 and HS578T cells to study the role of this protein in cell proliferation, migration and apoptosis in vitro. We studied the effects of KIAA1199 knockdown in vivo in two groups of mice (n = 5). We carried out the SILAC LC-MS/MS based proteomic studies on the involvement of KIAA1199 in breast cancer. RESULTS: KIAA1199 mRNA and protein was significantly overexpressed in breast tumor specimens and cell lines as compared with non-neoplastic breast tissues from large-scale microarray and studies of breast cancer cell lines and tumors. To gain deeper insights into the novel role of KIAA1199 in breast cancer, we modulated KIAA1199 expression using shRNA-mediated knockdown in two breast cancer cell lines (MDA-MB-231 and HS578T), expressing higher levels of KIAA1199. The KIAA1199 knockdown cells showed reduced motility and cell proliferation in vitro. Moreover, when the knockdown cells were injected into the mammary fat pads of female athymic nude mice, there was a significant decrease in tumor incidence and growth. In addition, quantitative proteomic analysis revealed that knockdown of KIAA1199 in breast cancer (MDA-MB-231) cells affected a broad range of cellular functions including apoptosis, metabolism and cell motility. CONCLUSIONS: Our findings indicate that KIAA1199 may play an important role in breast tumor growth and invasiveness, and that it may represent a novel target for biomarker development and a novel therapeutic target for breast cancer

Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale

Detecting Spatiotemporal Changes in Vegetation with the BFAST Model in the Qilian Mountain Region during 2000–2017

The Qilian Mountain ecosystems play an irreplaceable role in maintaining ecological security in western China. Vegetation, as an important part of the ecosystem, has undergone considerable changes in recent decades in this area, but few studies have focused on the process of vegetation change. A long normalized difference vegetation index (NDVI) time series dataset based on remote sensing is an effective tool to investigate large-scale vegetation change dynamics. The MODerate resolution Imaging Spectroradiometer (MODIS) NDVI dataset has provided very detailed regional to global information on the state of vegetation since 2000. The aim of this study was to explore the spatial-temporal characteristics of abrupt vegetation changes and detect their potential drivers in the Qilian Mountain area using MODIS NDVI data with 1 km resolution from 2000 to 2017. The Breaks for Additive Season and Trend (BFAST) algorithm was adopted to detect vegetation breakpoint change times and magnitudes from satellite observations. Our results indicated that approximately 80.1% of vegetation areas experienced at least one abrupt change from 2000 to 2017, and most of these areas were distributed in the southern and northern parts of the study area, especially the area surrounding Qinghai Lake. The abrupt browning changes were much more widespread than the abrupt greening changes for most years of the study period. Environmental factors and anthropogenic activities mainly drove the abrupt vegetation changes. Long-term overgrazing is likely the main cause of the abrupt browning changes. In addition, our results indicate that national ecological protection policies have achieved positive effects in the study area

Molecular dynamics simulation for surface melting and self-preservation effect of methane hydrate

The surface melting process of structure sI methane hydrate is simulated at T = 240, 260, 280, and 300 K using NVT molecular dynamics method. The simulation results show that a quasi-liquid layer will be formed during the melting process. The density distribution, translation, orientation, and dynamic properties of water molecules in the quasi-liquid layer are calculated as a function of the distance normal to the interface, which indicates the performance of quasi-liquid layer exhibits a continuous change from crystal-like to liquid-like. The quasi-liquid layer plays as a resistance of mass transfer restraining the diffusion of water and methane molecules during the melting process. The resistance of quasi-liquid layer will restrain methane molecules diffuse from hydrate phase to gas phase and slow the melting process, which can be considered as a possible mechanism of self-preservation effect. The performance of quasi-liquid layer is more crystal-like when the temperature is lower than the melting-point of water, which will exhibit an obvious self-preservation. The self-preservation will weaken while the temperature is higher than the melting-point of water because of the liquid-like performance of the quasi-liquid layer

Molecular dynamics simulation for surface melting and self-preservation effect of methane hydrate

The surface melting process of structure sI methane hydrate is simulated at T = 240, 260, 280, and 300 K using NVT molecular dynamics method. The simulation results show that a quasi-liquid layer will be formed during the melting process. The density distribution, translation, orientation, and dynamic properties of water molecules in the quasi-liquid layer are calculated as a function of the distance normal to the interface, which indicates the performance of quasi-liquid layer exhibits a continuous change from crystal-like to liquid-like. The quasi-liquid layer plays as a resistance of mass transfer restraining the diffusion of water and methane molecules during the melting process. The resistance of quasi-liquid layer will restrain methane molecules diffuse from hydrate phase to gas phase and slow the melting process, which can be considered as a possible mechanism of self-preservation effect. The performance of quasi-liquid layer is more crystal-like when the temperature is lower than the melting-point of water, which will exhibit an obvious self-preservation. The self-preservation will weaken while the temperature is higher than the melting-point of water because of the liquid-like performance of the quasi-liquid layer