Small RNA interference-mediated gene silencing of heparanase abolishes the invasion, metastasis and angiogenesis of gastric cancer cells

<p>Abstract</p> <p>Background</p> <p>Heparanase facilitates the invasion and metastasis of cancer cells, and is over-expressed in many kinds of malignancies. Our studies indicated that heparanase was frequently expressed in advanced gastric cancers. The aim of this study is to determine whether silencing of heparanase expression can abolish the malignant characteristics of gastric cancer cells.</p> <p>Methods</p> <p>Three heparanase-specific small interfering RNA (siRNAs) were designed, synthesized, and transfected into cultured gastric cancer cell line SGC-7901. Heparanase expression was measured by RT-PCR, real-time quantitative PCR and Western blot. Cell proliferation was detected by MTT colorimetry and colony formation assay. The <it>in vitro </it>invasion and metastasis of cancer cells were measured by cell adhesion assay, scratch assay and matrigel invasion assay. The angiogenesis capabilities of cancer cells were measured by tube formation of endothelial cells.</p> <p>Results</p> <p>Transfection of siRNA against 1496-1514 bp of encoding regions resulted in reduced expression of heparanase, which started at 24 hrs and lasted for 120 hrs post-transfection. The siRNA-mediated silencing of heparanase suppressed the cellular proliferation of SGC-7901 cells. In addition, the <it>in vitro </it>invasion and metastasis of cancer cells were attenuated after knock-down of heparanase. Moreover, transfection of heparanase-specific siRNA attenuated the <it>in vitro </it>angiogenesis of cancer cells in a dose-dependent manner.</p> <p>Conclusions</p> <p>These results demonstrated that gene silencing of heparanase can efficiently abolish the proliferation, invasion, metastasis and angiogenesis of human gastric cancer cells <it>in vitro</it>, suggesting that heparanase-specific siRNA is of potential values as a novel therapeutic agent for human gastric cancer.</p

Small RNAs Targeting Transcription Start Site Induce Heparanase Silencing through Interference with Transcription Initiation in Human Cancer Cells

Heparanase (HPA), an endo-h-D-glucuronidase that cleaves the heparan sulfate chain of heparan sulfate proteoglycans, is overexpressed in majority of human cancers. Recent evidence suggests that small interfering RNA (siRNA) induces transcriptional gene silencing (TGS) in human cells. In this study, transfection of siRNA against −9/+10 bp (siH3), but not −174/−155 bp (siH1) or −134/−115 bp (siH2) region relative to transcription start site (TSS) locating at 101 bp upstream of the translation start site, resulted in TGS of heparanase in human prostate cancer, bladder cancer, and gastric cancer cells in a sequence-specific manner. Methylation-specific PCR and bisulfite sequencing revealed no DNA methylation of CpG islands within heparanase promoter in siH3-transfected cells. The TGS of heparanase did not involve changes of epigenetic markers histone H3 lysine 9 dimethylation (H3K9me2), histone H3 lysine 27 trimethylation (H3K27me3) or active chromatin marker acetylated histone H3 (AcH3). The regulation of alternative splicing was not involved in siH3-mediated TGS. Instead, siH3 interfered with transcription initiation via decreasing the binding of both RNA polymerase II and transcription factor II B (TFIIB), but not the binding of transcription factors Sp1 or early growth response 1, on the heparanase promoter. Moreover, Argonaute 1 and Argonaute 2 facilitated the decreased binding of RNA polymerase II and TFIIB on heparanase promoter, and were necessary in siH3-induced TGS of heparanase. Stable transfection of the short hairpin RNA construct targeting heparanase TSS (−9/+10 bp) into cancer cells, resulted in decreased proliferation, invasion, metastasis and angiogenesis of cancer cells in vitro and in athymic mice models. These results suggest that small RNAs targeting TSS can induce TGS of heparanase via interference with transcription initiation, and significantly suppress the tumor growth, invasion, metastasis and angiogenesis of cancer cells

Remote Sensing Estimates of Boreal and Temperate Forest Woody Biomass: Carbon Pools, Sources, and Sinks

The relation between satellite measurements of the normalized difference vegetation index (NDVI), cumulated over the growing season, and inventory estimates of forest woody biomass carbon is estimated statistically with data from 167 provinces and states in six countries (Canada, Finland, Norway, Russia and the USA for a single time period and Sweden for two periods). Statistical tests indicate that the regression model can be used to represent the relation between forest biomass and NDVI across spatial, temporal and ecological scales for relatively long time scales. For the 1.42 billion ha of boreal and temperate forests in the Northern Hemisphere, the woody biomass carbon pools and sinks are estimated at a relatively high spatial resolution (8 8 km). We estimate the carbon pool to be 61 F 20 gigatons (10 ) carbon (Gt C) during the late 1990s and the biomass sink to be 0.68 F 0.34 Gt C/year between the 1982 and 1999. The geographic detail of carbon sinks provided here can contribute to a potential monitoring program for greenhouse gas emission reduction commitments under the Kyoto Protocol. D 2002 Elsevier Science Inc. All rights reserved

Multiscale analysis and validation of the MODIS LAI product

The development of appropriate ground-based validation techniques is critical to assessing uncertainties associated with satellite databased products. In this paper, the second of a two-part series, we present a method for validation of the Moderate Resolution Imaging Spectroradiometer Leaf Area Index (MODIS LAI) product with emphasis on the sampling strategy for field data collection. Using a hierarchical scene model, we divided 30-m resolution LAI and NDVI images from Maun (Botswana), Harvard Forest (USA) and Ruokulahti Forest (Finland) into individual scale images of classes, region and pixel. Isolating the effects associated with different landscape scales through decomposition of semivariograms not only shows the relative contribution of different characteristic scales to the overall variation, but also displays the spatial structure of the different scales within a scene. We find that (1) patterns of variance at the class, region and pixel scale at these sites are different with respect to the dominance in order of the three levels of landscape organization within a scene; (2) the spatial structure of LAI shows similarity across the three sites, that is, ranges of semivariograms from scale of pixel, region and class are less than 1000 m. Knowledge gained from these analyses aids in formulation of sampling strategies for validation of biophysical products derived from moderate resolution sensors such as MODIS. For a homogeneous (within class) site, where the scales of class and region account for most of the spatial variation, a sampling strategy should focus more on using accurate land cover maps and selection of regions. However, for a heterogeneous (within class) site, accurate point measurements and GPS readings are needed

Overview of the north American land data assimilation system (NLDAS)

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), together with its NOAA Climate Program Office (CPO) Climate Prediction Program of the Americas (CPPA) partners, have established a North American Land Data Assimilation System (NLDAS). The system runs multiple land surface models (LSMs) over the Continental United States (CONUS) to generate long-term hourly, 1/8th degree hydrological and meteorological products. NLDAS was initiated in 1998 as a collaborative project between NOAA, NASA, and several universities to improve the generation of initial land surface conditions for numerical weather prediction models. The first phase of NLDAS (NLDAS-1, 1998-2005) centered on the construction of the overall NLDAS system and on the assessment of the ability of the four NLDAS LSMs to accurately simulate water fluxes, energy fluxes, and state variables. These LSMs included the Noah, Mosaic, Sacramento Soil Moisture Accounting (SAC-SMA), and Variable Infiltration Capacity (VIC) models. Building on the results of NLDAS-1, the project entered into a second phase (NLDAS-2, 2006-present) which has included upgraded forcing data and LSMs, model intercomparison studies, real-time monitoring of extreme weather events, and seasonal hydrologic forecasts. NLDAS-1 and NLDAS-2 have also spurred and supported other modeling activities, including high-resolution 1 km land surface modeling and the establishment of regional and global land data assimilation systems. NLDAS-2 operates on both a real-time monitoring mode and an ensemble seasonal hydrologic forecast mode. In the monitoring mode, land states (soil moisture and snow water equivalent) and water fluxes (evaporation, total runoff, and streamflow) from real-time LSM executions are depicted as anomalies and percentiles with respect to their own modelbased climatology. One key application of the real-time updates is for drought monitoring over the CONUS, and NLDAS supports both NOAA Climate Prediction Center (CPC) and US National Integrated Drought Information System (NIDIS) drought monitoring activities. The uncoupled ensemble seasonal forecast mode generates downscaled ensemble seasonal forecasts of surface forcing based on a climatological Ensemble Stream flow Prediction (ESP) type approach, a method utilizing CPC Official Seasonal Climate Outlooks, and a third approach using NCEP Climate Forecast System (CFS) ensemble dynamical model predictions. The three sets of forcing ensembles are then used to drive a chosen LSM (currently VIC) in seasonal forecast mode over 14 large river basins that together span the CONUS domain. One-to six-month ensemble seasonal forecast products such as air temperature, precipitation, soil moisture, snowpack, total runoff, evaporation, and stream flow are derived using each forecasting approach. The anomalies and percentiles of the predicted products and the drought probability forecast based on the predicted total column soil moisture for each forcing approach can be used for the purpose of drought prediction over the CONUS, and provide key support for NIDIS and CPC drought forecast efforts.</p