Drug and Cell Type-Specific Regulation of Genes with Different Classes of Estrogen Receptor β-Selective Agonists

Estrogens produce biological effects by interacting with two estrogen receptors, ERα and ERβ. Drugs that selectively target ERα or ERβ might be safer for conditions that have been traditionally treated with non-selective estrogens. Several synthetic and natural ERβ-selective compounds have been identified. One class of ERβ-selective agonists is represented by ERB-041 (WAY-202041) which binds to ERβ much greater than ERα. A second class of ERβ-selective agonists derived from plants include MF101, nyasol and liquiritigenin that bind similarly to both ERs, but only activate transcription with ERβ. Diarylpropionitrile represents a third class of ERβ-selective compounds because its selectivity is due to a combination of greater binding to ERβ and transcriptional activity. However, it is unclear if these three classes of ERβ-selective compounds produce similar biological activities. The goals of these studies were to determine the relative ERβ selectivity and pattern of gene expression of these three classes of ERβ-selective compounds compared to estradiol (E2), which is a non-selective ER agonist. U2OS cells stably transfected with ERα or ERβ were treated with E2 or the ERβ-selective compounds for 6 h. Microarray data demonstrated that ERB-041, MF101 and liquiritigenin were the most ERβ-selective agonists compared to estradiol, followed by nyasol and then diarylpropionitrile. FRET analysis showed that all compounds induced a similar conformation of ERβ, which is consistent with the finding that most genes regulated by the ERβ-selective compounds were similar to each other and E2. However, there were some classes of genes differentially regulated by the ERβ agonists and E2. Two ERβ-selective compounds, MF101 and liquiritigenin had cell type-specific effects as they regulated different genes in HeLa, Caco-2 and Ishikawa cell lines expressing ERβ. Our gene profiling studies demonstrate that while most of the genes were commonly regulated by ERβ-selective agonists and E2, there were some genes regulated that were distinct from each other and E2, suggesting that different ERβ-selective agonists might produce distinct biological and clinical effects

Increased DNA microarray hybridization specificity using sscDNA targets

Abstract Background The most widely used amplification method for microarray analysis of gene expression uses T7 RNA polymerase-driven in vitro transcription (IVT) to produce complementary RNA (cRNA) that can be hybridized to arrays. However, multiple rounds of amplification are required when assaying very small amounts of starting RNA. Moreover, certain cRNA-DNA mismatches are more stable than the analogous cDNA-DNA mismatches and this might increase non-specific hybridization. We sought to determine whether a recently developed linear isothermal amplification method (ribo-SPIA) that produces single stranded cDNA would offer advantages over traditional IVT-based methods for microarray-based analyses of transcript expression. Results A single round of ribo-SPIA amplification produced sufficient sscDNA for hybridizations when as little as 5 ng of starting total RNA was used. Comparisons of probe set signal intensities obtained from replicate amplifications showed consistently high correlations (r = 0.99). We compared gene expression in two different human RNA samples using ribo-SPIA. Compared with one round IVT, ribo-SPIA had a larger dynamic range and correlated better with quantitative PCR results even though we used 1000-fold less starting RNA. The improved dynamic range was associated with decreases in hybridization to mismatch control probes. Conclusion The use of amplified sscDNA may offer substantial advantages over IVT-based amplification methods, especially when very limited amounts of starting RNA are available. The use of sscDNA targets instead of cRNA targets appears to improve hybridization specificity.</p

Timosaponin AIII is preferentially cytotoxic to tumor cells through inhibition of mTOR and induction of ER stress.

The aqueous extract of Anemarrhena asphodeloides (BN108) induces apoptosis in various cancer cell lines but is significantly less cytotoxic in non-transformed cells. Chemical fractionation of BN108 showed that its cytotoxicity is associated with timosaponins, steroidal saponins of coprostane type. Timosaponin BII (TBII) is a major saponin in BN108, but it shows little cytotoxicity. A much less abundant TAIII induces cell death in tumor cells but not in normal cells, reproducing the selectivity of the total extract BN108. Glycosidase treatment, by removing the extra sugar moiety in TBII, converts it to TAIII and confers cytotoxic activity. Analysis of the mechanisms of death induced by TAIII revealed activation of two distinct pro-apoptotic pathways: first, inhibition of mTORC1 manifested in much reduced phosphorylation of mTORC1 targets; second, induction of endoplasmic reticulum stress culminating in phosphorylation of eIF2alpha and activation of caspase 4. These pro-apoptotic pathways are activated by TAIII selectively in tumor cells but not in normal cells. Both pathways play a causative role in TAIII cytotoxicity, as restoration of either mTOR activity or relief of ER stress alone offer only partial protection from TAIII. Inhibition of mTORC1 and induction of ER stress apparently contribute to the induction of the previously reported autophagic response in TAIII-treated cells. TAIII induced autophagy plays a protective role in TAIII induced death signaling, and failure to mount autophagic response is associated with heightened sensitivity to TAIII induced apoptosis. The multiple death-promoting and apparently tumor-selective responses to TAIII, its ability to inhibit mTORC1, and the possibility of further enhancing its cytotoxicity by pharmacological inhibition of autophagy, make TAIII an attractive candidate for development as a cancer therapeutic agent

Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery

Deep learning (DL) convolutional neural networks (CNNs) have been rapidly adapted in very high spatial resolution (VHSR) satellite image analysis. DLCNN-based computer visions (CV) applications primarily aim for everyday object detection from standard red, green, blue (RGB) imagery, while earth science remote sensing applications focus on geo object detection and classification from multispectral (MS) imagery. MS imagery includes RGB and narrow spectral channels from near- and/or middle-infrared regions of reflectance spectra. The central objective of this exploratory study is to understand to what degree MS band statistics govern DLCNN model predictions. We scaffold our analysis on a case study that uses Arctic tundra permafrost landform features called ice-wedge polygons (IWPs) as candidate geo objects. We choose Mask RCNN as the DLCNN architecture to detect IWPs from eight-band Worldview-02 VHSR satellite imagery. A systematic experiment was designed to understand the impact on choosing the optimal three-band combination in model prediction. We tasked five cohorts of three-band combinations coupled with statistical measures to gauge the spectral variability of input MS bands. The candidate scenes produced high model detection accuracies for the F1 score, ranging between 0.89 to 0.95, for two different band combinations (coastal blue, blue, green (1,2,3) and green, yellow, red (3,4,5)). The mapping workflow discerned the IWPs by exhibiting low random and systematic error in the order of 0.17&ndash;0.19 and 0.20&ndash;0.21, respectively, for band combinations (1,2,3). Results suggest that the prediction accuracy of the Mask-RCNN model is significantly influenced by the input MS bands. Overall, our findings accentuate the importance of considering the image statistics of input MS bands and careful selection of optimal bands for DLCNN predictions when DLCNN architectures are restricted to three spectral channels

An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems

An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems