Feed-Forward Inhibition of Androgen Receptor Activity by Glucocorticoid Action in Human Adipocytes

SummaryWe compared transcriptomes of terminally differentiated mouse 3T3-L1 and human adipocytes to identify cell-specific differences. Gene expression and high content analysis (HCA) data identified the androgen receptor (AR) as both expressed and functional, exclusively during early human adipocyte differentiation. The AR agonist dihydrotestosterone (DHT) inhibited human adipocyte maturation by downregulation of adipocyte marker genes, but not in 3T3-L1. It is interesting that AR induction corresponded with dexamethasone activation of the glucocorticoid receptor (GR); however, when exposed to the differentiation cocktail required for adipocyte maturation, AR adopted an antagonist conformation and was transcriptionally repressed. To further explore effectors within the cocktail, we applied an image-based support vector machine (SVM) classification scheme to show that adipocyte differentiation components inhibit AR action. The results demonstrate human adipocyte differentiation, via GR activation, upregulates AR but also inhibits AR transcriptional activity

High Throughput Method to Quantify Anterior-Posterior Polarity of T-Cells and Epithelial Cells

The virologic synapse (VS), which is formed between a virus-infected and uninfected cell, plays a central role in the transmission of certain viruses, such as HIV and HTLV-1. During VS formation, HTLV-1-infected T-cells polarize cellular and viral proteins toward the uninfected T-cell. This polarization resembles anterior-posterior cell polarity induced by immunological synapse (IS) formation, which is more extensively characterized than VS formation and occurs when a T-cell interacts with an antigen-presenting cell. One measure of cell polarity induced by both IS or VS formation is the repositioning of the microtubule organizing center (MTOC) relative to the contact point with the interacting cell. Here we describe an automated, high throughput system to score repositioning of the MTOC and thereby cell polarity establishment. The method rapidly and accurately calculates the angle between the MTOC and the IS for thousands of cells. We also show that the system can be adapted to score anterior-posterior polarity establishment of epithelial cells. This general approach represents a significant advancement over manual cell polarity scoring, which is subject to experimenter bias and requires more time and effort to evaluate large numbers of cells

Automated analysis of Human Protein Atlas immunofluorescence images

The Human Protein Atlas is a rich source of location proteomics data. In this work, we present an automated approach for processing and classifying major subcellular patterns in the Atlas images. We demonstrate that two different classification frameworks (support vector machine and random forest) are effective at determining subcellular locations; we can analyze over 3500 Atlas images with a high degree of accuracy, up to 87.5 % for all of the samples and 98.5 % when only considering samples in whose classification assignments we are most confident. Moreover, the features obtained in both of these frameworks are observed to be highly consistent and generalizable. Additionally, we observe that the features relating the proteins to cell markers are especially important in automated learning approaches. Index Terms Image classification; microscopy; location proteomics; machine learning; feature selection 1

SB Driver Analysis: A \u3cem\u3eSleeping Beauty\u3c/em\u3e Cancer Driver Analysis Framework for Identifying and Prioritizing Experimentally Actionable Oncogenes and Tumor Suppressors

Cancer driver prioritization for functional analysis of potential actionable therapeutic targets is a significant challenge. Meta-analyses of mutated genes across different human cancer types for driver prioritization has reaffirmed the role of major players in cancer, including KRAS, TP53 and EGFR, but has had limited success in prioritizing genes with non-recurrent mutations in specific cancer types. Sleeping Beauty (SB) insertional mutagenesis is a powerful experimental gene discovery framework to define driver genes in mouse models of human cancers. Meta-analyses of SB datasets across multiple tumor types is a potentially informative approach to prioritize drivers, and complements efforts in human cancers. Here, we report the development of SB Driver Analysis, an in-silico method for defining cancer driver genes that positively contribute to tumor initiation and progression from population-level SB insertion data sets. We demonstrate that SB Driver Analysis computationally prioritizes drivers and defines distinct driver classes from end-stage tumors that predict their putative functions during tumorigenesis. SB Driver Analysis greatly enhances our ability to analyze, interpret and prioritize drivers from SB cancer datasets and will continue to substantially increase our understanding of the genetic basis of cancer

SB Driver Analysis: A \u3cem\u3eSleeping Beauty\u3c/em\u3e Cancer Driver Analysis Framework for Identifying and Prioritizing Experimentally Actionable Oncogenes and Tumor Suppressors

Cancer driver prioritization for functional analysis of potential actionable therapeutic targets is a significant challenge. Meta-analyses of mutated genes across different human cancer types for driver prioritization has reaffirmed the role of major players in cancer, including KRAS, TP53 and EGFR, but has had limited success in prioritizing genes with non-recurrent mutations in specific cancer types. Sleeping Beauty (SB) insertional mutagenesis is a powerful experimental gene discovery framework to define driver genes in mouse models of human cancers. Meta-analyses of SB datasets across multiple tumor types is a potentially informative approach to prioritize drivers, and complements efforts in human cancers. Here, we report the development of SB Driver Analysis, an in-silico method for defining cancer driver genes that positively contribute to tumor initiation and progression from population-level SB insertion data sets. We demonstrate that SB Driver Analysis computationally prioritizes drivers and defines distinct driver classes from end-stage tumors that predict their putative functions during tumorigenesis. SB Driver Analysis greatly enhances our ability to analyze, interpret and prioritize drivers from SB cancer datasets and will continue to substantially increase our understanding of the genetic basis of cancer

Example of sub-patterns identified by clustering.

<p>Proteins “neuronal pentraxin receptor” and “eukaryotic translation initiation factor 5″ were visually annotated as “cytoplasm,” but hierarchical clustering assigned them to separate clusters in the first round. The images indicate that they indeed display two cytoplasmic sub-patterns.</p

Examples of mis-annotated proteins identified by the hierarchical clustering reannotation method.

<p>(a) Protein “S100 calcium binding protein A12” was identified in the first round analysis. The image of the protein was visually annotated as “nucleus” but was annotated as “nucleus without nucleoli” by clustering. (b) Protein “Rho/Rac guanine nucleotide exchange factor (GEF) 2″ was identified in the second round analysis. The image of the protein was visually annotated as “nucleus” but was annotated as “nucleus without nucleoli” by clustering. In both cases the latter annotation was chosen upon re-examination.</p

Classification results before first round of reannotation.

<p>Cell level feature classification confusion matrix. Bold values indicate agreement between the classifier and the true class. Overall classification accuracy is 82.4%. The number of proteins in each class is shown in parenthesis after the class name.</p

Classification results before second round of reannotation.

<p>Cell level feature classification confusion matrix. Bold values indicate agreement between the classifier and the true class. Overall classification accuracy is 77.9%. The number of proteins in each class is shown in parenthesis after the class name.</p