P42 Ebp1 functions as a tumor suppressor in non-small cell lung cancer

Although the short isoform of ErbB3-binding protein 1 (Ebp1), p42 has been considered to be a potent tumor suppressor in a number of human cancers, whether p42 suppresses tumorigenesis of lung cancer cells has never been clarified. In the current study we investigated the tumor suppressor role of p42 in non-small cell lung cancer cells. Our data suggest that the expression level of p42 is inversely correlated with the cancerous properties of NSCLC cells and that ectopic expression of p42 is sufficient to inhibit cell proliferation, anchorage-independent growth, and invasion as well as tumor growth in vivo. Interestingly, p42 suppresses Akt activation and overexpression of a constitutively active form of Akt restores the tumorigenic activity of A549 cells that is ablated by exogenous p42 expression. Thus, we propose that p42 Ebp1 functions as a potent tumor suppressor of NSCLC through interruption of Akt signaling. [BMB Reports 2015; 48(3): 159-165]     

Distribution and morphology of ghrelin immunostained cells in the adrenal gland of the African ostrich

Ghrelin is the endogenous ligand for the growth hormone secretagogue receptor. We investigated the distribution and morphological characteristics of ghrelin-immunopositive (ghrelin-ip) cells in the African ostrich adrenal gland. We found that the adrenal gland of the African ostrich consisted of three parts: capsule, inter-renal tissue and chromaffin cells. The inter-renal tissue and chromaffin cells interdigitated irregularly. The inter-renal tissue consisted of a peripheral zone and a central inner zone. The peripheral zone could be divided into an outer subcapsular zone and an inner zone. The subcapsular zone cells were arranged as a bow, while the inner area cells formed cords that were perpendicular to the capsule. The central inner zone exhibited irregular clumps and the cells were morphologically similar to chromaffin cells. Ghrelin-ip cells were located throughout the adrenal gland except the capsule. The majority of ghrelin-ip cells were found among the chromaffin cells. The number of ghrelin-ip cells in the inter-renal tissue decreased gradually from the central inner zone, to the inner zone to the subcapsular zone. The ghrelin-ip cells were oval or irregular in shape and exhibited cytoplasmic staining. Our findings suggest that ghrelin may play a role in regulating adrenal hormone secretion in the African ostrich.     

Power analysis and sample size estimation for RNA-Seq differential expression

It is crucial for researchers to optimize RNA-seq experimental designs for differential expression detection. Currently, the field lacks general methods to estimate power and sample size for RNA-Seq in complex experimental designs, under the assumption of the negative binomial distribution. We simulate RNA-Seq count data based on parameters estimated from six widely different public data sets (including cell line comparison, tissue comparison, and cancer data sets) and calculate the statistical power in paired and unpaired sample experiments. We comprehensively compare five differential expression analysis packages (DESeq, edgeR, DESeq2, sSeq, and EBSeq) and evaluate their performance by power, receiver operator characteristic (ROC) curves, and other metrics including areas under the curve (AUC), Matthews correlation coefficient (MCC), and F-measures. DESeq2 and edgeR tend to give the best performance in general. Increasing sample size or sequencing depth increases power; however, increasing sample size is more potent than sequencing depth to increase power, especially when the sequencing depth reaches 20 million reads. Long intergenic noncoding RNAs (lincRNA) yields lower power relative to the protein coding mRNAs, given their lower expression level in the same RNA-Seq experiment. On the other hand, paired-sample RNA-Seq significantly enhances the statistical power, confirming the importance of considering the multifactor experimental design. Finally, a local optimal power is achievable for a given budget constraint, and the dominant contributing factor is sample size rather than the sequencing depth. In conclusion, we provide a power analysis tool (http://www2.hawaii.edu/~lgarmire/RNASeqPowerCalculator.htm) that captures the dispersion in the data and can serve as a practical reference under the budget constraint of RNA-Seq experiments.     

mirMark: a site-level and UTR-level classifier for miRNA target prediction

MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features. By combining Correlation-based Feature Selection with a variety of statistical or machine learning methods for the site- and UTR-level classifiers, mirMark significantly improves the overall predictive performance compared to existing publicly available methods. MirMark is available from https://github.com/lanagarmire/MirMark.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0500-5) contains supplementary material, which is available to authorized users.     

Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data

Annotating cell types is a critical step in single-cell RNA sequencing(scRNA-seq) data analysis. Some supervised or semi-supervised classification methods have recently emerged to enable automated cell type identification. However, comprehensive evaluations of these methods are lacking. Moreover, it is not clear whether some classification methods originally designed for analyzing other bulk omics data are adaptable to scRNA-seq analysis. In this study, we evaluated ten cell type annotation methods publicly available as R packages. Eight of them are popular methods developed specifically for single-cell research, including Seurat, scmap, SingleR, CHETAH, SingleCellNet, scID, Garnett, and SCINA. The other two methods were repurposed from deconvoluting DNA methylation data, i.e., linear constrained projection(CP) and robust partial correlations(RPC). We conducted systematic comparisons on a wide variety of public scRNA-seq datasets as well as simulation data. We assessed the accuracy through intra-dataset and inter-dataset predictions; the robustness over practical challenges such as gene filtering, high similarity among cell types, and increased cell type classes; as well as the detection of rare and unknown cell types. Overall, methods such as Seurat, SingleR, CP, RPC, and SingleCellNet performed well, with Seurat being the best at annotating major cell types. Additionally, Seurat, SingleR, CP, and RPC were more robust against downsampling. However, Seurat did have a major drawback at predicting rare cell populations, and it was suboptimal at differentiating cell types highly similar to each other,compared to SingleR and RPC. All the code and data are available from https://github.com/qianhuiSenn/scRNA_cell_deconv_benchmark.     

A Novel Model to Combine Clinical and Pathway-Based Transcriptomic Information for the Prognosis Prediction of Breast Cancer

Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS) based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g. P53 pathway) that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12) and three testing data sets (log rank p-value<0.0005). Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.