Characterization of BTBD1 and BTBD2, two similar BTB-domain-containing Kelch-like proteins that interact with Topoisomerase I

BACKGROUND: Two-hybrid screening for proteins that interact with the core domain of human topoisomerase I identified two novel proteins, BTBD1 and BTBD2, which share 80% amino acid identities. RESULTS: The interactions were confirmed by co-precipitation assays demonstrating the physical interaction of BTBD1 and BTBD2 with 100 kDa topoisomerase I from HeLa cells. Deletion mapping using two-hybrid and GST-pulldown assays demonstrated that less than the C-terminal half of BTBD1 is sufficient for binding topoisomerase I. The topoisomerase I sequences sufficient to bind BTBD2 were mapped to residues 215 to 329. BTBD2 with an epitope tag localized to cytoplasmic bodies. Using truncated versions that direct BTBD2 and TOP1 to the same cellular compartment, either the nucleus or the cytoplasm, co-localization was demonstrated in co-transfected Hela cells. The supercoil relaxation and DNA cleavage activities of topoisomerase I in vitro were affected little or none by co-incubation with BTBD2. Northern analysis revealed only a single sized mRNA for each BTBD1 and BTBD2 in all human tissues tested. Characterization of BTBD2 mRNA revealed a 255 nucleotide 90% GC-rich region predicted to encode the N-terminus. BTBD1 and BTBD2 are widely if not ubiquitously expressed in human tissues, and have two paralogs as well as putative orthologs in C. elegans and D. melanogaster. CONCLUSIONS: BTBD1 and BTBD2 belong to a small family of uncharacterized proteins that appear to be specific to animals. Epitope-tagged BTBD2 localized to cytoplasmic bodies. The characterization of BTBD1 and BTBD2 and their interaction with TOP1 is underway

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer

Multivariate Analysis and Visualization of Splicing Correlations in Single-Gene Transcriptomes

BACKGROUND: RNA metabolism, through 'combinatorial splicing', can generate enormous structural diversity in the proteome. Alternative domains may interact, however, with unpredictable phenotypic consequences, necessitating integrated RNA-level regulation of molecular composition. Splicing correlations within transcripts of single genes provide valuable clues to functional relationships among molecular domains as well as genomic targets for higher-order splicing regulation. RESULTS: We present tools to visualize complex splicing patterns in full-length cDNA libraries. Developmental changes in pair-wise correlations are presented vectorially in 'clock plots' and linkage grids. Higher-order correlations are assessed statistically through Monte Carlo analysis of a log-linear model with an empirical-Bayes estimate of the true probabilities of observed and unobserved splice forms. Log-linear coefficients are visualized in a 'spliceprint,' a signature of splice correlations in the transcriptome. We present two novel metrics: the linkage change index, which measures the directional change in pair-wise correlation with tissue differentiation, and the accuracy index, a very simple goodness-of-fit metric that is more sensitive than the integrated squared error when applied to sparsely populated tables, and unlike chi-square, does not diverge at low variance. Considerable attention is given to sparse contingency tables, which are inherent to single-gene libraries. CONCLUSION: Patterns of splicing correlations are revealed, which span a broad range of interaction order and change in development. The methods have a broad scope of applicability, beyond the single gene – including, for example, multiple gene interactions in the complete transcriptome