Comparative analysis of the human serine hydrolase OVCA2 to the model serine hydrolase homolog FSH1 from S. cerevisiae.

Over 100 metabolic serine hydrolases are present in humans with confirmed functions in metabolism, immune response, and neurotransmission. Among potentially clinically-relevant but uncharacterized human serine hydrolases is OVCA2, a serine hydrolase that has been linked with a variety of cancer-related processes. Herein, we developed a heterologous expression system for OVCA2 and determined the comprehensive substrate specificity of OVCA2 against two ester substrate libraries. Based on this analysis, OVCA2 was confirmed as a serine hydrolase with a strong preference for long-chain alkyl ester substrates (>10-carbons) and high selectivity against a variety of short, branched, and substituted esters. Substitutional analysis was used to identify the catalytic residues of OVCA2 with a Ser117-His206-Asp179 classic catalytic triad. Comparison of the substrate specificity of OVCA2 to the model homologue FSH1 from Saccharomyces cerevisiae illustrated the tighter substrate selectivity of OVCA2, but their overlapping substrate preference for extended straight-chain alkyl esters. Conformation of the overlapping biochemical properties of OVCA2 and FSH1 was used to model structural information about OVCA2. Together our analysis provides detailed substrate specificity information about a previously, uncharacterized human serine hydrolase and begins to define the biological properties of OVCA2.

Characterizing heterogeneous cellular responses to perturbations.

Cellular populations have been widely observed to respond heterogeneously to perturbation. However, interpreting the observed heterogeneity is an extremely challenging problem because of the complexity of possible cellular phenotypes, the large dimension of potential perturbations, and the lack of methods for separating meaningful biological information from noise. Here, we develop an image-based approach to characterize cellular phenotypes based on patterns of signaling marker colocalization. Heterogeneous cellular populations are characterized as mixtures of phenotypically distinct subpopulations, and responses to perturbations are summarized succinctly as probabilistic redistributions of these mixtures. We apply our method to characterize the heterogeneous responses of cancer cells to a panel of drugs. We find that cells treated with drugs of (dis-)similar mechanism exhibit (dis-)similar patterns of heterogeneity. Despite the observed phenotypic diversity of cells observed within our data, low-complexity models of heterogeneity were sufficient to distinguish most classes of drug mechanism. Our approach offers a computational framework for assessing the complexity of cellular heterogeneity, investigating the degree to which perturbations induce redistributions of a limited, but nontrivial, repertoire of underlying states and revealing functional significance contained within distinct patterns of heterogeneous responses.

Systems-level analyses identify extensive coupling among gene expression machines.

Here, we develop computational methods to assess and consolidate large, diverse protein interaction data sets, with the objective of identifying proteins involved in the coupling of multicomponent complexes within the yeast gene expression pathway. From among approximately 43 000 total interactions and 2100 proteins, our methods identify known structural complexes, such as the spliceosome and SAGA, and functional modules, such as the DEAD-box helicases, within the interaction network of proteins involved in gene expression. Our process identifies and ranks instances of three distinct, biologically motivated motifs, or patterns of coupling among distinct machineries involved in different subprocesses of gene expression. Our results confirm known coupling among transcription, RNA processing, and export, and predict further coupling with translation and nonsense-mediated decay. We systematically corroborate our analysis with two independent, comprehensive experimental data sets. The methods presented here may be generalized to other biological processes and organisms to generate principled, systems-level network models that provide experimentally testable hypotheses for coupling among biological machines.

Genome-scale identification of nucleosome positions in S. cerevisiae.

The positioning of nucleosomes along chromatin has been implicated in the regulation of gene expression in eukaryotic cells, because packaging DNA into nucleosomes affects sequence accessibility. We developed a tiled microarray approach to identify at high resolution the translational positions of 2278 nucleosomes over 482 kilobases of Saccharomyces cerevisiae DNA, including almost all of chromosome III and 223 additional regulatory regions. The majority of the nucleosomes identified were well-positioned. We found a stereotyped chromatin organization at Pol II promoters consisting of a nucleosome-free region approximately 200 base pairs upstream of the start codon flanked on both sides by positioned nucleosomes. The nucleosome-free sequences were evolutionarily conserved and were enriched in poly-deoxyadenosine or poly-deoxythymidine sequences. Most occupied transcription factor binding motifs were devoid of nucleosomes, strongly suggesting that nucleosome positioning is a global determinant of transcription factor access.

Multidimensional drug profiling by automated microscopy.

We present a method for high-throughput cytological profiling by microscopy. Our system provides quantitative multidimensional measures of individual cell states over wide ranges of perturbations. We profile dose-dependent phenotypic effects of drugs in human cell culture with a titration-invariant similarity score (TISS). This method successfully categorized blinded drugs and suggested targets for drugs of uncertain mechanism. Multivariate single-cell analysis is a starting point for identifying relationships among drug effects at a systems level and a step toward phenotypic profiling at the single-cell level. Our methods will be useful for discovering the mechanism and predicting the toxicity of new drugs.