Biasogram: visualization of confounding technical bias in gene expression data.

Gene expression profiles of clinical cohorts can be used to identify genes that are correlated with a clinical variable of interest such as patient outcome or response to a particular drug. However, expression measurements are susceptible to technical bias caused by variation in extraneous factors such as RNA quality and array hybridization conditions. If such technical bias is correlated with the clinical variable of interest, the likelihood of identifying false positive genes is increased. Here we describe a method to visualize an expression matrix as a projection of all genes onto a plane defined by a clinical variable and a technical nuisance variable. The resulting plot indicates the extent to which each gene is correlated with the clinical variable or the technical variable. We demonstrate this method by applying it to three clinical trial microarray data sets, one of which identified genes that may have been driven by a confounding technical variable. This approach can be used as a quality control step to identify data sets that are likely to yield false positive results

Identification of novel targets for breast cancer by exploring gene switches on a genome scale

<p>Abstract</p> <p>Background</p> <p>An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.</p> <p>Results</p> <p>We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.</p> <p>Conclusions</p> <p>Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.</p

The creatine kinase pathway is a metabolic vulnerability in EVI1-positive acute myeloid leukemia

Expression of the MECOM (also known as EVI1) proto-oncogene is deregulated by chromosomal translocations in some cases of acute myeloid leukemia (AML) and is associated with poor clinical outcome. Here, through transcriptomic and metabolomic profiling of hematopoietic cells, we reveal that EVI1 overexpression alters cellular metabolism. A screen using pooled short hairpin RNAs (shRNAs) identified the ATP-buffering, mitochondrial creatine kinase CKMT1 as necessary for survival of EVI1-expressing cells in subjects with EVI1-positive AML. EVI1 promotes CKMT1 expression by repressing the myeloid differentiation regulator RUNX1. Suppression of arginine-creatine metabolism by CKMT1-directed shRNAs or by the small molecule cyclocreatine selectively decreased the viability, promoted the cell cycle arrest and apoptosis of human EVI1-positive cell lines, and prolonged survival in both orthotopic xenograft models and mouse models of primary AML. CKMT1 inhibition altered mitochondrial respiration and ATP production, an effect that was abrogated by phosphocreatine-mediated reactivation of the arginine-creatine pathway. Targeting CKMT1 is thus a promising therapeutic strategy for this EVI1-driven AML subtype that is highly resistant to current treatment regimens. Keywords: AML; RUNX1; CKMT1; cyclocreatine; arginine metabolismNational Cancer Institute (U.S.) (NIH 1R35 CA210030-01)Stand Up To CancerBridge ProjectNational Cancer Institute (U.S.) (David H. Koch Institute for Integrative Cancer Research at MIT. Grant P30-CA14051

Biomedical Discovery Acceleration, with Applications to Craniofacial Development

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work

Human cytomegalovirus immediate-early 1 protein rewires upstream STAT3 to downstream STAT1 signaling switching an IL6-type to an IFNγ-like response

MN and CP were supported by the Wellcome Trust (www.wellcome.ac.uk) Institutional Strategic Support Fund and CP was supported by the Deutsche Forschungsgemeinschaft (PA 815/2-1; www.dfg.de).The human cytomegalovirus (hCMV) major immediate-early 1 protein (IE1) is best known for activating transcription to facilitate viral replication. Here we present transcriptome data indicating that IE1 is as significant a repressor as it is an activator of host gene expression. Human cells induced to express IE1 exhibit global repression of IL6- and oncostatin M-responsive STAT3 target genes. This repression is followed by STAT1 phosphorylation and activation of STAT1 target genes normally induced by IFNγ. The observed repression and subsequent activation are both mediated through the same region (amino acids 410 to 445) in the C-terminal domain of IE1, and this region serves as a binding site for STAT3. Depletion of STAT3 phenocopies the STAT1-dependent IFNγ-like response to IE1. In contrast, depletion of the IL6 receptor (IL6ST) or the STAT kinase JAK1 prevents this response. Accordingly, treatment with IL6 leads to prolonged STAT1 instead of STAT3 activation in wild-type IE1 expressing cells, but not in cells expressing a mutant protein (IE1dl410-420) deficient for STAT3 binding. A very similar STAT1-directed response to IL6 is also present in cells infected with a wild-type or revertant hCMV, but not an IE1dl410-420 mutant virus, and this response results in restricted viral replication. We conclude that IE1 is sufficient and necessary to rewire upstream IL6-type to downstream IFNγ-like signaling, two pathways linked to opposing actions, resulting in repressed STAT3- and activated STAT1-responsive genes. These findings relate transcriptional repressor and activator functions of IE1 and suggest unexpected outcomes relevant to viral pathogenesis in response to cytokines or growth factors that signal through the IL6ST-JAK1-STAT3 axis in hCMV-infected cells. Our results also reveal that IE1, a protein considered to be a key activator of the hCMV productive cycle, has an unanticipated role in tempering viral replication.Publisher PDFPeer reviewe

Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

<p>Abstract</p> <p>Background</p> <p>A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs). MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence.</p> <p>Results</p> <p>We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM²): an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM) algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM² substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods.</p> <p>Conclusions</p> <p>This work provides a novel, accelerated version of a likelihood-based parameter estimation method that can be readily applied to stochastic biochemical systems. In addition, our results suggest opportunities for added efficiency improvements that will further enhance our ability to mechanistically simulate biological processes.</p