Inferring Genetic Interactions via a Data-Driven Second Order Model

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R3) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm

Inferring genetic interactions via a nonlinear model and an optimization algorithm

<p>Abstract</p> <p>Background</p> <p>Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.</p> <p>Results</p> <p>An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in <it>S. cerevisiae</it>, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.</p> <p>Conclusions</p> <p>GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.</p

WebPARE: web-computing for inferring genetic or transcriptional interactions

Summary: Inferring genetic or transcriptional interactions, when done successfully, may provide insights into biological processes or biochemical pathways of interest. Unfortunately, most computational algorithms require a certain level of programming expertise. To provide a simple web interface for users to infer interactions from time course gene expression data, we present WebPARE, which is based on the pattern recognition algorithm (PARE). For expression data, in which each type of interaction (e.g. activator target) and the corresponding paired gene expression pattern are significantly associated, PARE uses a non-linear score to classify gene pairs of interest into a few subclasses of various time lags. In each subclass, PARE learns the parameters in the decision score using known interactions from biological experiments or published literature. Subsequently, the trained algorithm predicts interactions of a similar nature. Previously, PARE was shown to infer two sets of interactions in yeast successfully. Moreover, several predicted genetic interactions coincided with existing pathways; this indicates the potential of PARE in predicting partial pathway components. Given a list of gene pairs or genes of interest and expression data, WebPARE invokes PARE and outputs predicted interactions and their networks in directed graphs

Corneal perforation in ocular graft-versus-host disease

PURPOSE: Corneal perforation is a rare, vision-threatening complication of ocular graft-versus-host disease (GVHD) and is not well understood. Our objective was to examine the clinical disease course and histopathologic correlation in patients who progressed to this outcome. METHODS: This study is a retrospective case series from four academic centers in the United States. All patients received a hematopoietic stem cell transplant (HSCT) prior to developing ocular GVHD. Variables of interest included patient demographics, time interval between HSCT and ocular events, visual acuity throughout clinical course, corticosteroid and infection prophylaxis regimens at time of corneal perforation, medical/surgical interventions, and histopathology. RESULTS: Fourteen eyes from 14 patients were analyzed. Most patients were male (86%) and Caucasian (86%), and average age at time of hematopoietic stem cell transplant was 47 years. The mean interval between hematopoietic stem cell transplant and diagnosis of ocular graft-versus-host disease was 9.5 months, and between hematopoietic stem cell transplant and corneal perforation was 37 months. Initial best-corrected visual acuity was 20/40 or better in 9 eyes, and all eyes had moderate or poor visual outcomes despite aggressive management, including corneal gluing in all patients followed by keratoplasty in 8 patients. The mean follow-up after perforation was 34 months (range 2-140 months). Oral prednisone was used prior to perforation in 11 patients (79%). On histopathology, representative specimens in the acute phase demonstrated ulcerative keratitis with perforation but minimal inflammatory cells and no microorganisms, consistent with sterile corneal melt in the setting of immunosuppression; and in the healed phase, filling in of the perforation site with fibrous scar. CONCLUSIONS: In these patients, an extended time interval was identified between the diagnosis of ocular graft-versus-host disease and corneal perforation. This represents a critical window to potentially prevent this devastating outcome. Further study is required to identify those patients at greatest risk as well as to optimize prevention strategies

Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling

<p>Abstract</p> <p>Background</p> <p>With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality.</p> <p>Results</p> <p>Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted.</p> <p>Conclusion</p> <p>SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.</p

H2B ubiquitylation is part of chromatin architecture that marks exon-intron structure in budding yeast

<p>Abstract</p> <p>Background</p> <p>The packaging of DNA into chromatin regulates transcription from initiation through 3' end processing. One aspect of transcription in which chromatin plays a poorly understood role is the co-transcriptional splicing of pre-mRNA.</p> <p>Results</p> <p>Here we provide evidence that H2B monoubiquitylation (H2BK123ub1) marks introns in <it>Saccharomyces cerevisiae</it>. A genome-wide map of H2BK123ub1 in this organism reveals that this modification is enriched in coding regions and that its levels peak at the transcribed regions of two characteristic subgroups of genes. First, long genes are more likely to have higher levels of H2BK123ub1, correlating with the postulated role of this modification in preventing cryptic transcription initiation in ORFs. Second, genes that are highly transcribed also have high levels of H2BK123ub1, including the ribosomal protein genes, which comprise the majority of intron-containing genes in yeast. H2BK123ub1 is also a feature of introns in the yeast genome, and the disruption of this modification alters the intragenic distribution of H3 trimethylation on lysine 36 (H3K36me3), which functionally correlates with alternative RNA splicing in humans. In addition, the deletion of genes encoding the U2 snRNP subunits, Lea1 or Msl1, in combination with an <it>htb-K123R </it>mutation, leads to synthetic lethality.</p> <p>Conclusion</p> <p>These data suggest that H2BK123ub1 facilitates cross talk between chromatin and pre-mRNA splicing by modulating the distribution of intronic and exonic histone modifications.</p

POD Analysis of Cavity Flow Instability

A Mach 1.5 turbulent cavity flow develops large-amplitude oscillations, pressure drag and noise. This type of flow instability affects practical engineering applications, such as aircraft store bays. A simple model of the flow instability is sought towards developing a real-time model-based active control system for simple geometries, representative of open aircraft store bays. An explicit time marching second-order accurate finite-volume scheme has been used to generate time-dependent benchmark cavity flow data. Then, a simpler and leaner numerical predictor for the unsteady cavity pressure was developed, based on a Proper Orthogonal Decomposition of the benchmark data. The low order predictor gives pressure oscillations in good agreement with the benchmark CFD method. This result highlights the importance of large-scale phase-coherent structures in the Mach 1.5 turbulent cavity flow. At the selected test conditions, the significant pressure ‘energy’ content of these structures enabled an effective reduced order model of the cavity dynamic system. Directions and methods to further streamline and simplify the unsteady pressure predictor have been highlighted