Global analysis of phase locking in gene expression during cell cycle: the potential in network modeling

<p>Abstract</p> <p>Background</p> <p>In nonlinear dynamic systems, synchrony through oscillation and frequency modulation is a general control strategy to coordinate multiple modules in response to external signals. Conversely, the synchrony information can be utilized to infer interaction. Increasing evidence suggests that frequency modulation is also common in transcription regulation.</p> <p>Results</p> <p>In this study, we investigate the potential of phase locking analysis, a technique to study the synchrony patterns, in the transcription network modeling of time course gene expression data. Using the yeast cell cycle data, we show that significant phase locking exists between transcription factors and their targets, between gene pairs with prior evidence of physical or genetic interactions, and among cell cycle genes. When compared with simple correlation we found that the phase locking metric can identify gene pairs that interact with each other more efficiently. In addition, it can automatically address issues of arbitrary time lags or different dynamic time scales in different genes, without the need for alignment. Interestingly, many of the phase locked gene pairs exhibit higher order than 1:1 locking, and significant phase lags with respect to each other. Based on these findings we propose a new phase locking metric for network reconstruction using time course gene expression data. We show that it is efficient at identifying network modules of focused biological themes that are important to cell cycle regulation.</p> <p>Conclusions</p> <p>Our result demonstrates the potential of phase locking analysis in transcription network modeling. It also suggests the importance of understanding the dynamics underlying the gene expression patterns.</p

A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis

BackgroundThe overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-ΔF508) accounts for most of the disease. In cell models, CFTR-ΔF508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-ΔF508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-ΔF would manifest phenotypically.MethodsTo gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC transporter, we employed yeast genetics to develop a 'phenomic' model, in which the CFTR-ΔF508-equivalent residue of a yeast homolog is mutated (Yor1-ΔF670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-ΔF undergoes protein misfolding and has reduced half-life, analogous to CFTR-ΔF. Gene interaction was then assessed quantitatively by growth curves for approximately 5,000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1.ResultsFrom a comparative genomic perspective, yeast gene interactions influencing Yor1-ΔF biogenesis were representative of human homologs previously found to modulate processing of CFTR-ΔF in mammalian cells. Additional evolutionarily conserved pathways were implicated by the study, and a ΔF-specific pro-biogenesis function of the recently discovered ER membrane complex (EMC) was evident from the yeast screen. This novel function was validated biochemically by siRNA of an EMC ortholog in a human cell line expressing CFTR-ΔF508. The precision and accuracy of quantitative high throughput cell array phenotyping (Q-HTCP), which captures tens of thousands of growth curves simultaneously, provided powerful resolution to measure gene interaction on a phenomic scale, based on discrete cell proliferation parameters.ConclusionWe propose phenomic analysis of Yor1-ΔF as a model for investigating gene interaction networks that can modulate cystic fibrosis disease severity. Although the clinical relevance of the Yor1-ΔF gene interaction network for cystic fibrosis remains to be defined, the model appears to be informative with respect to human cell models of CFTR-ΔF. Moreover, the general strategy of yeast phenomics can be employed in a systematic manner to model gene interaction for other diseases relating to pathologies that result from protein misfolding or potentially any disease involving evolutionarily conserved genetic pathways

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

2006 MULTIMODALITY IMAGE REGISTRATION

Image registration has been a major area of research in the last few decades. It is a complex problem that involves handling large data sets. The relation between the real object and the image is very intricate and encompasses the object’s shape, surface properties, position, and illumination. In this thesis, a general solution to this problem was devised that can be applied to multimodality and within-modality image registration. The approach followed here is such that various factors such as illumination do not affect the algorithm. The algorithm considers the intensity of the images as raw data for the registration. The method presented in this research applies mutual information (MI) to measure the information redundancy. The redundancy is calculated between the image intensities of corresponding voxels in both images. If the images are aligned with each other closely, then the MI is assumed to be maximal. Maximization of MI is a powerful criterion for image registration. The result of the image registration algorithm is validated for computed tomography and magnetic resonance imaging by comparing with a sub-pixel registration based on intensity. Image registration is obtained automatically, as well as manually, without following any preprocessing steps such as segmentation and feature selection. ii DEDICATION To my parents for making it all possible iii ACKNOWLEDGMENTS The following contributed to the writer’s understanding and supported the research with helpful suggestions and information. Professor Murat M. Tanik, Committee Chair, for assisting in the understanding of entropy and mutual information Drs. Gary Grimes and John Hartman, committee members, for helpful suggestion

A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis

Background: The overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-ΔF508) accounts for most of the disease. In cell models, CFTR-ΔF508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-ΔF508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-ΔF would manifest phenotypically. Methods To gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC transporter, we employed yeast genetics to develop a 'phenomic' model, in which the CFTR-ΔF508-equivalent residue of a yeast homolog is mutated (Yor1-ΔF670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-ΔF undergoes protein misfolding and has reduced half-life, analogous to CFTR-ΔF. Gene interaction was then assessed quantitatively by growth curves for approximately 5,000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1. Results From a comparative genomic perspective, yeast gene interactions influencing Yor1-ΔF biogenesis were representative of human homologs previously found to modulate processing of CFTR-ΔF in mammalian cells. Additional evolutionarily conserved pathways were implicated by the study, and a ΔF-specific pro-biogenesis function of the recently discovered ER membrane complex (EMC) was evident from the yeast screen. This novel function was validated biochemically by siRNA of an EMC ortholog in a human cell line expressing CFTR-ΔF508. The precision and accuracy of quantitative high throughput cell array phenotyping (Q-HTCP), which captures tens of thousands of growth curves simultaneously, provided powerful resolution to measure gene interaction on a phenomic scale, based on discrete cell proliferation parameters. Conclusion We propose phenomic analysis of Yor1-ΔF as a model for investigating gene interaction networks that can modulate cystic fibrosis disease severity. Although the clinical relevance of the Yor1-ΔF gene interaction network for cystic fibrosis remains to be defined, the model appears to be informative with respect to human cell models of CFTR-ΔF. Moreover, the general strategy of yeast phenomics can be employed in a systematic manner to model gene interaction for other diseases relating to pathologies that result from protein misfolding or potentially any disease involving evolutionarily conserved genetic pathways.Science, Faculty ofStatistics, Department ofNon UBCReviewedFacult

The SWI/SNF Chromatin Remodeling Complex Influences Transcription by RNA Polymerase I in <em>Saccharomyces cerevisiae</em>

<div><p>SWI/SNF is a chromatin remodeling complex that affects transcription initiation and elongation by RNA polymerase II. Here we report that SWI/SNF also plays a role in transcription by RNA polymerase I (Pol I) in <i>Saccharomyces cerevisiae</i>. Deletion of the genes encoding the Snf6p or Snf5p subunits of SWI/SNF was lethal in combination with mutations that impair Pol I transcription initiation and elongation. SWI/SNF physically associated with ribosomal DNA (rDNA) within the coding region, with an apparent peak near the 5′ end of the gene. In <i>snf6</i>Δ cells there was a ∼2.5-fold reduction in rRNA synthesis rate compared to WT, but there was no change in average polymerase occupancy per gene, the number of rDNA gene repeats, or the percentage of transcriptionally active rDNA genes. However, both ChIP and EM analyses showed a small but reproducible increase in Pol I density in a region near the 5′ end of the gene. Based on these data, we conclude that SWI/SNF plays a positive role in Pol I transcription, potentially by modifying chromatin structure in the rDNA repeats. Our findings demonstrate that SWI/SNF influences the most robust transcription machinery in proliferating cells.</p> </div

Strains used in this study.

<p>Strains used in this study.</p

A small accumulation of Pol I complexes in the 5′ end of rDNA in <i>snf6</i>Δ.

<p>(A) Distribution frequency for the number of polymerases per gene was revealed by EM analysis of Miller chromatin spreads in <i>snf6</i>Δ (DAS647) and WT (DAS648). (B) More than 100 rDNA genes from Miller chromatin spreads were analyzed in WT and <i>snf6</i>Δ. Pol I density and percentage of actively transcribed genes in <i>snf6</i>Δ and WT are similar. (C) Polymerase occupancy as a function of position within the transcribed region of rDNA in <i>snf6</i>Δ and WT cells. A small peak of Pol I occupancy in the 5′ end of rDNA in <i>snf6</i>Δ is indicated by an asterisk. All mappable genes in the dataset were analyzed, corresponding to >60 genes per strain and >2500 polymerases per strain. Schematic below the X-axis represents the Pol I transcribed region of the rDNA. (D) Same as C but an additional two WT control strains (NOY388 and BY4741) are plotted. BY4741 data are from El Hage et al. 2010 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056793#pone.0056793-ElHage1" target="_blank">[34]</a>.</p