Importance of replication in analyzing time-series gene expression data: Corticosteroid dynamics and circadian patterns in rat liver

<p>Abstract</p> <p>Background</p> <p>Microarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses. However, experimental data usually contain potential sources of uncertainty and thus many experiments are now designed with repeated measurements to better assess such inherent variability. Many computational methods have been proposed to account for the variability in replicates. As yet, there is no model to output expression profiles accounting for replicate information so that a variety of computational models that take the expression profiles as the input data can explore this information without any modification.</p> <p>Results</p> <p>We propose a methodology which integrates replicate variability into expression profiles, to generate so-called 'true' expression profiles. The study addresses two issues: (i) develop a statistical model that can estimate 'true' expression profiles which are more robust than the average profile, and (ii) extend our previous micro-clustering which was designed specifically for clustering time-series expression data. The model utilizes a previously proposed error model and the concept of 'relative difference'. The clustering effectiveness is demonstrated through synthetic data where several methods are compared. We subsequently analyze <it>in vivo </it>rat data to elucidate circadian transcriptional dynamics as well as liver-specific corticosteroid induced changes in gene expression.</p> <p>Conclusions</p> <p>We have proposed a model which integrates the error information from repeated measurements into the expression profiles. Through numerous synthetic and real time-series data, we demonstrated the ability of the approach to improve the clustering performance and assist in the identification and selection of informative expression motifs.</p

Comparative analysis of acute and chronic corticosteroid pharmacogenomic effects in rat liver: Transcriptional dynamics and regulatory structures

<p>Abstract</p> <p>Background</p> <p>Comprehensively understanding corticosteroid pharmacogenomic effects is an essential step towards an insight into the underlying molecular mechanisms for both beneficial and detrimental clinical effects. Nevertheless, even in a single tissue different methods of corticosteroid administration can induce different patterns of expression and regulatory control structures. Therefore, rich <it>in vivo </it>datasets of pharmacological time-series with two dosing regimens sampled from rat liver are examined for temporal patterns of changes in gene expression and their regulatory commonalities.</p> <p>Results</p> <p>The study addresses two issues, including (1) identifying significant transcriptional modules coupled with dynamic expression patterns and (2) predicting relevant common transcriptional controls to better understand the underlying mechanisms of corticosteroid adverse effects. Following the orientation of meta-analysis, an extended computational approach that explores the concept of agreement matrix from consensus clustering has been proposed with the aims of identifying gene clusters that share common expression patterns across multiple dosing regimens as well as handling challenges in the analysis of microarray data from heterogeneous sources, e.g. different platforms and time-grids in this study. Six significant transcriptional modules coupled with typical patterns of expression have been identified. Functional analysis reveals that virtually all enriched functions (gene ontologies, pathways) in these modules are shown to be related to metabolic processes, implying the importance of these modules in adverse effects under the administration of corticosteroids. Relevant putative transcriptional regulators (e.g. RXRF, FKHD, SP1F) are also predicted to provide another source of information towards better understanding the complexities of expression patterns and the underlying regulatory mechanisms of those modules.</p> <p>Conclusions</p> <p>We have proposed a framework to identify significant coexpressed clusters of genes across multiple conditions experimented from different microarray platforms, time-grids, and also tissues if applicable. Analysis on rich <it>in vivo </it>datasets of corticosteroid time-series yielded significant insights into the pharmacogenomic effects of corticosteroids, especially the relevance to metabolic side-effects. This has been illustrated through enriched metabolic functions in those transcriptional modules and the presence of GRE binding motifs in those enriched pathways, providing significant modules for further analysis on pharmacogenomic corticosteroid effects.</p

Circadian signatures in rat liver: from gene expression to pathways

<p>Abstract</p> <p>Background</p> <p>Circadian rhythms are 24 hour oscillations in many behavioural, physiological, cellular and molecular processes that are controlled by an endogenous clock which is entrained to environmental factors including light, food and stress. Transcriptional analyses of circadian patterns demonstrate that genes showing circadian rhythms are part of a wide variety of biological pathways.</p> <p>Pathway activity method can identify the significant pattern of the gene expression levels within a pathway. In this method, the overall gene expression levels are translated to a reduced form, pathway activity levels, via singular value decomposition (SVD). A given pathway represented by pathway activity levels can then be as analyzed using the same approaches used for analyzing gene expression levels. We propose to use pathway activity method across time to identify underlying circadian pattern of pathways.</p> <p>Results</p> <p>We used synthetic data to demonstrate that pathway activity analysis can evaluate the underlying circadian pattern within a pathway even when circadian patterns cannot be captured by the individual gene expression levels. In addition, we illustrated that pathway activity formulation should be coupled with a significance analysis to distinguish biologically significant information from random deviations. Next, we performed pathway activity level analysis on a rich time series of transcriptional profiling in rat liver. The over-represented five specific patterns of pathway activity levels, which cannot be explained by random event, exhibited circadian rhythms. The identification of the circadian signatures at the pathway level identified 78 pathways related to energy metabolism, amino acid metabolism, lipid metabolism and DNA replication and protein synthesis, which are biologically relevant in rat liver. Further, we observed tight coordination between cholesterol biosynthesis and bile acid biosynthesis as well as between folate biosynthesis, one carbon pool by folate and purine-pyrimidine metabolism. These coupled pathways are parts of a sequential reaction series where the product of one pathway is the substrate of another pathway.</p> <p>Conclusions</p> <p>Rather than assessing the importance of a single gene beforehand and map these genes onto pathways, we instead examined the orchestrated change within a pathway. Pathway activity level analysis could reveal the underlying circadian dynamics in the microarray data with an unsupervised approach and biologically relevant results were obtained.</p

Utilizing Human Factors to Improve Perioperative Adverse Event Investigations: An Integrated Approach

Objective: Apply Human Factors (HF), systems engineering, and high reliability organizational principles to improve adverse event investigations in a regional hospital system. Background: Given the complexity of medicine and healthcare systems, innovative thinking is required to ensure these systems are resilient to error. Understanding the work system and its constituent parts is fundamental to understanding how errors begin and propagate. Method: This paper provides a discussion on employing a systems-based approach to improve perioperative adverse event investigations within a hospital system. Results: Data was collected across 13 investigations. The findings are summarized into 16 contributing factors, with 10 specific examples of critical/serious risks that were addressed by the hospital system. Conclusion: Modern medicine needs to look to HF to improve safety and reduce errors. This manuscript provides a systems-based approach grounded in HF and organizational theories to improve how investigations are conducted and the approach to human error within a large hospital system. Application: This work provides practical guidance for those who want to improve postoperative investigations within their own units or hospitals. Precis: This article describes research that evolves the approach to accident investigation to improve perioperative adverse event investigations in hospital settings

Identification of Global Transcriptional Dynamics

One of the challenges in exploiting high throughput measurement techniques such as microarrays is the conversion of the vast amounts of data obtained into relevant knowledge. Of particular importance is the identification of the intrinsic response of a transcriptional experiment and the characterization of the underlying dynamics.The proposed algorithm seeks to provide the researcher a summary as to various aspects relating to the dynamic progression of a biological system, rather than that of individual genes. The approach is based on the identification of smaller number of expression motifs that define the transcriptional state of the system which quantifies the deviation of the cellular response from a control state in the presence of an external perturbation. The approach is demonstrated with a number of data sets including a synthetic base case and four animal studies. The synthetic dataset will be used to establish the response of the algorithm on a "null" dataset, whereas the four different experimental datasets represent a spectrum of possible time course experiments in terms of the degree of perturbation associated with the experiment as well as representing a wide range of temporal sampling strategies. This wide range of experimental datasets will thus allow us to explore the performance of the proposed algorithm and determine its ability identify relevant information.In this work, we present a computational approach which operates on high throughput temporal gene expression data to assess the information content of the experiment, identify dynamic markers of important processes associated with the experimental perturbation, and summarize in a concise manner the evolution of the system over time with respect to the experimental perturbation

Long-term safety and efficacy of pegunigalsidase alfa: A multicenter 6-year study in adult patients with Fabry disease

Purpose: Fabry disease (FD) is a rare lysosomal storage disorder caused by pathogenic variants in the GLA gene encoding α-galactosidase (α-Gal)-A. We evaluated long-term safety/efficacy of pegunigalsidase alfa, a novel PEGylated α-Gal-A enzyme replacement therapy (ERT) now approved for FD. Methods: In a phase-1/2 dose-ranging study, 15 ERT-naive adults with FD completed 12 months of pegunigalsidase alfa and enrolled in this 60-month open-label extension of 1 mg/kg pegunigalsidase alfa infusions every 2 weeks. Results: Fifteen patients enrolled (8 males; 7 females); 10 completed ≥48 months (60 months total treatment), and 2 completed 60 months (72 months total treatment). During treatment, most treatment-emergent adverse events were mild/moderate in severity and all infusion-related reactions were mild/moderate in severity. Four patients were transiently positive for anti-pegunigalsidase alfa IgG. Patients showed continuous reduction in plasma lyso-Gb3 concentrations with mean (standard error) reduction of 76.1 [25.1] ng/mL from baseline to month 24. At 60 months, the estimated glomerular filtration rate slope was comparable to that observed in patients treated with other ERTs. Cardiac function assessments revealed stability; no cardiac fibrosis was observed. Conclusion: In this first long-term assessment of pegunigalsidase alfa administration in patients with FD, we found favorable safety/efficacy. Our data suggest long-term continuous benefits of pegunigalsidase alfa treatment in adults with FD

Safety and efficacy of pegunigalsidase alfa in patients with Fabry disease who were previously treated with agalsidase alfa: results from BRIDGE, a phase 3 open-label study

BACKGROUND: Pegunigalsidase alfa is a novel, PEGylated α-galactosidase-A enzyme-replacement therapy approved in the EU and US to treat patients with Fabry disease (FD). OBJECTIVE/METHODS: BRIDGE is a phase 3 open-label, switch-over study designed to assess safety and efficacy of 12 months of pegunigalsidase alfa (1 mg/kg every 2 weeks) treatment in adults with FD who had been previously treated with agalsidase alfa (0.2 mg/kg every 2 weeks) for ≥ 2 years. RESULTS: Twenty-seven patients were screened; 22 met eligibility criteria; and 20 (13 men, 7 women) completed the study. Pegunigalsidase alfa was well-tolerated, with 97% of treatment-emergent adverse events (TEAEs) being of mild or moderate severity. The incidence of treatment-related TEAEs was low, with 2 (9%) discontinuations due to TEAEs. Five patients (23%) reported infusion-related reactions. Overall mean (SD; n = 22) baseline estimated glomerular filtration rate (eGFR) was 82.5 (23.4) mL/min/1.73 m2 and plasma lyso-Gb3 level was 38.3 (41.2) nmol/L (men: 49.7 [45.8] nmol/L; women: 13.8 [6.1] nmol/L). Before switching to pegunigalsidase alfa, mean (standard error [SE]) annualized eGFR slope was − 5.90 (1.34) mL/min/1.73 m2/year; 12 months post-switch, the mean eGFR slope was − 1.19 (1.77) mL/min/1.73 m2/year; and mean plasma lyso-Gb3 reduced by 31%. Seven (35%) out of 20 patients were positive for pegunigalsidase alfa antidrug antibodies (ADAs) at ≥ 1 study timepoint, two of whom had pre-existing ADAs at baseline. Mean (SE) changes in eGFR slope for ADA-positive and ADA-negative patients were + 5.47 (3.03) and + 4.29 (3.15) mL/min/1.73 m2/year, respectively, suggesting no negative impact of anti-pegunigalsidase alfa ADAs on eGFR slope. CONCLUSION: Pegunigalsidase alfa may offer a safe and effective treatment option for patients with FD, including those previously treated with agalsidase alfa. TRN: NCT03018730. Date of registration: January 2017