Identification of global transcriptional dynamics

Modeling Pathway Dynamics of the Skeletal Muscle Response to Intravenous Methylprednisolone (MPL) Administration in Rats: Dosing and Tissue Effects

A model-based approach for the assessment of pathway dynamics is explored to characterize metabolic and signaling pathway activity changes characteristic of the dosing-dependent differences in response to methylprednisolone in muscle. To consistently compare dosing-induced changes we extend the principles of pharmacokinetics and pharmacodynamics and introduce a novel representation of pathway-level dynamic models of activity regulation. We hypothesize the emergence of dosing-dependent regulatory interactions is critical to understanding the mechanistic implications of MPL dosing in muscle. Our results indicate that key pathways, including amino acid and lipid metabolism, signal transduction, endocrine regulation, regulation of cellular functions including growth, death, motility, transport, protein degradation, and catabolism are dependent on dosing, exhibiting diverse dynamics depending on whether the drug is administered acutely of continuously. Therefore, the dynamics of drug presentation offer the possibility for the emergence of dosing-dependent models of regulation. Finally, we compared acute and chronic MPL response in muscle with liver. The comparison revealed systematic response differences between the two tissues, notably that muscle appears more prone to adapt to MPL.

Pathway-Based Analysis of the Liver Response to Intravenous Methylprednisolone Administration in Rats: Acute Versus Chronic Dosing

Pharmacological time-series data, from comparative dosing studies, are critical to characterizing drug effects. Reconciling the data from multiple studies is inevitably difficult; multiple in vivo high-throughput -omics studies are necessary to capture the global and temporal effects of the drug, but these experiments, though analogous, differ in (microarray or other) platforms, time-scales, and dosing regimens and thus cannot be directly combined or compared. This investigation addresses this reconciliation issue with a meta-analysis technique aimed at assessing the intrinsic activity at the pathway level. The purpose of this is to characterize the dosing effects of methylprednisolone (MPL), a widely used anti-inflammatory and immunosuppressive corticosteroid (CS), within the liver. A multivariate decomposition approach is applied to analyze acute and chronic MPL dosing in male adrenalectomized rats and characterize the dosing-dependent differences in the dynamic response of MPL-responsive signaling and metabolic pathways. We demonstrate how to deconstruct signaling and metabolic pathways into their constituent pathway activities, activities which are scored for intrinsic pathway activity. Dosing-induced changes in the dynamics of pathway activities are compared using a model-based assessment of pathway dynamics, extending the principles of pharmacokinetics/pharmacodynamics (PKPD) to describe pathway activities. The model-based approach enabled us to hypothesize on the likely emergence (or disappearance) of indirect dosing-dependent regulatory interactions, pointing to likely mechanistic implications of dosing of MPL transcriptional regulation. Both acute and chronic MPL administration induced a strong core of activity within pathway families including the following: lipid metabolism, amino acid metabolism, carbohydrate metabolism, metabolism of cofactors and vitamins, regulation of essential organelles, and xenobiotic metabolism pathway families. Pathway activities alter between acute and chronic dosing, indicating that MPL response is dosing dependent. Furthermore, because multiple pathway activities are dominant within a single pathway, we observe that pathways cannot be defined by a single response. Instead, pathways are defined by multiple, complex, and temporally related activities corresponding to different subgroups of genes within each pathway.

Understanding Physiology in the Continuum: Integration of Information from Multiple -Omics Levels

In this paper, we discuss approaches for integrating biological information reflecting diverse physiologic levels. In particular, we explore statistical and model-based methods for integrating transcriptomic, proteomic and metabolomics data. Our case studies reflect responses to a systemic inflammatory stimulus and in response to an anti-inflammatory treatment. Our paper serves partly as a review of existing methods and partly as a means to demonstrate, using case studies related to human endotoxemia and response to methylprednisolone (MPL) treatment, how specific questions may require specific methods, thus emphasizing the non-uniqueness of the approaches. Finally, we explore novel ways for integrating -omics information with PKPD models, toward the development of more integrated pharmacology models.

Tandem analysis of transcriptome and proteome changes after a single dose of corticosteroid: a systems approach to liver function in pharmacogenomics

Corticosteroids (CS) such as methylprednisolone (MPL) affect almost all liver functions through multiple mechanisms of action, and long-term use results in dysregulation causing diverse side effects. The complexity of involved molecular mechanisms necessitates a systems approach. Integration of information from the transcriptomic and proteomic responses has potential to provide deeper insights into CS actions. The present report describes the tandem analysis of rich time-series transcriptomic and proteomic data in rat liver after a single dose of MPL. Hierarchical clustering of the common genes represented in both mRNA and protein datasets displayed two dominant patterns. One of these patterns exhibited complementary mRNA and protein expression profiles indicating that MPL affected the regulation of these genes at the transcriptional level. Some of the classic pharmacodynamic markers for CS actions, including tyrosine aminotransferase (TAT), were among this group, together with genes encoding urea cycle enzymes and ribosomal proteins. The other pattern was rather unexpected. For this group of genes, MPL had distinctly observable effects at the protein expression level, although a change in the reverse direction occurred at the transcriptional level. These genes were functionally associated with metabolic processes that might be essential to elucidate side effects of MPL on liver, most importantly including modulation of oxidative stress, fatty acid oxidation, and bile acid biosynthesis. Furthermore, profiling of gene and protein expression data was also done independently of one another by a two-way sequential approach. Prominent temporal shifts in expression and relevant cellular functions were described together with the assessment of changes in the complementary side.

Modeling physiologic variability in human endotoxemia

The control and management of inflammation is a key aspect of clinical care for critical illnesses such as sepsis. In an ideal reaction to injury, the inflammatory response provokes a strong enough response to heal the injury and then restores homeostasis. When inflammation becomes dysregulated, a persistent inflammatory state can lead to significant deleterious effects and clinical challenges. Thus, gaining a better biological understanding of the mechanisms driving the inflammatory response is of the utmost importance. In this review, we discuss our work with the late Stephen F. Lowry to investigate systemic inflammation through systems biology of human endotoxemia. We present our efforts in modeling the human endotoxemia response with a particular focus on physiologic variability. Through modeling, with a focus ultimately on translational applications, we obtain more fundamental understanding of relevant physiological processes. And by taking advantage of the information embedded in biological rhythms, ranging in time scale from high-frequency autonomic oscillations reflected in heart rate variability to circadian rhythms in inflammatory mediators, we gain insight into the underlying physiology.

Translational applications of evaluating physiologic variability in human endotoxemia

Dysregulation of the inflammatory response is a critical component of many clinically challenging disorders such as sepsis. Inflammation is a biological process designed to lead to healing and recovery, ultimately restoring homeostasis; however, the failure to fully achieve those beneficial results can leave a patient in a dangerous persistent inflammatory state. One of the primary challenges in developing novel therapies in this area is that inflammation is comprised of a complex network of interacting pathways. Here, we discuss our approaches towards addressing this problem through computational systems biology, with a particular focus on how the presence of biological rhythms and the disruption of these rhythms in inflammation may be applied in a translational context. By leveraging the information content embedded in physiologic variability, ranging in scale from oscillations in autonomic activity driving short-term heart rate variability to circadian rhythms in immunomodulatory hormones, there is significant potential to gain insight into the underlying physiology.

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.

Optimality and thermodynamics determine the evolution of transcriptional regulatory networks

Transcriptional motifs are small regulatory interaction patterns that regulate biological functions in highly-interacting cellular networks. Recently, attempts have been made to explain the significance of transcriptional motifs through dynamic function. However, fundamental questions remain unanswered. Why are certain transcriptional motifs with similar dynamic function abundant while others occur rarely? What are the criteria for topological generalization of these motifs into complex networks? Here, we present a novel paradigm that combines non-equilibrium thermodynamics with multiobjective-optimality for network analysis. We found that energetic cost, defined herein as specific dissipation energy, is minimal at the optimal environmental conditions and it correlates inversely with the abundance of the network motifs obtained experimentally for E. coli and S. cerevisiae. This yields evidence that dissipative energetics is the underlying criteria used during evolution for motif selection and that biological systems during transcription tend towards evolutionary selection of subgraphs which produces minimum specific heat dissipation under optimal conditions, thereby explaining the abundance/rare occurrence of some motifs. We show that although certain motifs had similar dynamical functionality, they had significantly different energetic cost, thus explaining the abundance/rare occurrence of these motifs. The presented insights may establish global thermodynamic analysis as a backbone in designing and understanding complex networks systems, such as metabolic and protein interaction networks.

A quantitative model of thermal injury-induced acute inflammation

Severe burns are among the most common causes of death from unintentional injury. The induction and resolution of the burn-induced systemic inflammatory response are mediated by a network of factors and regulatory proteins. Numerous mechanisms operate simultaneously, thus requiring a systems level approach to characterize their overall impact. Towards this goal, we propose an in silico semi-mechanistic model of burn-induced systemic inflammation using liver-specific gene expression from a rat burn model. Transcriptional responses are coupled with extracellular signals through a receptor mediated indirect response (IDR) and transit compartment model. The activation of the innate immune system in response to the burn stimulus involves the interaction between extracellular signals and critical receptors which triggers downstream signal transduction cascades leading to transcriptional changes. The resulting model consists of fifteen (15) coupled ordinary differential equations capturing key aspects of inflammation such as pro-inflammation, anti-inflammation and hypermetabolism. The model was then evaluated through a series of biologically relevant scenarios aiming at revealing the non-linear behavior of acute inflammation including: investigating the implication of effect of different severity of thermal injury; examining possible mechanistic dysregulation of IKK-NF-κB system which may reflect secondary effects that lead to potential malfunction of the response; and exploring the outcome of administration of receptor antagonist or anti-body to significant cytokines.

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

Background

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.

Results

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 in vivo rat data to elucidate circadian transcriptional dynamics as well as liver-specific corticosteroid induced changes in gene expression.

Conclusions

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.