Modeling of corticosteroid pharmacogenomics in rat liver using gene microarrays

Systems pharmacodynamic model of combined gemcitabine and trabectedin in pancreatic cancer cells. Part I.Çô Effects on signal transduction pathways related to tumor growth

Pancreatic ductal adenocarcinoma (PDAC) is often chemotherapy-resistant, and novel drug combinations would fill an unmet clinical need. Previously we reported synergistic cytotoxic effects of gemcitabine and trabectedin on pancreatic cancer cells, but underlying protein-level interaction mechanisms remained unclear. We employed a reliable, sensitive, comprehensive, quantitative, high-throughput IonStar proteomic workflow to investigate the time course of gemcitabine and trabectedin effects, alone and combined, upon pancreatic cancer cells. MiaPaCa-2 cells were incubated with vehicle (controls), gemcitabine, trabectedin, and their combinations over 72 hours. Samples were collected at intervals and analyzed using the label-free IonStar liquid chromatography-mass spectrometry (LC-MS/MS) workflow to provide temporal quantification of protein expression for 4,829 proteins in four experimental groups. To characterize diverse signal transduction pathways, a comprehensive systems pharmacodynamic (SPD) model was developed. The analysis is presented in two parts. Here, Part I describes drug responses in cancer cell growth and migration pathways included in the full model: receptor tyrosine kinase- (RTK), integrin-, G-protein coupled receptor- (GPCR), and calcium-signaling pathways. The developed model revealed multiple underlying mechanisms of drug actions, provides insight into the basis of drug interaction synergism, and offers a scientific rationale for potential drug combination strategies.

Plasticity of colonic enteric nervous system following spinal cord injury in male and female rats

BACKGROUND

Neurogenic bowel is a dysmotility disorder following spinal cord injury (SCI) that negatively impacts quality of life, social integration, and physical health. Colonic transit is directly modulated by the enteric nervous system. Interstitial Cells of Cajal (ICC) distributed throughout the small intestine and colon serve as specialized pacemaker cells, generating rhythmic electrical slow waves within intestinal smooth muscle, or serve as an interface between smooth muscle cells and enteric motor neurons of the myenteric plexus. Interstitial Cells of Cajal loss has been reported for other preclinical models of dysmotility, and our previous experimental SCI study provided evidence of reduced excitatory and inhibitory enteric neuronal count and smooth muscle neural control.

METHODS

Immunohistochemistry for the ICC-specific marker c-Kit was utilized to examine neuromuscular remodeling of the distal colon in male and female rats with experimental SCI.

KEY RESULTS

Myenteric plexus ICC (ICC-MP) exhibited increased cell counts 3 days following SCI in male rats, but did not significantly increase in females until 3 weeks after SCI. On average, ICC-MP total primary arborization length increased significantly in male rats at 3-day, 3-week, and 6-week time points, whereas in females, this increase occurred most frequently at 6 weeks post-SCI. Conversely, circular muscle ICC (ICC-CM) did not demonstrate post-SCI changes.

CONCLUSIONS AND INFERENCES

These data demonstrate resiliency of the ICC-MP in neurogenic bowel following SCI, unlike seen in other related disease states. This plasticity underscores the need to further understand neuromuscular changes driving colonic dysmotility after SCI in order to advance therapeutic targets for neurogenic bowel treatment.

Advances in remission induction therapy for ANCA-associated vasculitis

Since its first description 40 years ago, huge strides have been made in the management of ANCA-associated vasculitis with improved patient outcomes. The use of cyclophosphamide and/or B-cell depleting therapy alongside glucocorticoids remains the cornerstone of therapy in organ or life-threatening disease, but recent trials have re-evaluated existing treatment strategies, alongside the development of new treatment targets. This has led to refinement of the role of plasma exchange, the use of reduced dosing of oral glucocorticoids with improved patient outcomes, as well as other treatment adjuvants/options of steroid minimization including C5a receptor antagonism and IL-5 inhibition. In this review we examine developments in remission induction therapy for ANCA-associated vasculitis.

Role of 5-HT in the enteric nervous system and enteroendocrine cells

Since the 1950s, considerable circumstantial evidence had been presented that endogenous 5-HT (serotonin) synthesized from within the wall of the gastrointestinal (GI) tract played an important role in GI motility and transit. However, identifying the precise functional role of gut-derived 5-HT has been difficult to ascertain, for a number of reasons. Over the past decade, as recording techniques have advanced significantly and access to new genetically modified animals improved, there have been major new insights and major changes in our understanding of the functional role of endogenous 5-HT in the GI tract. Data from many different laboratories have shown that major patterns of GI motility and transit still occur with minor or no, change when all endogenous 5-HT is pharmacologically or genetically ablated from the gut. Furthermore, antagonists of 5-HT3 receptors are equally, or more potent at inhibiting GI motility in segments of intestine that are completely depleted of endogenous 5-HT. Here, the most recent findings are discussed with regard to the functional role of endogenous 5-HT in enterochromaffin cells and enteric neurons in gut motility and more broadly in some major homeostatic pathways.

Teaching computational systems biology with an eye on quantitative systems pharmacology at the undergraduate level: Why do it, who would take it, and what should we teach?

Computational systems biology (CSB) is a field that emerged primarily as the product of research activities. As such, it grew in several directions in a distributed and uncoordinated manner making the area appealing and fascinating. The idea of not having to follow a specific path but instead creating one fueled innovation. As the field matured, several interdisciplinary graduate programs emerged attempting to educate future generations of computational systems biologists. These educational initiatives coordinated the dissemination of information across student populations that had already decided to specialize in this field. However, we are now entering an era where CSB, having established itself as a valuable research discipline, is attempting the next major step: Entering undergraduate curricula. As interesting as this endeavor may sound, it has several difficulties, mainly because the field is not uniformly defined. In this manuscript, we argue that this diversity is a significant advantage and that several incarnations of an undergraduate-level CSB biology course could, and should, be developed tailored to programmatic needs. In this manuscript, we share our experiences creating a course as part of a Biomedical Engineering program.

Tegaserod for the Treatment of Irritable Bowel Syndrome

Background: Tegaserod (Zelnorm®) is a 5-hydroxytryptamine (serotonin) type 4 receptor agonist for the treatment of hypomotility disorders of the lower gastrointestinal tract associated with the irritable bowel syndrome with constipation (IBS-C).

Objective: The authors provide the reader with a better understanding on tegaserod mechanism of action, on its pharmacodynamics and pharmacokinetic properties, on safety and tolerability, with a summary of the key published clinical trials conducted in patients with irritable bowel syndrome (IBS). Its effects on colon inflammation have also been described.

Results: Tegaserod was withdrawn in 2007 due to increased risks of cardiovascular adverse effects. The manufacturer denied this, because pre-existing cardiovascular disease or risk factors were attributed to all affected patients. Thus, no causal relationship between tegaserod use and cardiovascular events was clearly shown. A matched case-control study of tegaserod-treated with untreated patients found no association between tegaserod and adverse cardiovascular outcomes. Despite its adverse effects, tegaserod resulted to be effective in treating chronic constipation in adult women aged < 65 years with IBS-C, while the safety and effectiveness of tegaserod in men with IBS-C have not been established.

Conclusion: Tegaserod was resubmitted to the Food and Drug Administration in 2018 for use in a low-risk population. Moreover, tegaserod has also been shown to improve symptoms, enhance gastric accommodation and significantly attenuate visceral pain arising from the colon in functional dyspepsia patients. Treatment with tegaserod seems also to exert a protective effect in inflamed colons, reducing the severity of colitis in animal models.

Prokinetic actions of luminally acting 5-HT4 receptor agonists

BACKGROUND

5-HT₄ receptor (5-HT₄ R) agonists exert prokinetic actions in the GI tract, but non-selective actions and potential for stimulation of non-target 5-HT₄ Rs have limited their use. Since 5-HT₄ Rs are expressed in the colonic epithelium and their stimulation accelerates colonic propulsion in vitro, we tested whether luminally acting 5-HT₄ R agonists promote intestinal motility.

METHODS

Non-absorbed 5-HT₄ R agonists, based on prucalopride and naronapride, were assessed for potency at the 5-HT₄ R in vitro, and for tissue and serum distribution in vivo in mice. In vivo assessment of prokinetic potential included whole gut transit, colonic motility, fecal output, and fecal water content. Colonic motility was also studied ex vivo in mice treated in vivo. Immunofluorescence was used to evaluate receptor distribution in human intestinal mucosa.

KEY RESULTS

Pharmacological screening demonstrated selectivity and potency of test agonists for 5-HT₄ R. Bioavailability studies showed negligible serum detection. Gavage of agonists caused faster whole gut transit and colonic motility, increased fecal output, and elevated fecal water content. Prokinetic actions were blocked by a 5-HT₄ R antagonist and were not detected in 5-HT₄ R knockout mice. Agonist administration promoted motility in models of constipation. Evaluation of motility patterns ex vivo revealed enhanced contractility in the middle and distal colon. Immunoreactivity for 5-HT₄ R is present in the epithelial layer of the human small and large intestines.

CONCLUSIONS AND INFERENCES

These findings demonstrated that stimulation of epithelial 5-HT₄ Rs can potentiate propulsive motility and support the concept that mucosal 5-HT₄ Rs could represent a safe and effective therapeutic target for the treatment of constipation.

YH12852, a Potent and Selective Receptor Agonist of 5-hydroxytryptamine, Increased Gastrointestinal Motility in Healthy Volunteers and Patients With Functional Constipation

Gastrointestinal (GI) motility disorders are common, decreases quality of life, and imposes a substantial economic burden. YH12852 is a novel agonist of 5‐hydroxytryptamine for the treatment of GI motility disorders. This phase I/IIa study assessed the tolerability, pharmacodynamic (PD) and pharmacokinetic (PK) profiles of YH12852. In the multiple dose (MD) cohort, healthy subjects and patients with functional constipation were randomized and received orally YH12852 at 0.3, 0.5, 1, 2, or 3 mg or prucalopride 2 mg or their matching placebo, once daily for 14 days after breakfast. In the multiple low‐dose cohort (MLD), healthy subjects randomly received once‐daily oral doses of YH12852 at 0.05 or 0.1 mg for 14 days after breakfast. Questionnaires, gastric emptying breath test for PDs, and plasma samples for PKs were collected. In the MD cohort, a total of 56 subjects (29 healthy volunteers and 27 patients with functional constipation) were randomized, of whom 48 completed the study. In the MLD cohort, a total of 16 healthy subjects were randomized, and 15 subjects completed the study. YH12852 increased the average weekly frequency of spontaneous bowel movements and loosened the stool. In addition, YH12852 increased quality of life satisfaction, and decreased severity of constipation symptom and GI symptoms. YH12852 was safe and well‐tolerated up to 3 mg and showed nearly dose proportional PKs. In conclusion, YH12852 was safe and enhanced GI motility. YH12852 can be developed as an effective treatment option for GI motility disorders, including functional constipation. Further studies are warranted to confirm this possibility.

Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids

Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.

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.

Serotonin transporter untranslated regions influence mRNA abundance and protein expression

The serotonin transporter (SERT, SLC6A4) is a Na⁺-dependent transporter that regulates the availability of serotonin (5-HT, 5-hydroxytryptamine), a key neurotransmitter and hormone in the brain and the intestine. The human SERT gene consists of two alternate promoters that drive expression of an identical SERT protein. However, there are different mRNA transcript variants derived from these two promoters that differ in their 5' untranslated region (5'UTR), which is the region of the mRNA upstream from the protein-coding region. Two of these transcripts contain exon-1a and are abundant in neuronal tissue, whereas the third transcript contains exon-1c and is abundant in the intestine. The 3'UTR is nearly identical among the transcripts. Current studies tested the hypothesis that the UTRs of SERT influence its expression in intestinal epithelial cells (IECs) by controlling mRNA or protein levels. The SERT UTRs were cloned into luciferase reporter plasmids and luciferase mRNA and activity were measured following transient transfection of the UTR constructs into the model IEC Caco-2. Luciferase activity and mRNA abundance were higher than the empty vector for two of the three 5'UTR variants. Calculation of translation index (luciferase activity divided by the relative luciferase mRNA level) revealed that the exon-1a containing 5'UTRs had enhanced translation when compared to the exon-1c containing 5'UTR which exhibited a low translation efficiency. Compared to the empty vector, the SERT 3'UTR markedly decreased luciferase activity. In silico analysis of the SERT 3'UTR revealed many conserved potential miRNA binding sites that may be responsible for this decrease. In conclusion, we have shown that the UTRs of SERT regulate mRNA abundance and protein expression. Delineating the molecular basis by which the UTRs of SERT influence its expression will lead to an increased understanding of post-transcriptional regulation of SERT in GI disorders associated with altered 5-HT availability.

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.

Modeling Corticosteroid Pharmacogenomics and Proteomics in Rat Liver

Corticosteroids (CS) regulate the expression of numerous genes at the mRNA and protein levels. The time course of CS pharmacogenomics and proteomics were examined in livers obtained from adrenalectomized rats given a 50-mg/kg bolus dose of methylprednisolone. Microarrays and mass spectrometry-based proteomics were employed to quantify hepatic transcript and protein dynamics. One-hundred, sixty-three differentially expressed mRNA and their corresponding proteins (163 genes) were clustered into two dominant groups. The temporal profiles of most proteins were delayed compared with their mRNA, attributable to synthesis delays and slower degradation kinetics. On the basis of our fifth-generation model of CS, mathematical models were developed to simultaneously describe the emergent time patterns for an array of steroid-responsive mRNA and proteins. The majority of genes showed time-dependent increases in mRNA and protein expression before returning to baseline. A model assuming direct, steroid-mediated stimulation of mRNA synthesis was applied. Some mRNAs and their proteins displayed down-regulation following CS. A model assuming receptor-mediated inhibition of mRNA synthesis was used. More complex patterns were observed for other genes (e.g., biphasic behaviors and opposite directionality in mRNA and protein). Models assuming either stimulation or inhibition of mRNA synthesis coupled with dual secondarily induced regulatory mechanisms affecting mRNA or protein turnover were derived. These findings indicate that CS-regulated gene expression manifested at the mRNA and protein levels are controlled via mechanisms affecting key turnover processes. Our quantitative models of CS pharmacogenomics were expanded from mRNA to proteins and provide extended hypotheses for understanding the direct, secondary, and downstream mechanisms of CS actions.

Receptor/gene/protein-mediated signaling connects methylprednisolone exposure to metabolic and immune-related pharmacodynamic actions in liver

A multiscale pharmacodynamic model was developed to characterize the receptor-mediated, transcriptomic, and proteomic determinants of corticosteroid (CS) effects on clinically relevant hepatic processes following a single dose of methylprednisolone (MPL) given to adrenalectomized (ADX) rats. The enhancement of tyrosine aminotransferase (TAT) mRNA, protein, and enzyme activity were simultaneously described. Mechanisms related to the effects of MPL on glucose homeostasis, including the regulation of CCAAT-enhancer binding protein-beta (C/EBPβ) and phosphoenolpyruvate carboxykinase (PEPCK) as well as insulin dynamics were evaluated. The MPL-induced suppression of circulating lymphocytes was modeled by coupling its effect on cell trafficking with pharmacogenomic effects on cell apoptosis via the hepatic (STAT3-regulated) acute phase response. Transcriptomic and proteomic time-course profiles measured in steroid-treated rat liver were utilized to model the dynamics of mechanistically relevant gene products, which were linked to associated systemic end-points. While time-courses of TAT mRNA, protein, and activity were well described by transcription-mediated changes, additional post-transcriptional processes were included to explain the lack of correlation between PEPCK mRNA and protein. The immune response model quantitatively discerned the relative roles of cell trafficking versus gene-mediated lymphocyte apoptosis by MPL. This systems pharmacodynamic model provides insights into the contributions of selected molecular events occurring in liver and explores mechanistic hypotheses for the multi-factorial control of clinically relevant pharmacodynamic outcomes.

On the analysis of complex biological supply chains: From Process Systems Engineering to Quantitative Systems Pharmacology

The use of models in biology has become particularly relevant as it enables investigators to develop a mechanistic framework for understanding the operating principles of living systems as well as in quantitatively predicting their response to both pathological perturbations and pharmacological interventions. This application has resulted in a synergistic convergence of systems biology and pharmacokinetic-pharmacodynamic modeling techniques that has led to the emergence of quantitative systems pharmacology (QSP). In this review, we discuss how the foundational principles of chemical process systems engineering inform the progressive development of more physiologically-based systems biology models.

Bayesian state space models for dynamic genetic network construction across multiple tissues

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Functional proteomic analysis of corticosteroid pharmacodynamics in rat liver: Relationship to hepatic stress, signaling, energy regulation, and drug metabolism

Corticosteroids (CS) are anti-inflammatory agents that cause extensive pharmacogenomic and proteomic changes in multiple tissues. An understanding of the proteome-wide effects of CS in liver and its relationships to altered hepatic and systemic physiology remains incomplete. Here, we report the application of a functional pharmacoproteomic approach to gain integrated insight into the complex nature of CS responses in liver in vivo. An in-depth functional analysis was performed using rich pharmacodynamic (temporal-based) proteomic data measured over 66h in rat liver following a single dose of methylprednisolone (MPL). Data mining identified 451 differentially regulated proteins. These proteins were analyzed on the basis of temporal regulation, cellular localization, and literature-mined functional information. Of the 451 proteins, 378 were clustered into six functional groups based on major clinically-relevant effects of CS in liver. MPL-responsive proteins were highly localized in the mitochondria (20%) and cytosol (24%). Interestingly, several proteins were related to hepatic stress and signaling processes, which appear to be involved in secondary signaling cascades and in protecting the liver from CS-induced oxidative damage. Consistent with known adverse metabolic effects of CS, several rate-controlling enzymes involved in amino acid metabolism, gluconeogenesis, and fatty-acid metabolism were altered by MPL. In addition, proteins involved in the metabolism of endogenous compounds, xenobiotics, and therapeutic drugs including cytochrome P450 and Phase-II enzymes were differentially regulated. Proteins related to the inflammatory acute-phase response were up-regulated in response to MPL. Functionally-similar proteins showed large diversity in their temporal profiles, indicating complex mechanisms of regulation by CS.

SIGNIFICANCE

Clinical use of corticosteroid (CS) therapy is frequent and chronic. However, current knowledge on the proteome-level effects of CS in liver and other tissues is sparse. While transcriptomic regulation following methylprednisolone (MPL) dosing has been temporally examined in rat liver, proteomic assessments are needed to better characterize the tissue-specific functional aspects of MPL actions. This study describes a functional pharmacoproteomic analysis of dynamic changes in MPL-regulated proteins in liver and provides biological insight into how steroid-induced perturbations on a molecular level may relate to both adverse and therapeutic responses presented clinically.

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.

Time-Dependent Effects in Algae for Chemicals with Different Adverse Outcome Pathways: A Novel Approach

Chemicals affect unicellular algae as a result of toxicokinetic and toxicodynamic processes. The internal concentration of chemicals in algae cells typically reaches equilibrium within minutes, while damage cumulatively increases over hours. The time gap between the steady state of internal exposure and damage development is thus suspected to span up to hours, mainly due to toxicodynamic processes. The quantification of rate-limited toxicodynamic processes, aggregated as a progressive effect from an initiating molecular event through biological key events toward the adverse outcome on algae growth inhibition, might discriminate between different adverse outcome pathways (AOPs). To support our hypothesis, we selected six chemicals according to different physicochemical properties and three distinctly dissimilar AOPs. The time courses of internal concentrations were linked to the observed affected Scenedesmus vacuolatus growth using toxicokinetic-toxicodynamic modeling. Effects on cell growth were explained by effect progression and not by the time to reach internal equilibrium concentration. Effect progression rates ranged over 6 orders of magnitude for all chemicals but varied by less than 1 order of magnitude within similar AOP (photosystem II inhibitors > reactive chemicals > lipid biosynthesis inhibitors), meaning that inhibitors of photosystem II advance an effect toward algae growth fastest compared to reactive chemicals and inhibitors of lipid biosynthesis.

Quantitative Systems Pharmacology: A Framework for Context

Quantitative Systems Pharmacology (QSP) is receiving increased attention. As the momentum builds and the expectations grow it is important to (re)assess and formalize the basic concepts and approaches. In this short review, I argue that QSP, in addition to enabling the rational integration of data and development of complex models, maybe more importantly, provides the foundations for developing an integrated framework for the assessment of drugs and their impact on disease within a broader context expanding the envelope to account in great detail for physiology, environment and prior history. I articulate some of the critical enablers, major obstacles and exciting opportunities manifesting themselves along the way. Charting such overarching themes will enable practitioners to identify major and defining factors as the field progressively moves towards personalized and precision health care delivery.

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.

Circadian characteristics of permissive and suppressive effects of cortisol and their role in homeostasis and the acute inflammatory response

In this work we explore a semi-mechanistic model that considers cortisol's permissive and suppressive effects through the regulation of cytokine receptors and cytokines respectively. Our model reveals the proactive role of cortisol during the resting period and its reactive character during the body's activity phase. Administration of an acute LPS dose during the night, when cortisol's permissive effects are higher than suppressive, leads to increased cytokine levels compared to LPS administration at morning when cortisol's suppressive effects are higher. Interestingly, our model presents a hysteretic behavior where the relative predominance of permissive or suppressive effects results not only from cortisol levels but also from the previous states of the model. Therefore, for the same cortisol levels, administration of an inflammatory stimulus at cortisol's ascending phase, that follows a time period where cytokine receptor expression is elevated ultimately sensitizing the body for the impending stimulus, leads to higher cytokine expression compared to administration of the same stimulus at cortisol's descending phase.

Inference of gene regulatory networks incorporating multi-source biological knowledge via a state space model with L1 regularization

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid kinetics/dynamics, literature-recorded pathways and transcription factor (TF) information.

An efficient method of exploring simulation models by assimilating literature and biological observational data

Recently, several biological simulation models of, e.g., gene regulatory networks and metabolic pathways, have been constructed based on existing knowledge of biomolecular reactions, e.g., DNA-protein and protein-protein interactions. However, since these do not always contain all necessary molecules and reactions, their simulation results can be inconsistent with observational data. Therefore, improvements in such simulation models are urgently required. A previously reported method created multiple candidate simulation models by partially modifying existing models. However, this approach was computationally costly and could not handle a large number of candidates that are required to find models whose simulation results are highly consistent with the data. In order to overcome the problem, we focused on the fact that the qualitative dynamics of simulation models are highly similar if they share a certain amount of regulatory structures. This indicates that better fitting candidates tend to share the basic regulatory structure of the best fitting candidate, which can best predict the data among candidates. Thus, instead of evaluating all candidates, we propose an efficient explorative method that can selectively and sequentially evaluate candidates based on the similarity of their regulatory structures. Furthermore, in estimating the parameter values of a candidate, e.g., synthesis and degradation rates of mRNA, for the data, those of the previously evaluated candidates can be utilized. The method is applied here to the pharmacogenomic pathways for corticosteroids in rats, using time-series microarray expression data. In the performance test, we succeeded in obtaining more than 80% of consistent solutions within 15% of the computational time as compared to the comprehensive evaluation. Then, we applied this approach to 142 literature-recorded simulation models of corticosteroid-induced genes, and consequently selected 134 newly constructed better models. The method described here was found to be capable of efficiently exploring candidate simulation models and obtaining better models within a short span of time. Furthermore, the results suggest that there may be room for improvement in literature recorded pathways and that they can be systematically updated using biological observational data.

Meta-modeling of methylprednisolone effects on glucose regulation in rats

A retrospective meta-modeling analysis was performed to integrate previously reported data of glucocorticoid (GC) effects on glucose regulation following a single intramuscular dose (50 mg/kg), single intravenous doses (10, 50 mg/kg), and intravenous infusions (0.1, 0.2, 0.3 and 0.4 mg/kg/h) of methylprednisolone (MPL) in normal and adrenalectomized (ADX) male Wistar rats. A mechanistic pharmacodynamic (PD) model was developed based on the receptor/gene/protein-mediated GC effects on glucose regulation. Three major target organs (liver, white adipose tissue and skeletal muscle) together with some selected intermediate controlling factors were designated as important regulators involved in the pathogenesis of GC-induced glucose dysregulation. Assessed were dynamic changes of food intake and systemic factors (plasma glucose, insulin, free fatty acids (FFA) and leptin) and tissue-specific biomarkers (cAMP, phosphoenolpyruvate carboxykinase (PEPCK) mRNA and enzyme activity, leptin mRNA, interleukin 6 receptor type 1 (IL6R1) mRNA and Insulin receptor substrate-1 (IRS-1) mRNA) after acute and chronic dosing with MPL along with the GC receptor (GR) dynamics in each target organ. Upon binding to GR in liver, MPL dosing caused increased glucose production by stimulating hepatic cAMP and PEPCK activity. In adipose tissue, the rise in leptin mRNA and plasma leptin caused reduction of food intake, the exogenous source of glucose input. Down-regulation of IRS-1 mRNA expression in skeletal muscle inhibited the stimulatory effect of insulin on glucose utilization further contributing to hyperglycemia. The nuclear drug-receptor complex served as the driving force for stimulation or inhibition of downstream target gene expression within different tissues. Incorporating information such as receptor dynamics, as well as the gene and protein induction, allowed us to describe the receptor-mediated effects of MPL on glucose regulation in each important tissue. This advanced mechanistic model provides unique insights into the contributions of major tissues and quantitative hypotheses for the multi-factor control of a complex metabolic system.

Erg and AP-1 as determinants of glucocorticoid response in acute lymphoblastic leukemia

Glucocorticoids (GCs) are among the most widely prescribed medications in clinical practice. The beneficial effects of GCs in acute lymphoblastic leukemia (ALL) are based on their ability to induce apoptosis, but the underlying transcriptional mechanisms remain poorly defined. Computational modeling has enormous potential in the understanding of biological processes such as apoptosis and the discovery of novel regulatory mechanisms. We here present an integrated analysis of gene expression kinetic profiles using microarrays from GC sensitive and resistant ALL cell lines and patients, including newly generated and previously published data sets available from the Gene Expression Omnibus. By applying time-series clustering analysis in the sensitive ALL CEM-C7-14 cells, we identified 358 differentially regulated genes that we classified into 15 kinetic profiles. We identified GC response element (GRE) sequences in 33 of the upregulated known or potential GC receptor (GR) targets. Comparative study of sensitive and resistant ALL showed distinct gene expression patterns and indicated unexpected similarities between sensitivity-restored and resistant ALL. We found that activator protein 1 (AP-1), Ets related gene (Erg) and GR pathways were differentially regulated in sensitive and resistant ALL. Erg protein levels were substantially higher in CEM-C1-15-resistant cells, c-Jun was significantly induced in sensitive cells, whereas c-Fos was expressed at low levels in both. c-Jun was recruited on the AP-1 site on the Bim promoter, whereas a transient Erg occupancy on the GR promoter was detected. Inhibition of Erg and activation of GR lead to increased apoptosis in both sensitive and resistant ALL. These novel findings significantly advance our understanding of GC sensitivity and can be used to improve therapy of leukemia.

Entrainment of peripheral clock genes by cortisol

Circadian rhythmicity in mammals is primarily driven by the suprachiasmatic nucleus (SCN), often called the central pacemaker, which converts the photic information of light and dark cycles into neuronal and hormonal signals in the periphery of the body. Cells of peripheral tissues respond to these centrally mediated cues by adjusting their molecular function to optimize organism performance. Numerous systemic cues orchestrate peripheral rhythmicity, such as feeding, body temperature, the autonomic nervous system, and hormones. We propose a semimechanistic model for the entrainment of peripheral clock genes by cortisol as a representative entrainer of peripheral cells. This model demonstrates the importance of entrainer's characteristics in terms of the synchronization and entrainment of peripheral clock genes, and predicts the loss of intercellular synchrony when cortisol moves out of its homeostatic amplitude and frequency range, as has been observed clinically in chronic stress and cancer. The model also predicts a dynamic regime of entrainment, when cortisol has a slightly decreased amplitude rhythm, where individual clock genes remain relatively synchronized among themselves but are phase shifted in relation to the entrainer. The model illustrates how the loss of communication between the SCN and peripheral tissues could result in desynchronization of peripheral clocks.

5-HT(3) and 5-HT(4) receptors contribute to the anti-motility effects of Garcinia buchananii bark extract in the guinea-pig distal colon

BACKGROUND

Garcinia buchananii bark extract is an anti-motility diarrhea remedy. We investigated whether G. buchananii bark extract has components that reduce gastrointestinal peristaltic activity via 5-HT(3) and 5-HT(4) receptors.

METHODS

Aqueous G. buchananii extract was separated into fractions using preparative thin layer chromatography (PTLC), and major chemical components were identified using standard tests. The anti-motility effects of the extract and its fractions (PTLC1-5) were studied through pellet propulsion assays using isolated guinea-pig distal colons.

KEY RESULTS

Anti-motility (PTLC1 & PTLC5) and pro-motility (PTLC2) fractions were isolated from the extract. Flavonoids, steroids, alkaloids, tannins, and phenols were identified in the extract and PTLC1&5. The potency of the extract applied via the mucosal surface was reduced by 5-HT, 5-HT(3) receptor agonist RS-56812, 5-HT(4) receptor agonists cisapride and CJ-033466, 5-HT(3) receptor antagonist granisetron, and 5-HT(4) receptor antagonist GR-113808. The anti-motility effects of the aqueous extract and PTLC1&5 when applied serosally were reversed by RS-56812, cisapride, and CJ-033466. The 5-HT(3) receptor antagonists, granisetron and ondansetron, reduced the effects of the extract to an extent and completely reversed the anti-motility effects of PTLC1&5. GR-113808 inhibited the actions of the extract during the initial 10 min, but enhanced the extracts' anti-motility effects after 15 min. GR-113808 augmented the anti-motility activities of PTLC1 and PTLC5 by 30%.

CONCLUSIONS & INFERENCES

These results indicate that the anti-motility effects of G. buchananii aqueous extract are potentially mediated by compounds that affect 5-HT(3) and 5-HT(4) receptors. Identification and characterization of the bioactive compounds within G. buchananii could lead to the discovery of new non-opiate anti-diarrhea formulations.

Modeling autonomic regulation of cardiac function and heart rate variability in human endotoxemia

Heart rate variability (HRV), the quantification of beat-to-beat variability, has been studied as a potential prognostic marker in inflammatory diseases such as sepsis. HRV normally reflects significant levels of variability in homeostasis, which can be lost under stress. Much effort has been placed in interpreting HRV from the perspective of quantitatively understanding how stressors alter HRV dynamics, but the molecular and cellular mechanisms that give rise to both homeostatic HRV and changes in HRV have received less focus. Here, we develop a mathematical model of human endotoxemia that incorporates the oscillatory signals giving rise to HRV and their signal transduction to the heart. Connections between processes at the cellular, molecular, and neural levels are quantitatively linked to HRV. Rhythmic signals representing autonomic oscillations and circadian rhythms converge to modulate the pattern of heartbeats, and the effects of these oscillators are diminished in the acute endotoxemia response. Based on the semimechanistic model developed herein, homeostatic and acute stress responses of HRV are studied in terms of these oscillatory signals. Understanding the loss of HRV in endotoxemia serves as a step toward understanding changes in HRV observed clinically through translational applications of systems biology based on the relationship between biological processes and clinical outcomes.

Biological assessment of robust noise models in microarray data analysis

Motivation: Although several recently proposed analysis packages for microarray data can cope with heavy-tailed noise, many applications rely on Gaussian assumptions. Gaussian noise models foster computational efficiency. This comes, however, at the expense of increased sensitivity to outlying observations. Assessing potential insufficiencies of Gaussian noise in microarray data analysis is thus important and of general interest.

Results: We propose to this end assessing different noise models on a large number of microarray experiments. The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom. Inference of differentially expressed genes is taken into consideration at a second mixing level. For attaining far reaching validity, our investigations cover a wide range of analysis platforms and experimental settings. As the most striking result, we find irrespective of the chosen preprocessing and normalization method in all experiments that a heavy-tailed noise model is a better fit than a simple Gaussian. Further investigations revealed that an appropriate choice of noise model has a considerable influence on biological interpretations drawn at the level of inferred genes and gene ontology terms. We conclude from our investigation that neglecting the over dispersed noise in microarray data can mislead scientific discovery and suggest that the convenience of Gaussian-based modelling should be replaced by non-parametric approaches or other methods that account for heavy-tailed noise.

Contact:peter.sykacek@boku.ac.at

Availability:http://bioinf.boku.ac.at/alexp/robmca.html.

Assessment of pharmacologic area under the curve when baselines are variable

PURPOSE

The area under the curve (AUC) is commonly used to assess the extent of exposure of a drug. The same concept can be applied to generally assess pharmacodynamic responses and the deviation of a signal from its baseline value. When the initial condition for the response of interest is not zero, there is uncertainty in the true value of the baseline measurement. This necessitates the consideration of the AUC relative to baseline to account for this inherent uncertainty and variability in baseline measurements.

METHODS

An algorithm to calculate the AUC with respect to a variable baseline is developed by comparing the AUC of the response curve with the AUC of the baseline while taking into account uncertainty in both measurements. Furthermore, positive and negative components of AUC (above and below baseline) are calculated separately to allow for the identification of biphasic responses.

RESULTS

This algorithm is applied to gene expression data to illustrate its ability to capture transcriptional responses to a drug that deviate from baseline and to synthetic data to quantitatively test its performance.

CONCLUSIONS

The variable nature of the baseline is an important aspect to consider when calculating the AUC.

Quantitative analysis and modeling of glucocorticoid-controlled gene expression

Aims: Glucocorticoid hormones are used extensively in the clinic for the treatment of acute lymphoblastic leukemia. Despite intensive research, the molecular mechanisms of glucocorticoid receptor (GR)-mediated transcriptional events that lead to the induction of apoptosis of leukemia cells, as well as the causes for the development of resistance in leukemia patients, are not yet understood. It is thought that the B-cell lymphoma 2 family members that control apoptosis, including some of the GR target genes, may play an important role in deciding cell fate. In this report we have employed pathway modeling due to the recent discovery of its usefulness as a tool for improving understanding of the mechanisms of cellular signaling, and in discovering new therapeutic targets for the treatment of various diseases. Materials & methods: Detailed kinetics of GR autoregulation, as well as the kinetics of expression of its target genes and proteins Bcl-xL, Bim, Bmf and GILZ in glucocorticoid responsive and resistant leukemia cell lines were carried out. Subsequently in order to obtain further insight into the molecular mechanisms of GR signaling in this pathway a dynamic model of the induction of these genes and proteins by GR was constructed. Results: The simulations were in good agreement with the observed experimental data suggesting that Bim was induced between 6 and 10 h after the addition of the synthetic glucocorticoid dexamethasone, possibly through rapid glucocorticoid dependent modulation of an unknown factor. Simulations and experimental results also suggested that Bmf induction did not require novel protein synthesis, and is a potential direct GR target. Conclusion: This combination of experimental analysis and model development initiates a virtuous cycle enabling further data integration and model expansion, and constitutes a novel promising framework towards a global mechanistic understanding of GR function.

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

Background

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 in vivo 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.

Results

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.

Conclusions

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 in vivo 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.

Oxygenomics in environmental stress

Environmental stressors such as chemicals and physical agents induce various oxidative stresses and affect human health. To elucidate their underlying mechanisms, etiology and risk, analyses of gene expression signatures in environmental stress-induced human diseases, including neuronal disorders, cancer and diabetes, are crucially important. Recent studies have clarified oxidative stress-induced signaling pathways in human and experimental animals. These pathways are classifiable into several categories: reactive oxygen species (ROS) metabolism and antioxidant defenses, p53 pathway signaling, nitric oxide (NO) signaling pathway, hypoxia signaling, transforming growth factor (TGF)-beta bone morphogenetic protein (BMP) signaling, tumor necrosis factor (TNF) ligand-receptor signaling, and mitochondrial function. This review describes the gene expression signatures through which environmental stressors induce oxidative stress and regulate signal transduction pathways in rodent and human tissues.

A new symbolic representation for the identification of informative genes in replicated microarray experiments

Microarray experiments generate massive amounts of data, necessitating innovative algorithms to distinguish biologically relevant information from noise. Because the variability of gene expression data is an important factor in determining which genes are differentially expressed, analysis techniques that take into account repeated measurements are critically important. Additionally, the selection of informative genes is typically done by searching for the individual genes that vary the most across conditions. Yet because genes tend to act in groups rather than individually, it may be possible to glean more information from the data by searching specifically for concerted behavior in a set of genes. Applying a symbolic transformation to the gene expression data allows the detection overrepresented patterns in the data, in contrast to looking only for genes that exhibit maximal differential expression. These challenges are approached by introducing an algorithm based on a new symbolic representation that searches for concerted gene expression patterns; furthermore, the symbolic representation takes into account the variance in multiple replicates and can be applied to long time series data. The proposed algorithm's ability to discover biologically relevant signals in gene expression data is exhibited by applying it to three datasets that measure gene expression in the rat liver.

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.

Multiscale model for the assessment of autonomic dysfunction in human endotoxemia

Severe injury and infection are associated with autonomic dysfunction. The realization that a dysregulation in autonomic function may predispose a host to excessive inflammatory processes has renewed interest in understanding the role of central nervous system (CNS) in modulating systemic inflammatory processes. Assessment of heart rate variability (HRV) has been used to evaluate systemic abnormalities and as a predictor of the severity of illness. Dissecting the relevance of neuroimmunomodulation in controlling inflammatory processes requires an understanding of the multiscale interplay between CNS and the immune response. A vital enabler in that respect is the development of a systems-based approach that integrates data across multiple scales, and models the emerging host response as the outcome of interactions of critical modules. Thus, a multiscale model of human endotoxemia, as a prototype model of systemic inflammation in humans, is proposed that integrates processes across the host from the cellular to the systemic host response level. At the cellular level interacting components are associated with elementary signaling pathways that propagate extracellular signals to the transcriptional response level. Further, essential modules associated with the neuroendocrine immune crosstalk are considered. Finally, at the systemic level, phenotypic expressions such as HRV are incorporated to assess systemic decomplexification indicative of the severity of the host response. Thus, the proposed work intends to associate acquired endocrine dysfunction with diminished HRV as a critical enabler for clarifying how cellular inflammatory processes and neural-based pathways mediate the links between patterns of autonomic control (HRV) and clinical outcomes.

NETWORKS, BIOLOGY AND SYSTEMS ENGINEERING: A CASE STUDY IN INFLAMMATION

Biological systems can be modeled as networks of interacting components across multiple scales. A central problem in computational systems biology is to identify those critical components and the rules that define their interactions and give rise to the emergent behavior of a host response. In this paper we will discuss two fundamental problems related to the construction of transcription factor networks and the identification of networks of functional modules describing disease progression. We focus on inflammation as a key physiological response of clinical and translational importance.

Serotonin release and uptake in the gastrointestinal tract

The afferent innervation of the gastrointestinal (GI) tract consists of intrinsic and extrinsic sensory neurons that respond to nutrients, chemicals or mechanical stimuli within the gut lumen. Most stimuli do not interact directly with the afferent nerves but instead activate specialised cells in the epithelium in a process of sensory transduction. It is thought that one of the first steps in this process is the release of serotonin (5-HT) from the enterochromaffin (EC) cells. The EC cells are a sub-type of enteroendocrine (EE) cells which are found among the enterocytes of the intestinal epithelium. The EC cells are responsible for the production and storage of the largest pool of 5 HT in the body. Released 5-HT can act on the intrinsic nerves and vagal endings. This review will focus on the role of 5-HT in sensory transduction and examine how the EC cell produces and releases 5-HT. We will explore recent developments that have helped to elucidate some of the proteins that allow EC cells to sense the luminal environment. Finally, we will highlight some of the findings from new studies using electrochemical techniques which allow the real-time recording of 5-HT concentrations near to the EC cell.

Identification of global transcriptional dynamics

Background

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.

Methodology and Findings

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.

Conclusions and Significance

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.

In silico simulation of corticosteroids effect on an NFkB- dependent physicochemical model of systemic inflammation

BACKGROUND

During the onset of an inflammatory response signaling pathways are activated for "translating" extracellular signals into intracellular responses converging to the activation of nuclear factor (NF)-kB, a central transcription factor in driving the inflammatory response. An inadequate control of its transcriptional activity is associated with the culmination of a hyper-inflammatory response making it a desired therapeutic target. Predicated upon the nature of the response, a systems level analysis might provide rational leads for the development of strategies that promote the resolution of the response.

METHODOLOGY AND FINDINGS

A physicochemical host response model is proposed to integrate biological information in the form of kinetic rules and signaling cascades with pharmacokinetic models of drug action for the modulation of the response. The unifying hypothesis is that the response is triggered by the activation of the NFkB signaling module and corticosteroids serve as a template for assessing anti-inflammatory strategies. The proposed in silico model is evaluated through its ability to predict and modulate uncontrolled responses. The pre-exposure of the system to hypercortisolemia, i.e. 6 hr before or simultaneously with the infectious challenge "reprograms" the dynamics of the host towards a balanced inflammatory response. However, if such an intervention occurs long before the inflammatory insult a symptomatic effect is observed instead of a protective relief while a steroid infusion after inducing inflammation requires much higher drug doses.

CONCLUSIONS AND SIGNIFICANCE

We propose a reversed engineered inflammation model that seeks to describe how the system responds to a multitude of external signals. Timing of intervention and dosage regimes appears to be key determinants for the protective or symptomatic effect of exogenous corticosteroids. Such results lie in qualitative agreement with in vivo human studies exposed both to LPS and corticosteroids under various time intervals thus improving our understanding of how interacting modules generate a behavior.

Pharmacodynamics of glucose regulation by methylprednisolone. I. Adrenalectomized rats

Mechanisms related to the adverse effects of corticosteroids on glucose homeostasis were studied. Five groups of adrenalectomized (ADX) rats were given methylprednisolone (MPL) intravenously at 10 and 50 mg/kg, or a continuous 7 day infusion at rates of 0, 0.1, 0.3 mg/kg/h via subcutaneously implanted Alzet mini-pumps. Plasma concentrations of MPL, glucose and insulin were determined at various time points up to 72 h after injection or 336 h after infusion. The pharmacokinetics of MPL was captured with a two-compartment model. The Adapt II software was used in modeling. Injection of MPL caused a temporary glucose increase over 6 h by stimulating gluconeogenesis. The glucose changes stimulated pancreatic beta-cell secretion yielding a later insulin peak at around 10 h. In turn, insulin can stimulate glucose disposition. However, long-term MPL treatment caused continuous hyperglycemia during and after infusion. Insulin was increased during infusion, and immediately returned to baseline after the infusion was terminated, despite the almost doubled glucose concentration. A disease progression model incorporating the reduced endogenous glucose disposition was included to capture glucose homeostasis under different treatments. The results exemplify the importance of the steroid dosing regimen in mediating pharmacological and adverse metabolic effects. This mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model quantitatively describes the induction of hyperglycemia and provides additional insights into metabolic disorders such as diabetes.