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.

Since the development of high-density microarray technology in the late 1990s, global host gene expression changes in response to various stimuli have been extensively studied. More than a dozen peer-reviewed publications have investigated the effect of Leishmania infection in various models since 2001. This review covers the transcriptional changes in macrophage models induced by various Leishmania species and summarizes the resulting impact these studies have on our understanding of the host response to leishmaniasis in vitro. Characterization of the similarities and differences between various model systems will not only further our understanding of Leishmania-induced changes to macrophage gene expression but also identify potential therapeutic targets in the future.

The purpose of this study was to evaluate the effects of butyrate infusion on rumen epithelial transcriptome. Next-generation sequencing (NGS) and bioinformatics are used to accelerate our understanding of regulation in rumen epithelial transcriptome of cattle in the dry period induced by butyrate infusion at the level of the whole transcriptome. Butyrate, as an essential element of nutrients, is a histone deacetylase (HDAC) inhibitor that can alter histone acetylation and methylation, and plays a prominent role in regulating genomic activities influencing rumen nutrition utilization and function. Ruminal infusion of butyrate was following 0-hour sampling (baseline controls) and continued for 168 hours at a rate of 5.0 L/day of a 2.5 M solution as a continuous infusion. Following the 168-hour infusion, the infusion was stopped, and cows were maintained on the basal lactation ration for an additional 168 hours for sampling. Rumen epithelial samples were serially collected via biopsy through rumen fistulae at 0-, 24-, 72-, and 168-hour (D1, D3, D7) and 168-hour post-infusion (D14). In comparison with pre-infusion at 0 hours, a total of 3513 genes were identified to be impacted in the rumen epithelium by butyrate infusion at least once at different sampling time points at a stringent cutoff of false discovery rate (FDR) < 0.01. The maximal effect of butyrate was observed at day 7. Among these impacted genes, 117 genes were responsive consistently from day 1 to day 14, and another 42 genes were lasting through day 7. Temporal effects induced by butyrate infusion indicate that the transcriptomic alterations are very dynamic. Gene ontology (GO) enrichment analysis revealed that in the early stage of rumen butyrate infusion (on day 1 and day 3 of butyrate infusion), the transcriptomic effects in the rumen epithelium were involved with mitotic cell cycle process, cell cycle process, and regulation of cell cycle. Bioinformatic analysis of cellular functions, canonical pathways, and upstream regulator of impacted genes underlie the potential mechanisms of butyrate-induced gene expression regulation in rumen epithelium. The introduction of transcriptomic and bioinformatic technologies to study nutrigenomics in the farm animal presented a new prospect to study multiple levels of biological information to better apprehend the whole animal response to nutrition, physiological state, and their interactions. The nutrigenomics approach may eventually lead to more precise management of utilization of feed resources in a more effective approach.

Quantitative Systems Pharmacology (QSP) modeling is increasingly used as a quantitative tool for advancing mechanistic hypotheses on the mechanism of action of a drug, and its pharmacological effect in relevant disease phenotypes, to enable linking the right drug to the right patient. Application of QSP models relies on creation of virtual populations for simulating scenarios of interest. Creation of virtual populations requires 2 important steps, namely, identification of a subset of model parameters that can be associated with a phenotype of disease and development of a sampling strategy from identified distributions of these parameters. We improve on existing sampling methodologies by providing a means of representing the structural relationship across model parameters and describing propagation of variability in the model. This gives a robust, systematic method for creating a virtual population. We have developed the Linear-In-Flux-Expressions (LIFE) method to simulate variability in patient pharmacokinetics and pharmacodynamics using relationships between parameters at baseline to create a virtual population. We demonstrate the importance of this methodology on a model of cholesterol metabolism. The LIFE methodology brings us a step closer toward improved QSP simulators through enhanced capture of the observed variability in drug and disease clinical data.

Reduction in low-density lipoprotein cholesterol (LDL-C) is associated with decreased risk for cardiovascular disease. Alirocumab, an antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9), significantly reduces LDL-C. Here, we report development of a quantitative systems pharmacology (QSP) model integrating peripheral and liver cholesterol metabolism, as well as PCSK9 function, to examine the mechanisms of action of alirocumab and other lipid-lowering therapies, including statins. The model predicts changes in LDL-C and other lipids that are consistent with effects observed in clinical trials of single or combined treatments of alirocumab and other treatments. An exploratory model to examine the effects of lipid levels on plaque dynamics was also developed. The QSP platform, on further development and qualification, may support dose optimization and clinical trial design for PCSK9 inhibitors and lipid-modulating drugs. It may also improve our understanding of factors affecting therapeutic responses in different phenotypes of dyslipidemia and cardiovascular disease.

Signal-transducing functions driven by the cytoplasmic domain of membrane type-1 matrix metalloproteinase (MT1-MMP) are believed to regulate many inflammation-associated cancer cell functions including migration, proliferation, and survival. Aside from upregulation of the inflammation biomarker cyclooxygenase-2 (COX-2) expression, MT1-MMP's role in relaying intracellular signals triggered by extracellular pro-inflammatory cues remains poorly understood. Here, we triggered inflammation in HT1080 fibrosarcoma cells with phorbol-12-myristate-13-acetate (PMA), an inducer of COX-2 and of MT1-MMP. To assess the global transcriptional regulatory role that MT1-MMP may exert on inflammation biomarkers, we combined gene array screens with a transient MT1-MMP gene silencing strategy. Expression of MT1-MMP was found to exert both stimulatory and repressive transcriptional control of several inflammasome-related biomarkers such as interleukin (IL)-1B, IL-6, IL-12A, and IL-33, as well as of transcription factors such as EGR1, ELK1, and ETS1/2 in PMA-treated cells. Among the signal-transducing pathways explored, the silencing of MT1-MMP prevented PMA from phosphorylating extracellular signal-regulated kinase, inhibitor of κB, and p105 nuclear factor κB (NF-κB) intermediates. We also found a signaling axis linking MT1-MMP to MMP-9 transcriptional regulation. Altogether, our data indicate a significant involvement of MT1-MMP in the transcriptional regulation of inflammatory biomarkers consolidating its contribution to signal transduction functions in addition to its classical hydrolytic activity.

Hypoxia is an important environmental stressor leading to endocrine disruption and reproductive impairment in fish. Although the hypoxia-inducible factor 1 (HIF-1) is known to regulate the transcription of various genes mediating oxygen homeostasis, its role in modulating steroidogenesis-related gene expression remains poorly understood. In this study, the regulatory effect of HIF-1 on the expression of 9 steroidogenic enzyme genes was investigated in zebrafish embryos using a "gain-of-function and loss-of-function" approach. Eight of the genes, CYP11a, CYP11b2, 3β-HSD, HMGCR, CYP17a1, 17β-HSD2, CYP19a, and CYP19b, were found to be differentially upregulated at 24 and 48 hpf following zHIF-1α-ΔODD overexpression (a mutant zebrafish HIF-1α protein with proline-414 and proline-557 deleted). Knockdown of zHIF-1α also affected the expression pattern of the steroidogenic enzyme genes. Overexpression of zHIF-1α and hypoxia exposure resulted in downregulated StAR expression but upregulated CYP11a and 3β-HSD expression in zebrafish embryos. Conversely, the expression patterns of these 3 genes were reversed in embryos in which zHIF-1α was knocked down under normoxia, suggesting that these 3 genes are regulated by HIF-1. Overall, the findings from this study indicate that HIF-1-mediated mechanisms are likely involved in the regulation of specific steroidogenic genes.

Drug-induced liver injury (DILI) remains an adverse event of significant concern for drug development and marketed drugs, and the field would benefit from better tools to identify liver liabilities early in development and/or to mitigate potential DILI risk in otherwise promising drugs. DILIsym software takes a quantitative systems toxicology approach to represent DILI in pre-clinical species and in humans for the mechanistic investigation of liver toxicity. In addition to multiple intrinsic mechanisms of hepatocyte toxicity (ie, oxidative stress, bile acid accumulation, mitochondrial dysfunction), DILIsym includes the interaction between hepatocytes and cells of the innate immune response in the amplification of liver injury and in liver regeneration. The representation of innate immune responses, detailed here, consolidates much of the available data on the innate immune response in DILI within a single framework and affords the opportunity to systematically investigate the contribution of the innate response to DILI.

In the present report, we examined the responses of diabetic Goto-Kakizaki (GK) rats and control Wistar-Kyoto (WKY) rats fed either a standard chow or high-fat diet (HFD) from weaning to 20 weeks of age. This comparison included gene expression profiling of skeletal muscle using Affymetrix gene array chips. The expression profiling is interpreted within the context of a wide array of physiological measurements. Genes whose expressions are different between the 2 strains regardless of diet, as well as genes that differ between strains only with HFD, were identified. In addition, genes that were regulated by diet in 1 or both strains were identified. The results suggest that both strains respond to HFD by an increased capacity to oxidize lipid fuels in the musculature but that this adaptation occurs more rapidly in WKY rats. The results also demonstrated an impaired cytokine signalling and heightened inflammatory status in the GK rats.

The feeding and fasting cycles are strong behavioral signals that entrain biological rhythms of the periphery. The feeding rhythms synchronize the activities of the metabolic organs, such as liver, synergistically with the light/dark cycle primarily entraining the suprachiasmatic nucleus. The likely phase misalignment between the feeding rhythms and the light/dark cycles appears to induce circadian disruptions leading to multiple physiological abnormalities motivating the need to investigate the mechanisms behind joint light-feeding circadian entrainment of peripheral tissues. To address this question, we propose a semimechanistic mathematical model describing the circadian dynamics of peripheral clock genes in human hepatocyte under the control of metabolic and light rhythmic signals. The model takes the synergistically acting light/dark cycles and feeding rhythms as inputs and incorporates the activity of sirtuin 1, a cellular energy sensor and a metabolic enzyme activated by nicotinamide adenine dinucleotide. The clock gene dynamics was simulated under various light-feeding phase relations and intensities, to explore the feeding entrainment mechanism as well as the convolution of light and feeding signals in the periphery. Our model predicts that the peripheral clock genes in hepatocyte can be completely entrained to the feeding rhythms, independent of the light/dark cycle. Furthermore, it predicts that light-feeding phase relationship is a critical factor in robust circadian oscillations.

1[2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oyl]imidazole (CDDO-Im), a synthetic derivative of oleanolic acid that exhibits antioxidant and anti-inflammatory activity in several animal and in vitro models, has been shown to be beneficial if given after injury. Although induction of heme oxygenase 1 appears to be a major effector of cytoprotection, the mechanism by which the overall effect is mediated is largely unknown. This study evaluated temporal gene expression profiles to better characterize the early transcriptional events and their relationship to the dynamics of the cytoprotective response in human umbilical vein endothelial cells (HUVEC) to CDDO-Im. Time-course gene expression profiling was performed on HUVEC treated with CDDO-Im for 0.5, 1, 3, 6, and 24 hours. More than 10 000 genes were statistically altered in their expression in at least 1 time point across the time course. Large alterations in immediate-early gene expression were readily detectable within 0.5 hour after administration of CDDO-Im.

Drug-induced toxicity is a major public health concern that leads to patient morbidity and mortality. To address this problem, the Food and Drug Administration is working on the PredicTox initiative, a pilot research program on tyrosine kinase inhibitors, to build mechanistic and predictive models for drug-induced toxicity. This program involves integrating data acquired during preclinical studies and clinical trials within pharmaceutical company development programs that they have agreed to put in the public domain and in publicly available biological, pharmacological, and chemical databases. The integration process is accommodated by biomedical ontologies, a set of standardized vocabularies that define terms and logical relationships between them in each vocabulary. We describe a few programs that have used ontologies to address biomedical questions. The PredicTox effort is leveraging the experience gathered from these early initiatives to develop an infrastructure that allows evaluation of the hypothesis that having a mechanistic understanding underlying adverse drug reactions will improve the capacity to understand drug-induced clinical adverse drug reactions.

A main source of failures in systems projects (including systems pharmacology) is poor communication level and different expectations among the stakeholders. A common and not ambiguous language that is naturally comprehensible by all the involved players is a boost to success. We present bStyle, a modeling tool that adopts a graphical language close enough to cartoons to be a common media to exchange ideas and data and that it is at the same time formal enough to enable modeling, analysis, and dynamic simulations of a system. Data analysis and simulation integrated in the same application are fundamental to understand the mechanisms of actions of drugs: a core aspect of systems pharmacology.

Hepatic de-novo lipogenesis is a metabolic process implemented in the pathogenesis of type 2 diabetes. Clinically, the rate of this process can be ascertained by use of labeled acetate and stimulation by fructose administration. A systems pharmacology model of this process is desirable because it facilitates the description, analysis, and prediction of this experiment. Due to the multiple enzymes involved in de-novo lipogenesis, and the limited data, it is desirable to use single functional expressions to encapsulate the flux between multiple enzymes. To accomplish this we developed a novel simplification technique which uses the available information about the properties of the individual enzymes to bound the parameters of a single governing 'transfer function'. This method should be applicable to any model with linear chains of enzymes that are well stimulated. We validated this approach with computational simulations and analytical justification in a limiting case. Using this technique we generated a simple model of hepatic de-novo lipogenesis in these experimental conditions that matched prior data. This model can be used to assess pharmacological intervention at specific points on this pathway. We have demonstrated this with prospective simulation of acetyl-CoA carboxylase inhibition. This simplification technique suggests how the constituent properties of an enzymatic chain of reactions gives rise to the sensitivity (to substrate) of the pathway as a whole.

We describe a novel computational approach to identify transcription factors (TFs) that are candidate regulators in a human cell type of interest. Our approach involves integrating cell type-specific expression quantitative trait locus (eQTL) data and TF data from chromatin immunoprecipitation-to-tag-sequencing (ChIP-seq) experiments in cell lines. To test the method, we used eQTL data from human monocytes in order to screen for TFs. Using a list of known monocyte-regulating TFs, we tested the hypothesis that the binding sites of cell type-specific TF regulators would be concentrated in the vicinity of monocyte eQTLs. For each of 397 ChIP-seq data sets, we obtained an enrichment ratio for the number of ChIP-seq peaks that are located within monocyte eQTLs. We ranked ChIP-seq data sets according to their statistical significances for eQTL overlap, and from this ranking, we observed that monocyte-regulating TFs are more highly ranked than would be expected by chance. We identified 27 TFs that had significant monocyte enrichment scores and mapped them into a protein interaction network. Our analysis uncovered two novel candidate monocyte-regulating TFs, BCLAF1 and SIN3A. Our approach is an efficient method to identify candidate TFs that can be used for any cell/tissue type for which eQTL data are available.