Gene regulation and systems biology最新文献

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.