Comparative Transcriptional Network Modeling of Three PPAR-α/γ Co-Agonists Reveals Distinct Metabolic Gene Signatures in Primary Human Hepatocytes

Aims: To compare the molecular and biologic signatures of a balanced dual peroxisome proliferator-activated receptor (PPAR)-α/γ agonist, aleglitazar, with tesaglitazar (a dual PPAR-α/γ agonist) or a combination of pioglitazone (Pio; PPAR-γ agonist) and fenofibrate (Feno; PPAR-α agonist) in human hepatocytes. Methods and Results: Gene expression microarray profiles were obtained from primary human hepatocytes treated with EC50-aligned low, medium and high concentrations of the three treatments. A systems biology approach, Causal Network Modeling, was used to model the data to infer upstream molecular mechanisms that may explain the observed changes in gene expression. Aleglitazar, tesaglitazar and Pio/Feno each induced unique transcriptional signatures, despite comparable core PPAR signaling. Although all treatments inferred qualitatively similar PPAR-α signaling, aleglitazar was inferred to have greater effects on high- and low-density lipoprotein cholesterol levels than tesaglitazar and Pio/Feno, due to a greater number of gene expression changes in pathways related to high-density and low-density lipoprotein metabolism. Distinct transcriptional and biologic signatures were also inferred for stress responses, which appeared to be less affected by aleglitazar than the comparators. In particular, Pio/Feno was inferred to increase NFE2L2 activity, a key component of the stress response pathway, while aleglitazar had no significant effect. All treatments were inferred to decrease proliferative signaling. Conclusions: Aleglitazar induces transcriptional signatures related to lipid parameters and stress responses that are unique from other dual PPAR-α/γ treatments. This may underlie observed favorable changes in lipid profiles in animal and clinical studies with aleglitazar and suggests a differentiated gene profile compared with other dual PPAR-α/γ agonist treatments

Small molecule activators of SIRT1 replicate signaling pathways triggered by calorie restriction in vivo

<p>Abstract</p> <p>Background</p> <p>Calorie restriction (CR) produces a number of health benefits and ameliorates diseases of aging such as type 2 diabetes. The components of the pathways downstream of CR may provide intervention points for developing therapeutics for treating diseases of aging. The NAD⁺-dependent protein deacetylase SIRT1 has been implicated as one of the key downstream regulators of CR in yeast, rodents, and humans. Small molecule activators of SIRT1 have been identified that exhibit efficacy in animal models of diseases typically associated with aging including type 2 diabetes. To identify molecular processes induced in the liver of mice treated with two structurally distinct SIRT1 activators, SIRT501 (formulated resveratrol) and SRT1720, for three days, we utilized a systems biology approach and applied Causal Network Modeling (CNM) on gene expression data to elucidate downstream effects of SIRT1 activation.</p> <p>Results</p> <p>Here we demonstrate that SIRT1 activators recapitulate many of the molecular events downstream of CR <it>in vivo</it>, such as enhancing mitochondrial biogenesis, improving metabolic signaling pathways, and blunting pro-inflammatory pathways in mice fed a high fat, high calorie diet.</p> <p>Conclusion</p> <p>CNM of gene expression data from mice treated with SRT501 or SRT1720 in combination with supporting <it>in vitro </it>and <it>in vivo </it>data demonstrates that SRT501 and SRT1720 produce a signaling profile that mirrors CR, improves glucose and insulin homeostasis, and acts via SIRT1 activation <it>in vivo</it>. Taken together these results are encouraging regarding the use of small molecule activators of SIRT1 for therapeutic intervention into type 2 diabetes, a strategy which is currently being investigated in multiple clinical trials.</p

The Role of Hypoxia in 2-Butoxyethanol–Induced Hemangiosarcoma

To understand the molecular mechanisms underlying compound-induced hemangiosarcomas in mice, and therefore, their human relevance, a systems biology approach was undertaken using transcriptomics and Causal Network Modeling from mice treated with 2-butoxyethanol (2-BE). 2-BE is a hemolytic agent that induces hemangiosarcomas in mice. We hypothesized that the hemolysis induced by 2-BE would result in local tissue hypoxia, a well-documented trigger for endothelial cell proliferation leading to hemangiosarcoma. Gene expression data from bone marrow (BM), liver, and spleen of mice exposed to a single dose (4 h) or seven daily doses of 2-BE were used to develop a mechanistic model of hemangiosarcoma. The resulting mechanistic model confirms previous work proposing that 2-BE induces macrophage activation and inflammation in the liver. In addition, the model supports local tissue hypoxia in the liver and spleen, coupled with increased erythropoeitin signaling and erythropoiesis in the spleen and BM, and suppression of mechanisms that contribute to genomic stability, events that could be contributing factors to hemangiosarcoma formation. Finally, an immunohistochemistry method (Hypoxyprobe) demonstrated that tissue hypoxia was present in the spleen and BM. Together, the results of this study identify molecular mechanisms that initiate hemangiosarcoma, a key step in understanding safety concerns that can impact drug decision processes, and identified hypoxia as a possible contributing factor for 2-BE–induced hemangiosarcoma in mice

Harnessing big data for precision medicine: A panel of experts elucidates the data challenges and proposes key strategic decisions points

A group of disparate translational bioinformatics experts convened at the 6th Annual Precision Medicine Partnership Meeting, October 29–30, 2014 to discuss big data challenges and key strategic decisions needed to advance precision medicine, emerging solutions, and the anticipated path to success. This article reports the panel discussion

Treatment-induced RNA and gene expression changes.

<p>(<b>A</b>) Total number of RNA state changes (up- or downregulation) in human hepatocytes treated with aleglitazar, tesaglitazar or Pio/Feno. (<b>B</b>) Unique and shared gene expression following treatment with aleglitazar, tesaglitazar or Pio/Feno.</p

Clustering of the hypotheses into broader biologic processes/pathways.

<p>Numbers (in the boxes) indicate the number of genes/hypotheses in molecular pathway clusters.</p

Molecular pathways modulated in response to treatment with aleglitazar, tesaglitazar or Pio/Feno.

<p>Number of RNA expression changes observed following treatment with aleglitazar, tesaglitazar or Pio/Feno. Of the 2,000 unique mechanisms represented in the knowledgebase, 280 were considered statistically significant in at least one treatment condition and are designated as “hypotheses”.</p

Cellular stress response-associated RNA changes and inferred signaling pathways.

<p>(<b>A</b>) Biologic processes associated with the cellular stress response inferred to increase or decrease upon treatment with aleglitazar, tesaglitazar or Pio/Feno. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035012#pone-0035012-g002" target="_blank"><i>Figure 2A</i></a> for treatment dosages. As in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035012#pone-0035012-g005" target="_blank"><i>Figure 5A</i></a>, proxy hypotheses are shown. (<b>B</b>) Molecular signaling pathway, predicted by RNA state changes shown in panel A, that could lead to changes in the stress response. Numbers in the blue or yellow boxes indicate the number of gene expression state changes that support the inference: negative numbers indicate an inferred decrease and positive numbers indicate an inferred increase. Numbers in the green or red boxes indicate the log fold change. * Denotes genes/pathways responsive to the agent cited. ER, endoplasmic reticulum; Med, medium; ROS, reactive oxygen species.</p