Lipid network for control-group with parameter values ε = 0.4 and minimum modular size = 3.

<p>Significant lipids from the test for differential connectivity of a single lipid are circulated. Modules consisting of lipids belonging to the same lipid class are highlighted with a rectangle as well.</p

Test for differential modular structure in the case and control networks for MI LURIC data.

<p>For comparison, the same statistics and p-values are given for complete case (CC) data with subgroup of lipids from which 90% of the values were detected.</p

The 13 imputed lipids having significantly different mean concentrations between case and control groups by the marginal analysis implemented by using Rubin’s rules.

<p>The 13 imputed lipids having significantly different mean concentrations between case and control groups by the marginal analysis implemented by using Rubin’s rules.</p

The 10 most differentially connected lipids for MI LURIC data based on the test for differential connectivity of individual lipids between case and control groups.

<p>The 10 most differentially connected lipids for MI LURIC data based on the test for differential connectivity of individual lipids between case and control groups.</p

The three fully observed lipids having significantly different mean concentrations between case and control groups by the marginal analysis.

<p>The three fully observed lipids having significantly different mean concentrations between case and control groups by the marginal analysis.</p

Drug-Induced Rhabdomyolysis: From Systems Pharmacology Analysis to Biochemical Flux

The goal of this study was to integrate systems pharmacology and biochemical flux to delineate drug-induced rhabdomyolysis by leveraging prior knowledge and publicly accessible data. A list of 211 rhabdomyolysis-inducing drugs (RIDs) was compiled and curated from multiple sources. Extended pharmacological network analysis revealed that the intermediators directly interacting with the pharmacological targets of RIDs were significantly enriched with functions such as regulation of cell cycle, apoptosis, and ubiquitin-mediated proteolysis. A total of 78 intermediators were shown to be significantly connected to at least five RIDs, including estrogen receptor 1 (ESR1), synuclein gamma (SNCG), and janus kinase 2 (JAK2). Transcriptomic analysis of RIDs profiled in Connectivity Map on the global scale revealed that multiple pathways are perturbed by RIDs, including ErbB signaling and lipid metabolism pathways, and that carnitine palmitoyl transferase 2 (CPT2) was in the top 1 percent of the most differentially perturbed genes. <i>CPT</i>2 was downregulated by nine drugs that perturbed the genes significantly enriched in oxidative phosphorylation and energy-metabolism pathways. With statins as the use case, biochemical pathway analysis on the local scale implicated a role for CPT2 in statin-induced perturbation of energy homeostasis, which is in agreement with reports of statin–<i>CPT2</i> interaction. Considering the complexity of human biology, an integrative multiple-approach analysis composed of a biochemical flux network, pharmacological on- and off-target networks, and transcriptomic signature is important for understanding drug safety and for providing insight into clinical gene–drug interactions

PCSK9 Genotype frequencies (%) for cases and controls.

<p>Statistics: p1  = χ 2 test, p2 = Logistic regression analysis, with adjustment for: hypertension, hyperlipemia, diabetes, obesity, smoking, alcohol consumption</p><p>LVA = large-vessel atherosclerosis, SVO = small-vessel occlusion, IS = ischemic stroke, p3 = after controlling for multiple testing with Bonferoni correction.</p

Clinical characteristics of stroke patients and controls.

<p>χ2 test</p><p>MI = myocardial infarction, BMI = body mass index</p><p>IS ischemic stroke</p

Partial least squares discriminant analysis (PLS/DA) of serum lipidomics data.

<p>Results after 8 week treatment from placebo (N = 11), atorvastatin (N = 14), and simvastatin (N = 12) groups, with 132 identified lipid species included in analysis as variables. For each molecular species and each subject, its level after the 8 week treatment period was scaled by subtracting its median level across all subjects prior to treatment and divided by corresponding standard deviation. Four latent variables were used in the model (<i>Q</i>² = 0.46). The labels are patient ID numbers. The lines outlining different groups are shown as a guide. (A) The scores for Latent Variables (LV) 1 and 3 reveal serum lipid changes specific to the statin treatment (LV1) as well as statin-specific changes (LV3). (B) Loadings on LV3 for most important lipids in simvastatin or atorvastatin groups selected by VIP analysis. Only lipids for which at least one of the two groups has VIP value greater than 2 are shown.</p

PLS/DA analysis on combined muscle gene expression and serum lipid data.

<p>Results after intervention for the subjects from placebo (N = 5), atorvastatin (N = 6), and simvastatin (N = 6) groups. Total 38 genes from four enriched pathways and 132 lipids were included in the analysis as variables. Data was autoscaled prior to multivariate analysis. Three latent variables were used in the model (<i>Q</i>² = 0.50). The labels are patient ID numbers. (A) The PLS/DA score plot reveals treatment-specific differences between the treatments are observed in molecular profiles after intervention. (B) Loadings for the first two latent variables reveal plasma lipid classes and muscle pathways associated with specific interventions. LPC is shorthand for lysophosphatidylcholine (for example GPCho(18∶0/0∶0)).</p