Identification of a Kinase Profile that Predicts Chromosome Damage Induced by Small Molecule Kinase Inhibitors

Kinases are heavily pursued pharmaceutical targets because of their mechanistic role in many diseases. Small molecule kinase inhibitors (SMKIs) are a compound class that includes marketed drugs and compounds in various stages of drug development. While effective, many SMKIs have been associated with toxicity including chromosomal damage. Screening for kinase-mediated toxicity as early as possible is crucial, as is a better understanding of how off-target kinase inhibition may give rise to chromosomal damage. To that end, we employed a competitive binding assay and an analytical method to predict the toxicity of SMKIs. Specifically, we developed a model based on the binding affinity of SMKIs to a panel of kinases to predict whether a compound tests positive for chromosome damage. As training data, we used the binding affinity of 113 SMKIs against a representative subset of all kinases (290 kinases), yielding a 113×290 data matrix. Additionally, these 113 SMKIs were tested for genotoxicity in an in vitro micronucleus test (MNT). Among a variety of models from our analytical toolbox, we selected using cross-validation a combination of feature selection and pattern recognition techniques: Kolmogorov-Smirnov/T-test hybrid as a univariate filter, followed by Random Forests for feature selection and Support Vector Machines (SVM) for pattern recognition. Feature selection identified 21 kinases predictive of MNT. Using the corresponding binding affinities, the SVM could accurately predict MNT results with 85% accuracy (68% sensitivity, 91% specificity). This indicates that kinase inhibition profiles are predictive of SMKI genotoxicity. While in vitro testing is required for regulatory review, our analysis identified a fast and cost-efficient method for screening out compounds earlier in drug development. Equally important, by identifying a panel of kinases predictive of genotoxicity, we provide medicinal chemists a set of kinases to avoid when designing compounds, thereby providing a basis for rational drug design away from genotoxicity

REGULATORY NETWORKS OF PXR, CAR AND LXR IN CHOLESTEROL AND BILE ACID METABOLISM

The orphan nuclear receptors Pregnane X Receptor (PXR) and Constitutive Androstane Receptor (CAR) have been proposed to play an important role in the detoxification of xeno- and endobiotics by regulating the expression of detoxifying enzymes and transporters. We showed that the combined loss of PXR and CAR resulted in a significantly heightened sensitivity to bile acid toxicity in a sex-specific manner. The increased sensitivity in males was associated with genotype-specific suppression of bile acid transporters and loss of bile acid-mediated down regulation of small heterodimer partner, whereas the transporter suppression was modest or absent in the female DKO mice. The liver X receptors (LXRs), including the alpha and beta isoforms were identified as sterol sensors that regulate cholesterol and lipid homeostasis and macrophage functions. We found that activation of LXRĄ in transgenic mice or with LXR ligands confers a female-specific resistance to lithocholic acid (LCA)-induced hepatotoxicity and bile duct ligation (BDL)-induced cholestasis. In contrast, LXR alpha and beta double knockout mice (LXR DKO) exhibited heightened cholestatic sensitivity. The LCA and BDL resistance in transgenic mice was associated with an increased expression of bile acid detoxifying sulfotransferase 2A (SULT2A) and selected members of the bile acid transporters. We also showed that genetic or pharmacological activation of the orphan nuclear receptor liver X receptor (LXR) sensitized mice to cholesterol gallstone disease (CGD) induced by a high cholesterol lithogenic diet. LXR-promoted CGD was associated with increased expression of several canalicular transporters that efflux cholesterol and phospholipids, leading to higher biliary concentrations of cholesterol and phospholipids. The biliary bile salt concentration was reduced in these mice, resulting in increased cholesterol saturation index (CSI). Interestingly, the lithogenic effect of LXR was completely abolished in the low-density lipoprotein receptor (LDLR) null background or when the mice were treated with Ezetimibe, a cholesterol-lowering drug that blocks the intestinal dietary cholesterol absorption. We propose that LXRs have evolved to have dual function in maintaining cholesterol and bile acid homeostasis

Direct reprogramming of human fibroblasts to hepatocyte-like cells by synthetic modified mRNAs.

Direct reprogramming by overexpression of defined transcription factors is a promising new method of deriving useful but rare cell types from readily available ones. While the method presents numerous advantages over induced pluripotent stem (iPS) cell approaches, a focus on murine conversions and a reliance on retroviral vectors limit potential human applications. Here we address these concerns by demonstrating direct conversion of human fibroblasts to hepatocyte-like cells via repeated transfection with synthetic modified mRNAs. Hepatic induction was achieved with as little as three transcription factor mRNAs encoding HNF1A plus any two of the factors, FOXA1, FOXA3, or HNF4A in the presence of an optimized hepatic growth medium. We show that the absolute necessity of exogenous HNF1A mRNA delivery is explained both by the factor's inability to be activated by any other factors screened and its simultaneous ability to strongly induce expression of other master hepatic transcription factors. Further analysis of factor interaction showed that a series of robust cross-activations exist between factors that induce a hepatocyte-like state. Transcriptome and small RNA sequencing during conversion toward hepatocyte-like cells revealed global preferential activation of liver genes and miRNAs over those associated with other endodermal tissues, as well as downregulation of fibroblast-associated genes. Induced hepatocyte-like cells also exhibited hepatic morphology and protein expression. Our data provide insight into the process by which direct hepatic reprogramming occurs in human cells. More importantly, by demonstrating that it is possible to achieve direct reprogramming without the use of retroviral gene delivery, our results supply a crucial step toward realizing the potential of direct reprogramming in regenerative medicine

Improving Risk Assessment

A U.S. government initiative to engineer nonclinical cell-based models that mimic human biology may improve predictions of drug-related adverse events.</jats:p

Robust compensation and interaction between the transcription factors of 6TF.

<p>(A) Cells were reprogrammed with six different combinations of 5TF. The factor missing from each 5TF sample is indicated on the x-axis, and its expression compared to primary hepatocytes is indicated (log2 ratio). The top of each clear bar indicates the level of each factor when delivered to fibroblasts via 6TF; the bottom of each clear bar indicates the endogenous level found in untransfected fibroblasts. Taken together these bars show the compensation each factor receives from the other five factors of 6TF, in relation to hepatocytes, fibroblasts, and 6TF samples. <i>FOXA1</i>, <i>FOXA2</i>, <i>GATA4</i>, and <i>HNF4A</i> were compensated at or above the levels found endogenously in hepatocytes. <i>FOXA3</i> levels were compensated near those of hepatocytes. However, <i>HNF1A</i> received almost no compensation with levels remaining closest to those found in fibroblasts. (B) The first column indicates the effects reprogramming with <i>HNF1A</i> alone exerts on the remaining five factors of 6TF over fibroblast control levels. The remaining columns indicate the effect reprogramming with <i>HNF1A</i> plus one additional factor have on the remaining four factors over the levels found when cells are reprogrammed with <i>HNF1A</i> alone. Upregulations are in red and downregulations in blue. Extensive interactions between the factors are observed. (C) The interactions of the previous matrix are binned into bright red activations (10-fold or more increase in expression), light red prospective activations (2-fold or more increase), and light blue prospective inhibitions (2-fold or more decrease). (D) The interactions defined by the previous binned matrix represented as a diagram between the factors of 6TF during reprogramming. Prospective interactions are dashed.</p

Global distribution of genes and small RNAs in 6TF, 11TF, and control.

<p>R² values are calculated based on the log2 of the sequencing expression value for genes and small RNAs. The full expression data for all genes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s010" target="_blank">Tables S4</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s011" target="_blank">S5</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s012" target="_blank">S6</a>) and small RNAs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s013" target="_blank">Tables S7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s014" target="_blank">S8</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s015" target="_blank">S9</a>) are provided in the supporting information.</p

<i>HNF1A</i> is necessary for reprogramming and sufficient in combination with two interchangeable transcription factors.

<p>(A–B) Concurrent staining of reprogrammed samples for AFP and Foxa2 shows that AFP-positive converted cells can exist with or without Foxa2 receipt and expression, indicating that not all six factors of 6TF are necessary for reprogramming. Nuclei are stained blue by Hoechst. (C–E) Consistent loss of <i>AFP</i> induction with removal of <i>HNF1A</i> (blue) from reprogramming cocktails, 6TF, 5TF, and 4TF, with little or no decrease of induction due to removal of other factors. (F–H) Albumin induction measurement shows the same trend as <i>AFP</i> induction. Reprogramming can be achieved with as little as three transcription factors, <i>HNF1A</i> plus two of the following three factors, <i>FOXA1</i>, <i>FOXA3</i>, or <i>HNF4A</i>. Reprogramming could not be achieved at levels resemebling those of 6TF with less than three factors (I–J). Data points shown are meanSD. Stars indicate p-val 0.001. (K) 5TF sample lacking <i>HNF1A</i> clusters with vehicle control separately from 6TF and other 5TF samples and away from hepatocytes based on 33 hepatic genes, supporting the absolute necessity of <i>HNF1A</i>.</p

Hepatocyte-like state induced by 11TF and 6TF within five days.

<p>(A) Massive induction of hepatic genes, <i>ALB</i> and <i>AFP</i>, within five days of reprogramming (meanSD). Distinct albumin (B), AFP (C), and neutral lipid droplet (D) positive cells also appear within 5 days. Nuclei are stained blue by Hoechst.</p

Global gene and small RNA sequencing analysis of reprogrammed cells.

<p>(A–B) Genes and small RNAs significantly upregulated more than 2-fold over control (red), significantly downregulated more than 2-fold below control (green), and genes without significant changes of 2-fold or more (blue) are plotted logarithmically for 6TF versus vehicle control. Well-known liver and liver-repair associated genes are upregulated (labeled in red), whereas fibroblast associated genes are downregulated (labeled in green). Pluripotency genes and control genes are unchanged (labeled in blue). Well-known hepatic miRNAs, such as miR-122, are upregulated (labeled in red). False discovery rates less than 0.001 for genes and p-values less than 0.05 for small RNAs were deemed significant. (C) Of the top 25 most upregulated genes in reprogrammed cells, twelve (nearly half) are associated with liver or liver-repair (red), four are associated with other endodermal tissues (blue), and four are histones (green). Histone genes were also globally upregulated in reprogrammed cells (D); mean and 75% and 25% quantiles are indicated. The full expression data for all genes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s010" target="_blank">Tables S4</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s011" target="_blank">S5</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s012" target="_blank">S6</a>) and small RNAs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s013" target="_blank">Tables S7</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s014" target="_blank">S8</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100134#pone.0100134.s015" target="_blank">S9</a>) are provided in the supporting information.</p