Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis

Alcoholic hepatitis (AH) is a life-threatening condition characterized by profound hepatocellular dysfunction for which targeted treatments are urgently needed. Identification of molecular drivers is hampered by the lack of suitable animal models. By performing RNA sequencing in livers from patients with different phenotypes of alcohol-related liver disease (ALD), we show that development of AH is characterized by defective activity of liver-enriched transcription factors (LETFs). TGFβ1 is a key upstream transcriptome regulator in AH and induces the use of HNF4α P2 promoter in hepatocytes, which results in defective metabolic and synthetic functions. Gene polymorphisms in LETFs including HNF4α are not associated with the development of AH. In contrast, epigenetic studies show that AH livers have profound changes in DNA methylation state and chromatin remodeling, affecting HNF4α-dependent gene expression. We conclude that targeting TGFβ1 and epigenetic drivers that modulate HNF4α-dependent gene expression could be beneficial to improve hepatocellular function in patients with AH

Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.

One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology

A Quantitative Systems Pharmacology Platform Reveals NAFLD Pathophysiological States and Targeting Strategies

Non-alcoholic fatty liver disease (NAFLD) has a high global prevalence with a heterogeneous and complex pathophysiology that presents barriers to traditional targeted therapeutic approaches. We describe an integrated quantitative systems pharmacology (QSP) platform that comprehensively and unbiasedly defines disease states, in contrast to just individual genes or pathways, that promote NAFLD progression. The QSP platform can be used to predict drugs that normalize these disease states and experimentally test predictions in a human liver acinus microphysiology system (LAMPS) that recapitulates key aspects of NAFLD. Analysis of a 182 patient-derived hepatic RNA-sequencing dataset generated 12 gene signatures mirroring these states. Screening against the LINCS L1000 database led to the identification of drugs predicted to revert these signatures and corresponding disease states. A proof-of-concept study in LAMPS demonstrated mitigation of steatosis, inflammation, and fibrosis, especially with drug combinations. Mechanistically, several structurally diverse drugs were predicted to interact with a subnetwork of nuclear receptors, including pregnane X receptor (PXR; NR1I2), that has evolved to respond to both xenobiotic and endogenous ligands and is intrinsic to NAFLD-associated transcription dysregulation. In conjunction with iPSC-derived cells, this platform has the potential for developing personalized NAFLD therapeutic strategies, informing disease mechanisms, and defining optimal cohorts of patients for clinical trials

Heterogeneity in the activation STAT3 in Cal33 cells.

<p>Cal33 cells were treated with IL-6 (50 ng/ml) for 15 min. then fixed and labeled with an antibody to phospho-STAT3-Y705. A) Pseudocolor image of STAT3 activation shows a high degree of heterogeneity in the intensity of the Cy5-labeled secondary antibody (color scale at lower right indicates mapping of relative fluorescent intensities to colors). Scale bar is 100 um (lower left). B) The standard deviation of the well average STAT3 activity in replicate wells (EC50 = 3.3 ng/ml, error bars are ±1σ, N = 8) indicates the assay is highly reproducible despite the observed cellular heterogeneity (Z’ = 0.54) C) The standard deviation of the cellular STAT3 activity (error bars are ±1σ) indicates the high variability in the cell-to-cell STAT3 Activity consistent with the appearance of the image (A).</p

Comparison of the activation of STAT3 across 5 cell lines.

<p>Application of the HI's to the data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102678#pone-0102678-g002" target="_blank">Figure 2</a>. Left Panel) Activation of pSTAT3 by exposure to IL-6 for 15 min at the indicated concentrations. Right Panel) Activation of pSTAT3 by exposure to Oncostatin M for 15 min at the indicated concentrations. Red Bars) Diversity index (DIV) indicating the relative heterogeneity associated with the activation of pSTAT3. The horizontal red line indicates the selected threshold for classifying populations a heterogeneous. Green Bars) The non-Normality index (nNRM) indicating the extent of deviation from a single, normally distributed population. The green horizontal line indicates the selected threshold for classifying a population as having macro-heterogeneity. Blue Bars) The percent outliers (%OL) indicates the percentage of cells with an activity level that is above the upper inner fence or below the lower inner fence. The horizontal blue line indicates the selected threshold that is used to classify a population as having more than the expected number of outliers.</p

Heterogeneity analysis applied throughout the early drug discovery process.

<p>Heterogeneity analysis is required to guide decisions throughout the drug discovery process, beginning with defining disease relevant biology in clinical samples, and establishing benchmarks for subsequent analyses. Next disease relevant models, which by necessity will be heterogeneous, are developed and optimized. Heterogeneity is characterized in the models, and thresholds for HI's are established along with potency criteria to select hits. Screening hits are advanced to secondary assays based on their potency and HI profile. Heterogeneity of response to compounds will be model dependent, and assessing heterogeneity in orthogonal secondary assays will provide insights into understanding the MOA. Monitoring the heterogeneity profile during SAR and lead optimization is essential to keeping lead development on target and mechanism of the disease relevant biology.</p

Decision tree for interpreting the Heterogeneity Indices.

<p>Using thresholds established for each index, DIV, nNRM and %OL, a binary decision tree can be used to characterize heterogeneity in a given sample. The thresholds for DIV (0.03) and nNRM (0.05) were selected as the mean +3 SD for each index in replicate negative control wells for Cal33 cells. The threshold for %OL (4.5%) is the percent outliers expected for a normal distribution.</p

Variation in the cellular distributions of STAT3 activation by IL-6 and OSM in several cell types.

<p>Top series) Histo-box plots of the activation of STAT3 by IL-6 after 15 min exposure to IL-6 at the indicated concentrations in 2 HNSCC cell lines, 1 breast cell line and 2 breast cancer cell lines. Bottom series) The activation of STAT3 by OSM was measured at 15 min. in the same 5 cell lines as above. Note: 686LN cells were found to be much more sensitive to IL-6 and much less sensitive to OSM than the other cell lines, so the concentrations were adjusted appropriately.</p

Three indices for characterizing cellular heterogeneity.

<p>Three indices that provide information about the distribution were chosen. Cell Diversity (DIV) characterizes the overall heterogeneity in the population without regard for the specific shape of the distribution, using the Quadratic Entropy, a metric that is sensitive to the spread of the distribution as well as the magnitude of the differences between phenotypes in the distribution. Non-Normality (nNRM) indicates deviation from a normal distribution, distinguishing between micro- and macro-heterogeneity. %Outliers (%OL) indicates the fraction of cells that respond differently than the majority.</p