Genome-Wide Small RNA Sequencing and Gene Expression Analysis Reveals a microRNA Profile of Cancer Susceptibility in ATM-Deficient Human Mammary Epithelial Cells

<div><p>Deficiencies in the ATM gene are the underlying cause for ataxia telangiectasia, a syndrome characterized by neurological, motor and immunological defects, and a predisposition to cancer. MicroRNAs (miRNAs) are useful tools for cancer profiling and prediction of therapeutic responses to clinical regimens. We investigated the consequences of ATM deficiency on miRNA expression and associated gene expression in normal human mammary epithelial cells (HME-CCs). We identified 81 significantly differentially expressed miRNAs in ATM-deficient HME-CCs using small RNA sequencing. Many of these have been implicated in tumorigenesis and proliferation and include down-regulated tumor suppressor miRNAs, such as hsa-miR-29c and hsa-miR-16, as well as over-expressed pro-oncogenic miRNAs, such as hsa-miR-93 and hsa-miR-221. MicroRNA changes were integrated with genome wide gene expression profiles to investigate possible miRNA targets. Predicted mRNA targets of the miRNAs significantly regulated after ATM depletion included many genes associated with cancer formation and progression, such as SOCS1 and the proto-oncogene MAF. While a number of miRNAs have been reported as altered in cancerous cells, there is little understanding as to how these small RNAs might be driving cancer formation or how they might be used as biomarkers for cancer susceptibility. This study provides preliminary data for defining miRNA profiles that may be used as prognostic or predictive biomarkers for breast cancer. Our integrated analysis of miRNA and mRNA expression allows us to gain a better understanding of the signaling involved in breast cancer predisposition and suggests a mechanism for the breast cancer-prone phenotype seen in ATM-deficient patients.</p> </div

Depletion of ATM in non-cancerous cells reveals effects on miRNAs and target mRNAs that suggest an early event in transformation to cancer.

<p>A) Gene Families representing the 202 significantly regulated genes determined using GSEA to give a functional overview of the types of genes affected by changes in miRNA expression. B) Top Functions analysis of ATM-dependent correlated miRNAs and possible mRNA targets by IPA. Only selected significant functional groups are depicted. The dashed line indicates a <i>p</i>-value of 0.01.</p

Differential expression of 259 present miRNAs in both wild type and ATM-deficient HME-CCs.

<p>Differential miRNA expression between wild type and ATM-deficient HME-CCs obtained from three independent replicates of each. The <i>y</i>-axis displays the ATM-deficient to WT expression ratio, the <i>x</i>-axis displays the average expression of each miRNA; both axes are in logarithmic scale. Differentially expressed miRNAs of <i>p</i>-value ≤0.05 and at least 1.5 fold change are blue. Representative significant miRNAs are labelled. Each sample had a separately generated sequencing library and was run in an individual sequencing lane.</p

Depletion of ATM leads to deregulation of miRNAs important in cancer formation.

<p>A) List of 4 known tumor suppressors and 8 oncomirs with significant expression changes after the depletion of ATM. B) Expression levels, tags per million (TpM), of four examples of deregulated miRNAs. Dark gray bars represent wild type expression and light gray bars represent expression in ATM-deficient cells. Error bars are standard error from the expression of 3 independent replicates of each genotype.</p

Process Map and Summary of Next Gen Sequencing data.

<p>A) Small RNA Sequencing pipeline overview. B) Summary statistics of Small RNA sequencing data at different stages of data analysis.</p

Real-time cell toxicity profiling of Tox21 10K compounds reveals cytotoxicity dependent toxicity pathway linkage

<div><p>Cytotoxicity is a commonly used <i>in vitro</i> endpoint for evaluating chemical toxicity. In support of the U.S. Tox21 screening program, the cytotoxicity of ~10K chemicals was interrogated at 0, 8, 16, 24, 32, & 40 hours of exposure in a concentration dependent fashion in two cell lines (HEK293, HepG2) using two multiplexed, real-time assay technologies. One technology measures the metabolic activity of cells (i.e., cell viability, <i>glo</i>) while the other evaluates cell membrane integrity (i.e., cell death, <i>flor</i>). Using <i>glo</i> technology, more actives and greater temporal variations were seen in HEK293 cells, while results for the <i>flor</i> technology were more similar across the two cell types. Chemicals were grouped into classes based on their cytotoxicity kinetics profiles and these classes were evaluated for their associations with activity in the Tox21 nuclear receptor and stress response pathway assays. Some pathways, such as the activation of H2AX, were associated with the fast-responding cytotoxicity classes, while others, such as activation of TP53, were associated with the slow-responding cytotoxicity classes. By clustering pathways based on their degree of association to the different cytotoxicity kinetics labels, we identified clusters of pathways where active chemicals presented similar kinetics of cytotoxicity. Such linkages could be due to shared underlying biological processes between pathways, for example, activation of H2AX and heat shock factor. Others involving nuclear receptor activity are likely due to shared chemical structures rather than pathway level interactions. Based on the linkage between androgen receptor antagonism and Nrf2 activity, we surmise that a subclass of androgen receptor antagonists cause cytotoxicity via oxidative stress that is associated with Nrf2 activation. In summary, the real-time cytotoxicity screen provides informative chemical cytotoxicity kinetics data related to their cytotoxicity mechanisms, and with our analysis, it is possible to formulate mechanism-based hypotheses on the cytotoxic properties of the tested chemicals.</p></div

Number of chemicals that reached their maximum cytotoxic effect in each time interval by assay.

<p>Number of chemicals that reached their maximum cytotoxic effect in each time interval by assay.</p

Comparison of number of actives in four assays.

<p>The number of actives detected in the four assays at the six different time points. Blue: HEK293 cell line; red: HepG2 cell line. Filled circle: <i>glo</i> assay technology; hollow circle: <i>flor</i> assay technology.</p

Activity potency comparison between the known nuclear receptor antagonists clustered based on their cytotoxicity kinetics activity data.

<p>a) AR antagonists. b) ER antagonists. Dendrogram on the left represents the clustering using the cytotoxicity kinetics activity data and color represents the groupings; the symbols represent the most potent activity in the respective assays.</p

Cytotoxicity kinetics data of mitomycin C in HEK293 cell line using <i>glo</i> technology.

<p>a) The percent of activity is plotted as the function of hour. The color represents different concentrations of the chemical. The darker color (redder) is equivalent to higher concentrations. b) The concentration-response data at three representative time points (0, 16, 40 hour). The total effect across concentrations can be summarized as wAUC. c) The total effect (log₁₀(wAUC+1)) is plotted as the function of exposure duration in hours.</p