The Response of the Prostate to Circulating Cholesterol: Activating Transcription Factor 3 (ATF3) as a Prominent Node in a Cholesterol-Sensing Network

Elevated circulating cholesterol is a systemic risk factor for cardiovascular disease and metabolic syndrome, however the manner in which the normal prostate responds to variations in cholesterol levels is poorly understood. In this study we addressed the molecular and cellular effects of elevated and suppressed levels of circulating cholesterol on the normal prostate. Integrated bioinformatic analysis was performed using DNA microarray data from two experimental formats: (1) ventral prostate from male mice with chronically elevated circulating cholesterol and (2) human prostate cells exposed acutely to cholesterol depletion. A cholesterol-sensitive gene expression network was constructed from these data and the transcription factor ATF3 was identified as a prominent node in the network. Validation experiments confirmed that elevated cholesterol reduced ATF3 expression and enhanced proliferation of prostate cells, while cholesterol depletion increased ATF3 levels and inhibited proliferation. Cholesterol reduction in vivo alleviated dense lymphomononuclear infiltrates in the periprostatic adipose tissue, which were closely associated with nerve tracts and blood vessels. These findings open new perspectives on the role of cholesterol in prostate health, and provide a novel role for ATF3, and associated proteins within a large signaling network, as a cholesterol-sensing mechanism

Impact of Circulating Cholesterol Levels on Growth and Intratumoral Androgen Concentration of Prostate Tumors

Prostate cancer (PCa) is the second most common cancer in men. Androgen deprivation therapy (ADT) leads to tumor involution and reduction of tumor burden. However, tumors eventually reemerge that have overcome the absence of gonadal androgens, termed castration resistant PCa (CRPC). Theories underlying the development of CRPC include androgen receptor (AR) mutation allowing for promiscuous activation by non-androgens, AR amplification and overexpression leading to hypersensitivity to low androgen levels, and/or tumoral uptake and conversion of adrenally derived androgens. More recently it has been proposed that prostate tumor cells synthesize their own androgens through de novo steroidogenesis, which involves the step-wise synthesis of androgens from cholesterol. Using the in vivo LNCaP PCa xenograft model, previous data from our group demonstrated that a hypercholesterolemia diet potentiates prostatic tumor growth via induction of angiogenesis. Using this same model we now demonstrate that circulating cholesterol levels are significantly associated with tumor size (R = 0.3957, p = 0.0049) and intratumoral levels of testosterone (R = 0.41, p = 0.0023) in LNCaP tumors grown in hormonally intact mice. We demonstrate tumoral expression of cholesterol uptake genes as well as the spectrum of steroidogenic enzymes necessary for androgen biosynthesis from cholesterol. Moreover, we show that circulating cholesterol levels are directly correlated with tumoral expression of CYP17A, the critical enzyme required for de novo synthesis of androgens from cholesterol (R = 0.4073, p = 0.025) Since hypercholesterolemia does not raise circulating androgen levels and the adrenal gland of the mouse synthesizes minimal androgens, this study provides evidence that hypercholesterolemia increases intratumoral de novo steroidogenesis. Our results are consistent with the hypothesis that cholesterol-fueled intratumoral androgen synthesis may accelerate the growth of prostate tumors, and suggest that treatment of CRPC may be optimized by inclusion of cholesterol reduction therapies in conjunction with therapies targeting androgen synthesis and the AR

Growth of LNCaP xenograft tumors in relation to serum cholesterol levels.

<p>Mean tumor weight (<b>A</b>) and volume (<b>B</b>) (with standard deviation and range) in mice with cholesterol levels above or below the median serum cholesterol level. P values from unpaired two sample t-tests. Mean tumor weight (<b>C</b>) and volume (<b>D</b>) (with standard deviation and range) in mice grouped by quartile of serum cholesterol levels. P values from one way ANOVA of mean values in the four quartiles, with a post test for linear trend. The mean cholesterol levels in tumors above or below the median, or in each quartile of cholesterol, and the number of mice in each group, are indicated below the x-axis in each graph.</p

Correlation of serum cholesterol levels with tumor weight, tumors androgens and expression of CYP17A in LNCaP xenografts.

<p>The correlation of serum cholesterol levels with tumor weight (<b>A</b>), testosterone (<b>B</b>) and DHT (<b>C</b>) in aggregated tumors from all four treatment cohorts (n = 52). Tumor androgens were determined by mass spectrometry. Correlation coefficients and p values from linear regression analysis.</p

Expression of steroidogenic enzymes necessary for <i>de novo</i> synthesis of androgens from cholesterol in LNCaP xenografts.

<p>(<b>A</b>) Enzymes and intermediates in the steroid bio-synthetic pathway leading from cholesterol to the formation of testosterone and DHT. LDLR and SR-B1 mediate cholesterol uptake; STAR and STARD3 mediate transport of cholesterol across the mitochondrial membrane where steroidogenesis is initiated. CYB5A is an important cofactor for the lyase activity of CYP17A. (<b>B</b>) Transcript profiling of tumors from all 4 treatment groups by qRT-PCR for the androgen receptor (AR), the androgen regulate gene PSA, and genes involved in cholesterol transport. (<b>C</b>) Transcript profiling for the expression of steroidogenic genes and the CYB5 cofactor. The mean cycle threshold (CT) for detection of each transcript was normalized to expression of the housekeeping gene RPL13A in the same sample (delta or dCT). (<b>D</b>) The correlation of serum cholesterol levels with transcript expression of CYP17A as measured by qRT-PCR. Correlation coefficients and p values from linear regression analysis.</p

Serum cholesterol and testosterone levels in murine cohorts receiving cholesterol targeted treatment.

<p>(<b>A</b>) Mean cholesterol levels (with standard deviation and range) in mice randomized to 12 weeks of treatment with the indicated combinations of a low fat/no cholesterol diet (LFNC) or high fat/high cholesterol diet (HFHC) ± ezetimibe (drug). One way ANOVA with a post test for linear trend was used to compare values in the four treatment groups. P values<0.05 were considered significant. (<b>B</b>) Measurement of serum testosterone levels by ELISA in mice receiving the indicated HFHC or LFNC diet (n = 10/group). Data are presented as testosterone (T) levels (ng/ml) vs. diet group ± SE. Differences between mice fed the two diets were not statistically significant (unpaired two sample t-test).</p

Linking single-cell measurements of mass, growth rate, and gene expression

Mass and growth rate are highly integrative measures of cell physiology not discernable via genomic measurements. Here, we introduce a microfluidic platform enabling direct measurement of single-cell mass and growth rate upstream of highly multiplexed single-cell profiling such as single-cell RNA sequencing. We resolve transcriptional signatures associated with single-cell mass and growth rate in L1210 and FL5.12 cell lines and activated CD8+ T cells. Further, we demonstrate a framework using these linked measurements to characterize biophysical heterogeneity in a patient-derived glioblastoma cell line with and without drug treatment. Our results highlight the value of coupled phenotypic metrics in guiding single-cell genomics. Keywords: Single-cell RNA-Seq, Mass, Growth, Serial suspended microchannel resonator, Multi-omics, Single cell, T cell activation, Glioblastoma, GBM, Drug response, Microfluidics, Biophysical propertie

Linking single-cell measurements of mass, growth rate, and gene expression

Mass and growth rate are highly integrative measures of cell physiology not discernable via genomic measurements. Here, we introduce a microfluidic platform enabling direct measurement of single-cell mass and growth rate upstream of highly multiplexed single-cell profiling such as single-cell RNA sequencing. We resolve transcriptional signatures associated with single-cell mass and growth rate in L1210 and FL5.12 cell lines and activated CD8+ T cells. Further, we demonstrate a framework using these linked measurements to characterize biophysical heterogeneity in a patient-derived glioblastoma cell line with and without drug treatment. Our results highlight the value of coupled phenotypic metrics in guiding single-cell genomics.status: publishe