Disabled cell density sensing leads to dysregulated cholesterol synthesis in glioblastoma

A hallmark of cellular transformation is the evasion of contact-dependent inhibition of growth. To find new therapeutic targets for glioblastoma, we looked for pathways that are inhibited by high cell density in astrocytes but not in glioma cells. Here we report that glioma cells have disabled the normal controls on cholesterol synthesis. At high cell density, astrocytes turn off cholesterol synthesis genes and have low cholesterol levels, but glioma cells keep this pathway on and maintain high cholesterol. Correspondingly, cholesterol pathway upregulation is associated with poor prognosis in glioblastoma patients. Densely-plated glioma cells increase oxygen consumption, aerobic glycolysis, and the pentose phosphate pathway to synthesize cholesterol, resulting in a decrease in reactive oxygen species, TCA cycle intermediates, and ATP. This constitutive cholesterol synthesis is controlled by the cell cycle, as it can be turned off by cyclin-dependent kinase inhibitors and it correlates with disabled cell cycle control though loss of p53 and RB. Finally, glioma cells, but not astrocytes, are sensitive to cholesterol synthesis inhibition downstream of the mevalonate pathway, suggesting that specifically targeting cholesterol synthesis might be an effective treatment for glioblastoma.


Cell culture and RNA harvesting for microarrays Glioma TS cells
Cells were grown as suspension cultures. 3-4 biological replicates of each cell line were harvested on different days as matched sets of sparse and dense. Sparse = 5 × 10 4 cells/mL, in 3 Corning T75 flasks (= 75 cm 2 ); dense = 3 × 10 5 cell/mL, in 1 Corning T75 flask (= 75 cm 2 ). Day 1: Cells were plated 7.5 mL medium. Day 2: Cells were fed with 2.5 mL medium. Day 5: cells were harvested with the RNeasy Mini Kit, with modification: RW1 wash = 350 µL RW1, followed by on column DNA digestion with Ambion Turbo DNase (#AM2238) 37°C 30 minutes (50 µL total), then remaining 350 µL RW1 wash. NHAs: 3 biological replicates were harvested on different days as matched sets of sparse and dense. Sparse = 3.5 × 10 5 cells and dense = 5.2 × 10 6 cells on 10 mm Corning TC dishes (= 55 cm 2 ). Cells were plated on day 1 in 12 mL medium, and then fed with 4 mL more on day 2. Final conc. of cells = 5.3 × 10 4 /mL sparse, 3.25 × 10 5 /mL dense. Cells were harvested on Day 5 as for glioma cells. RNA purity and quality was quantitated using an Agilent 2100 Bioanalyzer with the RNA 6000 Nano Kit (Agilent). RNA was only used for microarrays if it had 260/280 ratios of 1.7-2.1, 28S/18S ratios around 2, and an RNA integrity number of at least 7.

Microarray processing and data analysis
Microarrays were processed using Partek Genomics Suite version 6.6. Normalization was performed using RMA background correction with the following Partek settings: adjust for GC content, quantile normalization, probeset summarization = mean. Batch removal was performed across experimental replicates. Probesets were collapsed to gene IDs from the probeset with the lowest p-value. Cell density enriched genes were found via twosample t-test, equal variance comparing sparse and dense for each cell line. Hierarchical clustering analysis was performed on processed data in Partek Genomics Suite using the following parameters: columns were shifted to mean of zero, row and column dissimilarity = Euclidean, row and column method = average linkage. Venn diagrams were generated in Partek Genomics Suite. Self-Organizing Maps were generated for an 8x8 grid using 20,000 training iterations.

Survival and gene expression analyses
The Kaplan Meier method and log-rank test was used on GBM TCGA data to assess the clinical relevance of genes in the cholesterol and mevalonate pathways. For each gene, comparisons between patients with low and high expression were made by separating patients into quantile groups and selecting either low expression group (gene expression value < = 2nd quantile) and high expression group (gene expression value > = 4th quantile). Survival analysis was performed in R using "survival" package and Log-rank p-value computed for each plot. Z-scores for gene expression relative to normal brain were downloaded from cBioPortal (http://www.cbioportal.org/), up-to-date as of December 2016.

Cholesterol extraction and quantitation
Cholesterol was extracted using a modified Bligh and Dyer method carried out entirely with glass labware [5]. Briefly, 0.5-1 × 10 6 live cells were resuspended in 200 µL PBS and either frozen on dry ice or immediately processed. To cell suspension, 700 µL of a 2:1 methanol:chloroform solution was added and the tube vortexed followed by addition of 300 µL chloroform and a second round of vigorous vortexing. Finally, 250 µL of 1 M NaCl was added and mixed by vortexing. Samples were centrifuged at 3000 × g for 15 minutes at 4°C and the organic (bottom) phase collected with a Pasteur pipette into a fresh glass test tube. Samples were either vacuum dried for 1 hour or allowed to air dry in a chemical hood for no more than 18 hours. Cholesterol was resuspended in 80 µL of 5X Amplex buffer with periodic vortexing for 1 hour, followed by dilution with additional 320 µL water (to final 1X buffer concentration). Amplex Red Cholesterol Assay (Life Technologies) was carried out according to manufacturer instructions. To analyze cholesterol levels by flow cytometry, 0.5-1.0 × 10 6 cells were fixed in 1% paraformaldehyde for 25 minutes, centrifuged 10 minutes at 1000 rpm, and resuspended in 1.5 µg/mL Filipin III (Sigma #F4767). Cells were stained for 2 hours then analyzed using an LSR Fortessa flow cytometer (BD Biosciences). 10,000 ungated events were analyzed with FlowJo Single Cell Analysis software.

Metabolic measurements
Oxygen consumption rates (OCR) were evaluated using an XFe96 Extracellular Flux Analyzer (Seahorse Bioscience). XFe96 plates were coated with 0.01 ug/ mL poly-L-ornithine overnight, washed 3x with water, then incubated with 10 µg/mL laminin in glioma TS cell medium overnight. Wells were rinsed 1x with medium and cells seeded at indicated densities for 24 hours. The XFe96 sensor cartridge was hydrated overnight in XF calibrant, per manufacturer instructions. Prior to analysis, cells were rinsed twice with XF assay medium (XF Base Medium supplemented with 17.5 mM glucose, 0.5 mM pyruvate, 2.5 mM glutamine, 10 ng/mL EGF, 10 ng/mL bFGF, and 2 µg/mL heparin) then incubated for one hour in XF assay medium at 37°C in a CO 2 -free incubator. Injection cartridges were loaded with compounds as indicated. For each experiment, average OCR values were determined from a minimum of 6 technical replicates and a minimum of 3 measurement cycles per condition. To account for cell density, all extracellular flux assays were normalized to SYTO 60 Red Fluorescent Nucleic Acid Stain (Life Technologies) after completion of the experiment. Briefly, media was removed from the cell plate and replaced with 1:5000 SYTO 60 in TBS. Cell plate was incubated with SYTO 60 at room temperature for 30 minutes, then rinsed with TBS. After removing all liquid from the wells, the plate was scanned using a LI-COR Odyssey CLx. Well intensities were quantitated using Image Studio software (LI-COR).

ROS, MMP, and measurements
MitoTracker Red CMXRos (Life Technologies) was used at a concentration of 100 nM to assess mitochondrial membrane potential (MMP). CellROX Green (Life Technologies) was used at a concentration of 2.5 μM to assess cellular ROS. Both were diluted as indicated in pre-warmed (37°C) medium and the slide incubated with the solution for 15-30 minutes. Stain was then replaced with fresh, warmed medium and slides imaged. Images were captured on an EVOS fl fluorescence microscope (ThermoFisher). ROS quantitation was performed using ImageJ. Five random fields were imaged for each well. For each field, the background was calculated as an average of four cell-free areas, and then the signal intensity of fifty random, representative cells was measured. For each cell, the Corrected Fluorescence = Integrated Density -(Area * Mean Background Fluorescence). Dot plots depict these 250 data points and mean and standard deviation were calculated in GraphPad Prism. Each set of images within a figure panel were cropped and levels were adjusted using identical settings in Adobe Photoshop. For ATP measurements, cells were plated at the indicated densities in 75 µL media per well of a white 96-well plate. After 24 hours, 75 µL CellTiter-Glo (Promega) was added per well and plates mixed for 2 minutes at 450 rpm. Plates were incubated in the dark for 15-20 minutes then the luminescence analyzed on a Wallac Victor2 1420 Multilabel Counter. For each experiment, the average luminescence was calculated from four technical replicates per density. Relative ATP level was calculated by dividing the average luminescence for each density by the average luminescence of the samples plated at 15,625 cells/ cm 2 ("sparse"). The ratios of 3 independent biological replicates were averaged and presented as mean +/− standard error of the mean.

Lactate measurements
Analysis of lactate levels was performed using the L-Lactate Assay Kit (Cayman Chemical Company) according to manufacturer protocols for intra-and extracellular L-lactate. Prior to analysis, cells were plated sparsely or densely for 24 hours then collected and counted. Equal cell numbers were processed for analysis of intracellular lactate in sparse and dense samples. To analyze extracellular lactate, 600 µL media supernatant was collected after cells were pelleted for counting. Samples were process as indicated in manufacturer's protocol and analyzed. To account for differential cell density, raw fluorescence values were normalized by multiplying by a density factor derived from the number of cells in the sparse and dense cultures.

Metabolomics
TS543 and TS616 cells were cultured as spheroids at 50,000 cells/mL (sparse) or 300,000 cells/mL (dense) in suspension. 1 × 10 7 cells were pelleted and flash frozen in liquid nitrogen. Metabolites were profiled by Metabolon (Durham, NC, USA) as follows: following normalization to Bradford protein concentration, log transformation and imputation of missing values, if any, with the minimum observed value for each compound, Sample Accessioning: Following receipt, samples were inventoried and immediately stored at -80°C. Each sample received was accessioned into the Metabolon LIMS system and was assigned by the LIMS a unique identifier that was associated with the original source identifier only. This identifier was used to track all sample handling, tasks, results, etc. The samples (and all derived aliquots) were tracked by the LIMS system. All portions of any sample were automatically assigned their own unique identifiers by the LIMS when a new task was created; the relationship of these samples was also tracked. All samples were maintained at −80 o C until processed. Sample Preparation: Samples were prepared using the automated MicroLab STAR ® system from Hamilton Company. A recovery standard was added prior to the first step in the extraction process for QC purposes. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min (Glen Mills GenoGrinder 2000) followed by centrifugation. The resulting extract was divided into five fractions: one for analysis by UPLC-MS/MS with positive ion mode electrospray ionization, one for analysis by UPLC-MS/MS with negative ion mode electrospray ionization, one for LC polar platform, one for analysis by GC-MS, and one sample was reserved for backup. Samples were placed briefly on a TurboVap (Zymark) to remove the organic solvent. For LC, the samples were stored overnight under nitrogen before preparation for analysis. For GC, each sample was dried under vacuum overnight before preparation for analysis. QA/QC: Several types of controls were analyzed in concert with the experimental samples: a pooled matrix sample generated by taking a small volume of each experimental sample (or alternatively, use of a pool of well-characterized human plasma) served as a technical replicate throughout the data set; extracted water samples served as process blanks; and a cocktail of QC standards that were carefully chosen not to interfere with the measurement of endogenous compounds were spiked into every analyzed sample, allowed instrument performance monitoring and aided chromatographic alignment. Instrument variability was determined by calculating the median relative standard deviation (RSD) for the standards that were added to each sample prior to injection into the mass spectrometers. Overall process variability was determined by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of the pooled matrix samples.

Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS):
The LC/MS portion of the platform was based on a Waters ACQUITY ultraperformance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The sample extract was dried then reconstituted in acidic or basic LCcompatible solvents, each of which contained 8 or more injection standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion optimized conditions and the other using basic negative ion optimized conditions in two independent injections using separate dedicated columns (Waters UPLC BEH C 18-2.1 × 100 mm, 1.7 µm). Extracts reconstituted in acidic conditions were gradient eluted from a C18 column using water and methanol containing 0.1% formic acid. The basic extracts were similarly eluted from C18 using methanol and water, however with 6.5 mM Ammonium Bicarbonate. The third aliquot was analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1 × 150 mm, 1.7 µm) using a gradient consisting of water and acetonitrile with 10mM Ammonium Formate. The MS analysis alternated between MS and data-dependent MS2 scans using dynamic exclusion, and the scan range was from 80-1000 m/z. Raw data files are archived and extracted as described below.

Gas Chromatography-Mass Spectroscopy (GC-MS):
The samples destined for analysis by GC-MS were dried under vacuum for a minimum of 18 h prior to being derivatized under dried nitrogen using bistrimethylsilyltrifluoroacetamide. Derivatized samples were separated on a 5% diphenyl / 95% dimethyl polysiloxane fused silica column (20 m × 0.18 mm ID; 0.18 um film thickness) with helium as carrier gas and a temperature ramp from 60° to 340°C in a 17.5 min period. Samples were analyzed on a Thermo-Finnigan Trace DSQ fastscanning single-quadrupole mass spectrometer using electron impact ionization (EI) and operated at unit mass resolving power. The scan range was from 50-750 m/z. Raw data files are archived and extracted as described below. LIMS: The purpose of the Metabolon LIMS system was to enable fully auditable laboratory automation through a secure, easy to use, and highly specialized system. The scope of the Metabolon LIMS system encompasses sample accessioning, sample preparation and instrumental analysis and reporting and advanced data analysis. All of the subsequent software systems are grounded in the LIMS data structures. It has been modified to leverage and interface with the in-house information extraction and data visualization systems, as well as third party instrumentation and data analysis software. Data Extraction and Compound Identification: Raw data was extracted, peak-identified and QC processed using Metabolon's hardware and software. These systems are built on a web-service platform utilizing Microsoft's .NET technologies, which run on high-performance application servers and fiber-channel storage arrays in clusters to provide active failover and load-balancing. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Metabolon maintains a library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and chromatographic data (including MS/MS spectral data) on all molecules present in the library. Furthermore, biochemical identifications are based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library +/− 0.005 amu, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores are based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum. While there may be similarities between these molecules based on one of these factors, the use of all three data points can be utilized to distinguish and differentiate biochemicals. More than 3300 commercially available purified standard compounds have been acquired and registered into LIMS for distribution to both the LC-MS and GC-MS platforms for determination of their analytical characteristics. Additional mass spectral entries have been created for structurally unnamed biochemicals, which have been identified by virtue of their recurrent nature (both chromatographic and mass spectral). These compounds have the potential to be identified by future acquisition of a matching purified standard or by classical structural analysis. Curation: A variety of curation procedures were carried out to ensure that a high quality data set was made available for statistical analysis and data interpretation. The QC and curation processes were designed to ensure accurate and consistent identification of true chemical entities, and to remove those representing system artifacts, misassignments, and background noise. Metabolon data analysts use proprietary visualization and interpretation software to confirm the consistency of peak identification among the various samples. Library matches for each compound were checked for each sample and corrected if necessary. Metabolite Quantification and Data Normalization: Peaks were quantified using area-underthe-curve. For studies spanning multiple days, a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound was corrected in run-day blocks by registering the medians to equal one (1.00) and normalizing each data point proportionately (termed the "block correction"). For studies that did not require more than one day of analysis, no normalization is necessary, other than for purposes of data visualization. In certain instances, biochemical data may have been normalized to an additional factor (e.g., cell counts, total protein as determined by Bradford assay, osmolality, etc.) to account for differences in metabolite levels due to differences in the amount of material present in each sample. Identification of metabolites with a statistically significant fold change in dense cells versus sparse cells was performed in GraphPad Prism 7.01 using a grouped twoway ANOVA analysis (grouped by cell line and cell harvest date), without correction for multiple comparisons using a Fisher's LSD test.