Multiplexed dose response to identify specific small molecule inhibitors of Ras and Ras-related GTPases specifically Ras activated mutant
Ras and related small molecular weight GTPases function in the regulation of signaling and cell growth, and collectively serve to control cell proliferation, differentiation and apoptosis [Takai et al. 2001; Wennerberg et al. 2005]. The Ras-related GTPases are divided into four subfamilies with the Rab proteins regulating membrane transport, Rho proteins (including Rac and Cdc 42) regulating more ..
BioActive Compounds: 9
University of New Mexico Assay Overview:
Assay Support: NIH I RO3 MH081231-01
HTS to identify specific small molecule inhibitors of Ras and Ras-related GTPases
PI: Angela Wandinger-Ness, Ph.D.
Co-PI: Larry Sklar, Ph.D.
Assay Development: Zurab Surviladze, Ph.D.
Assay Implementation: Zurab Surviladze, Danuta Wlodek, Terry Foutz, Mark Carter, Anna Waller
Target Team Leader for the Center: Larry Sklar (firstname.lastname@example.org)
Dose Response Assay Background and Significance:
Ras and related small molecular weight GTPases function in the regulation of signaling and cell growth, and collectively serve to control cell proliferation, differentiation and apoptosis [Takai et al. 2001; Wennerberg et al. 2005]. The Ras-related GTPases are divided into four subfamilies with the Rab proteins regulating membrane transport, Rho proteins (including Rac and Cdc 42) regulating cytoskeletal rearrangements and responses to signaling, Arf/Sar proteins regulating membrane and microtubule dynamics as well as protein transport, and Ran proteins controlling nucleocytoplasmic transport. This project focuses on representative Ras, Rho, and Rab family members to validate the approach for the identification of new chemical compounds with novel therapeutic potential in cell signaling and growth control.
Ras and Ras-related GTPase functions are tightly regulated, and dysregulation is causal in a wide variety of human diseases. Ras mutations resulting in impaired GTP hydrolysis and plasma membrane hyperactivation are linked to many human cancers [Farnsworth et al. 1991; Sukumar et al. 1983; Taparowsky et al. 1982; Boylan et al. 1990; Hruban et al. 2004; Abrams et al. 1996]. Point mutations in the Rab and Rho GTPases are also causal in diverse human diseases affecting pigmentation, immune, and neurologic functions [Houlden et al. 2004; Verhoeven et al 2003; Williams et al. 2000; Bahadoran et al. 2003; and preliminary findings]. Rab and Rho mutants identified in human disease act as dominant negatives either due to a failure to bind GTP or due to inappropriate coupling of the active proteins with downstream effectors. To date, inhibition of Ras and Ras-related proteins has relied largely on altering membrane recruitment with various drugs affecting prenylation [Morgillo F Lee HY, 2006; Russell RG, 2006; Park, et al. 2002]. Generally, Ras proteins must be farnesylated for proper membrane localization, while Rab and Rho proteins are geranylated. Such strategies lack specificity and are problematic because each of these prenylation machineries is required for the proper function of many Ras superfamily members. Rational drug design has only recently been applied to identify the first two small molecule inhibitors of Rho GTPase family members [Gao, et al. 2004; Nassar et al. 2006]. Therefore, broadly testing the Ras-related GTPases as targets for small molecule inhibitors and activators is expected to identify new classes of compounds that may be useful in the treatment of human disease, as well as in unraveling the molecular details of how Ras-related GTPases function.
In the dose response study, the compounds that satisfied hit selection criteria in the primary screen (change in % activity greater than 20% from baseline) were tested to confirm activity and determine potency.
Bead sets are coated with individual GST-small G proteins, blocked with Buffer (0.01% NP-40; 30mM HEPES pH 7.5; 100mM KCl; 20mM NaCl; 1mM EDTA; 0.1% BSA and 1mM DTT), incubated overnight at 4 degrees C, and finally washed in buffer. The different bead sets, acquired from Duke Scientific, have similar size (~ 4 microm diameter) however they are distinguished by varied magnitude of emission at 665 +/-10 nm with excitation at 635 nm.
The assay is conducted in 384-well microplates in a total well volume of 10.1 microliters (5 microliters of bead mixture, 0.1 microliters of test compound, and 5 microliters of 200nM Bodipy-FL-GTP in buffer containing BSA and DTT for a final concentration of GTP of 100nM). Positive Controls, which contain bead mixture and fluorescent GTP but no test compound, are located in columns 1 and 2 on plate. Negative Controls containing bead mixture with fluorescent GTP and 0.5 mM unlabeled GTP, are collected from a separate test tube. Plates are placed on rotators and incubated for 40-45 minutes at 4 degrees C.
Test compounds were cherry-picked from compound storage plates at 10 milliM in DMSO, then serially diluted 1:3 eight times for a total of nine different test compound concentrations in DMSO. Final compound dilutions in DMSO ranged from 1 microM to 10mM. These dilutions were then diluted 1 to 100 to give an assay concentration range of 10 nanoM to 100 microM.
Beads were coated with proteins as described in the primary screening procedure. Dose response experiments reported here include one multiplex format for: Rab7wt, Rab2 wt, H-Ras wt, H-Ras constitutively active, Cdc42 wt, and Cdc42 constitutively active, and 3 single-plexes for: Rac1 wt, Rac1 constitutively active and GST-GFP.
Sample acquisition and preliminary analysis is conducted with the HyperCyt(R) high throughput flow cytometry platform. The HyperCyt system interfaces a flow cytometer and autosampler for high-throughput microliter volume sampling from 384-well microtiter plates [Kuckuck, et al., 2001; Ramirez, et al., 2003]. The stream of particles is excited at 488 nm and 635 nm, and flow cytometric data of light scatter and fluorescence emission at 530 +/- 20 nm (FL1) and emission at 665 +/- 10 nm (FL8) are collected on a Cyan Flow Cytometer (Dako). Analysis of the time-resolved acquisition data file uses IDLeQuery software to merge the flow cytometry data files with compound worklist files generated by HyperSip software. The raw data are parsed in IDLeQuery to produce annotated fluorescence summary data for each well. The parsed data are then processed through an Excel template file constructed specifically for the assay to segregate data for each target and the fluorescence scavenger in the multiplex. Gating based on forward scatter (FS) and side scatter (SS) parameters is used to identify singlet bead populations. Gating based on FL8 emission distinguishes the beads coated with different proteins, and the green median fluorescence intensity (MFI) per bead population (well) is calculated.
In dose response experiments, the assay was performed without compound (DMSO control) and with nine different concentrations of compound, from 10 nanoM to 100 microM, to produce a series of 9 data points. IDLeQuery calculates the median channel fluorescence (MCF) for each of these ligand concentrations, generating competition curves.
Ligand competition curves were fitted by Prism(R) software (GraphPad Software, Inc., San Diego, CA) using nonlinear least-squares regression in a sigmoidal dose response model with variable slope, also known as the four parameter logistic equation. Curve fit statistics were used to determine the following parameters of the model: EC50, microM - concentration of added test compound competitor that inhibited fluorescent ligand binding by 50 percent; LOGEC50 - the logarithm of EC50; TOP - the response value at the top plateau; BOTTOM - the response value at the bottom plateau; HILLSLOPE - the slope factor, or the Hill coefficient; STD_LOGEC50, STD_TOP, STD_BOTTOM, STD_HILLSLOPE - standard errors of LOGEC50, TOP, BOTTOM, and HILLSLOPE ; EC50_95CI_LOW, EC50_95CI_HIGH - the low and high boundaries of the 95% confidence interval of the EC50 estimate, RSQR - the correlation coefficient (r squared) indicative of goodness-of-fit.
In order to be considered active and get a score > 0, the compounds have to pass the following criteria:
- -8 < LOGEC50 < -4 (the computed EC50 value should be in the interval of tested concentrations)
- 0.5 < |HILLSLOPE| < 2 (the absolute value of HILLSLOPE should be higher than 0.5 and lower than 2)
- [TOP - STD_TOP] > [BOTTOM + STD_BOTTOM] (the amplitude of the biological signal should be statistically significant)
- |LOGEC50| > STD_LOGEC50 (the standard error of LOGEC50 should be lower than the absolute value of LOGEC50)
- |HILLSLOPE| > STD_HILLSLOPE (idem for the HILLSLOPE)
- [TOP - BOTTOM] scavenger < 0.5*[TOP - BOTTOM] target (the inherent fluorescence of the test compound should be lower than 50% of the biological signal)
- [TOP - BOTTOM]/TOP for GST-GFP < 0.5*[TOP - BOTTOM]/TOP for the target (the interference of the compound with the GST/GSH interaction should be lower than 50% of the biological signal)
The PUBCHEM_ACTIVITY_SCORE was based on the following equation;
PUBCHEM_ACTIVITY_SCORE = 1000 * (-4 - LOGEC50)/4 * [(TOP-STD_TOP)-(BOTTOM+STD_BOTTOM)]/TOP * (1 - ||HILLSLOPE|-1|) * (1-STD_LOGEC50/|LOGEC50|)
In this assay active compounds have the activity score higher or equal than 1, and for inactive compounds the activity score is 0.
Keywords: NIH Roadmap, NMMLSC, high throughput flow cytometry,Rac 1 wt, Rab7 wt, Rac 1 activated, Ras wt, Ras activated, Rab 2 wt., CDC wt, CDC activated, multiplex, bead-based, screening, dose response
* Activity Concentration. ** Test Concentration.
Data Table (Concise)