High-throughput microRNA functional screening using the Acumen e X3 to identify repressors of a tumorigenic signal transduction pathway Neil Kubica, Janie Zhang, Greg Hoffman and John Blenis Department of Cell Biology Harvard Medical School US Acumen Users Group Meeting (UGM) British Consulate – General Cambridge, MA May 18, 2010
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High-throughput microRNA functional screening using the Acumen eX3 to identify repressors of a tumorigenic signal
transduction pathway
Neil Kubica, Janie Zhang, Greg Hoffman and John Blenis
Department of Cell Biology
Harvard Medical School
US Acumen Users Group Meeting (UGM)
British Consulate – General
Cambridge, MA
May 18, 2010
mTORC1 Integrates Multiple Upstream Signals to Determine the Balance Between Cellular Anabolism and Cellular Catabolism
mTOR
RaptorLST8
AminoAcids
GrowthFactors
Energy
RibosomalBiogenesis
mRNATranslationAutophagy
Rapamycin
The mTORC1 signaling network is populated by a plethora of oncogenes and tumor suppressors
mTORC1 is hyperactivated in ~80-90% of all human cancers
Biomarker
Phosphatase and Tensin Homolog Deleted on Chromosome 10 (PTEN)Function
IRS-1
PI3K
PTEN
PIP2 PIP3
PDK1
Akt
CellSurvival Cell
Growth
CellDivision
Cell Membrane Extracellular
Cytosol
mTOR
RaptorLST8
PTEN loss-of-function (LOF) results in constitutive hyperactivation of the PI3K/Akt/mTORC1 signaling axis
IRS-1
PI3K
PIP2 PIP3
PDK1
Akt
CellGrowth
CellDivision
Cell Membrane Extracellular
Cytosol
mTOR
RaptorLST8
CellSurvival
ConstitutiveHyperactivation
PTEN is one of the most frequently mutated tumor suppressors in primary human cancers
PTEN LOF
EndometrialCarcinoma(50-80%)
Glioblastoma(50-80%)
Prostate Cancer(50-80%)
Breast Cancer(30-50%)
Generally, PTEN +/- is associated with early-stage disease (e.g. formation/progression), while complete LOF (PTEN -/-) is associated with advanced
stages of cancer (e.g. metastatic disease)
Colon Cancer(30-50%)
Lung Cancer(30-50%)
Molecular Genetics and Prostate Cancer Progression
Is mTORC1 hyperactivation downstream of PTEN LOF important for prostate cancer formation/progression?
Genetic inactivation of mTOR suppresses Pten-null-driven prostate cancer (CaP)
Nardella, C. et al. (2009) Sci. Signal. 2: 1-10
PTENpc-/-: PTENloxP/loxP x PB-Cre4
mTorpc-/-: mTorloxP/loxP x PB-Cre4
PB-Cre4 transgenic mice express Cre recombinaseunder the control of the ARR2-probasin promoter,Which is turned on in the prostate epithelium afterpuberty
What about small regulatory RNAs (e.g. microRNAs)?
microRNA (miRNA) expression is dramatically altered in human cancer
Widespread loss of miRNA expression in cancer suggests most miRNAs function as tumor suppressors, while a minority of overexpressed miRNAs function as oncogenes
Lu, J. et al. Nature 435 (7043): 834-838 Gaur, A. et al. Cancer Res 67: 2456-2468
Normal Tissue vs. 1° Tumor Normal Tissue vs. NCI60 Cell Lines
miRNAs can act as tumor suppressors by repressing the expression of signal transduction proteins that serve as powerful oncogenes
(e.g. Ras and let-7)
Esquela-Kerscher, A & Slack, FJ.(2006) Nat Rev Cancer 6: 259-69
HepG2 Cells:
Human 1° Lung Tumors:
miRNA MimicNeg. Control
let-7 Mimic
Adapted From: Johnson, SM, et al. (2005) Cell 120: 635-47
miRNAs can act as tumor suppressors by repressing the expression of signal transduction proteins that serve as powerful oncogenes
(e.g. Ras and let-7)
Adapted From: Trang, P et al. (2010) Oncogene 29: 1580-87
Mouse Strain: LSL-K-Ras G12D
This strain carries a latent point mutant allele of Kras2 (K-RasG12D).
Cre-mediated recombination leads to deletion of a transcriptional termination sequence (Lox-Stop-Lox) and expression of the oncogenic protein.
Intranasal infection with Cre adenovirus results in very high frequency of lung tumors at baseline.
Intranasal infection of a lentivirus encoding let-7 reduces lung tumor burden
Jackson, EL et al. (2001) Genes Dev 15: 3243-8
Project: Identify and characterize miRNAs and miRNA inhibitors that repress
the mTORC1 pathway in cell-based models of PTEN -/- prostate cancer.
The microRNA Screening Consortium @ the Institute of Chemistry and Cell Biology-Longwood (ICCB-L) Screening
Facility (HMS)
The microRNA Screeners Consortium @ the ICCB-L
ICCB-LHarvard
Medical School
Ragon Instituteof MGH, MIT and Harvard Immune Disease
Institute
Dana-FarberCancer Institute
Children’s HospitalBoston
Blenis Lab(Cell Bio)
Struhl Lab(BCMP)
Brass LabLieberman Lab
Daley Lab
Chowdhury Lab
Shimaoka Lab
The microRNA Screeners Consortium @ the ICCB-L
• Consortium model allowed for shared purchase and evaluation of miRNA gain-of-function and loss-of-function libraries.
Gain-of-Function Libraries:
miScript miRNA Mimic Library (Qiagen)
Pre-miR miRNA Mimic Library (Ambion)
Loss-of-Function Library:
miRCURY LNA miRNA Knockdown Library (Exiqon)
Phase 2. miRNA 1° Screen Optimization
Primary Screen:
1. Transfection of miRNA gain-of-function and miRNA loss-of-function reagents into PC-3 cells (PTEN -/- human prostate cancer cell line) in a 384-well format.
2. Monitoring of mTORC1 function using an In-Cell Western (ICW) fluorescence-based assay. The screening assay involves antibody-based detection of endogenous ribosomal protein S6 Ser-235/236 phosphorylation (Cell Signaling Technology).
3. Detection with an Alexa 488-conjugated secondary antibody and counterstaining with the DNA intercalating agent propidium iodide (PI).
4. Data is collected using the Acumen eX3 microplate cytometer (TTP LabTech).
20X
40X DroshaDicer
Phase 2. miRNA 1° Screen Optimization
2A. Validation of the 1° screening assay in PC-3 cells
2B. Small RNA transfection protocol for PC-3 cells
2C. siRNA/miRNA positive and negative control selection in PC-3 cells
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.
ReverseTransfection
(384-well)
FeedCells
Fix Permeabilize
Block&
1° Ab
Alexa-488 2° Ab
&PI
DNA Stain
Image&
Data Analysis
Bravo AutomatedLiquid Handling
Platform(Velocity 11)
24h 24h 24h Store@
4°C
Optional:SerumStarve
24h
PC-3 Cells (PTEN -/-)
LST8
PI3K
N
Akt
TSC1/2
mTOR
S6K1/2
S6
PTEN
SerumWithdrawal
RISC
Raptor
LST8&
S6K1/2k.d.
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.siRNAs
NTC
IGF-1
RIR
S1IR
S2
IRS-1
/2
P13K p
110α
PDK1Rheb
RagA
RagB
RagA/B
RagC
RagD
RagC/D
mTOR
Rapto
r
LST8
Ricto
r
mSin
1S6K
1S6K
2
S6K1/
2
PTENTSC1
TSC2
PRAS400
20
40
60
80
100
120
140
Mea
n %
p-S
6 A
ctiv
e(%
Co
ntr
ol)
Experiment 1NTC siRNA pool vs. siRNA pool positive control panelN = 4/group600 cells/well
Asynchronously-growing (+serum)
Starve (-serum)
LST8: 52%/25%
S6K1/2: 31%/10%
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.siRNAs
Experiment 2Z’ Factor Calculation Matrix: NTC vs. LST8, S6K1/2 and LST8 + S6K1/2N = 24/group500-1000 cells/well
Cell siRNA Pool(s)
Number LST8 S6K1/2LST8 + S6K1/2
500 0.310 0.219 0.632
600 0.343 0.471 0.673
700 0.519 0.693 0.780
800 0.666 0.738 0.797
900 0.418 0.673 0.752
1000 0.337 0.702 0.749
Cell siRNA Pool(s)
Number LST8 S6K1/2LST8 + S6K1/2
500 0.671 0.785 0.879
600 0.646 0.763 0.832
700 0.631 0.768 0.820
800 0.709 0.812 0.841
900 0.700 0.803 0.846
1000 0.673 0.755 0.808
(+) serum (-) serum
Z’ Factor0.2 0.9
*
* *
*
* *
Under optimal conditions the Z’-factor values obtained from our siRNA positive control optimization rival those achieved in our small molecule validation study
(Z’ = 0.852)
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.miRNAs
Experiment 1Mock vs. miRNA negative controlsN = 24/group600 cells/well
Asynchronously-growing (+serum)
Starve (-serum)
0
20
40
60
80
100
120
Mea
n %
p-S
6 A
ctiv
e(%
Co
ntr
ol)
Mea
n C
ell N
um
ber
(% C
on
tro
l)0
20
40
60
80
100
120
Q1M A1 A2 E1 E2 Q1M A1 A2 E1 E2
2C. siRNA/miRNA positive and negative control selection for PC-3 cells.Odyssey® WB Validation