Hit Triage in RNAi Screens マking Use of Interaction and Pathway Data - Enhance Hit Selection
35
Hit Triage in RNAi Screens Making Use of Interac1on and Pathway Data to Enhance Hit Selec1on Rajarshi Guha NIH Chemical Genomics Center March 18, 2010 GeneGO User Group Mee5ng, Boston, MA
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Transcript of Hit Triage in RNAi Screens マking Use of Interaction and Pathway Data - Enhance Hit Selection
Hit Triage in RNAi Screens Making Use of Interac1on and Pathway Data
to Enhance Hit Selec1on
Rajarshi Guha
NIH Chemical Genomics Center
March 18, 2010
GeneGO User Group Mee5ng, Boston, MA
Outline
• The NCGC RNAi infrastructure • Selec:on & Triage • Case Studies
Trans‐NIH RNAi Ini6a6ve ‐ Mission
• Gene func:on • Pathway analysis • Target ID • Compound MoA • Drug antagonist/agonist
To establish a state of the art RNAi screening facility to perform genome-wide RNAi screens with investigators in the intramural NIH community.
Pilot Phase Screens
RNAi Informa6cs Toolset
• Local databases (screen data, pathways, interac:ons, etc).
• Commercial pathway tools.
• Custom soKware for loading, analysis and visualiza:on.
Back End Services
• Currently all computa:onal analysis performed on the backend
• R & Bioconductor code • Custom R package (ncgcrnai) to support NCGC infrastructure – Partly derived from cellHTS2 – Supports QC metrics, normaliza:on, adjustments, selec:ons, triage, (sta:c) visualiza:on, reports
• Some Java tools for – Data loading – Library and plate registra:on
User Accessible Tools
• Summary sta:s:cs
• Correc:ons
QC
• Median • Quar:le • Background
Normaliza:on • Thresholding • Hypothesis tes:ng
• Sum of ranks
Hit Selec:on
• GO seman:c similarity
• Pathways • Interac:ons
Hit Triage
RNAi Analysis Workflow
Raw and Processed
Data
GO annota:ons Pathways Interac:ons
Hit List Follow‐up
Hit Selec6on Methods
• Thresholding • Hypothesis tes:ng • Sum/mean of ranks
• In all cases, some degree of abitrariness
• In many cases, poor concordance between selec:on methods
• Gives us a selec:on of siRNA’s • Iden:fy genes based on number of siRNA’s
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05
01
00
15
0
Plate Number
Sig
na
l
Negative
Positive
Sample
Hit Triage Methods
• Given that we end up with a rela:vely small number of hit genes, we need to expand it
• Triage iden:fies other “relevant” or “borderline” genes to be included for follow‐up
• Methods – Protein‐protein interac:on data – Pathway membership data – GO terms – Network sta:s:cs
CPT Sensi6za6on
TOP1 poisons prevent DNA religation resulting in replication-dependent double strand breaks. Cell activates DNA damage response (e.g. ATR).
Pommier, Y., Nat. Rev. Cancer, 2006.
Screening Protocol
Screen conducted in the human breast cancer cell line MDA-MB-231. Many variables to optimize including transfection conditions, cell seeding density, assay conditions, and the selection of positive and negative controls.
Hit Selection Follow-Up Dose Response Analysis
CPT (Log M)
ATR
MAP3K7IP2
CPT (Log M)
Viab
ility
(%)
Viab
ility
(%)
siNeg siATR-A siATR-B siATR-C
siNeg siMAP3K7IP2-A siMAP3K7IP2-B siMAP3K7IP2-C siMAP3K7IP2-D
Multiple active siRNAs for ATR, MAP3K7IP2, and BCL2L1.
Screen #2
Screen #1
Sensitization Ranked by Log2 Fold Change
Sensitization Ranked by Log2 Fold Change
Triage with PPIs
• For each gene in the hit list iden:fy interac:on partners – Currently using HPRD – Will be switching to MIMI
• This added MAP3K7 to the hit list, which reconfirmed nicely
•
MAP3K7
CPT (Log M)
Viab
ility
(%) siNeg
siMAP3K7-A siMAP3K7-B siMAP3K7-C siMAP3K7-D
http://www.eecs.umich.edu/db/mimi/
Looking in Pathways
• Using the ini:al hit list based on differen:al analysis we get a list of relevant pathways
Looking in Pathways
• We then iden:fy genes from these pathways that were not ini:ally selected
• 3 genes from the IL‐10 signalling pathway were each knocked down by a single siRNA – CD14 – PIK3CD – PIK3R3
• Similarly, we add in genes from other pathways
Looking in Pathways
• The reconfirma:on rate of pathway‐derived hit list members was low (< 10%)
• Not too surprising • PPI‐derived hit list members worked beger
Are These Genes Relevant?
• Some are well known to be CPT‐sensi:zers • Consider a HPRD PPI sub‐network corresponding to the Qiagen HDG gene set
• How “central” are these selected genes? – Larger values of betweenness indicate that the node lies on many shortest paths
– Makes sense ‐ a number of them are stress‐related
– But some of them have very low betweenness values
log Betweenness
log
Fre
qu
en
cy
0 2 4 6
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Are These Genes Relevant?
• Most selected genes are densely connected
• A few are not – Generally did not reconfirm
• Network metrics could be used to provide confidence in selec:ons
!
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ACTC1
TWF1
BMPR2
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COL4A1
CD44
PLG
LCN2
COL1A1
COL1A2
MMP9
MMP7
PRSS2
AREG
COL4A3
COL4A2
COL4A4
COL4A5
COL4A6
FN1
THBS1
IL8
CXCL1
HAPLN1
MMP10
THBS2
TIMP3
KISS1
PZP
BTC
RECK
MMP26
CXCL5
TFPI
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SNCA
! PSEN1
CASP1
BCL2
BCAP31
TP53
MAPK8
IRS1
BCL2L1
CASP8
BAX
CYCS
IRS2
BCL2L11
CAPN1PSEN2
ANTXR1 BAD
FKBP8BAK1
CASP9
VDAC1
CRYAA
CRYAB
BAG1
SIVA1
PPP1CA
CFLAR
BNIP1
BNIP3
BIK
HRK
RAD9A
BECN1
BCL2L14
BMF
BCL2L10
RTN4
BNIP3L
PMAIP1
BCLAF1
MOAP1
NLRP1
IKZF3
TEGT
AVEN
BCL2L12
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RB1
ACTB
CASP3
HD
ALDOA
TGM2
TUBB
FN1
ITGB1ITGB3
SPARC
HIST2H2BE
SERPINF2
GSTP1
LTBP1
ANXA1
RHOA
PLCD1
TMSB4X
MAP3K12
EIF5AS100A7
KPNA3KPNA4PPHLN1
HIST1H2BG
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SPTB
FYN
PRNP
FGFR1L1CAM
NCAM1
BDNF
NCAN
ST8SIA2
ST8SIA4
ST8SIA3
GFRA1
GDNF
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EP300
MDM2
TP53
DAXX
CDKN1A
HCK
AR
GGA3
GGA1
TSG101
DNMT1
DMAP1
HGS
AATF
PDCD6IP
UBA52
VPS28
VPS37A
LRSAM1
VPS37C
VPS37D
VPS37B
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RAC1
PPP1R9A
MAPK1
CDC2
PPP1R9B
PPP2CA
PDPK1
AKT1
EIF4EBP1
CDC42
RPS6KB1
EEF2K
TERT
FRAP1
TRAF4
STK11
NCBP1
RPS6
NEK6
COASY
POLDIP3
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EP300
CREBBP
PDC
CRX
BANF1
NR2E3
NRL
IPO13RAXL1
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CHD4
TP53
BRCA1
KDR
E2F1
XRCC5
NBN
CHEK2
CLSPN
CHEK1
MSH2
ATR
TREX1
XPA
RHEB
FLT1
RAD17
EEF1E1
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FYNGRB2
IFNAR1
LCK
PLCG1
SHC1
RASA1
PTPRC
MUC1
CRK
PTPN6
SOS1
CD79B
VAV1
CBL
ABL1
FCGR3A
CD5 CD3E
TUBB
SHB
PTK2B
LCP2
SH3BP2
LAT
CBLB
SIT1
SH2B3
PAG1
GAB2
LAX1
ACP1
TUBA4A
DEF6
CD247
PRLR
DUSP3
ZAP70
WIPF1
SLA
SLAMF6
SLA2
TYROBP
DBNL
PTPN3
FCRL3
NFAM1
CARD11
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GNA12
YWHAG
THRAYWHAB
YWHAZ
GNAQ
ESR2
ESR1
YWHAE
PRKAR2A
PPARA
AKAP13
RHOA
RXRB
CTNNAL1
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GP1BA
GP9
GP1BB
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RB1
SRC
LEPR
PRKCA
PLCG1
HRAS
SNTA1
RBL1
RBL2
DMD
SNTB1
SNTB2
DGKZ
SNTG1
RASGRP1
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STAT3
RELA
SMAD3
!
MAP2K4
MAPK14
EIF2AK2
MAPK8
MAP3K5
MAP4K4
NRIP1
MAP4K1
TRAF6
TRAF3IP2
FOS
CHUKIKBKB
PPM1B
IRAK1
HGS
MAP3K14
IL17RD
SMAD6
IKBKAP
BIRC4
PEBP1
HIPK2
MAP3K7
MAP2K6MAP3K7IP1
SMAD7
PELI3
MAP3K3
TNFRSF11ABIRC1
PPM1L
BCL10
ALS2CR2
CARD11
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LEF1
ALX4
CART1
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MC4R
GHRL
MC5R
MC3R
AGRP
Literature Novelty
• For selected genes, we evaluate to what extent it has been discussed in the literature
• Makes use of pre‐processed NCBI PubMed annota:ons on gene iden:fiers
• Lower scores indicate novelty (or lack of interest!)
• Priori:za:on method
RNAi & Small Molecule Screens
Goal: Develop systems level view of small molecule activity
• Reuse pre-existing MLI data • Develop new annotated libraries
TACGGGAACTACCATAATTTA
CAGCATGAGTACTACAGGCCA
• Run parallel RNAi screen
What targets mediate activity of siRNA and compound
Pathway elucidation, identification of interactions
Target ID and validation
Link RNAi generated pathway peturbations to small molecule activities. Could provide insight into polypharmacology
HTS for NF‐κB Antagonists
• NF‐κB controls DNA transcrip:on
• Involved in cellular responses to s:muli – Immune response, memory forma:on
– Inflamma:on, cancer, auto‐immune diseases
hgp://www.genego.com
HTS for NF‐κB Antagonists
• ME‐180 cell line • S:mulate cells using TNF, leading to NF‐κB ac:va:on, readout via a β‐lactamase reporter
• Iden:fy small molecules and siRNA’s that block the resultant ac:va:on
Small Molecule HTS Summary
• 2,899 FDA‐approved compounds screened
• 55 compounds retested ac:ve
• Which components of the NF‐κB pathway do they hit? – 17 molecules have target/pathway informa:on in GeneGO
– Literature searches list a few more
!9 !8 !7 !6 !5
!60
!40
!20
0
log Concentration (uM)
Activity
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!9 !8 !7 !6 !5
!100
!60
!20
0
log Concentration (uM)
Activity
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!9 !8 !7 !6 !5
!60
!40
!20
0
log Concentration (uM)
Activity
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Most Potent Actives Proscillaridin A
Trabectidin
Digoxin
Miller, S.C. et al, Biochem. Pharmacol., 2010, ASAP
RNAi HTS Summary
• Qiagen HDG library – 6886 genes, 4 siRNA’s per gene
• A total of 567 genes were knocked down by 1 or more siRNA’s – We consider >= 2 as a “reliable” hit
– 16 reliable hits – Added in 66 genes for follow up via triage procedure
The Obvious Conclusion
• The ac:ve compounds target the one or more of the 16 hits (at least) from the RNAi screen – Useful if the RNAi screen was small & focused
• But what if we’re inves:ga:ng a larger system? – Is there a way to get more specific?
– Can compound data suggest RNAi non‐hits? – Can RNAi hits suggest compound targets?
Small Molecule Targets
• Some small molecules interact with core components
Bortezomib (proteosome inhibitor)
Daunorubicin (IκBα inhibitor)
!9 !8 !7 !6 !5
!100
!80
!60
!40
!20
0
log Concentration (uM)
Activity
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!9 !8 !7 !6 !5
!120
!80
!60
!40
!20
0
log Concentration (uM)
Activity
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Small Molecule Targets
• Others are ac:ve against upstream targets
• We also get an idea of off ‐target effects
Montelukast (LDT4 antagonist)
!9 !8 !7 !6 !5
!100
!80
!60
!40
!20
0
log Concentration (uM)
Activity
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Compound Networks ‐ Similarity
• Evaluate fingerprint‐based similarity matrix for the 55 ac:ves
• Connect pairs that exhibit Tc> 0.7
• Edges are weighted by the Tc value
• Most groupings are obvious
Compound Networks ‐ Targets
• Predict targets for the ac:ves using SEA • Target based compound network maps nearly iden:cally to the similarity based network
• But depending on the predicted target quality we get poor (or no) mappings to the RNAi targeted genes
Keiser, M.J. et al, Nat. Biotech., 2007, 25, 197‐206
(Reduced) Gene Networks – Pathways
• Nodes are 526 genes with >= 1 siRNA showing knockdown
• Edge indicates two genes/proteins are involved in the same pathway
Pathway Based Integra6on
• Direct matching of targets is not very useful • Try and map compounds to siRNA targets if the compounds’ predicted target(s) and siRNA targets are in the same pathway – Considering 16 reliable hits, we cover 26 pathways – Predicted compound targets cover 131 pathways
• For 18 out of 41 compounds
– 3 RNAi‐derived pathways not covered by compound‐derived pathways • Rhodopsin, alterna:ve NFkB, FAS
The Use of GeneGO
• Excellent resource! • The web interface is nice for simple/few queries, visualiza:on
• Programma:c access could be improved – REST interface – Access to algorithms, datasets via REST interface
Conclusions
• Hit triage is a necessary step in our screening pipeline • Have mul:ple methods and data sources to consider. • Building up a wealth of small molecule and RNAi data • Challenges involve integra:ng RNAi data with other sources
• Primary bogleneck is dimensionality of the data – Simple flourescence‐based approaches do not provide sufficient resolu:on
– High‐content is required
Acknowledgements
• Scog Mar:n • Pinar Tuzmen
![Drosophila: RNAi and Non-RNAi - biblio.ugent.be · subsequently in Drosophila [3] and Caenorhabditis elegans [4]. In Drosophila, the major RNAi pathway involved in antiviral immunity](https://static.fdocuments.in/doc/165x107/5ccc3ff188c99335448bc823/drosophila-rnai-and-non-rnai-subsequently-in-drosophila-3-and-caenorhabditis.jpg)
