Estimating genetic interactions from combinatorial rnai screens
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Estimating genetic interactions from combinatorial RNAi screens. IASC Yokohama 7 December 2008 Wolfgang Huber European Molecular Biology Laboratory European Bioinformatics Institute. Many genes are functionally uncharacterized. Homo sapiens25,000 Drosophila15,000 C. elegans17,000

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Estimating genetic interactions from combinatorial RNAi screens

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Estimating genetic interactions fromcombinatorial RNAi screens

IASC

Yokohama

7 December 2008

Wolfgang Huber

European Molecular Biology Laboratory

European Bioinformatics Institute


Many genes are functionally uncharacterized

Homo sapiens25,000

Drosophila15,000

C. elegans17,000

S. cerevisiae 6,000

}

< 40-50%

~ 60%

Genes

Functional information

In addition to protein-coding genes, there are 1000s of non-coding RNA and 100,000s of regulatory elements


Much of our genetic knowledge is from perturbation (loss or partial loss of function) phenotypes

wild typeknock-out


biological (sub)system

I n p u t s

O u t p u t s


Perturbation

RNAi

drug

cDNA expression

System

cell-based assay

insect embryo

Phenotypic readout

luminescene reader

cytometry (distribution ofcell cycle states)

microscopy


RNA inhibition (RNAi)

A natural mechanism: on seeing a double stranded RNA, 'all' existing gene transcripts in the cell with the same sequence are destroyed - effectively shutting down this gene.

This natural mechanism can be exploited for targeted gene knock down experiments.

The evolutionary origins of RNAi are thought to lie in defense against viruses.

He and Hannon, 2004


neg. controlIAP RNAi

Kc167

S2R+

cells

72h post transfection

RNAi to analyze loss of function of a specific gene - parallelized to whole genome

Boutros & Ahringer, Nat Rev Genet 2008


RNAi screening to generate quantitative perturbation profiles

RNAi screen for modifiers of

cell viability

...:replicate 2

quantitative "fitness" readout: replicate 1

Distinguish phenotypic changes

of <15%

siRNAs in 384-well assay plates

RNAi in cultured cells by bathing or transfection

2-5 days incubation

Phenotypic readout

>40,000 measurements per day


cellHTS2

an R package providing data management and complete workflow for analysing a cell-based assay experiment

www.bioconductor.org


example report


What is a genetic interaction?

Conceptually: when the action of one gene is modified by one or several other genes

direct

  • protein – protein interaction

  • protein – DNA interaction

    indirect

  • gene products act in the same pathway

  • redundancy (buffering)

    • proteins

    • pathways

  • ...

    Empirically: when the phenotype of a combination of losses (or gains) of function is different from what is expected from individual phenotypes.


Why is it interesting?

1. Genes that interact may be related

2. Genes that interact similarly with everyone else may have similar functions

Yeh et al. found that drugs with similar mechanism showed the same interactions with other drugsNature Genetics 38 (2006)

Tong et al. found that interacting genes tended to share GO annotationsScience 303 (2004)‏


Genetic interactions

phenotype

Gene A

Gene B

positive

(synergistic)

interaction

phenotype

wt

A

B

A+B

negative (antagonistic)

interaction

A

phenotype

B

phenotype

wt

A

B

A+B

B

buffering

interaction

phenotype

A

wt

A

B

A+B

phenotype


A cell-based combinatorial RNAi co-knock-down screen

Set of 84 phosphatases

  • they dephosphorylate other molecules (opposite function of kinases)

  • activate or deactivate enzymes

  • involved in many signal transduction pathways

  • not well understood

    Test all vs all for interactions: interactions might reveal which phosphatases are involved in similar processes

    Scalability: pilot experiment for a genome-wide combinatorial screen

    Joint work with M. Boutros, T. Horn (DKFZ) and R. Gentleman (Seattle)


Experimental setup: Criss-cross design

4 "column plates" (4x21=84)

6 "row plates" (6x14=84)

column j

row i

X

dsRNAs

384-well plate

384-well plate

well i:j

Measure

>3500 pair wise phospatase-phosphatase interactions

(4 replicates each)

84 interactions of phosphatases with positive controls

84 ... with negative controls

24 "mix plates" (4x6)


Quality assessmentreciprocity of pairwise interactions: Pi:Pj vs Pj:Pi

Bad

Good

Kc167

SL2


Reproducibility

Three replicate experiments (1,2,3) with 2 directions (Pi:Pj, Pj:Pi) each (a,b)


Estimating interaction effects

Yijkdata: i,j{1,...,84}(genes), k{1,...,3} (replicates)

Pimain effect of gene i

Pijinteraction i:j

There are three regions in data space from which we could estimate the single gene effects Pi

1.)j = 0, negative control (assuming P0 = 0, Pi0 = Pi )

2.) j = i (assuming Pii = Pi )

3.)set aside 1.+2., minimize ||||. Attractive option: penalize Pij 0

Differences between 1, 2 and 3 can be used as diagnostic for experiment quality / model fit.


Estimating interaction effects

viability hits

synergistic interactions

antagonistic interactions


In a previous pilot experiment:

unexpected dosage dependence


Related phosphatases show similar interaction profiles

cluster of lipid phosphatases

(CG11437, CG11438, wun)

interact with JNK signaling

synergistic

antagonistic

interactions


Graph(ical) representation of sparse interaction matrix

Limited number of PP with many interactions, e.g.

puc

PP1- a96


Discussion

Importance of data quality control and diagnostics for checking modeling assumptions

Linear modeling approach seems natural, but:

on which scale? (measured fluorescence vs log scale)

caveat saturation and background signal

Regularisation to achieve sparsity seems desirable


Next steps

Expansion to larger gene sets (genome-wide instead of 84): what is the most economic experimental design?

More complex phenotypes (rather than 'viability'): automated quantification of cytometry or microscopy data

Application to RNAi-drug interactions


Thank you

DKFZ

Michael Boutros

Florian Fuchs

Christoph Budjan

Thomas Horn

David Zhang

FHCRC Seattle

Robert Gentleman

Florian Hahne

Martin Morgan

The contributors to the R and Bioconductor projects

Simon Anders

Elin Axelsson

Richard Bourgon

Rémy Clement

Kristen Feher

Bernd Fischer

Audrey Kauffmann

Daniel Murell

Gregoire Pau

Ramona Schmid

Oleg Sklyar

Jörn Tödling


What is a phenotype: it all depends on the assay

Any cellular process can be probed.

- (de-)activation of a signalling pathway

- cell differentiation

- changes in the cell cycle dynamics

- morphological changes

- activation of apoptosis

Similarly, for organisms (e.g. fly embryos, worms)

Phenotypes can be registered at various levels of detail

- yes/no alternative

- single quantitative variable

- tuple of quantitative variables

- image

- time course


Rescue from positive control (DIAP1)‏

  • DIAP, Drosophila Inhibitor of Apoptosis

  • commonly used as positive control

  • co-depletion with P71 shows rescue phenotype

  • reproducible in different cell lines

S2

A*B*

wt

A*

B*

negative (suppressing)

interaction


Normalization: Plate effects

k-th well

i-th plate

Percent of control

Normalized percent

inhibition

z-score


Zhang JH, Chung TD, Oldenburg KR, "A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays." J Biomol Screen.1999;4(2):67-73.

NB:

would be more efficient


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