The FXM, as of May 2004

An auspicious beginning . . .

The FXM, as of May 2004

but . . .

Knotty problems abound

We can (and do) congratulate ourselves for . . .

New cell, new experimental conditions

Parts list and network map

Primary assays: Ca2+, p-Akt, PH-Akt

. . . in cell populations and single cells

RNAi works, at (relatively) high throughput

RNAi Knockdowns (KDs) so far

37 KD lines, against 31 targets

KD robust: >99% in one third, >90% in two thirds, >80% in almost all

Throughput: prepare and assay 4 lines per week

Many KDs produce stable Ca2+ response phenotypes; of these, many are not expected

*KDs of 28 targets change ~30 % of Ca2+

responses to FXM ligands

FXM: Challenges, questions

Weak IgG2a responses

Need to validate RNAi phenotypes

Need more/better assays for network intermediates

Modeling is just beginning

KDs without phenotypes

Many phenotypes are unexpected, often with gain of function rather than loss

Multiple KD phenotypes: delight vs. disaster

Uncovering unsuspected complexity and generating fascinating puzzles?

Opening a Pandora’s box of misleading, biologically irrelevant phenomena?

Hell?

or

Are we . . .Heaven?

Validating knockdowns: the questions

Can an shRNAi exert off-target effects?

Are we selecting clones with compensatory mutations or long-term adaptations?

What are other sources of variability? How should we deal with them?

Are shRNAi KDs reliable, in general and in individual cell lines?

What should we do about any/all of these?

(Do we want to study such adaptations?)

Validating knockdowns: compensatory mutations/adaptations

To make such compensations less likely, knock down the target faster . . .

Antisense RNA vs. the same target

Replicate the phenotype with a KD down- stream (to rule out compensation at sites between the first and second targets)

Transiently transfect siRNA vs. the same target

and/or

Validating knockdowns: coping with variability

Early days! . . . We don’t know yet how much variation to expect, from any/all sources

Replicate cell lines with different shRNAi sequences (some already replicate the phenotype)Multiple determinations of responses, to assess general experimental variability

Initially, with several ‘unexpected’ phenotypes:

mRNA arrays, antisense, siRNAi, as above

Devise/apply better statistical criteria for comparing responses

Validating knockdowns: reverse the phenotype

Express the target protein in the shRNAi, line, using a cDNA it cannot affect (e.g.,

human vs. mouse DNA sequence)

Reversal of the shRNAi phenotype will indicate that the phenotype was indeed produced by KD of the target protein*

*But will not rule out compensatory mutations/adaptations

Validating knockdowns: test a good hypothesis

An shRNAi phenotype is more likely to be due to KD of the target protein if it is predictably affected by a second perturbation

E.g., the PTEN KD* appears to increase the Ca2+ response to C5a

Hypothesis 1: Effect is due to elevated PIP3

Hypothesis 2: Elevated PIP3 increases Ca2+ response by targeting PLC to membrane

PI3K inhibitor should reverse

PLC KD should reverse

(What we always want, of course!)

*Caution: Reproducibility of PTEN KD phenotype needs to be confirmed

‘Creative tension’

Test moreHypotheses!

Get more data!

Magical inductionism vs. needlepoint nihilism

From unbiased datathe truth will accrue

Data without ideas = ignorance

Relieve your creative tension!

In the AfCS

Hypothesis

center

AfCS data

Hypothesis

One experiment that would disprove it

Each hypothesis will include . . .

Intermediate signals

Pressing need to assay many more intermediate variables

Phosphorylation disappointing: few, often not robust

Plan/hope: SILAC, AQUApeptide technologies

XFP translocations

Screening under way

FRET assays

Lipids, PIP3

PIP3, IP3, DAG vexingly hard to measure

Network models

From the modelers we ask a lot

Construct a model network that . . .

Represents a comprehensive set of molecular interactions responsible for key responses

Can vary strengths of interactions & activities, in silico, to simulate responses

Predicts and evaluates responses in the cell

Easily incorporates (& even suggests) new hypotheses (feedbacks, connections, nodes)

Evaluates experimental tests of these new hypotheses

Network models

The bad news

A difficult task, likely to remain so

Good precedents are rare, but not unknown

The good news

Will model responses of cell populations AND of single cells

Abundant data kindles modelers’ enthusiasm

Overcast . . .

but full of promise

It’s a new day!

IgG2a responses

IgG2a elicits little detectable tyrosine phosphorylation (because Syk is poorly expressed?)

Ca2+ & p-Akt responses are quantitatively similar to C5a responses

Single cell responses are weak, not yet reproducible

This tyrosine-phosphorylation pathway makes an immensely attractive target to study, and . . .

But:

How can we begin to understand an IgG2a- triggered network without measuring phosphotyrosine responses

But . . .

IgG2a responses

Adapt more sensitive technology (SILAC or AQUApeptide?)

So

Signaling mechanisms differ from those of GPCR pathways

On the one hand . . .

Already see potentially interesting (& unexpected) shRNAi phenotypes

And . . . ?

KDs without phenotypes

E.g. IP3R KDs (so far)

KD ineffective: assess by western, RT-PCR; try alternative shRNAi sequences

Redundant isoforms: double (& ? triple) KDs with multiple lentiviruses

Redundant signals: regulation predominantly by a different pathway (which we must find)

Validating knockdowns: off-target effects

Can an shRNAi exert off-target effects?

Probably yes, as already reported with siRNA

But how frequently? In a specific cell line?

Immunoblots against unrelated target proteins

mRNA arrays in multiple control vs. shRNAi-expressing lines

To estimate how often this occurs . . .

Intermediate signals

Ligand h j oCa2+/PIP3

p

i

k

m n

We need to measure these to understand information flow through the network

What will a KD at i, j, or k do to a signal transmitted at nodes n, o, or p?