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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: Ca 2+ , p-Akt, PH-Akt. - PowerPoint PPT Presentation
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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?