Uses of micr
related meth
animal b
Bruce Walsh, jbwa
University
(Depts. of Ecology & Evolutionary Bi
Plant Sciences, Animal Sciences, a
roarrays and
hodologies in
breeding
[email protected]
of Arizona
iology, Molecular & Cellular Biology,
and Epidemology & Biostatistics)
The basic idea
expressio
• With a complete (or p
sequence in hand, one
from genes of interes
slide, or a membrane
• mRNA is extracted fr
and hybridized to the
• Genes showing differe
can be detected
a behind gene
on arrays
partial) genome
can array sequences
st on small chip, glass
rom cells of interest
array
ent levels of mRNA
Types of m
• Synthetic oligonucleo
– Chemically synthesize ol
directly on slide/chip/me
photolithography)
– Affymetrix, Agilent
• Spotted cDNA array
– PCR products from clone
are spotted on a glass sli
– Extracted cellular mRNA
transcribed into cDNAs
microarrays
otide arrays
igonucleotide sequences
embrane (e.g., using
ys
es of genes of interest
ide using a robot
A is reverse-
for hybridization
Cell type 1
Extract mR
Label mRNA with red
fluorescent dye (Cy5)
Cell type 2
RNA
Label mRNA with Green
fluorescent dye (Cy3)
Cell
Type 1
Hybridize mRNA to array
Each spo
correspo
gene
Cell type 2
ot (or feature)
onds to a different
The color of the spot
corresponds mtoRNtAhemeqauianllymix
relative concferonmtrcCeaelltlTiToyypnpeess
of mRNAs fo12rantdh2at gene
in the two cell types
gimneigmGtAmRnnceyNRebReenpcnNuNslAeeelenslsAsmAtsldysmtsaofopynorofffepecrrrrreeoe2otaommh1biuaneugbttsnbhuhhedloneyeatdsshneaeetqncutealll
Analysis of mic
• Image processing and
• Detecting significant
• Clustering and classif
– Clustering: detecting
genes
– Classification: finding
changes in mRNA expr
phenotype
croarray data
d normalization
t changes in expression
fication
g groups of co-expressed
g those genes at which
ression level predicts
Significance t
Yklijk = u + Ak +Rkl + T
ArrRaeyplkic
ktr-ethatsmpIgeoenntitnntteieniargrioarnaco
k
testing-- GLM
Ti + Gj + TGij +elkijk
caTterelaGtemneenjt i
rnacoantfryideogkpntelrnibeceeaatttjwmeueenlendontefrjarray
Problem of very m
vs. few actual
• Expectation: A large
interactions will be si
– Controlling experiment
overly conservative (fu
strongly correlated)
• Generating a reduced
future consideration (
– FDR (false discovery r
– PFP (proportion of fals
– Empirical Bayes approa
many tests (genes)
l data vectors
number of the GxT
ignificant
t-wide p value is very
urther, tests may be
d set of genes for
(data mining)
rate)
se positives)
aches
Which loci contro
changes in mRN
• Cis-acting factors
– Control regions immed
gene
• Trans-acting factors
– Diffusable factors un
to the gene of interes
• Global (Master) regul
– Trans-acting factors
number of genes
ol array-detected
NA expression?
diately adjacent to the
s
nlinked (or loosely linked)
st
lators
that influence a large
David Treadgill
exper
• Recombinant Inbre
cross of DBA/2J a
• The level of mRNA
(measured by array
treated as a quanti
QTL analysis perfo
gene in the array
l’s (UNC) mouse
riment
ed lines from a
and C57BL
A expression
y analysis) is
itative trait and
ormed for each
TRANS-modifiers C
Genomic location of genes on arrayDistribution
of >12,000
gene
interactions
Genomic lo
CIS-modifiers MASTER modifiers
ocation of mRNA level modifiers
Candidate loci :
Gene Expression
• Correlate difference
expression with trait
• Map factors underlyi
expression
– These are (very) ofte
• Difference between s
regulatory alleles
– Different structural a
on an array analysis
: Differences in
n between lines
es in levels of
t levels
ing changes in
en trans-acting factors
structural alleles and
alleles may go undetected
Expanded selecti
offered by m
• GxE
– Candidate genes may be
levels of mRNA express
major environments
– With candidates in hand
selection of genes showi
expression over critical
• Breaking (or at least re
deleterious genetic cor
– Look for variation in gen
any) trans-acting effect
ion opportunities
microarrays
e suggested by examining
sion over different
d, potential for
ing reduced variance in
environments
educing) potentially
rrelations
nes that have little (if
ts on other genes
Towards t
• Selection decisions us
gene networks / pathw
• Microarrays are one t
reconstructing gene n
• Additional tools for e
protein interactions
– Two hybrid screens
– FRET & FRAP
– 2D Protein gels
the future
sing information on
ways
tool for
networks
examining protein-
Analysis and Ex
Gene and Metab
• Graph theory
• Most estimation a
unresolved
• Major (current) a
Kascer-Burns Sen
xploitation of
bolic Networks
and statistical issues
analytic tool:
nsitivity Analysis
Gene network
ks are graphs
Kascer-Burns Sen
(aka. Metabolic C
““NAolltmhoedoerylssahroeulwdrfointga, lslotm
th(Beofxa)cts are wrong” (N.
nsitivity Analysis
Control Analysis)
tmhe mfaocdteslsbeacraeuusseesfouml”e of
Bohr)
Flu
Perhaps we increase thpea
A e1 B e2 D
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ux = production rate of a
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D e3 E e4 F
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Flux Control Coe
The control coefficient f
a pathway associated with
Cij = @F i Ej
@E j fi
Roughly speaking, the
the percentage change
percentage change in e
efficients, C j
i
for the flux at step i in
h enzyme j,
= @ln Fi
@ln E j
control coefficient is
e in flux divided by
enzyme activity
FluxWhy many mutations are r
reduction in activity (the h
results in only a very smal
Activi
WWhheenn tthhee aaccttiivviitt
CC iiss cclloossee ttoo 1zero
recessive: a 50%
heterozygote)
ll change in the flux
ity
ttyy ooff EE iiss lnaeragre,zero,
o
Kacser-Bu
summation
X
C
i
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urns Flux
n theorem:
C j = 1
i
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ol coefficients
“rate-limiting” st
SinmcHraelela-nsKecadecs,betyrhatenherlio-mfroeitlmdin:igntchirn
f f =f = 1 °
1°
20
0.2 0.4
18
16
14
12
10
8
6
4
2
0
0.0
Control
teps in pathways
hrneecafrsaeecatinosreafcitnbivyfitwyihsoicfhEfilsux is
11
°CEj1
°r r CEj
4 0.6 0.8 1.0
l Coefficient C
Using estima
Coefficients as
• Loci with larger C val
faster to selection
• Such loci are obvious
of natural variation (c
• Selection with reduce
– Tallis or Kempthorne - No
index
– Select on loci with large C
for other fluxes not of co
– Positive selection on C for
reduce flux changes in oth
ated Control
s selection aids
lues should respond
s targets for screens
candidate loci)
ed correlations
ordskog restricted selection
C for flux of interest, smallest C
oncern
r flux of interest, selection to
her pathways
e6
e5
A e1 B e2 D e3
A moTrheecionritrieacltapaprporaocahchm, ih
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