close

Вход

Забыли?

вход по аккаунту

?

Potential use of microarrays and related methodologies in animal

код для вставкиСкачать
Uses of microarrays and
related methodologies in
animal breeding
QuickTimeтДв and a TIFF ( Uncompr essed) decompressor are needed to see this pictur e.
Bruce Walsh, jbwalsh@u.arizona.edu
University of Arizona
(Depts. of Ecology & Evolutionary Biology, Molecular & Cellular Biology,
Plant Sciences, Animal Sciences, and Epidemology & Biostatistics)
The basic idea behind gene
expression arrays
тАв With a complete (or partial) genome
sequence in hand, one can array sequences
from genes of interest on small chip, glass
slide, or a membrane
тАв mRNA is extracted from cells of interest
and hybridized to the array
тАв Genes showing different levels of mRNA
can be detected
Types of microarrays
тАв Synthetic oligonucleotide arrays
тАУ Chemically synthesize oligonucleotide sequences
directly on slide/chip/membrane (e.g., using
photolithography)
тАУ Affymetrix, Agilent
тАв Spotted cDNA arrays
тАУ PCR products from clones of genes of interest
are spotted on a glass slide using a robot
тАУ Extracted cellular mRNA is reversetranscribed into cDNAs for hybridization
Cell type 1
Cell type 2
Extract mRNA
Label mRNA with red
fluorescent dye (Cy5)
Label mRNA with Green
fluorescent dye (Cy3)
Cell
Type 1
Cell type 2
Hybridize mRNA to array
The color of the spot
corresponds mRNA
to the
equal mix
from cell Types
relative concentrations
1 and 2
of mRNAs for that gene
in the two cell types
mRNAsfor
from
these
mRNAs
from
these
mRNAs
these
Genes
of roughly
equal
genes
more
abundant
genes
more
abundant
Abundance
celltype
type21in both cell
inin
cell
types
Analysis of microarray data
тАв Image processing and normalization
тАв Detecting significant changes in expression
тАв Clustering and classification
тАУ Clustering: detecting groups of co-expressed
genes
тАУ Classification: finding those genes at which
changes in mRNA expression level predicts
phenotype
Significance testing-- GLM
Yklijk = u + Ak +Rkl + Ti + Gj + TGij +elkijk
Array
k
Replicate
l Gene j i
Treatment
in array k between
Interaction
k-th spotting of gene j under
genei ion
and
treatment
treatment
replicate
l of jarray
k
Problem of very many tests (genes)
vs. few actual data vectors
тАв Expectation: A large number of the GxT
interactions will be significant
тАУ Controlling experiment-wide p value is very
overly conservative (further, tests may be
strongly correlated)
тАв Generating a reduced set of genes for
future consideration (data mining)
тАУ FDR (false discovery rate)
тАУ PFP (proportion of false positives)
тАУ Empirical Bayes approaches
Which loci control array-detected
changes in mRNA expression?
тАв Cis-acting factors
тАУ Control regions immediately adjacent to the
gene
тАв Trans-acting factors
тАУ Diffusable factors unlinked (or loosely linked)
to the gene of interest
тАв Global (Master) regulators
тАУ Trans-acting factors that influence a large
number of genes
David TreadgillтАЩs (UNC) mouse
experiment
тАв Recombinant Inbred lines from a
cross of DBA/2J and C57BL
тАв The level of mRNA expression
(measured by array analysis) is
treated as a quantitative trait and
QTL analysis performed for each
gene in the array
Distribution
of >12,000
gene
interactions
CIS-modifiers
MASTER modifiers
Genomic location of genes on array
TRANS-modifiers
Genomic location of mRNA level modifiers
Candidate loci : Differences in
Gene Expression between lines
тАв Correlate differences in levels of
expression with trait levels
тАв Map factors underlying changes in
expression
тАУ These are (very) often trans-acting factors
тАв Difference between structural alleles and
regulatory alleles
тАУ Different structural alleles may go undetected
on an array analysis
Expanded selection opportunities
offered by microarrays
тАв GxE
тАУ Candidate genes may be suggested by examining
levels of mRNA expression over different
major environments
тАУ With candidates in hand, potential for
selection of genes showing reduced variance in
expression over critical environments
тАв Breaking (or at least reducing) potentially
deleterious genetic correlations
тАУ Look for variation in genes that have little (if
any) trans-acting effects on other genes
Towards the future
тАв Selection decisions using information on
gene networks / pathways
тАв Microarrays are one tool for
reconstructing gene networks
тАв Additional tools for examining proteinprotein interactions
тАУ Two hybrid screens
тАУ FRET & FRAP
тАУ 2D Protein gels
Analysis and Exploitation of
Gene and Metabolic Networks
тАв Graph theory
тАв Most estimation and statistical issues
unresolved
тАв Major (current) analytic tool:
Kascer-Burns Sensitivity Analysis
Gene networks are graphs
Kascer-Burns Sensitivity Analysis
(aka. Metabolic Control Analysis)
тАЬAll theory
modelsshould
are wrong,
are usefulтАЭ
тАЬNo
fit allsome
the models
facts because
some of
(Box)
the
facts are wrongтАЭ (N. Bohr)
Flux = production rate of a
Perhaps we increase the
concentration
of ehere
particular
product,
F
1
However, it may be more efficient
The flux control coefficient, introduced by
To increase the concentration of e
Kascer and Burns, provides a quantitative solution 4
How best to increase the flux through this
to this problem
pathway?
Flux Control Coefficients, C
The control coefficient for the flux at step i in
a pathway associated with enzyme j,
j
Ci
=
@F i E j
@E j f i
=
@ln F i
@ln E j
Roughly speaking, the control coefficient is
the percentage change in flux divided by
percentage change in enzyme activity
j
i
.
Flux
Why many mutations are recessive: a 50%
reduction in activity (the heterozygote)
results in only a very small change in the flux
Activity
When
When the
the activity
activity of
of E
E is
is large,
near zero,
C
C is
is close
close to
to zero
1
Kacser-Burns Flux
summation theorem:
X
C
j
i
= 1
i
тАвтАв While
Coefficients
are
notproteins
intrinsic
properties
most
valuescoefficient
of C for
are
positive,
If
a
control
is
greatly
тАв
Truly
rate-limiting
steps
are
rare
negative
regulators
(repressors)
give
negativesystem
values,
of
an enzyme,
but
rather
a (local)
increased
in value,
this decreases
the
allowing for C values > 1.
property
values of other control coefficients
тАЬrate-limitingтАЭ steps in pathways
Small-Kacser theorem: the factor f by which flux is
Hence, the limiting increase in f is
increased by an r-fold increase in activity of E is
1
1
f =
j
f =
1 ┬░r ┬░C E1 j
1┬░
CE
r
Using estimated Control
Coefficients as selection aids
тАв Loci with larger C values should respond
faster to selection
тАв Such loci are obvious targets for screens
of natural variation (candidate loci)
тАв Selection with reduced correlations
тАУ Tallis or Kempthorne - Nordskog restricted selection
index
тАУ Select on loci with large C for flux of interest, smallest C
for other fluxes not of concern
тАУ Positive selection on C for flux of interest, selection to
reduce flux changes in other pathways
We wish this flux to
remain unchanged
A more
Thecorrect
initial approach
approach,
might
however
be to
is to
Flux
we
wish to increase
Pick try
theeither
step(s)e3that
or emaximize
CF while
e1 orminimizing
e2
CH
4, rather than
Index selection on pathways
тАв The elements of selection include both
phenotype and C, and (possibly) marker
markers as well
тАв Problems:
тАУ C is a local estimate, changing as the pathway
evolves
тАУ Still have all the standard concerns with a
selection index (e.g., stability of inverse of
genetic covariance matrix)
тАУ These are important caveats to consider even
under the rosy scenaro where all CтАЩs are known
What to call it?
MAS = Marker Assisted Selection
CAS = Control Coefficient Assisted Selection
CASH $ = Control Activity Selection Helper
Summary
тАв Microarray analysis = data mining
тАв Potential (immediate) useage:
тАУ Suggesting candidate loci
тАУ More efficient use of G X E
тАУ Reducing/breaking deleterious correlations
тАв Cis (easy) vs. trans (hard) control of
expression levels
тАв Future = analysis of pathways
тАУ Index selection (and all its problems)
Farewell from the тАЬdesertтАЭ
U of A Campus
QuickTimeтДв and a TIFF (Unc ompressed) dec ompressor are needed to see this picture.
Документ
Категория
Презентации по английскому языку
Просмотров
9
Размер файла
3 668 Кб
Теги
1/--страниц
Пожаловаться на содержимое документа