CG920 Genomics
Lesson 6
Gene Expression and Chemical Genetics
Jan Hejátko
Functional Genomics and Proteomics of Plants,
CEITEC - Central European Institute of Technology
And
National Centre for Bimolecular Research,
Faculty of Science,
Masaryk University, Brno
hejatko@sci.muni.cz, www.ceitec.eu
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 Literature sources for Chapter 05:
 Surpin, M. and Raikhel, N. (2004) Traffic jams affect plant development and
signal transduction. Nature Reviews/Molecular Cell Biology 5,100-109
 Zouhar, J., Hicks, G.R. and Raikhel, N.V. (2004) Sorting inhibitors (Sortins):
Chemical compounds to study vacuolar sorting in Arabidopsis. Proceedings of
the National Academy of Sciences of the U.S.A., 101, 9497–9501
 Nevo-Dinur, K., Nussbaum-Shochat, A., Ben-Yehuda, S., and Amster-Choder,
O. (2011). Translation-independent localization of mRNA in E. coli. Science
331, 1081-1084.
 Lecuyer, E., Yoshida, H., Parthasarathy, N., Alm, C., Babak, T., Cerovina, T.,
Hughes, T.R., Tomancak, P., and Krause, H.M. (2007). Global analysis of
mRNA localization reveals a prominent role in organizing cellular architecture
and function. Cell 131, 174-187.
 Schonberger, J., Hammes, U.Z., and Dresselhaus, T. (2012). In vivo
visualization of RNA in plants cells using the lambdaN(22) system and a
GATEWAY-compatible vector series for candidate RNAs. The Plant journal : for
cell and molecular biology 71, 173-181.
Literature
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3
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
 Regulation of gene expression in the identification of
gene function by gain-of-function approaches
 T-DNA activation mutagenesis
 Ectopic expression and regulated gene expression systems
 Chemical Genetics
Outline
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4
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
Outline
5
 Identification and cloning of the promoter region of the gene
 Preparation of recombinant DNA carrying the promoter and
the reporter gene (uidA, GFP)
TATA box
Iniciation of transcription
promoter
5’ UTR
ATG…ORF of reporter gene
Transcriptional Fusion
5
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 Identification and cloning of the promoter region of the gene
 Preparation of recombinant DNA carrying the promoter and
the reporter gene (uidA, GFP)
 Preparation of transgenic organisms carrying this
recombinant DNA and their histological analysis
Transcriptional Fusion
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GUS Reporter in Mouse
Embryos
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8
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
Outline
9
 Identification and cloning of the promoter and coding region of
the analyzed gene
 Preparation of a recombinant DNA carrying the promoter and
the coding sequence of the studied gene in a fusion with the
reporter gene (uidA, GFP)
TATA box
promoter
5’ UTR
ATG…ORF of analysed gene…..….ATG…ORF of reporter gene….….....STOP
Translational Fusion
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 Preparation of transgenic organisms carrying the
recombinant DNA and their histological analysis
 Compared to transcriptional fusion, translation fusion allows
analysis of intercellular localization of gene product (protein)
or its dynamics
Histone 2A-GFP in Drosophila embryo by PAMPIN1-GFP in Arabidopsis
Translational Fusion
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Translational Fusion
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12
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
Outline
13
Databases
□ Analysis of expression using Genevestigator (AHP1 and
AHP2, Arabidopsis, Affymetrix ATH 22K Array)
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Databases
□ Analysis of expression using Genevestigator (AHP1 and
AHP2, Arabidopsis, Affymetrix ATH 22K Array)
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Databases
□ Analysis of expression using ePlant
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Databases
□ Analysis of expression using ePlant
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□ Analysis of expression using Genevestigator (AHP1 and
AHP2, Arabidopsis, Affymetrix ATH 22K Array)
Databases
17
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18
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
Outline
19
Fluorescence-Activated Cell Sorting (FACS)
□ High-Resolution Expression Map in Arabidopsis Root
Expression Maps - RNA
Brady et al., Science, 2007
Microarray expression profiles of 19 fluorescently sorted GFP-marked lines were
analyzed (3–9, 23, 24). The colors associated with each marker line reflect the
developmental stage and cell types sampled. Thirteen transverse sections were
sampled along the root's longitudinal axis (red lines) (10). CC, companion cells.
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□ High-Resolution Expression Map in Arabidopsis Root
Expression Maps - RNA
Brady et al., Science, 2007
(A) The majority of enriched GO terms (hierarchically clustered) are associated
with individual cell types (blue bar). A smaller number are present across multiple
cell types (green bar). (fig. S2) (B) GO category enrichment for hair cells
confirms a previous report (15). Enriched cis-elements and an enriched TF family
were also identified. (C) From the top 50% of varying probe sets, 51 dominant
radial patterns were identified. Pattern expression values were mean-normalized
(rows) and log2 transformed to yield relative expression indices for each marker
line (columns). Marker line order is the same for all figures; see table S1 for
marker line abbreviations. (D) Pattern expression peaks were found across one
to five cell types. (E to G) Patterns where expression is enriched in single and
multiple cell types support transcriptional regulation of auxin flux and synthesis. In
all heat maps with probe sets, expression values were mean-normalized and log2
transformed. Expression is false-colored in representations of a root transverse
section, a cut-away of a root tip, and in a lateral root primordium. (E) Auxin
biosynthetic genes (CYP79B2, CYP79B3, SUPERROOT1, and SUPERROOT2)
are transcriptionally enriched in the QC, lateral root primordia, pericycle, and
phloem-pole pericycle (P = 1.99E–11, pattern 5). All AGI identifiers and TAIR
descriptions are found in table S14. (F) Auxin amido-synthases GH3.6 and
GH3.17 that play a role in auxin homeostasis show enriched expression in the
columella, just below the predicted auxin biosynthetic center of the QC (P =
8.82E–4, pattern 13). (G) The expression of the auxin transporter, PINFORMED2,
and auxin transport regulators (PINOID, WAG1) are enriched in the
columella, hair cells, and cortex (P = 1.03E–4, pattern 31).
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□ High-Resolution Expression Map in Drosophilla
Expression Maps - RNA
Nikos Karaiskos et al. Science 2017;science.aan3235
Deconstructing and reconstructing the embryo by single-cell
transcriptomics combined with spatial mapping.
(A) Single-cell sequencing of the Drosophila embryo: ~1000 handpicked stage 6
fly embryos are dissociated per Drop-seq replicate, cells are fixed and counted,
single cells are combined with barcoded capture beads, and libraries are
prepared and sequenced. Finally, single-cell transcriptomes are deconvolved,
resulting in a digital gene expression matrix for further analysis.
(B) Mapping cells back to the embryo: Single-cell transcriptomes are correlated
with high-resolution gene expression patterns across 84 marker genes, cells are
mapped to positions within a virtual embryo, and expression patterns are
computed by combining the mapping probabilities with the expression levels
(virtual in situ hybridization).
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Expression Maps - Proteins
Ponten et al., J Int Med, 2011
□ Human Protein Atlas
Schematic flowchart of the Human Protein Atlas. For each gene, a signature
sequence (PrEST) is defined from the human genome sequence, and following
RT-PCR, cloning and production of recombinant protein fragments, subsequent
immunization and affinity purification of antisera results inmunospecific
antibodies. The produced antibodies are tested and validated in various
immunoassays. Approved antibodies are used for protein profiling in cells
(immunofluorescence) and tissues (immunohistochemistry) to generate the
images and protein expression data that are presented in the Human Protein
Atlas (Ponten et al., J Int Med, 2011).
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□ Human Protein Atlas
(http://www.proteinatlas.org/)
Expression Maps - Proteins
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□ Human Protein Atlas
(http://www.proteinatlas.org/)
Expression Maps - Proteins
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25
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Quantitative analysis of gene expression
 DNA and protein chips
Outline
26
26
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Quantitative analysis of gene expression
 DNA and protein chips
Outline
27
 Method, which provides quick comparison of a large
number of genes/proteins between the test sample and
control
 Oligo DNA chips are used the most
 There are commercialy available kits for the whole genome
 company Operon (Qiagen), 29.110 of 70-mer oligonucleotides
representing 26.173 genes coding proteins, 28.964 transcripts and 87
microRNA genes of Arabidopsis thaliana
 Possibility of use for the preparation of photolithography chips – facilitation
of oligonucletide synthesis e.g. for the whole human genome (about 3,1 x
109 bp) jit is possible to prepare 25-mers in only 100 steps, by this
technique
Affymetrix ATH1 Arabidopsis genome array
 Chips not only for the analysis of
gene expression, but also for e.g.
Genotyping (SNPs, sequencing
with chips, …)
DNA Chips
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 For the correct interpretation of the results, good knowledge of
advanced statistical methods is required
 Control of accuracy of the measurement (repeated
measurements on several chips with the same
sample, comparing the same samples analysed on
different chips with each other)
 It is necessary to include a sufficient number of controls and
repeats
 Control of reproducibility of measurements
(repeated measurements with different samples
isolated under the same conditions on the same
chip – comparing with each other)
Che et al., 2002
 Identification of reliable measurement treshold
nespolehlivé
spolehlivé
 Finally comparing the experiment with the control
or comparing different conditions with each other >
the result
 Currently there‘s been a great number of results of various
experiments in publicly accessible databases
DNA Chips
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 Protein chips
 Chips with high density containing 104 proteins
 Analysis of protein-protein interactions, kinase substrates
and interactions with small molecules
 Possibility of using antibodies – more stable than proteins
Protein Chips
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 Identification of proteins interacting with integrin αIIbβ3
cytoplasmic domain of platelets
 Expression of cytoplasmic part as a
fusion peptide biotin-KVGFFKR
 Analysis of binding to the protein
chip containing 37.000 clones of E.
coli expressing human recombinant
proteins
 Confirmation of interaction by pulldown
analysis of peptides and by
coprecipitation of whole proteins as
well (e.g. chloride channel Icln)
 Other use: e.g. in the identification of
kinase substrates, when substrates
are bound to the chip and exposed
to kinases in the presense of
radiolabeled ATP (786 purified
proteins of barely, of which 21 were
identified as CK2α kinase
substrates; Kramer et al., 2004)
Lueking et al., 2005
Protein Chips
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31
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
Outline
32
WT hormonal mutant
Next Gen Transcriptional
Profiling
□ Transcriptional profiling via RNA sequencing
mRNA
Sequencing by Illumina and
number of transcripts determination
mRNA
cDNA cDNA
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Results of –omics Studies vs
Biologically Relevant Conclusions
□ Transcriptional profiling yielded more then 7K differentially
regulated genes…
gene locus sample_1 sample_2 status value_1 value_2 log2(fold_change) test_stat p_value q_value significant
AT1G07795 1:2414285-2414967 WT MT OK 0 1,1804 1.79769e+308
1.79769e+3
08 6.88885e-05
0,00039180
1 yes
HRS1 1:4556891-4558708 WT MT OK 0 0,696583 1.79769e+308
1.79769e+3
08 6.61994e-06
4.67708e-
05 yes
ATMLO14 1:9227472-9232296 WT MT OK 0 0,514609 1.79769e+308
1.79769e+3
08 9.74219e-05
0,00053505
5 yes
NRT1.6 1:9400663-9403789 WT MT OK 0 0,877865 1.79769e+308
1.79769e+3
08 3.2692e-08
3.50131e-
07 yes
AT1G27570 1:9575425-9582376 WT MT OK 0 2,0829 1.79769e+308
1.79769e+3
08 9.76039e-06 6.647e-05 yes
AT1G60095 1:22159735-22162419 WT MT OK 0 0,688588 1.79769e+308
1.79769e+3
08 9.95901e-08
9.84992e-
07 yes
AT1G03020 1:698206-698515 WT MT OK 0 1,78859 1.79769e+308
1.79769e+3
08 0,00913915 0,0277958 yes
AT1G13609 1:4662720-4663471 WT MT OK 0 3,55814 1.79769e+308
1.79769e+3
08 0,00021683 0,00108079 yes
AT1G21550 1:7553100-7553876 WT MT OK 0 0,562868 1.79769e+308
1.79769e+3
08 0,00115582 0,00471497 yes
AT1G22120 1:7806308-7809632 WT MT OK 0 0,617354 1.79769e+308
1.79769e+3
08 2.48392e-06
1.91089e-
05 yes
AT1G31370 1:11238297-11239363 WT MT OK 0 1,46254 1.79769e+308
1.79769e+3
08 4.83523e-05
0,00028514
3 yes
APUM10 1:13253397-13255570 WT MT OK 0 0,581031 1.79769e+308
1.79769e+3
08 7.87855e-06
5.46603e-
05 yes
AT1G48700 1:18010728-18012871 WT MT OK 0 0,556525 1.79769e+308
1.79769e+3
08 6.53917e-05
0,00037473
6 yes
AT1G59077 1:21746209-21833195 WT MT OK 0 138,886 1.79769e+308
1.79769e+3
08 0,00122789 0,00496816 yes
AT1G60050 1:22121549-22123702 WT MT OK 0 0,370087 1.79769e+308
1.79769e+3
08 0,00117953 0,0048001 yes
Ddii et al., unpublished
AT4G15242 4:8705786-8706997 WT MT OK 0,00930712 17,9056 10,9098 -4,405231.05673e-05 7.13983e-05 yes
AT5G33251 5:12499071-12500433 WT MT OK 0,0498375 52,2837 10,0349 -9,8119 0 0 yes
AT4G12520 4:7421055-7421738 WT MT OK 0,0195111 15,8516 9,66612 -3,900439.60217e-05 0,000528904 yes
AT1G60020 1:22100651-22105276 WT MT OK 0,0118377 7,18823 9,24611 -7,503826.19504e-14 1.4988e-12 yes
AT5G15360 5:4987235-4989182 WT MT OK 0,0988273 56,4834 9,1587 -10,4392 0 0 yes
Excample of an output of transcriptional profiling study using Illumina sequencing
performed in our lab. Shown is just a tiny fragment of the complete list,
copmprising about 7K genes revealing differential expression in the studied
mutant.
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34
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
 Regulation of gene expression in the identification of
gene function by gain-of-function approaches
 T-DNA activation mutagenesis
Outline
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 Methods for identification of gene function using gain-of-function
approaches
 T-DNA activation mutagenesis
 Method enabling isolation of dominant mutants by random
insertion of constitutive promoter, resulting in
overexpression of the gene and therefore in corresponding
phenotypic changes
 First step: preparation of mutant library prepared by
tansformation of a strong constitutive promoter or enhancer
 Next step: search of interesting phenotypes
 Identification of the affected gene, e.g. by plasmid-rescue
Gain-of-Function Approaches
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TF TF
TF
40S
60S
TF TF TF TF
40S
60S
40S
60S
40S
60S
40S
60S
TF TF
TF
Activation Mutagenesis
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Isolation of CKI1 Gene
- Isolation of the gene using activation mutagenesis
- Mutant phenotype is a phenocopy of
exogenous application of cytokinins (CKI1,
CYTOKININ INDEPENDENT 1)
*- Tatsuo Kakimoto, Science 274 (1996), 982-985 *
*
no hormones
t-zeatin
K1 K2plasmid
rescue
35S::CK
1 cDNA
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38
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
 Regulation of gene expression in the identification of
gene function by gain-of-function approaches
 T-DNA activation mutagenesis
 Ectopic expression and regulated gene expression systems
Outline
39
35S
LhG4
pOP
TATA
CKI1
activator
reporter
activator x reporter
x
Regulated Expression
Systems
39
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35S LhGR
pOP
TATA
CKI1
activator
reporter
activator x reporter
DEX DEX
+DEX DEX
DEX
DEX
x
Regulated Expression
Systems
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35S LhGR
pOP
TATA
CKI1
activator
reporter
activator x reporter
DEX DEX
+DEX DEX
DEX
DEX
x
pOP
TATA
GUS
DEX DEX
wt Col-
0
4C
Regulated Expression
Systems
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 UAS system
http://www.plantsci.cam.ac.uk/Haseloff/
 Regulatable gene expression systems
 Time- or site-specific regulation of gene expression, leading
to a change in phenotype and thereby identification of the
natural function of the gene
 pOP system
Regulated Expression
Systems
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43
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
 Regulation of gene expression in the identification of
gene function by gain-of-function approaches
 T-DNA activation mutagenesis
 Ectopic expression and regulated gene expression systems
 Chemical Genetics
Outline
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44
 New trends
 „chemical genetics“ – more than 50.000/120.417 records in PubMed
database (16.10. 2008/15.11. 2018, an increase of >240 %)
Chemical Genetics
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45
 New trends
 „chemical genetics“ – more than 50.000/130.437 records in PubMed
database (16.10. 2008/24.10. 2019, an increase of >260 %)
Chemical Genetics
 Like in the case of genetics, there are also „forward“ and
„reverse“ genetics approaches
 Unlike in „classical“ genetics approaches, the subject of
study is not a gene, but a protein
 Chemical genetics tries to identify either the target protein
after a chemical treatment and after following phenotypic
changes („forward“ chemical genetics) or chemicals able
to interact with protein of interest („reverse“ chemical
genetics)
 For that purpose there are carried out searches in the
libraries of various chemicals (thousands of entries,
comercially available)
 example: analysis of endomembrane transport in plants
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 Analysis of mechanisms of endomembrane transport by
chemical genetics approaches
 In plants cells there occurr very dynamic processes mediated mainly
by endomembrane transport
Chemical Genetics
GFP targeted to the ER
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 Endomembrane transport is an important regulatory mechanism in
signal transduction and regulation of cellular processes
 Analysis of mechanisms of endomembrane transport by
chemical genetics approaches
 In plants cells there occurr very dynamic processes mediated mainly
by endomembrane transport (see film, GFP targeting to the ER)
Chemical Genetics
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Richter et al., E J Cell Biol (2010)
Anterograde
transport
Retrograde
transport
Trans-Golgi
network
Multivesicular
bodies-late
endosome
(prevacuolar
compartment)
Recycling
endosome
Huang et al., 2010
PIN1-GFP
In the figure, there is simplified scheme of vesicle trafficking pathways, regulated
by GNOM and its closest relative, GNOM-LIKE1 (GNL1).
Secretory and membrane proteins are synthesised at the ER (blue) and passed
onto the Golgi apparatus (green) by anterograde trafficking in COPII-coated
vesicles.
The retrograde route from the Golgi apparatus to the ER is regulated by the ARFGEFs
GNOM (GN) and GNL1, which regulate the recruitment of COPI coats to
the Golgi membrane. On the secretory route, proteins are transported to the
sorting station, the trans-Golgi network (TGN; lilac).
From there, proteins are either transported to the vacuole (grey) via
multivesicular bodies (MVB, also called prevacuolar compartment, PVC, which
corresponds to the late endosome; deep blue) or trafficked to the plasma
membrane (PM).
Plasma membrane proteins like the auxin efflux carrier PIN1 (red), which
accumulates at the basal PM at steady state, are continually internalised and
trafficked to the TGN, which resembles the early endosome (EE) in plants.
From the TGN, PIN1 is recycled to the plasma membrane via the recycling
endosome (RE; light blue). This pathway is regulated by the ARF-GEF GNOM.
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 Analysis of mechanisms of endomembrane transport by
chemical genetics approaches
 By searching in the „library“ of chemicals there were
identified those, that lead to the secretion of enzyme
(carboxypeptidase Y) in yeast (S. cerevisiae) – this
enzyme is normally transported to the vacuole via the
endomembrane transport
 Analysis of changes in secretion using dotblot
and immunodetection of
carboxypeptidase Y in the culture medium
with monoclonal antibodies
Zouhar et al., 2004
Chemical structure of sortins
Detection of vacuole phenotype (tonoplast shape) of
yeast by staining with a specific color (MDY-64)
Immunodetection of carboxypeptidase
Chemical Genetics
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 Analysis of mechanisms of endomembrane transport by
chemical genetics approaches
 Identified compounds („sortins“) were able to induce
similar changes in Arabidopsis as well – transport
mechanisms are conserved in yeast and in plants
 For detailed identification of the molecular proces
affected by one of the identified „sortins“, the analysis
of its influence on a secretion of a marker protein
(AtCPY) was performed – sortin 1 specifically inhibits
only this secretory pathway
 Identifcation of mutants with altered sensitivity to
sortin 1 (hyper- or hypo-sensitive mutants) by EMS
mutagenesis
Zouhar et al., 2004
Shape of plant vacuoles using EGFP:-TIP
Phenotype of seedlings in the presence of sortins
Sortin 1
Sortin 2
 By searching in the „library“ of chemicals there were
identified those, that lead to the secretion of enzyme
(carboxypeptidase Y) in yeast (S. cerevisiae) – this
enzyme is normally transported to the vacuole via the
endomembrane transport
 Analysis of changes in secretion using dotblot
and immunodetection of
carboxypeptidase Y in the culture medium
with monoclonal antibodies
Chemical Genetics
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 Analysis of mechanisms of endomembrane
transport by chemical genetics approaches –
summary
 GFP::d-TIP vacuole membrane
(tonoplast) labelling and identification
of mutations leading to altered
tonoplast morphology
 Chemical genetics in combination
with classical genetics – identification
of proteins participating in regulation
of endomembrane transport
 Proteomics approaches –
identification and analysis of vacuole
proteome
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52
 Methods of gene expression analysis
 Qualitative analysis of gene expression
 Preparation of transcriptional fusion of promoter of analysed gene
with a reporter gene
 Preparation of translational fusion of the coding region of the
analysed gene with reporter gene
 Use of the data available in public databases
 Tissue- and cell-specific gene expression analysis
 Quantitative analysis of gene expression
 DNA and protein chips
 Next generation transcriptional profiling
 Regulation of gene expression in the identification of
gene function by gain-of-function approaches
 T-DNA activation mutagenesis
 Ectopic expression and regulated gene expression systems
 Chemical Genetics
Summary
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53
Discussion