Lecture 13 - PowerPoint Presentation

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Lecture 13 - PowerPoint Presentation Powered By Docstoc
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                                                                           disagree: red
1.   Proteomics is the large-scale identification and cataloging of proteins from a
     particular organism.
2.   MALDI-TOF stands for matrix assisted laser desorbtion ionization - time of flight.
3.   PAGE refers to the separation of proteins through agarose gels.
4.   IEF is the first step of 2D-PAGE and is based on a pH gradient
5.   In situ hybridization gives a better representation of gene expression than northern
     blot analyses
6.   Immunolocalization is to western blotting what in situ hybridization is to northern
7.   The use of a secondary antibody in western analyses increases sensitivity and
     decreases cost.
8.   GFP is the protein that allows fireflies to glow in the dark.
9.   The expression of reporter genes may be affected by position effects.
This is the last class!!
What you should have learned (or need to learn in the next week):

-gene structure, transcription, translation, global mechanisms controlling gene expression.

-gene cloning strategies.
     -targeted vs. random approaches.
     -when to use which technique
     -uses and limitations of various techniques (information, effort, cost).

-Transformation and genetic engineering
     -cloning vectors
     -transformation techniques
     -sense and antisense down-regulation of genes
     -ethical concerns

-identifying gene function                                             Final exam
      -BLAST searches                                                  Dec. 12, 7-9 pm, BCHM 102
      -northern hybridization/ in situ hybridization
      -western analysis/ immunolocalization                            60 pts for 1st half of the class
      -reporter genes (GFP, luciferase, GUS)                           60 points for 2nd half up to Exam II
      -promoter analysis                                               30 pts for recent topics

-metabolic profiling

-the use of your common sense.
-Dr. Irwin is usually right.
This is a short movie to illustrate the use of reporter genes. This movie and similar movies can be found
at the following web site: (Dr. Sullivan’s lab at the Scripps
Research Institute in la Jolla, CA).
In this movie we are looking at centromere dynamics during mitosis. The centromeres are being
visualized by a centromere DNA binding protein that is fused to the green fluorescent protein (from jelly
fish). The GFP is being visualized through excitation with UV light. In this case the reporter gene is used
to gain knowledge about cell division.
Inverse PCR

Inverse PCR is used to generate unknown DNA flanking a known sequence. Because you don’t know
the sequence of the unknown DNA it is impossible to design PCR primers. When we do inverse PCR
we use primers based on the known sequence, but the primers face opposite directions.

So we have a piece of dsDNA that contains a known sequence (such as T-DNA, a transposon or a
partially sequenced gene or cDNA) flanked by unknown sequence. We circularize the DNA by diluting
it in an aqueous solution that also contains DNA ligase. The dilution is necessary to maximize the
probability of the two ends of the DNA molecule “bumping into each other”. We want intramolecular
events as opposed to intermolecular events!The DNA ligase ligates the two ends together to form a
circular DNA molecule.

With the two primers specific for the known sequence we can perform PCR which results in the
amplification of the flanking sequence.
Metabolic profiling

Metabolic profiling, also called metabolomics, is a high throughput method to characterize many metabolites
in (tissues or organs of) an organism.

The analysis is typically performed using gas chromatography or liquid chromatography followed by mass
spectrometry for identification of compounds. This is abbreviated as GC-MS or LC-MS. You end up with
detailed chemical profiles of your samples and very large datasets. The datasets are then subjected to
statistical analyses to identify (sets of) compounds that differ significantly between samples. So you end up
being able to distinguish your samples based on chemical differences.

What is the rationale for doing this? The classic genetics paradigm states that the gene encodes a protein
and that the protein affects a trait. The protein affects the trait directly or through the chemical conversion of
a metabolite. So by looking at the metabolites we are as close to the trait as possible. Once we know which
metabolites are important determinants of the trait, we can (1) specifically monitor that metabolite while
developing new germplasm optimized for our trait and (2) define genes that affect that metabolite and use
targeted genetic strategies to affect the trait.

DNA/mRNA                Genomics (sequencing projects, EST databases)

Protein                 Proteomics (protein databases)

Metabolites             Metabolomics

We can also use metabolic profiling in combination with genomics approaches to infer gene function.
For example, metabolic profiling can be used to evaluate the effects of genetic modifications as a
result of mutations or genetic engineering. It is typically not very hard to identify (small) molecules.
So when the sequence of a gene of interest does not enable the prediction of the gene function
(because of lack of homology with known sequences in the databases), it may be helpful to at least
identify the chemical changes that occur when the gene is mutated.

Metabolic profiling is a technique that is still under development, but likely to become quite important.

The equipment:

Gas chromatograph – this is an oven that contains a 20-60 meter long capillary column (wound into a
coil). The internal diameter is 0.2-0.5 mm. The column contains a thin film of a resin inside. The
column is connected to a pressurized tank of an inert gas (usually helium) that creates a flow of gas
through the column. The sample (containing a mixture of compounds) is injected at the top of the
column when the temperature is low (typically between 40 and 80 C). The temperature is then raised
and the different compounds are separated based on their volatility and their affinity for the column.
The retention time is the time it takes a compound to elute off of the column.

Mass spectrometer – the traditional (not TOF) mass spectrometer ionizes the sample (in this case an
individual compound) with the use of electrons (electron impact ionization). The resulting ions are then
accelerated in an electric field and then subjected to a magnetic field that causes the ions to deviate
from a straight course. The deviation is a function of the mass-to-charge (m/z) ratio. Many of the ions
fragment upon electron impact. Between the m/z ratio of the molecular ion (unfragmented) and the
fragmentation pattern that is observed it is possible to determine the identity of the compound that
eluted off of the GC column.
A neat web site with an animated overview of GC-MS can be found
at :
The data






                         5        10        15             20               25               30                 35                40        45        50          55               60               65
                                                                                                                                                                  Retention time
 4000e 3
                                                                                 120                             2500e 3                                                                                   150

 3500e 3
               mass spectrum                                                                                     2250e 3

                                                                                                                 2000e 3
 3000e 3

                                                                                                                 1750e 3                                                                             135
 2500e 3
                                                      91                                                         1500e 3

 2000e 3
                                                                                                                 1250e 3

 1500e 3                                                                                                         1000e 3

 1000e 3
                                       65                                                                            500e3
  500e3                      51
                                                                                                                     250e3                             63
                                                 77                   105
                                                                                              136   144                                                                                 117                      166   175
    0e 3                                                                                                               0e 3
           0        25       50             75                  100                    125                150                 0        25        50         75              100               125          150         175

Data analysis: discriminant analysis (as an example)


                                                                                med         med

           3                                                                                            med med
                                                                                      med                med

                                                                         med                                      med
           1                                        high


                                   high                    high
           -1                             high
                                          high        high


           -3                                                                                                                            low




                   -6              -4                               -2                0                        2                          4                  6

                Chemical profiles were determined for ten maize lines with different feed quality (low,
                medium and high quality as determined by feeding trials). The chemical profiles can be
                used to classify the lines in the different quality groups. This helps understand the chemical
                basis for feed quality. Discriminant analysis is a statistical technique that is very useful for
                the analysis of large or complex datasets.

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