Download MaxPlanckInst-MolecularPlant

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Site:
Max-Planck-Institute (MPI) for Molecular Plant Physiology
Am Muehlenberg 1 Science Center Golm
14476 Golm, Potsdam, Germany
Date visited:
5 July 2004
WTEC Attendees:
Fumiaki Katagiri (report author), Marvin Cassman, Adam Arkin, Fred Heineken
Hosts:
L. Willmitzer, Tel: +49-331-567-8200/0, e-mail: [email protected],
M. Stitt, e-mail: [email protected], W. Scheible, M. Udvardi, V.
Nikiforova, J. Selbig, D. Steinhauser.
BACKGROUND:
An overview presentation was given by Mark Stitt.
Organization
The institute has 25 independent groups and ~300 people (including administration). It is organized into
departments, junior research groups, infrastructure groups, University guest groups, and guest groups. There
are three departments: one led by Lothar Willmitzer since 1995, which has 7 research groups; one led by
Mark Stitt since 2000, which has five research groups; and one that Ralph Bock is starting. The departments
have about 160 scientific staff members. Unlike other MPI, the departments in this institute are practically
merged. The institute has a close relationship with the University of Potsdam, which is adjacent to the
institute.
Founding mission of the institute (1994):
To follow an integrated research approach to solve basic questions in plant physiology, combining methods
from genetics, molecular biology, chemistry and physics.
CURRENT SITUATION
Informatics efforts were initiated out of necessity to handle a large amount of data generated by wet labs.
Thus, the research efforts have evolved into systems biology, which is strongly based on generation of high
throughput data in wet labs.
Research Projects
The general experimental scheme is described as “genetic diversity growing in defined environments is
subjected to broad phenotyping.” A variety of experimental design concepts are employed: biased vs.
unbiased, single gene vs. multiple genes, alter gene expression vs. alter proteins. Single gene-oriented
“rational metabolic engineering” often did not work due to the complexity of metabolic networks. This led
to development of broad phenotyping technology platforms to combine with systematic disruptions of genes
involved. More recently, an approach for study of polygenic traits is included in the program.
Genetic diversity:
(1)
(2)
(3)
(4)
(5)
Systematic or targeted over-expression of genes in major metabolic pathways and others
High throughput random gene silencing by antisense RNA
Systematic generation of T-DNA tagged knockout lines
Gain access to introgression lines (Arabidopsis, tomato, maize, pea)
TILLING, which identifies individuals with point mutations in genes of interest from a heavilymutagenized population.
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B. Site Reports
Phenotyping technologies:
(1) Expression profiling
(2) The Affymetrix array is used for Arabidopsis. In-house arrays are used for tomato and Lotus. A
technology gap was filled by real time RT-PCR for >1400 Arabidopsis transcription factor genes.
(3) Proteomics
(4) More than 20 quantitative enzyme assays, which were automated except for the protein extraction
step, were developed and used for measurements of protein levels. The assays are based on
enzymatic cycling assays (G3P-DAP, NADP+-NADPH, NAD+-NADH)
(5) Metabolic profiling
(6) Currently, 100 known and 500 unknown compounds can be profiled. Arabidopsis is believed to
have ~20,000 compounds. Multiple platforms, such as GC/TOF, LC, etc., are required for good
coverage. Stitt doubts that NMR would dramatically reduce the number of platforms needed. The
relative ratios to isotope standards are used for quantitation. ~20% of total runs are used for
controls.
(7) Specialized profiling system
(8) Cell wall profiling is performed by a combination of enzymes that specifically cut sugar chains and
MALDI-TOF.
(9) Efforts are being made to run various profiling technologies for different sub-cellular compartments
and with a single cell/tissue.
Integrate, display, and analyze data.
Interactions between a central Bioinformatics group (research and service) and each lab group are facilitated
by bioinformatics people in each group. A large bioinformatics tool box for mining and visualization is
provided for discovery by biologists (CDB.DB). Interpretation is aided by visualization against the
background of known pathways and processes (MapMan).
Single genes to multiple genes
To study polygenic traits, introgression lines are used. Each introgression line carries one chromosomal
region of one inbred line in the chromosomal background of another inbred line. Having a collection of
introgression lines that collectively cover the entire genome of the former inbred line will help dissect
polygenic traits that differ between two inbred lines into monogenic traits. Such introgression lines in tomato
(150 lines) have been established by Dani Zamir (Hebrew University) and a database including a break-point
map and phenotype information for each line is maintained at Cornell University. The MPI group takes part
in the international efforts of phenotyping the lines by using its metabolic profiling technology.
Specific projects were discussed in the following presentations.
Victoria Nujufiriva
Discovery of sulfur-starvation related genes based on correlations in expression and metabolite profiles was
presented. The threshold for significance was decided by comparing with correlations in shuffled data. A
network, including cause-effect directionality, was built based on the significant correlations and known
links. Two sub-networks were studied closely.
Joachim Selbig (Bioinformatics)
HARUSPEX, Arabidopsis expression database.
MapCave, systematic way to expand annotations.
MetaGeneAnalyse, a suite of analytical methods.
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B. Site Reports
PaVESy, pathway visualization and editing (KEGG is generic, need to specialize it for plants)
PDM (plant diagnostic module), supervised learning for diagnostics.
because it is easy to grasp the decision making process used.
Decision tree machine was used
For community use, the software was made to be easy to use, and in this way, more people use the software.
Consequently, more feedback can be obtained from users. The small size of the research community helps in
this respect.
Dirk Steinhauser
Comprehensive systems biology database (CSB.DB)
It holds publicly available expression profile data from different organisms. It allows co-response query and
returns a functional category summary. This helps identify candidate genes, which can be further analyzed
using CSB.DB, including use of MapMan, which is a functional category-classified expression viewer. Two
questions were raised: 1) How should particular software be compared with other similar ones and 2) what is
the best strategy for a research community to deal with competing developments?
Wolf-R Scheible
Forward genetics had not been very successful with nitrogen-regulation studies due to functionally duplicated
genes (recent duplication of the genome is common in plants). Therefore, a reverse genetic approach was
taken. Their real time RT-PCR platform for Arabidopsis transcription factor genes quantitates >1400 genes
(there are ~2000 transcription factor gene in total) with >90% reliability with 1 transcript/1000 cells
sensitivity, which is much better performance than an Affymetrix array can achieve. The development of the
platform required ~$300k initial investment. 40 nitrogen-regulated genes were identified and analyzed by
inducible overexpression.
Mark Stitt presented further details of enzyme activity profiling and MapMan. Enzyme activity profiling
was used to measure protein level changes during a day, and the protein level changes were compared with
the corresponding mRNA changes. Generally, no clear correlations between mRNA and protein level
changes were observed. The degree of correlations between mRNA and protein levels varies in different
settings. Thus, measuring mRNA and protein levels in each setting is important.
Budget:
The institute receives approximately €8m for everything except depreciation. Approximately €3.5m of this
and additional approximately €3.5m from competitive grants are used as the research budget. Generally, the
overhead rate is zero or up to 20%.
Education:
Most bioinformaticians begin as computer scientists. To develop computer scientists into bioinformaticians,
motivation to take up biological studies is an important factor. Germany should have initiated efforts in
bioinformatics education earlier. MPIs cannot influence college programs, which is an unfortunate situation
since MPIs are often leading research institutes in the country. Another challenge in changing education
programs at the college level is that universities cannot make decisions about curricula by themselves – new
curricula need to be approved by higher governmental organizations.
REFERENCES