Download MPI-Plant-Katagiri

July 5, morning
Max-Planck-Institute for Molecular Plant Physiology (Golm, Germany)
Hosts: Lothar Willmitzer, Mark Stitt, Wolf-R. Scheible, Michael Udvardi, Victoria
Nikiforova, Joachim Selbig, Dirk Steinhauser.
WTEC: Fumi Katagiri, Marvin Cassman, Adam Arkin, Fred Heineken
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. The three departments are one led by Lothar Willmitzer
since 1995, which has 7 research groups, one led by Mark Stitt since 2000, which has 5
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.
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) Systematic or targeted over-expression of genes in major metabolic pathways and
others
(2) High throughput random gene silencing by antisense RNA
(3) Systematic generation of T-DNA tagged knockout lines
(4) Gain access to introgression lines (Arabidopsis, tomato, maize, pea)
(5) TILLING, which identifies individuals with point mutations in genes of interest
from a heavily-mutagenized population.
Phenotyping technologies:
(1) Expression profiling
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.
(2) Proteomics
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)
(3) Metabolic profiling
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.
(4) Specialized profiling system
Cell wall profiling is performed by a combination of enzymes that specifically cut
sugar chains and MALDI-TOF.
(5) Efforts are being made to run various profiling technologies for different subcellular 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 subnetworks were studied closely.
Joachim Selbig (Bioinformatics)
HARUSPEX, Arabidopsis expression database.
MapCave, systematic way to expand annotations
MetaGeneAnalyse, a suite of analytical methods.
PaVESy, pathway visualization and editing (KEGG is generic, need to specialize it for
plants)
PDM (plant diagnostic module), supervised learning for diagnostics. Decision tree
machine was used because it is easy to grasp the decision making process 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 coresponse 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 ~€8m for everything except depreciation. ~€3.5m of this and
additional ~€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.