Download Exploring how the organelles are organized

RESEARCH HIGHLIGHTS
PROTEOMICS
© 2006 Nature Publishing Group http://www.nature.com/naturemethods
Exploring how the organelles are organized
New work from several laboratories
describes ongoing efforts to explore a
new proteomic frontier—mapping and
indexing the protein content of the cellular organelles.
The cell is more than just a sack of proteins, even if it can sometimes be convenient to think of it that way. Every cell
contains dozens of functionally distinct
compartments, and it is well established
that the Golgi, mitochondria and other
organelles each have unique proteomic
profiles. But until recently, efforts to
cleanly isolate individual organelle types
and analyze their protein content have
been met with limited success.
To improve the quality of their organelle analyses, Max Planck Institute
researcher Matthias Mann and his colleagues developed a technique called
protein correlation profiling (PCP), in
which mass-spectrometric data obtained
from gradient-fractionated cell extracts
are compared against proteins known to
localize to specific organelles, allowing
researchers to confidently map organelles
to particular fractions. Using PCP with
extracts obtained from mouse liver, Mann
and his colleagues obtained data that
allowed them to confidently assign nearly
1,500 proteins to 10 cellular organelles
(Foster et al., 2006). These data not only
took Mann’s multinational team a step
closer toward assembling a better cellular
proteomic map, but it also proved useful for the preliminary identification of
organelle-specific cis-regulatory elements
and the tentative assembly of networks of
coregulated genes.
Sometimes dividing the cellular proteome into compartments can also be a
pragmatic decision. “The fractionation
that we did to enrich our organelles, to
some degree, was to get around technical
limitations associated with mass spectrometry,” explains Andrew Emili of the
University of Toronto. “[With] a crude
extract, we’d probably identify far fewer
BIOINFORMATICS
MORE THAN JUST ‘DOING THE MATH’
Two new articles show how computational tools continue to
move beyond mere sequence-based bioinformatic analysis
into more advanced arenas of prediction, deduction and
network building.
As interest grows in the still-young field of organelle
proteomics, inventive in silico strategies are essential if
researchers are to construct accurate hypotheses from mountains
of raw data. Computational and experimental approaches have
a symbiotic relationship, explains Vamsi Mootha of the Broad
Institute of MIT and Harvard University: “They complement each
other—you can’t tease them apart. In order to support highquality computational approaches, you need to begin with highquality datasets.”
Mootha recently illustrated this relationship, describing a
‘smarter’ in silico approach for identifying mitochondrial proteins
(Calvo et al., 2006). Earlier strategies have largely emphasized
motif-based predictors, but the Mootha group’s ‘Maestro’ program
takes a more holistic approach, integrating eight different
‘predictors’, based on both structural and experimental data,
to generate scores predicting the likelihood of mitochondrial
localization. After training Maestro with a ‘gold standard’ set of
known positive and negative controls, Mootha’s team confirmed
hundreds of known mitochondrial proteins and confidently
identified nearly 500 that were previously unidentified.
Notably, Maestro also proved capable of tentatively identifying
genes associated with several human mitochondrial diseases,
including at least one that had not been previously recognized as
mitochondrial.
Søren Brunak, of the Technical University of Denmark, and
his colleagues recently described an alternative computational
tool for organelle proteomics and used in silico methods to
420 | VOL.3 NO.6 | JUNE 2006 | NATURE METHODS
predict protein complexes in the nucleolus (Hinsby et al.,
2006). They began by constructing an interaction atlas for
a collection of known human nucleolar proteins based on
publicly available interaction data and then subjected each
putative complex to component-by-component computational
analysis based on dozens of protein features, to predict
the likelihood of nucleolar localization. Using conservative
parameters, Brunak’s team confidently predicted 15 nucleolar
complexes; several of them were expected, but many were
rather surprising from a functional standpoint (for example,
proteins involved in DNA repair). This work also revealed 11
new nucleolar proteins, which were confirmed by experimental
data from Brunak’s collaborator, Matthias Mann, in a process
the two call ‘reverse proteomics’.
Both groups benefited from smart use of existing data sets,
and Mootha suggests that more data should mean more options
for future computational efforts. “More generally,” he says,
“if we get different types of really good functional genomics
data sets, it might be possible to reconstruct all organelles in
silico.” Both approaches, however, also illustrate the value of
using conservative cutoffs to eliminate ‘junk’ data and to ensure
confidence in one’s analysis. “Mapping something often means to
throw a lot of information away, and this is, I think, what we try
to do with our work,” says Brunak. “We would rather not waste
the precious time of the experimentalists!”
Michael Eisenstein
RESEARCH PAPERS
Calvo, S. et al. Systematic identification of human mitochondrial disease genes
through integrative genomics. Nat. Genet. 38, 576–582 (2006).
Hinsby, A.M. et al. A wiring of the human nucleolus. Mol. Cell 22, 285–295
(2006).
© 2006 Nature Publishing Group http://www.nature.com/naturemethods
RESEARCH HIGHLIGHTS
proteins.” Emili and his colleagues isolated 4 different cellular compartments—cytosol, plasma membrane, nucleus and mitochondria—via gradient separation, and then compared the proteomic
differences between these organelles in different mouse tissues
(Kislinger et al., 2006). Their study yielded tissue- and organellespecific data for nearly 5,000 different proteins, and their comparisons of proteomic profiles against high-quality microarray data
revealed a surprisingly tight relationship between mRNA and protein
expression levels.
Kathryn Lilley, of the University of Cambridge, encountered
similar problems to Emili’s in her initial studies of Arabidopsis sp.
proteomics. “We were seeing the same proteins over and over again,
because we were just sampling the abundant cytosolic proteins,” she
explains. She and her colleagues used localization of organelle proteins using isotope tagging, a technique called LOPIT, to perform an
organelle enrichment study of their own, with a particular emphasis
on the identification of membrane proteins (Dunkley et al., 2006).
They consistently detected approximately 700 proteins in multiple
experiments, 60% of which were putative membrane proteins, and
more than 75% of which could be confidently assigned to a particular organelle after careful computational analysis of the protein
content in various cellular fractions.
Technical limitations have posed a serious obstacle to studies like
these in the past, and Mann is quick to credit much of his data quality to the equipment at his disposal. “We used very new instrumentation,” he says, “[and] so we were able to get much more accurate
data.” Emili agrees: “If I had my way, a mass spectrophotometer
would be like a PCR machine, and every lab would have one. We were
in a luxury position... being able to dedicate an instrument to this.”
All three researchers agree that this is a field rapidly coming of age.
“I think it’s an exciting time to be involved in organelle proteomics,”
says Lilley. “There are so many different biological questions that
require a knowledge of where proteins are and where they traffic to
upon given perturbations that have largely been ignored in the past
because we haven’t had the tools.”
Authoritatively indexing the organelle proteome will require
more effort, as well as powerful computational tools to maximize
the value of the data. Mann sees this work as a starting point for
answering far more interesting questions about cellular dynamics:
“For example, if you have insulin signaling, how exactly does it signal
into the mitochondria?” He concludes, “I think there will be more
looking in these functional directions, and not just trying to build a
catalog.” Emili also sees this blossoming of ‘reductionist’ proteomics
as an important step toward understanding fundamentals of global
protein organization and behavior. “I think we’re going to take it to
the next level,” he says. “My view of where the field is going is that
in five years, we’ll not only be measuring the levels of protein in
various organs and cell types and tissues, but we’ll [also] know who
they’re associated with and we’ll have some holistic sense of the posttranslational modifications.”
Michael Eisenstein
RESEARCH PAPERS
Dunkley, T.P. et al. Mapping the Arabidopsis organelle proteome. Proc. Natl. Acad.
Sci. USA 103, 6518–6523 (2006).
Foster, L.J. et al. A mammalian organelle map by protein correlation profiling. Cell
125, 187–199 (2006).
Kislinger, T. et al. Global survey of organ and organelle protein expression in
mouse: combined proteomic and transcriptomic profiling. Cell 125, 173–186
(2006).
NEWS IN BRIEF
PROTEOMICS
Biochemical suppression of small-molecule inhibitors:
a strategy to identify inhibitor targets and signaling
pathway components
Peterson et al. describe a biochemical alternative to the widely
used genetic suppressor screen. Cells treated with a smallmolecule inhibitor are incubated with concentrated cytosolic
fractions from untreated cells; closer analysis of the fractions
that reverse the inhibitor phenotype can reveal drug targets—
including multiprotein complexes—and proteins that act
downstream of these targets.
Peterson, J.R. et al. Chem. Biol. 13, 443–452 (2006).
GENE REGULATION
Preventing gene silencing with human replicators
Transcriptional silencing presents a serious obstacle to the efficacy
and safety of insertion-based gene therapy. Previous research has
shown that transcriptionally active chromosomal regions tend to
undergo replication early in S phase, and Fu et al. demonstrate
that the extent of silencing can be greatly reduced by the
incorporation of active replicator sequences into transgenes.
Fu, H. et al. Nat. Biotechnol. 24, 572–576 (2006).
IMAGING AND VISUALIZATION
Assembly of the brainstem cochlear nuclear complex is
revealed by intersectional and subtractive genetic fate
maps
Analyzing the development of complex tissues often requires
the ability to distinguish between similar but distinct cell
populations. As a tool for such mapping projects, Farago et al.
have developed a indicator system that allows them to visually
differentiate cells that simultaneously express two genes of
interest from cells where only one of the two is being expressed.
Farago, A.F. et al. Neuron 50, 205–218 (2006).
MICROFLUIDICS
Microfabricated bioprocessor for integrated nanoliterscale Sanger DNA sequencing
Whereas some scientists foresee the impending demise of Sanger
sequencing, Blazej et al. still see advantages in this venerable
technique. They describe a microfabricated lab-on-a-chip system
capable of accurate Sanger sequencing from one femtomole of
template DNA and discuss the possibility of developing similar
nanoscale bioprocessors for other genomic applications.
Blazej, R.G. et al. Proc. Natl. Acad. Sci. USA 103, 7240–7245 (2006).
MICROSCOPY
STED microscopy reveals that synaptotagmin remains
clustered after synaptic vesicle exocytosis
Stimulated emission depletion (STED) considerably improves the
resolution of fluorescence microscopy, allowing the visualization
of objects tens of nanometers in diameter with a minimum of
effort by the investigator. Willig et al. demonstrate the power of
STED microscopy, imaging the clustering of synaptic vesicles at
the presynaptic membranes of rat neurons.
Willig, K.I. et al. Nature 440, 935–939 (2006).
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