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Systematic screens for human disease
genes, from yeast to human and back
Fabiana Perocchi,{ Eugenio Mancera{ and Lars M. Steinmetz*
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Published on 25 September 2007 on http://pubs.rsc.org | doi:10.1039/B709494A
DOI: 10.1039/b709494a
Systematic screens for human disease genes have emerged in recent years, due to
the wealth of information provided by genome sequences and large scale
datasets. Here we review how integration of genomic data in yeast and human is
helping to elucidate the genetic basis of mitochondrial diseases. The identification
of nearly all yeast mitochondrial proteins and many of their functional interactions
provides insight into the role of mitochondria in cellular processes. This
information enables prioritization of the candidate genes underlying
mitochondrial disorders. In an iterative fashion, the link between predicted human
candidate genes and their disease phenotypes can be experimentally tested back
in yeast.
Introduction
Genetic factors contribute to nearly all
diseases, either directly—through, for
example, malfunctioning genes—or by
influencing our susceptibility to disease.
Identifying the genetic variants associated with a disorder is often the first
step towards an understanding of the
molecular basis of the disease, and
frequently leads to the development of
diagnostic methods and effective treatments. Since the development of positional cloning about 20 years ago, more
than 2400 genes with mutations causing
mainly Mendelian disorders have
been discovered (http://www.ncbi.nlm.
European Molecular Biology Laboratory,
Meyerhofstrasse 1, 69117 Heidelberg,
Germany. E-mail: [email protected]
{ These authors contributed equally.
Fabiana Perocchi
nih.gov/omim/).1 However, the task
ahead is not straightforward and the
disease genes identified so far may be the
‘‘easy ones’’.2 The Online Mendelian
Inheritance in Man (OMIM) database
reports close to 3700 Mendelian or
suspected-Mendelian diseases with as
yet unidentified genetic underpinnings.
Even more demanding will be the identification of the responsible genes for
common complex-trait diseases, such as
type 1 diabetes, obesity and multiple
sclerosis.2 Complexities arise from the
lack of simple correspondence between
phenotype and genotype caused by
multifactorial inheritance, epistasis,
pleiotropy, genetic heterogeneity and
gene-environment interactions, among
other factors.3
Genomics offers new opportunities to
accelerate the identification of genes and
Fabiana Perocchi grew up in
Rome, Italy. She earned a BS
in Biology in 2003 from the
University of Tor Vergata in
Rome and is currently completing her PhD in Functional
Genomics at EMBL,
Heidelberg. Her thesis work,
under the guidance of Dr Lars
M. Steinmetz, focused on the
identification of mitochondrial
components and the analysis of
their transcriptional regulation
and functional interactions
through the integration of
genomic technologies in yeast.
18 | Mol. BioSyst., 2008, 4, 18–29
the molecular mechanisms underlying
human diseases. At its fundamental level,
identifying a disease gene requires establishing a link between phenotype and
genotype. In candidate gene approaches,
knowledge about gene function is
employed to prioritize genes in the
process of associating them with a
disease. New approaches, which investigate properties of more than one gene at
a time and systematically transfer this
information across species, permit a
more comprehensive prediction of gene
function and informed prioritization of
disease
candidate
genes.
These
approaches integrate information from
sequence conservation, gene expression,
protein interactions and gene knockout
effects, among other properties.
The yeast Saccharomyces cerevisiae is a
powerful model organism because of its
Eugenio Mancera grew up in
Guadalajara, Mexico, and
received his BS degree in biology from UNAM in Mexico
City in 2002. He earned his MS
in molecular and cellular biology from the University of
Heidelberg, Germany, in 2005.
Thereafter he moved to the
group of Lars Steinmetz at
EMBL where he is currently
doing his PhD.
Eugenio Mancera
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comparatively simple genome, its genetic
tractability and a range of unsurpassed
genetic tools that can be applied. These
advantages enabled the development of
numerous genomic technologies in yeast,
which have contributed to identifying
human disease genes.4 One example is
the identification of SURF–1, a gene
involved in Leigh’s disease, a condition
associated with cytochrome c oxidase
deficiency (LD[COX2]). SURF–1 was
singled out among genes within the
linkage interval for Leigh disease because
its yeast homologue encodes a product
that is targeted to the mitochondria and,
when mutated, impairs mitochondrial
respiration.5 This kind of information
transfer is possible for several processes
that are conserved between yeast and
human, including DNA replication,
recombination
and
repair,
RNA
transcription and translation, intracellular trafficking, general metabolism,
and
mitochondrial
function
and
biogenesis.6
The aim of this review is to highlight
how advances in yeast genomics have
been applied to the systematic analysis of
human disease genes. We place a particular focus on the mitochondrial organelle, as it plays a central role in disease
and cellular metabolism, and its study
serves as a stepping-stone for the analysis
of the entire cell. Many of the genetically
uncharacterized diseases are likely linked
to mitochondrial dysfunction. However,
an impediment to finding the causative
genes is that only one-third of the human
mitochondrial proteome has been characterized in detail.7 This review will detail
how large-scale data integration has
catalyzed identification of nearly all yeast
mitochondrial proteins and many of their
functional interactions, as well as how
this knowledge has aided the search for
new disease genes (Fig. 1). In turn, the
review also explores how the human
candidate genes proposed can be tested
back in yeast, where cell-based assays
can be performed in a high-throughput
manner.
Mitochondrial biology and
disease
Mitochondria are best known as the
‘‘powerhouses’’ of eukaryotic cells.
Besides production of ATP, the organelle
is a center for the metabolism of nitrogen, heme and steroid hormones, as well
as for the storage of calcium, iron and
copper. While the biochemistry of mitochondria has been subject to decades of
research, more recent insights into a
mitochondrial role in signal transduction, oxygen sensing, antiviral responses
and programmed cell death are expanding our view of mitochondria beyond
their existence as isolated compartments
of cells.8 The organelle is intimately
involved in cell sensing and signaling
and it undergoes dynamic morphological
changes which are critical in disease,
aging and development.9
Mitochondrial biogenesis requires the
coordinated action of two genomes:
the mitochondrial powerhouses and the
nuclear.10,11 Since its origin as an aerobic
prokaryote that was engulfed in a eukaryotic cell about 2 billion years ago,12 the
mitochondrion has lost most of the
ancestral bacterial genome.13 Notably,
only 13 proteins in human and 8 proteins
in yeast are encoded by the mitochondrial DNA (mtDNA). Nevertheless,
roughly 1500 proteins are expected to
Lars Steinmetz grew up in Germany,
Switzerland and the USA. He earned a
BS in molecular biophysics and biochemistry from Yale University and a PhD in
genetics from Stanford University. Since
2003 he has been a group leader in gene
expression and developmental biology at
EMBL, Heidelberg. His group focuses on
using and developing functional genomic
approaches and high-throughput methods
in yeast to study complex traits and the
mitochondrial organelle at a systems level.
Lars M. Steinmetz
This journal is ß The Royal Society of Chemistry 2008
localize within human mitochondria14
and about half this number are needed
for mitochondria in the simpler eukaryotic organism yeast15 (Table 1). As the
majority of mitochondrial proteins are
encoded by nuclear genes, a comprehensive knowledge of this organelle demands
the integration of information about its
resident proteins and proteins localized
outside the organelle (e.g. transcriptional
regulators, metabolic branches and signaling pathways involved in mitochondrial function).
Due to the genetic dichotomy of the
mitochondrial proteome, mitochondrial
diseases can be caused by mutations in
both mtDNA- and nuclear DNAencoded genes, and follow maternal,
nuclear or a combined mode of inheritance.16 While hundreds of point mutations and large-scale rearrangements in
mtDNA have been successfully associated with several maternally inherited
diseases, mutations in mtDNA are found
in less than 15% of patients with symptoms consistent with a mitochondrial
dysfunction.17 Culprits of mitochondrial
disorders, therefore, likely predominate
in nuclear genes.18 So far, mutations in
over 150 nuclear genes that encode
proteins localized to the organelle have
been implicated in mitochondrial diseases7 (Table 1).
Due to the wide range of functions
carried out by mitochondria, the spectrum of mitochondrial disorders is
broad. Epidemiological studies suggest
that overall mitochondrial disorders
are among the most common inherited
diseases, with a minimum prevalence of 1
in 5000 individuals.19 Dysfunctions of
the mitochondrial oxidative phosphorylation pathway (OXPHOS) can drastically affect organs with high energy
demands, such as brain and muscles,
but the ubiquitous tissue requirement for
ATP can result in almost any organ
being affected.20 To name a few examples: defects in the electron transport
chain are common in mitochondrial
encephalomyopathies;21 increased generation of free oxygen radicals (ROS)
is associated with neuronal cell
death, Parkinson’s disease, Alzheimer’s
disease, carcinogenesis, and aging;11,22
and dysregulation of glucose and mitochondrial fatty acid metabolism are
implicated in type II diabetes and
obesity.23
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Fig. 1 Systematic approach to identify disease genes: from yeast to human. (A) For any biological process, the prediction of parts (nodes) and (B)
their interactions (lines) can be achieved by the integration of multiple datasets across several organisms. (C) The ensuing network enables
extracting properties of its proteins, for example their conservation (doted lines) and interactions, and can be used for prioritizing disease candidate
genes (red nodes). By searching mapped disease intervals in the human genome for genes previously known to be implicated in the process under
study (blue nodes) as well as components newly predicted to be part of the process from the integrative analysis in yeast (red nodes), a list of genes
for further investigation can be identified. These genes are then examined for mutations responsible for disease.
Integrative genomics to define
the mitochondrial parts-list
Genome-scale approaches in cell biology,
biochemistry, genetics and computational biology have catalyzed the identification of mitochondrial proteins in
several organisms.24 Specific examples
of these technologies include analysis of
subcellular localization,25,26 deletion phenotypes,27,28 gene expression,29–31 mass
spectrometry-based proteomics30–34 and
computational prediction of mitochondrial signal peptides.35Using fluorescence
microscopy of proteins genetically tagged
with either GFP or an immunologically
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detectable epitope, over 10% of the yeast
proteome could be localized to mitochondria in yeast.25 Organellar proteomics, which relies on the isolation,
separation and identification of proteins
in organelles,36,37 has assigned over 800
proteins to yeast mitochondria30,32 and
roughly 700 to human heart mitochondria.33,38 Analysis of yeast deletion
mutants defective in respiration and measurement of gene transcript levels during
respiration expanded the catalog of
mitochondrial proteins to include proteins from other compartments that are
essential for the regulation, processing
and turnover of mitochondria.27–30,39
To complete the inventory of
mitochondrial proteins, genome-wide
approaches need to be combined so as
to complement their strengths and
compensate for their shortcomings24
(Fig. 1A). This has been shown in yeast,
mouse and human.7,30,31,40,41 Each individual approach is biased towards different functional subsets of proteins, as
illustrated by a comparison of proteomic
and deletion datasets in yeast.30 Massspectrometry based proteomics exhibits
bias towards proteins that are soluble
and abundant, and therefore it does not
encompass the wide range of hydrophobicity, copy number, molecular weight,
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Table 1 Mitochondrial databases.
Database
Description
Website
MitoDat
Database of nuclear-encoded proteins involved in
mitochondrial biogenesis and function
Database of human mitochondrial genomic sequences,
including polymorphisms and mutations
Database of human mitochondrial genomes
http://www-lecb.ncifcrf.gov/mitoDat/
MITOMAP
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Human Mitochondrial
Genome Database
HMPDb (Human
Mitochondrial
Protein Database)
MitoProteome
MitoP2
YMPD (Yeast
Mitochondrial
Protein Database)
YDPM (Yeast
Deletion Project
and Mitochondria)
OGRe (Organellar
Genome Retrieval)
MitoDrome
MiGenes
http://www.MITOMAP.org
http://www.genpat.uu.se/mtDB
Database of human mitochondrial and nuclear encoded proteins
involved in the biogenesis and function of the organelle
http://bioinfo.nist.gov/hmpd/
Database of human mitochondrial protein sequences,
mtDNA- and nuclear-encoded
Database of mitochondria-related genes, proteins, and diseases.
MitoP2 contains numerous datasets relevant to the study of
mitochondrial proteins in yeast, mouse, Arabidopsis,
Neurospora and human
Database containing a curated list of mitochondrial proteins in
budding yeast. The list includes mtDNA-encoded proteins and
nuclear DNA-encoded proteins that localize in mitochondria
as well as outside it (nuclear or cytoplasmic) with a role in
mitochondrial biogenesis or function
Database supporting the yeast deletion project and
mitochondrial proteomics and expression projects to study
mitochondrial protein composition and transcriptional regulation
Database containing the complete mitochondrial genome
sequences for over 250 Metazoan species
Database of Drosophila melanogaster nuclear genes
encoding mitochondrial
Database of mitochondrial proteins from several organisms
curated using gene ontology
http://www.mitoproteome.org/
isoelectric point and membrane association found in mitochondrial proteins.42
Furthermore, this method has difficulty
detecting proteins that execute important
mitochondrial functions but do not
localize stably to the organelle, such as
transcription factors and cytoplasmic
regulators. Instead, these proteins can
be captured by single gene deletion
phenotype screening, which identifies
genes that function in mitochondrial
processes irrespective of their localization.28 However, deletion phenotype
analysis overlooks genes that do not
have a specific knockout phenotype
because they are redundant or essential,
which is the case for components of
mitochondrial outer membrane transport.28,41 In addition high-throughput
datasets are prone to false discoveries.
For example, mitochondrial proteomic
datasets often include highly abundant
proteins from other cellular compartments, such as proteins of the cytosolic
ribosome and the cytoskeleton, which are
possible contaminants in mitochondrial
preparations.36,43
Due to the diversity of genome-wide
data available for yeast, it is possible to
systematically assess both the coverage
and accuracy of each data type. This was
addressed for 22 genome-wide datasets,
mostly centered on mitochondria, which
showed great heterogeneity in the number and type of mitochondrial proteins
identified.30 While measurements of
mRNA levels during growth on nonfermentable media captured only 10% of
previously-known mitochondrial localized proteins, deletion phenotype screening,
microscopy-based
localization
studies and mass-spectrometry-based
proteomics covered from 50 to 80%.30
Remarkably, although some datasets
show relatively high coverage, they have
modest overlap due to biases in the
methods. Mass-spectrometry-based proteomics and deletion phenotype screening, for instance, shared only 30% of
their protein hits. Therefore, a combined
analysis of several datasets can increase
coverage by considering proteins that
have been identified by multiple, but
not necessarily all methods.
One means of systematic data integration employs algorithms which are
trained to discriminate between mitochondrial and non-mitochondrial proteins using a benchmark dataset. A set
of proteins unambiguously localized to
mitochondria by single-gene studies can
provide a high confidence reference set
for benchmarking7,44 (Table 1). The
most comprehensive integration of
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http://www.mitop.de
http://bmerc-www.bu.edu/projects/mito/
http://www-deletion.stanford.edu/YDPM/
http://www.bioinf.man.ac.uk/ogre
http://www2.ba.itb.cnr.it/MitoDrome/
http://www.pharm.stonybrook.edu/migenes/
genome-wide data for the prediction of
mitochondrial components has been performed in yeast.7,30,41 Machine-learning
approaches have been applied to integrate over 22 genome-wide datasets
ranging from expression, protein localization and deletion phenotype to computational predictions based on signal
peptides. This has resulted in a score
representing the probability of mitochondrial localization for every gene in the
genome.7,30,41 So far, integrative analysis
has covered over 90% of the previously
known mitochondrial proteome and has
yielded high-confidence predictions for
hundreds of previously uncharacterized
components.41
Since the size, shape, number and
metabolism of mitochondria exhibit tissue specificity, completing the annotation
of the mammalian mitochondrial proteome will require tissue-based identification of mitochondrial proteins.31,45
Less than half of the mitochondrial
proteins from brain, liver, heart and
kidney were found to be expressed across
all cell types by proteomic analysis in
mice.31 Characterization was considerably improved by integrating genomewide datasets from different organisms
and mammalian cell types.40 Predictions
of mitochondrial targeting signals were
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combined with protein domain enrichments, yeast homology, ancestry, proteomics, co-expression and transcriptional
induction during mitochondrial biogenesis to expand the catalog of 654 known
human mitochondrial proteins by 490
novel predictions with an estimated false
discovery rate of 10%.40
Despite the diversity of integrated
datasets in yeast and human, a few
known mitochondrial proteins have
remained undetected by integrative
approaches; these are mainly proteins
with dual localization, low abundance
and low solubility. This indicates a need
for new genome-wide datasets that identify both transient and resident proteins
and that reflect the changes in the
mitochondrial proteome in response to
a wider range of environmental conditions. Finally, through an iterative process, predicted mitochondrial proteins,
once experimentally confirmed, can be
then added to the reference set, improving future data integration.
Integrative genomics to define
the mitochondrial interactome
Understanding the complexity of a whole
organelle requires knowledge about how
its individual parts interact to build up
the hierarchical organization of the
organelle. The most basic level of this
organization is represented by its functional modules, which are groups of
proteins that work together, for example
protein complexes, transcription factors
and gene targets, or enzymes in a pathway.46 Identifying these modules requires
establishing the type, timing and mode of
interaction among all the parts of the
system. The reconstruction of a global
interaction network allows (1) placement
of both known and uncharacterized
components into functional contexts
from which hypotheses can be generated
about gene function (Fig. 1B); and (2)
prediction of phenotypic changes in
response to perturbations of cellular
state, such as gene mutations or environmental stresses.
Yeast has been the model organism of
choice for global mapping of gene,
protein and metabolic networks, largely
because of the abundance and quality of
diverse high-throughput interaction data.
A large number of gene-expression profiles have been generated using both
22 | Mol. BioSyst., 2008, 4, 18–29
genetic (e.g. deletion) and environmental
(e.g. media type) perturbations. These
allow the grouping of genes into coexpression modules that may share a
common role and similar transcriptional
regulation.47,48 Key regulators of gene
expression and their target genes (protein–DNA interactions) have been identified genome-wide in yeast by combining
chromatin immunoprecipitation (ChIP)
with DNA microarray analysis (ChIPon-chip).49 Screening double deletion
mutants for cell death or reduced fitness
has identified genetic interactions among
genes whose products buffer one another
and are involved in the same biological
process.50 Functional proteomic technologies developed or implemented in yeast,
such as yeast two-hybrid screens, largescale protein-tagged affinity purifications
and protein chips, resulted in an en masse
detection of transient binary protein–
protein interactions,51 stable physical
protein complexes52,53 and substrate specificity of two-thirds of yeast kinases, as
well as interactions among protein
and lipids, and proteins and small
molecules.54
Interactions can also be computationally predicted. The wealth of genomic
information available for several organisms has driven the development of
computational gene context analyses that
infer functional associations based on the
comparison of multiple genomes.55 The
assumption is that proteins are most
likely to interact if: (1) their genes are
either present or absent together across
genomes (gene co-occurrences or phylogenetic profiles);56 (2) a gene fusion event
occurred in other species;57 or (3) the
genes are conserved in physical proximity
in phylogenetically distant genomes
(gene neighborhood).58 Moreover, automatic methods for extracting different
types of associations from scientific
literature (text mining) have been used
to infer biological interactions.59
Defining the mitochondrial parts list
cannot rely on a sole interaction data
source, and neither can a comprehensive
characterization of interaction networks
(Fig. 1B). Using manually-curated catalogues of known binary interactions (e.g.
protein complexes)60 and pathways,61
systematic comparisons of interaction
data types show that the interactions
predicted with the highest accuracy are
those supported by more than one
dataset.62,63 To increase sensitivity and
to improve confidence in predicted protein interactions, computational tools
have been developed which integrate
many
approaches.64,65
Integrative
approaches can be enhanced further by
collecting and transferring interaction
information across multiple species,
based
on
the
assumption
that
conserved proteins tend to have conserved interactions.65
Integrative analysis of heterogeneous
yet complementary data has been a
key strategy for reconstructing the
mitochondrial interactome, given that
mitochondrial metabolism is highly
condition-dependent in yeast and tissuespecific in human.66 In particular, many
mitochondrial pathways, like respiration,
are repressed when yeast is grown in the
presence of glucose (rich media). Since
most of the protein interaction data have
been generated in rich media, individual
datasets are especially scarce for interactions between mitochondrial proteins.
As an example, two genome-wide screens
for protein complexes provided valuable
insights into cellular machineries for over
70% of the yeast proteome, but wellknown mitochondrial protein complexes
such as the respiratory chain complexes
were not detected52. Similar constraints
arise with in silico predictions, the
majority of which are based on comparisons of bacterial genomes. Only 13% of
the yeast mitochondrial proteome
appears to be ancient and evolutionarily
conserved in proteobacteria, limiting
the number and type of functional
associations that can be identified
through inference from bacterial interactions.41 Text mining, however, can
assist in completing annotation of the
mitochondrial interactome, given that
most results from over 50 years of
biochemical and genetic studies on
mitochondrial proteins and their interactions are stored in the primary
literature.41
An initial version of the yeast mitochondrial network has recently been
reconstructed from over 15 data sources,
including physical protein interactions,
mRNA co-expression associations, functional associations from literature mining, and genome context analyses.41 The
coverage of annotated mitochondrial
protein complexes and metabolic pathways was improved by the integration of
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functional networks from about 200
organisms through inter-species data
transfer.41 A total of 876 proteins, both
known and predicted as mitochondrial,
were placed into 164 functional modules,
providing the first clue about the function of over 150 uncharacterized mitochondrial components. Moreover, by
reconstructing the mitochondrial network in the cellular context, the network
could be used to formulate hypotheses on
the proteins that mediate cross-talk
between mitochondria and other cellular
compartments.41,66
Although high-throughput protein–
protein interaction assays can be applied
to mammals, most of these assays are not
yet scalable to the whole human interactome.67 Therefore, the application of
integrative strategies in human remains
limited.62,68 Nevertheless, the integration
of data from the mitochondrial genome,
the heart mitochondrial proteome, the
annotated human genome, and literature
on mitochondrial metabolism enabled
reconstruction of a human mitochondrial
metabolic network comprised of 189
biochemical reactions and 230 metaboFurthermore,
genome-scale
lites.69
atlases of RNA expression across diverse
tissues have been used to measure
transcriptional coexpression of uncharacterized components with known mitochondrial genes.31 This co-expression
network grouped 386 genes into functional modules that share functional and
regulatory mechanisms.31
As interactions often remodel in
response to genetic or environmental
stresses, it will be an informative next
step to investigate the way in which
mitochondrial modules change in composition, mode of action and regulation
over time. A key idea is that diseases are
caused by the operation of perturbed
networks. Through a comparison of
normal and diseased networks, critical
nodes (proteins) can be identified
which, if modulated, are likely to reconfigure the perturbed network structure
toward its normal state or specifically
kill the diseased cells.70 Moreover, from
the integration of multiple types of
networks
(e.g.
physical-interaction,
regulatory, metabolic networks), cellular
responses to genetic or environmental
perturbations can be predicted, as
has been reported for bacteria and
yeast.71
Systematic analysis of disease
genes
Positional cloning is commonly invoked
for genetic mapping of human diseases.
However, due to limited recombination
resolution, the linkage of disease phenotypes to genetic loci often results in
genomic intervals that contain many
candidate genes. Patient samples and
screening costs often constitute limiting
factors to pinpointing disease genes. Any
knowledge about the biological process
in which a gene product is implicated can
be used to guide the selection of candidate genes (Fig. 1C). As an example, a
genome-wide association study had successfully mapped a form of autosomal
recessive cytochrome c oxidase (COX)
deficiency, known as Leigh syndrome
French-Canadian type (LSFC), to a
chromosomal region containing 30
known and predicted genes.72 The clinical manifestations and biochemical features of LSFC had suggested that
mitochondrial dysfunction underlies this
disorder, but none of the genes encoding
known mitochondrial proteins were
found within the disease interval. Since
only a third of the mitochondrial
proteome was known at that time,
mitochondrial gene products were overlooked. A few years later, the integration
of functional data of mRNA expression
and subcellular localization identified
new human mitochondrial components73, and named LRPPRC as the
top-ranking candidate gene within the
LSFC linkage region, based on its
observed mitochondrial localization and
the remarkably high co-regulation with
known mitochondrial genes. Subsequent
sequencing and genotyping analysis provided the genetic evidence that LRPPRC
is responsible for LSFC. Such an example illustrates that completing the catalog
of mitochondrial proteins holds great
potential to accelerate the identification
of mitochondrial disease genes.
Given the successes in the identification and functional characterization of
mitochondrial proteins in yeast, it is
beneficial to integrate orthology to
known yeast mitochondrial proteins
into the analysis of mitochondrial diseases (Fig. 1C). Indeed, a dataset based
on homology to yeast mitochondrial
proteins performed second-best in predicting human mitochondrial proteins,
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outperforming proteomics and coexpression datasets in mammals40.
Moreover, by exploiting orthology to
yeast genes, mitochondrial protein predictions in yeast have been systematically
combined with genetic linkage mapping
data of mitochondrial disorders.28 This
approach prioritized 14 genes among a
few hundred candidates annotated in a
linkage interval associated with seven
putative human mitochondrial disorders.
Clearly the transfer of information from
yeast to human is limited to conserved
mitochondrial proteins. Human mitochondrion is a more complex system
with a predicted proteome two times
larger than yeast.15 Furthermore,
mechanisms such as alternative splicing
that are common in human can increase
polypeptide diversity. Nevertheless, using
yeast orthology has been extremely
powerful.
Recently, an integrative approach that
directly predicted mitochondrial proteins
in human, prioritized eight candidates
among 151 genes annotated in a genomic
locus associated with hepatic mtDNA
depletion
syndrome
(MDDS).40,74
Among the eight candidates, the gene
MPV17 was shown to have a causative
mutation, which most likely causes
MDDS74. Both MPV17 and LRPPRC
have orthologs in yeast that are characterized as mitochondrial proteins. In
fact, further elucidation of the pathogenetic mechanism underlying MDDS was
achieved through experimental analysis
in yeast.74 Similarly, early clues on the
role and localization of LRPPRC in
human came from its orthology to a
well-known yeast mitochondrial translational activator of COX1 mRNA,
PET309.75
Apart from mitochondrial localization, additional properties of yeast orthologs can be used to prioritize candidate
genes
for
mitochondrial
diseases
(Fig. 1C). For a significant fraction of
the known mitochondrial disease genes,
the yeast orthologs have a-proteobacterial ancestry and a mild deletion phenotype change during respiration.41 In
particular, among the yeast mitochondrial proteins with proteobacterial ancestry and a non-severe growth defect of the
mutant strain, 25 have orthologs in
human of which 8 have already been
identified as genes involved in mitochondrial disorders. Given that lethal or
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‘petite’ phenotypes (small colony size)
are under-represented, it is likely that
essential genes for yeast mitochondrial
function correspond to loss-of-function
mutants incompatible with human
development.41
Further correlations can be extracted
from comparison of disease genes and
yeast protein interaction partners. The
interconnectivity
between
diseasecausing genes can identify additional
candidates based on the assumption that
diseases which share a common biochemical mechanism could be caused by
mutations in one of several genes that
interact in a pathway or protein complex.76 Recently, through the reconstruction of a yeast mitochondrial network,
human mitochondrial disease genes have
been analyzed for their functional relatedness in yeast.41 The orthologs of
human disease genes were found to
cluster together. Moreover, human
disease genes that had yeast orthologs
enriched in certain functional modules
showed similarities in clinical symptoms.
These findings enable ranking of candidate genes for specific mitochondrial
disorders even in the absence of genetic
linkage data. In principle new patients
could be associated with known mitochondrial disease genes based on phenotype similarity. In addition to the known
disease genes, the candidates to be
screened in the patients could be
extended to interactors of these disease
genes. In human, the reconstruction of a
network has the potential to explain even
complex mechanisms of pathogenesis. As
shown for human spinocerebellar ataxias,77 highly interconnected proteins
reveal key players of pathogenesis that
could be targeted for therapy. Finally,
the candidate genes proposed can be
tested back in yeast, where high-throughput cell-based assays are feasible.
Testing of human
mitochondrial disease genes
in yeast
Experimental evaluation of the role of
candidate genes in human disease can be
achieved by heterologous expression of
human proteins in yeast (Fig. 2). Human
cDNA was expressed in yeast for the first
time two decades ago.78 The objective of
these initial studies was to identify
human homologs of known yeast genes,
exploiting the ability of many human
gene products to restore growth of their
respective yeast null mutant. This
method, known as functional complementation (Fig 2A), has also rapidly
become effective for revealing the role
of mutations in the performance of
human enzymes.79 So far, several studies
have applied yeast functional complementation assays to testing candidate
disease genes for human mitochondrial
Fig. 2 Evaluating disease candidate genes by heterologous expression in yeast. (A) Scheme of functional complementation to test the functional
effect of different polymorphisms in a human gene. (B, C) Examples of alternative yeast cell-based assays to study human genes that may not have a
yeast ortholog. (B) Scheme of a yeast growth inhibition assay to test the effect of polymorphisms in a human gene. (C) Scheme of an assay that
measures human GPCR activity by its coupling to the pheromone yeast pathway through human Ga proteins.
24 | Mol. BioSyst., 2008, 4, 18–29
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disorders.80–82 Two illustrative examples
involve the nuclear-encoded genes
COX10 and BCS1L. A mutation in
COX10 was associated with tubulopathy
and leukodystrophy,81 and several polymorphisms in BCS1L were related to
heterogeneous clinical presentations
including GRACILE syndrome.82 In
both cases, yeast complementation
assays were central in defining the role
of the genes and their polymorphisms in
mitochondrial disorders when the
mutant alleles, unlike the wild-type, were
unable to restore growth of their respective yeast null mutants in non-fermentable medium.
Recent methodological advances have
suggested that yeast complementation
assays can be performed in a highthroughput manner. With the use of
two regulatable promoters (tetO and
pMET3), a versatile complementation
system to clone human cDNAs was
developed.83 The system employs the
promoters to independently tune the
expression level of the yeast and human
genes, maximizing complementation. As
a proof of concept, 25 yeast strains in
which an essential gene is controlled by
the tetO promoter were used to screen
two cDNA human libraries expressed
from the pMET3 promoter. The screens
succeeded in the isolation of six complementing human cDNAs. Further efforts
have resulted in the construction of more
than 1000 strains in which essential genes
are under the control of such a regulatable promoter.48,84 These strains could
be the first part of a collection of yeast
lines in which to study human genes.
Another interesting approach combined
the complementation of the yeast deletion strain, zwf1, by human glucose-6phosphate dehydrogenase (hG6PD) with
yeast competition assays, in order to
functionally test several alleles of the
human enzyme85 (Fig. 2A). The functionality of each allele was calculated
from the growth of the mutant strain
transformed with the human allele when
grown in a competition pool under
conditions selective for active hG6PD.
This strategy revealed the sensitivity
to missense mutations of the hG6PD
active site positions and demonstrated
that yeast competition assays can be
used to test in parallel human
enzymes that differ by a single point
mutation.
An important limitation when scaling
up yeast functional complementation
assays to test disease candidate genes is
that not all human genes are able to
complement their respective yeast deletion strains. The Princeton Protein
Orthology Database (P-POD; http://
ortholog.princeton.edu/) reports over
200 human genes that complement a
yeast function. These cases were collected
from literature documenting cross-species expression experiments and good
complementing examples like COX10
and BCS1L are still not in the database.
Another drawback of functional complementation assays in yeast is the difference
in complexity between yeast and human
mitochondria (larger protein number,
alternative splicing, etc.). Higher human
protein numbers would result in unique
pathways and in more players in the
common pathways, partially restricting
the conclusions that can be drawn from
the results of the complementation
assays.
Although a complementation experiment is the ‘‘classic’’ yeast cell-based
assay, a variety of other yeast cell-based
methods have been shown to be effective
in the study of human genes and their
associated diseases. Taking advantage of
the fact that yeast is an experimentally
amenable eukaryotic cell, these methods
couple the function of the human protein
to a measurable yeast readout such as
growth, shmooing or the expression of a
reporter gene, irrespective of whether the
human protein has a yeast counterpart
(Fig. 2B and C).86–89 For example, the
human protein p53, which has no ortholog in S. cerevisiae, causes a growth
defect when overexpressed in yeast.90
This phenotype was employed to find
hyperactive variants of the tumor suppressor among alleles generated by
random mutagenesis.91 Similar strategies
have also been efficient in screening
for inhibitors of apoptotic factors92
and
poly(ADP-ribose)
polymerases
(Fig. 2B).93 In order to increase the scale
of this approach, a rapid method to
introduce human cDNAs into yeast was
developed for the purpose of identifying
human genes causing yeast growth inhibition.94 Of the 29 human proteins
tested, which belonged to a variety of
functional categories, around 30%
caused a growth inhibition of 55% or
higher. In all such cases, mutations or
This journal is ß The Royal Society of Chemistry 2008
small molecule inhibitors were used to
show that the growth defect is caused by
the protein function itself and is not
related to the nonspecific toxicity
caused by overexpression of heterologous proteins.
Several methods that couple more
sophisticated readouts have been developed even though such assays require
further engineering of the yeast system
and a better understanding of the biology
of the tested human protein (Fig. 2C). A
notable example is the replacement of
proteins at the top end of the mating
pheromone signal transduction pathway
of yeast by human G-protein-coupled
receptors (GPCR) and accessory Ga
subunits95 (reviewed in ref. 96). Due to
the resemblance between the pheromone
yeast pathway and the human G-protein
initiated MAP kinase cascade, activation
of the human GPCRs can trigger the
pheromone signaling pathway, which
itself will result in the activation of
mating-specific genes or reporter genes
with mating-specific promoters. Several
studies have used this system to screen
for agonists, antagonists and native
ligands of human GPCRs97 as well as
mammalian nonreceptor modulators of
G-protein signaling pathways.98 As an
extension, the system could also be used
to test human GPCR alleles for functionality. Other successful examples include
assays to screen ligands of the estrogen
receptor,99 to find anti-prion disease
drugs,100 to test the activity of the human
beta-secretase BACE in the study of
Alzheimer’s disease101 and to detect p53
mutations102 (for more extensive reviews
of yeast cell-based assays please see ref.
86–88). Interestingly, the method to
assess p53 function permits screening
for mutations from patient samples,
including blood, tumors or cell lines.103
The fact that at least one yeast-cell based
assay exists for each of the four major
classes of human drug targets (ion
channels, nuclear receptors, GPCRs and
enzymes)87 demonstrates the versatility
of this approach.
The success with yeast cell-based
assays illustrated here and the considerable availability of high-throughput
approaches in yeast (deletion strain
collections, sets of strains with genes
under a regulatable promoter, competition assays, growth-inhibition assays,
etc.) indicate that S. cerevisiae is a
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promising system in which to experimentally assess disease candidate genes. For
example, one can foresee the generation
of a collection of yeast strains in which
each mitochondrial disease candidate
gene is replaced by its complementary
human ortholog. Identification of disease
mutations could then ensue by comparing patient and wildtype gene complemention ability. This could be done by
systematically competing strains carrying
patient and control genes and may be a
useful tool to ascertain genetic determinants of disease.
Conclusion and outlook
The use of yeast as a model system has
made significant contributions in at least
three ways to our systemic understanding
of mitochondria and disease. First, most
genomic technologies and approaches for
data integration have been piloted in
yeast. These efforts have shown that (a)
integration of genomic data accelerates
the identification of mitochondrial parts
and their function; (b) only the integration of complementary datasets that
capture different functional categories
of proteins provides the most thorough
characterization; and (c) comprehensive
knowledge of the parts and interactions
of a biological system facilitates the
identification of disease-causing genes.
Second, due to the significant conservation between yeast and human mitochondria, the information on mitochondrial
components, interactions and their properties gained from yeast integrative
genomics have helped characterize
human orthologs, in particular human
disease candidate genes. Third, employment of simple model organisms has
been beneficial in experimentally evaluating the effect of mutations in disease
genes.
The task ahead includes the development of new technologies and
approaches to address the limitations of
available datasets and the complexity of
mitochondria in both yeast and human.
For example, studies on changes undergone by the organelle in response to
stresses will be instrumental in modeling
the dynamics that human mitochondria
experience in disease states. In addition,
tissue-specific measures will reveal the
variation in mitochondrial components
and their interactions throughout the
26 | Mol. BioSyst., 2008, 4, 18–29
human body. Imminent advances in
technology will burst the speed and
quality of genomic, proteomic and metabolic data acquisition, and promises to
catalyze mitochondrial research over the
next couple years. Sequencing by synthesis (such as 454 Life Sciences’ and
Solexa’s techniques), one of several
dozen ideas for high-throughput sequencing currently under development, is
already about 100 times faster than the
fastest Sanger sequencing techniques.104
The new ease of sequence acquisition will
not only allow sequence variation to be
measured across more species and individuals but also yield an increase in
biological information at other levels,
including gene expression and protein–
DNA interactions. Likewise, new massspectrometry-based proteomics methods
offer opportunities to obtain quantitative
measures on large numbers of mitochondrial proteins. Maturation of both stable
isotope labeling methods and label-free
strategies, as well as the development of
statistical methods for interpretation of
quantitative protein data, promise to
enable application to whole mitochondrial proteomes.105 At the metabolite
level, mass spectrometry combined with
chromatographic technologies already
provide a powerful means to systematically identify and quantify complex
mixtures of hundreds of metabolites,
even from nanomolar sample concentrations. Since physiological status is
directly reflected in the metabolome,
metabolic profiles may be used to monitor the biochemistry of mitochondria, as
well as disease states, drug efficacy
and side effects. So-called targeted
approaches that focus on known metabolites are already being applied to
analyze pathway-related enzyme substrates and products which are involved
for example in amyotrophic lateral
sclerosis (ALS), schizophrenia and
aging.106 The challenge remains of scaling up current technologies to uncover
novel biomarkers to obtain a comprehensive and time-related biochemical
snapshot from hundreds of patients.
It is clear that no single technology will
answer why diseases and their symptoms
arise, or how we can treat them. Instead,
the integration of all measurements—at
the genetic, transcriptional, proteomic,
metabolic and phentoypic level—is
needed to move towards a personalized
medicine that assesses genetic predisposition, monitors health biomarkers and
develops individualized treatments for
patients with mitochondrial disorders.
To achieve these goals the transfer of
information and technical know-how
between model systems and human will
continue to play a pivotal role.
Acknowledgements
We thank Lior David, Raeka Aiyar,
Himanshu Sinha, Julien Gagneur and
Anna-Lynn Wegener for helpful comments on the manuscript. Research in the
authors’ laboratory is supported by
grants from the National Institutes of
Health and the Deutsche Forschungsgemeinschaft (LMS).
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