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First published online April 20, 2007
Journal of Experimental Biology 210, 1593-1601 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.000141
Review Article |
Functional genomics and proteomics of the cellular osmotic stress response in `non-model' organisms
1 Physiological Genomics Group, Department of Animal Science, One Shields
Avenue, University of California, Davis, CA 95616, USA
2 Laboratorio de Fisiología de Invertebrados Marinos, CIAD, A.C.
Carr. a la Victoria Km. 0.6, CP 83000, Hermosillo, Sonora,
México
* Author for correspondence (e-mail: dkueltz{at}ucdavis.edu)
Accepted 3 January 2007
 |
Summary |
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 TOP Summary Systems biology approaches in...Identification of an immediate...Identification of protein...Analysis of posttranslational...Conclusions and perspectiveReferences |
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Key words: salinity adaptation, osmotic stress, systems biology, euryhaline fish, proteomics
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Systems biology approaches in traditional comparative biology |
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TOPSummarySystems biology approaches in...Identification of an immediate...Identification of protein...Analysis of posttranslational...Conclusions and perspectiveReferences |
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).
Systems biology organizes data obtained by genomic, transcriptomic, proteomic
and metabolomic approaches to attempt to build descriptive and mechanistic
models of integrative biological phenomena such as development or interactions
of organisms with their environment.
The ultimate goal of this approach is to generate a mathematical model that
describes the biological system and has predictive power
(Aggarwal and Lee, 2003).
Achieving this tremendously ambitious goal depends on in-depth knowledge about
each element constituting the system of interest. For instance, experimental
data about the expression, regulation, function, compartmentation,
interaction, modification and stability of individual RNAs and proteins have
to be collected and integrated for each state of the system that is described
by the model. Systems biology approaches have largely been applied to
organisms whose genomes have been sequenced (so-called `model' organisms)
because many of the available bioinformatics tools are based on prior genome
sequencing and annotation of the encoded transcriptome and proteome. However,
most high-throughput technologies for analyzing the transcriptome, proteome
and metabolome do not strictly depend on prior knowledge of genomic sequence.
Therefore, these approaches can also be applied to `non-model' organisms for
which few if any genome sequence data are available. Such organisms account
for the majority of species used in traditional comparative biology.
We utilized high-throughput transcriptomics and proteomics approaches for identifying key molecular constituents associated with osmoregulatory functions in euryhaline tilapia and other animals. Our work shows that these data can be used for bioinformatics analysis to generate models about biological processes, cellular pathways and molecular functions associated with osmotic stress responses. Nevertheless, many obstacles are encountered when attempting a systems biology approach with non-model species. In the following, we briefly review our efforts to apply high-throughput transcriptomics and proteomics methods to euryhaline tilapia, dogfish shark and an intertidal sponge to pave the way towards a systems biology approach for studying osmoregulation.
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Identification of an immediate-early gene network involved in osmoregulation |
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TOPSummarySystems biology approaches in...Identification of an immediate...Identification of protein...Analysis of posttranslational...Conclusions and perspectiveReferences |
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;
Gracey, 2007), prior sequence
knowledge greatly facilitates their production and use and maximizes coverage
of the transcriptome during the analysis. Serial analysis of gene expression
(SAGE) and recent modifications of SAGE represent alternative approaches to
DNA microarrays that identify very short sequence tags that can then be
matched against existing sequences
(Maillard et al., 2005). In
addition to SAGE and DNA microarrays, the identification of a subset of RNAs
whose levels are regulated by salinity can be achieved by methods that are
based on differential analysis of RNA levels and less dependent on prior
sequence knowledge of the transcriptome. Such methods include differential
display (Stein and Liang,
2002), representational difference analysis
(Hubank and Schatz, 1999) and
suppression subtractive hybridization (SSH)
(Diatchenko et al., 1999).
Suppression subtractive hybridization of salinity-responsive tilapia genes
Because SSH works optimally even in the absence of prior sequence
knowledge, we have used this technique for enriching a subset of genes that
are rapidly upregulated by salinity stress in gill epithelial cells of
tilapia. Tilapia are strongly euryhaline teleosts that are capable of
adaptation to a wide range of environmental salinity. Therefore, mechanisms
and molecules involved in salinity adaptation are very prominent in this fish
and can be effectively studied.
SSH was performed with mRNA from control fish transferred for 4 h from
freshwater (FW) to FW (as the Driver sample) and the corresponding mRNA from
fish transferred for 4 h from FW to seawater (SW) (as the Tester sample). The
final resulting PCR products were cloned into pGEM-Teasy vector (Promega,
Madison, WI, USA) to generate a subtracted cDNA library. This library was
screened by colony PCR to yield 650 individual clones. Only 72 clones were
selected for sequencing because many of the 650 clones had similar lengths and
likely represented the same sequence. Thirty-two unique cDNAs were represented
by the 72 clones that were sequenced. Increased mRNA abundance after 4 h SW
transfer was confirmed for 22 of these 32 cDNAs by quantitative real-time PCR
(qPCR). This result demonstrates that the rate of false-positive
identification by SSH is low (less than one-third of the analyzed clones were
false positives). Full-length coding sequences for most of the 22 cDNAs were
obtained by RACE-PCR and degenerate primer PCR, starting with original SSH
clones, many of which represented 3' terminal fragments of cDNAs
(Fiol and Kültz, 2005;
Fiol et al., 2006a). Based on
amino acid homology in the coding sequences, we were able to identify more
than 90% of the SSH genes cloned from tilapia
(Table 1).
|
Induction kinetics suggests that tilapia SSH cDNAs are immediate-early genes
Hyperosmotic induction of tilapia SSH genes was confirmed and the kinetics
of their induction analyzed by real-time qPCR. Most SSH genes show a rapid and
transient increase in mRNA abundance, with peak levels observed between 2 and
8 h after SW transfer. An example of a time course depicting the
salinity-induced induction of a tilapia SSH gene (protein phosphatase 2A,
catalytic subunit) is shown in Fig.
1. Most of the SSH genes differ only slightly in the kinetics of
their induction, suggesting that they participate in a common cellular network
as part of an overall essential and coordinated adaptive response that
involves changes in metabolism, transport, damage repair, etc.
(Fiol and Kültz, 2005;
Fiol et al., 2006a). A notable
single exception is a cDNA clone (SSH#7) that displays a robust sustained
induction in response to SW transfer. This cDNA increases as rapidly as the
other SSH genes but it remains high even after a prolonged stay of tilapia in
SW, and the degree of its increase is much greater than for any other SSH
gene, exceeding five orders of magnitude
(Fiol et al., 2006a).
Unfortunately, we were not able to match the sequence of the SSH#7 cDNA to any
known sequence. The lack of an open reading frame (ORF), even after extensive
attempts to extend the sequence by RACE-PCR, may suggest that SSH#7 represents
a non-protein-coding RNA (ncRNA). Because ncRNAs are involved in
transcriptional and translational regulation, modulation of protein function,
and regulation of RNA and protein localization, SSH#7 may have an important
role for salinity adaptation even if it is an ncRNA
(Goodrich and Kugel, 2006).
For one of the SSH genes that encodes a protein [osmotic stress transcription
factor 1 (OSTF1)], we have already performed a follow-up study to analyze the
mechanisms of its regulation in more depth. The results of this study show
that hypertonicity per se, rather than indirect systemic factors, is
responsible for the salinity-induced OSTF1 increase, that the mechanism of
increase is based on transient mRNA stabilization and that a return to normal
OSTF1 levels at later time points is promoted by systemic factors
(Fiol et al., 2006b).
|
). IEGs are key regulators during environmental
adaptation because they control the transcriptional regulation of effector
genes (delayed response genes) necessary for adaptation. IEGs represent the
first set of genes that are induced in response to environmental signals and
encode mRNAs that do not require de novo protein synthesis for their
induction (Morgan and Curran,
1989). Induction of many IEGs is often mediated by
mitogen-activated protein kinases (MAPKs)
(Thomson et al., 1999), which
we have shown to be activated by salinity stress in euryhaline teleosts
(Kültz and Avila, 2001).
Moreover, IEGs function within a network of other, constitutively expressed,
proteins to control cellular regulation in response to environmental cues
(Lanahan and Worley, 1998).
Thus, we utilized the gene set identified by SSH for a systems biology
approach that is based on bioinformatics tools to gain insight into the
regulatory network that underlies salinity adaptation in tilapia.
Gene ontology and pathway analysis using the tilapia SSH gene set
To gain insight into the nature of regulatory networks that are activated
early in response to salinity stress in tilapia gill cells, we used
bioinformatics tools that extract and synthesize most available information
about any given gene. The SSH gene set was compared to templates for known
biochemical pathways, molecular functions and biological processes. Using
Pathway Studio software (Ariadne Genomics, Inc., Rockville, MD, USA), we
distilled all published information about biological relationships of
mammalian orthologs of the salinity-induced tilapia genes and modeled the
interaction of the SSH genes within a common stress response signal
transduction network (Fiol et al.,
2006a). This network contains 13 of the 22 identified tilapia SSH
genes (OSTF1, TFIIB, BMPR2, PPP2CB, MIG-6, MAP3K7IP2, ENO1, IDH2, FABP6,
APOD, GSN, LGALS4, ANXA11) (see Table
1). Furthermore, a different type of bioinformatics approach,
PANTHER (Mi et al., 2005),
revealed that mammalian homologs of 11 of the 22 identified tilapia SSH genes
participate in apoptotic and cell cycle regulatory pathways (OSTF1, TFIIB,
BMPR2, PPP2CB, MIG-6, MAP3K7IP2, RFP128, PAD2, GSN, LGALS4, ANXA11) (see
Table 1). Apoptosis and cell
cycle regulation are key processes of the cellular stress response, indicating
that these biological processes are targeted during osmotic stress in tilapia
gills (Kültz, 2005). In
addition to apoptosis and cell cycle signaling, cellular functions associated
with the 22 tilapia SSH genes are organic osmolyte accumulation, energy
metabolism, lipid transport and membrane protection, modulation of actin-based
cytoskeleton dynamics, and control of mRNA and protein stability
(Fiol et al., 2006a).
Regulation of these processes reflects the need for extensive remodeling and
rapid cellular turnover in tilapia gill epithelium during salinity stress.
Although the pathway analysis described above yielded remarkably consistent
and meaningful results, some potential pitfalls need to be considered. First,
comparisons are made based on data that are heavily biased towards a few model
organisms such as human, mouse and rat. However, the involvement of a
particular protein in cellular functions, molecular pathways and biological
processes in non-model species may differ from that in model species. This
first aspect represents a significant disadvantage for work with non-model
species. Second, when working with gene sets that are based on altered levels
of mRNA abundance, corresponding changes in protein abundance are often
assumed but frequently not observed (Hack,
2004). Thus, further analyses of proteins encoded by genes that
have been identified or independent proteome analysis are logical next
steps.
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Identification of protein networks involved in osmoregulation |
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TOPSummarySystems biology approaches in...Identification of an immediate...Identification of protein...Analysis of posttranslational...Conclusions and perspectiveReferences |
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).
Second, we have generated a new antibody against tilapia OSTF1 based on
immunization of rabbits with an immunogenic peptide. This antibody was used to
confirm the induction of OSTF1 protein in gill cells of tilapia after transfer
from FW to SW (Fiol and Kültz,
2005). Currently, we are working on generating additional
antibodies for more tilapia SSH genes. However, this approach is
work-intensive, expensive and time-consuming and, therefore, not well-suited
for the high-throughput data generation needed for systems biology approaches
that utilize unbiased discovery-driven screens. Nevertheless, such a more
traditional approach will greatly advance our knowledge of osmotic stress
signaling in tilapia because it is firmly focused on prior knowledge of a
limited set of elements involved in a specific biological process.
Discovery-driven proteomics using osmoregulatory tissues
An alternative approach for protein analysis that is much faster, less
time-consuming and more economical is based on comparing protein patterns of
two different samples. This can be achieved by separating proteins
via two-dimensional gel electrophoresis (2DGE) or two-dimensional
liquid chromatography (2DLC) followed by subsequent mass spectrometry (MS)
analysis. Both techniques (2DGE and 2DLC) are capable of resolving in the
order of 1500 proteins and they are complementary, with different advantages
and drawbacks (Wagner et al.,
2000; Ishihama,
2005; Hu et al.,
2005; Bunai and Yamane,
2005; Okano et al.,
2006). A common limitation of both techniques is their bias
towards high-abundance proteins, which is a result of the huge dynamic range
of protein abundance in biological samples. This limitation can be addressed
by extensive sample pre-fractionation, but such an approach requires a lot of
starting material and greatly increases the cost, time and work load for the
analysis. High-throughput proteomics using protein chips represents an
alternative approach that is capable of detecting low-abundance proteins.
However, protein arrays are expensive and depend on very high-quality
antibodies that are often not available for a large number of proteins from
non-model organisms.
Keeping these limitations in mind, we have compared the proteomes of four
osmoregulatory tissues (rectal gland, kidney, gill epithelium, intestinal
epithelium) with heart and brain from dogfish shark (Squalus
acanthias) by 2DGE and MS (Lee et
al., 2006). The rationale of this proteomics experiment was to
determine whether highly abundant proteins that are common to osmoregulatory
tissues can be identified and whether they are indicative of specific
signaling pathways and biological functions in osmoregulatory tissues. The
number of consistently resolved protein spots ranged from 984 in brain to 1230
in intestine, and overall 1465 unique protein spots were detected in the six
tissues. Thirty-six percent of these protein spots (535 spots) were present at
comparable abundance in all tissues. Two hundred and seventy protein spots
that were significantly over-represented in one specific tissue or common to
osmoregulatory tissues were identified using the Delta2D software package
(Decodon GmBH, Greifswald, Germany). To identify such proteins we applied a
robust and accurate method of protein quantitation that is based on 10
internal standard spots that are used for normalization of gel-to-gel loading
and staining differences (Valkova and
Kültz, 2006; Lee et al.,
2006). Gel-based protein quantitation procedures are of course not
without pitfalls but in our experience this method is more accurate than
isotope-labeling or other approaches that rely on MS quantitation. The 270
identified protein spots were cut out from the gel, in-gel digested with
trypsin, and analyzed by MS to determine their identity
(Lee et al., 2006).
|
). Fifty-four of these 62 proteins were correctly identified
using the MS-BLASTP2 algorithm (=87%), compared to 43 that were identified
with the Mascot search engine (=69%) at the same mass accuracy limit of 50
p.p.m. High-quality spectra and de novo sequence information were
obtained for many of the 270 analyzed protein spots, but a large portion was
not identified by Mascot or MSBLASTP2, probably because of significant amino
acid sequence variation in the shark proteins compared to their orthologs in
model species (Lee et al.,
2006). The genome of dogfish sharks has not been sequenced and few
cDNA/protein sequences of this species are available. Therefore,
identification of shark proteins by mass spectrometry poses unique challenges
that are not encountered when working on model species. In particular,
approaches based on peptide mass fingerprinting (Mascot, etc.), which
generally work very well for model organisms (exceptions are proteins that are
extensively post-translationally modified), are less successful in non-model
species. Even if homologous peptide sequences differ by only a single amino
acid, identification of the corresponding protein is prevented because peptide
masses will not match. Nevertheless, fish proteins with highly homologous
sequence stretches can still be identified using peptide mass fingerprinting
(Smith et al., 2005;
Monti, G. et al., 2005;
Zupanc et al., 2006;
Lee et al., 2006). Success
rates of protein identification in non-model species can be increased using an
MSBLAST approach that is exclusively based on sequence similarity and does not
require matching peptide masses (Lee et
al., 2006; Kjaersgard et al.,
2006; Russell et al.,
2006). Success rates of protein identification can be further
increased by combining different methods of peptide fragmentation for MS/MS
[e.g. collision-induced dissociation (CID) and post-source decay (PSD)] to
generate a greater number of informative daughter ions
(Lee et al., 2006).
Identification of sponge proteins by MS
Even invertebrate proteins for which close homologs from a related and
fully sequenced reference organism are currently unavailable can be identified
by MS. For instance, using MSBLASTP2 and a combination of CID and PSD for
MS/MS, we identified many proteins from the evolutionarily primitive sponge
Tetilla mutabilis (Fig.
2). In this experiment, we were interested in proteins that are
upregulated during emersion and post-emersion stress
(Fig. 2;
Table 2). Such stress is
experienced by this intertidal sponge during ebb-tide in its natural habitat
at Estero La Cruz in the Gulf of California. The nature of stressors
represents a mixture of hyperosmotic, heat, UV and oxidative stress. Sponges
were collected in their natural habitat before emersion, after being emersed
for 3 h and after being re-submersed for 2 h following 3 h emersion.
Twenty-seven upregulated proteins and nine other proteins were picked for MS
analysis, in which 25 of these 36 proteins (69%) were identified. This high
success rate seems surprising compared with the 25% success rate with shark
proteins (see above). However, for shark proteins we only considered proteins
successfully identified if at least three out of the four search methods
(PSD–Mascot, CID–Mascot, PSD–MSBLASTP2, CID–MSBLASTP2)
yielded consistent results (Lee et al.,
2006). If the same criteria are applied to the sponge samples,
then the success rate drops to 44%. Again, the MSBLASTP2 approach identified
five additional proteins (14% more) compared with the Mascot approach
(Table 2), illustrating that
database searches based on de novo peptide sequence alignments are
more successful in non-model species than PMF approaches.
|
To overcome current limitations for proteomics in non-model species and to
increase the success rate of protein identification, a number of technical
improvements can be made. In particular, efforts directed at increasing
protein sequence coverage in mass spectra are promising. Increased sequence
coverage can be achieved by combining multiple proteases with different
sequence-specific cutting patterns
(Biringer et al., 2006).
Likewise, top-down mass spectrometry approaches increase sequence coverage but
are currently limited by the low efficiency of elution of intact proteins from
polyacrylamide (PAG) gels (Bogdanov and
Smith, 2005). Thus, elution of whole proteins from PAG gels is
another area of research that should be addressed to improve the versatility
of proteomics approaches. The development of new methods for N- and C-terminal
protein sequencing by MS will also increase success rates for protein
identification based on cross-species comparison
(McDonald et al., 2005). If
terminal protein sequences are known, then BLAST searches can be greatly
narrowed and degenerate primers for full-length cloning and sequencing of the
corresponding cDNA can be designed. Finally, improvements in bioinformatics
tools such as MSBLASTP2 will increase success rates of protein identification
approaches that are based on sequence similarity searches
(Shevchenko et al., 2001).
Pathway analysis using shark protein sets
Once protein sets have been identified, they can be used for pathway
analysis as described for the SSH gene set above. We have performed such
analysis for the set of shark proteins that are common to osmoregulatory
tissues and unique to a single tissue. Even though the number of shark
proteins used for the analysis (62) was relatively small, it was sufficient to
provide some insight into molecular functions associated with osmoregulatory
tissues in shark (Lee et al.,
2006). Our analysis of molecular functions, cellular pathways and
biological processes using PANTHER revealed that stress response proteins such
as molecular chaperones and oxidoreductases are enriched and highly abundant
in shark osmoregulatory tissues. In addition, we identified the Rho-GTPase
pathway of cytoskeletal regulation (PANTHER pathway 00016) as being
significantly over-represented in shark osmoregulatory tissues
(Lee et al., 2006). Thus, the
bioinformatics analysis of protein sets identified by proteomics technology
generates specific targets for hypothesis-driven, focused and in-depth
follow-up research in non-model organisms.
An important limitation of using bioinformatics approaches for pathway
analysis based on identified protein sets in non-model organisms is the use of
pathway models and other information from model species as templates for
non-model species (see discussion above for SSH pathway analysis). In
addition, virtually all proteins have multiple functions that depend on their
subcellular compartmentation, posttranslational state and interaction with
other cellular constituents. However, currently, functions of proteins stored
in databases often do not take into account the aforementioned parameters and,
even if they do, information about these parameters is often not available for
protein sets of interest. This problem is exacerbated by mixing information
about protein functions across species boundaries, which is inevitable when
using high-throughput approaches with non-model species. These pitfalls
illustrate that, despite the rapid growth of sequencing data, there is still
an enormous need for the experimental analysis of proteomes and metabolomes.
Even for a single-celled organism, experimentally determining all possible
states of posttranslational modification, subcellular compartmentaion and
interaction of proteins and correlating those with environmentally induced and
developmental cell states may not be feasible. Thus, a narrow focus on a few
model species seems warranted for collecting as many data as possible to
achieve the ultimate goal of systems biology. Currently, this need for
focusing on a handful of model species appears to outweigh the classical
advantage of traditional comparative biology, as formulated elegantly by the
August Krogh principle: `For many problems there is an animal on which it
can be most conveniently studied'
(Krebs, 1975). For instance,
using stenohaline zebrafish for studying mechanisms of osmotic stress
adaptation and euryhalinity in fishes may be counterintuitive. Medaka is
better suited for such studies, but extremely euryhaline fishes such as
tilapia, killifish or desert pupfish are the organisms of choice from a
physiological perspective even if they are considered non-model species from a
genomic perspective. Future breakthroughs in high-throughput technologies and
bioinformatics, in combination with a reductionist focus on a single problem
(or biological process) that lends itself exceptionally well for study in a
non-model species, may provide a solution for optimally combining the
strengths of traditional comparative biology and systems biology.
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Analysis of posttranslational modification, interaction and compartmentation of osmotic stress response proteins |
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TOPSummarySystems biology approaches in...Identification of an immediate...Identification of protein...Analysis of posttranslational...Conclusions and perspectiveReferences |
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Posttranslational protein modification in response to osmotic stress can be
studied by using phospho-specific or other state-specific antibodies
(Kültz et al., 1997;
Kültz and Avila, 2001) or
MS following protein separation by 2DGE or 2DLC
(Dihazi et al., 2005;
Salih, 2005;
Valkova and Kültz, 2006).
For the latter approach, protein abundance is a limiting factor. If protein
sets have previously been identified by a proteomics approach, this is not a
problem because all identified proteins exceed the detection threshold for
2DGE or 2DLC. However, when further studying gene sets identified by
transcriptomic approaches, this limitation may represent a serious
obstacle.
Another property of proteins that critically controls protein regulation
and function is their association with other proteins, nucleic acids and
micromolecules (inorganic ions, organic osmolytes, metabolic intermediates).
The interactome (sum of bound macro- and micromolecules) can be determined for
each of the proteins previously identified to be involved in salinity
adaptation. Protein–protein interactions can be revealed using genetic
or biochemical approaches (Cusick et al.,
2005; Monti, M. et al.,
2005; Suter et al.,
2006). Genetic approaches require a cell line and knowledge about
strong promoters that may not be available for many non-model organisms.
Therefore, biochemical approaches may be easier to adapt to non-model
organisms. However, most of them rely on high-quality antibodies. If not
already available, such antibodies can be generated for a limited set of
proteins of interest. Once an antibody is available, co-immunoprecipitation of
proteins followed by separation of protein complexes by 2DGE or 2DLC and MS
can be used to identify interacting proteins. Chromatin immunoprecipitation
and variations of this method can be used to identify nucleic acid sequences
that bind a protein of interest. Finally, using these approaches,
salinity-dependent changes in the interactomes of osmotic stress response
proteins can be identified.
The tissue-specific, cellular and subcellular compartmentation of osmotic
stress response proteins can also be studied using genetic and biochemical
approaches (Giltnane and Rimm,
2004; Suter et al.,
2006). Again, biochemical/histological approaches for studying
protein localization seem more amendable to non-model organisms than genetic
approaches. However, like biochemical methods used for studying the
interactome, they also rely to a great extent on high-quality antibodies. In
particular, high-throughput immunohistochemical techniques such as tissue
microarrays are promising tools for large-scale studies of protein
compartmentation. We have recently used this technique to localize
Na+/K+-ATPase to chloride cells in gills of FW- and
SW-adapted tilapia (Lima and Kültz,
2004). This approach can be extended to other proteins involved in
salinity adaptation once appropriate antibodies are available.
|
Conclusions and perspective |
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TOPSummarySystems biology approaches in...Identification of an immediate...Identification of protein...Analysis of posttranslational...Conclusions and perspectiveReferences |
|---|
Sets of genes or proteins associated with salinity adaptation can be
effectively identified in non-model organisms as illustrated by the SSH and
proteomics examples outlined above and by recent cDNA microarray experiments
that are based on normalized cDNA libraries from non-model species or on
cross-species hybridization (Gracey et
al., 2001; Podrabsky and
Somero, 2004; Buckley et al.,
2006). A more severe barrier for work with non-model species is
the reliance on models of biochemical pathways, gene ontology databases and
other information that is largely based on data obtained with model species.
Although many biochemical pathways and biological processes are evolutionarily
highly conserved, a great amount of plasticity is inherent in cellular
metabolic and signal transduction networks. We would expect such plasticity to
be particularly pronounced when a non-model organism differs in its
properties, with regard to the biological process under study, from model
organisms. For instance, how valid are models of biochemical pathways/networks
based on sets of osmotic stress response proteins for a euryhaline fish when
modeled against templates based on information available for stenohaline
zebrafish? Even though the individual elements (proteins) may be
evolutionarily highly conserved, their posttranslational modification,
interaction, compartmentation and function during salinity stress may differ
substantially. Currently, there are no alternatives to this approach, but this
area undoubtedly requires further study.
In our opinion, the most promising and feasible avenue for merging strengths of traditional comparative biology with strengths of systems biology approaches is to select a single biological process and focus on it (rather than attempting to describe the system as a whole) using an organism selected according to the August Krogh principle. Gene and protein sets associated with the biological process of interest (e.g. salinity adaptation) can then be identified. Next, tools for studying the regulation (including posttranslational modification, interaction and compartmentation) and function of these proteins (e.g. antibodies, cell lines, siRNA) should be generated for a manageable set of proteins. Then, the regulation and function of these proteins can be analyzed in detail using such tools. Finally, models describing the biological process of interest can be generated based on the resulting data. This avenue of research depends on extensive collaboration and, therefore, represents an exciting path for the future.
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Acknowledgments |
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Footnotes |
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References |
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TOPSummarySystems biology approaches in...Identification of an immediate...Identification of protein...Analysis of posttranslational...Conclusions and perspectiveReferences |
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