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Genetics. May 2008; 179(1): 157–166.
PMCID: PMC2390595

Metabolomics- and Proteomics-Assisted Genome Annotation and Analysis of the Draft Metabolic Network of Chlamydomonas reinhardtii


We present an integrated analysis of the molecular repertoire of Chlamydomonas reinhardtii under reference conditions. Bioinformatics annotation methods combined with GCxGC/MS-based metabolomics and LC/MS-based shotgun proteomics profiling technologies have been applied to characterize abundant proteins and metabolites, resulting in the detection of 1069 proteins and 159 metabolites. Of the measured proteins, 204 currently do not have EST sequence support; thus a significant portion of the proteomics-detected proteins provide evidence for the validity of in silico gene models. Furthermore, the generated peptide data lend support to the validity of a number of proteins currently in the proposed model stage. By integrating genomic annotation information with experimentally identified metabolites and proteins, we constructed a draft metabolic network for Chlamydomonas. Computational metabolic modeling allowed an identification of missing enzymatic links. Some experimentally detected metabolites are not producible by the currently known and annotated enzyme set, thus suggesting entry points for further targeted gene discovery or biochemical pathway research. All data sets are made available as supplementary material as well as web-accessible databases and within the functional context via the Chlamydomonas-adapted MapMan annotation platform. Information of identified peptides is also available directly via the JGI-Chlamydomonas genomic resource database (http://genome.jgi-psf.org/Chlre3/Chlre3.home.html).

THE sequencing of whole genomes of species from all kingdoms of life progresses at an ever increasing pace. Once a full genome has been assembled, the main challenge lies in its annotation, i.e., in identifying the protein-coding genes and other functional units that are encoded in the genome. For gene detection, the two main approaches are EST mapping and computational gene prediction combined with homology-based search methods (Wortman et al. 2003). Despite its many limitations and problems, whole-genome annotation has become a standardized data flow and initial sets of all encoded proteins can be generated in a computer-assisted and automated way. Both types of annotation approaches have also been used in the first draft of the Chlamydomonas reinhardtii genome (Merchant et al. 2007), resulting in the prediction of ~15,000 protein-coding genes.

The integration of metabolomic and proteomics technologies into the annotation process may lead to further experimental validation of in silico gene models as well as to improved accuracy of existing gene models. These technologies enable fast and comprehensive analysis of the molecular plant phenotype (Naumann et al. 2007; Weckwerth 2008) as well as providing complementary means for probing the completeness of genome annotations. If metabolites are being detected that, given a metabolic network derived from whole-genome annotation, actually are not reachable via the predicted network of biochemical reactions, either the enzyme annotation may be incomplete or the metabolite is synthesized by an as-of-yet-unidentified biochemical pathway.

Like EST sequencing, proteomics methods provide actual evidence for the presence of gene products and thus can serve as validation of gene models. We report here results from large-scale shotgun proteomics experiments leading to the detection of ~1000 proteins.

The power of whole-genome annotation approaches lies in their inherent goal of completeness. In principle, once the complete parts list is known, it is possible to investigate which processes and biochemical reactions may occur in an organism and which ones are impossible (Palsson 2004). The availability of full-scale metabolic models has led to a new field of theoretical investigations of the biochemical capabilities of organisms. To name just a few examples, optimal growth rates of knockout mutants may be estimated (Fong and Palsson 2004), principle metabolic capabilities of organisms or mutants can be determined when they are provided with a particular combination of nutrient metabolites (Handorf et al. 2005), or even minimal nutritional diets may be inferred (Handorf et al. 2007). However, for most organisms, including Chlamydomonas, the network generation process results in a draft network that cannot be expected to be complete. Subsequently, these draft networks can be computationally tested for identifying pathway gaps and also for predicting which reactions are missing to fill these gaps.

Here, we present an approach in which gene model prediction and validation and computational metabolic modeling is complemented by proteomics and metabolomics data.


Growth conditions and Chlamydomonas strain:

For our studies, the cell-wall-deficient strain C. reinhardtii CC503 cw92 mt+ was obtained from the Chlamydomonas Centre. We used this strain as it was utilized as the source of DNA for the genome sequencing project at the Joint Genome Institute (JGI). CC503 was cultivated at 21° under 100 μE/(m2 · sec) of white light (Osram fluora) on an orbital shaker (110 rpm; INFORS HT Multitron). Two different light cycles have been used: 24 hr of continuous light and a 12 hr light/12 hr dark cycle. To achieve photoautotrophic growth conditions, we left out acetate from the standard Tris–acetate–phosphate medium (Harris 1989). As it severely disturbs the mass spectrometric signal, the buffer component Tris has been replaced by HEPES in various concentrations.

Integrative protein and metabolite sampling of Chlamydomonas:

Chlamydomonas cell culture was harvested by adding methanol cooled down to −20° to an end concentration of 30% to the growth medium. For protein extraction, quenched cells were subsequently centrifuged at 10,000 × g and at −20° for 10 min.

Protein prefractionation using fast performance liquid chromatography:

Protein extraction and fast performance liquid chromatography (FPLC) prefractionation was carried out as previously described (Wienkoop et al. 2004) with the following modifications. Frozen Chlamydomonas cell pellet (~1.0 g fresh weight) was ground in a chilled mortar using liquid nitrogen. Extraction buffer containing 50 mm Tris–HCl, pH 8.0, 5 mm dithiothreitol, 1 mm EDTA, 1 mm phenylmethylsulfonyl fluoride was added and crude extract was then centrifuged at 10,000 × g for 10 min and immediately desalted on a Sephadex G-25 column (1.5 cm), previously equilibrated with 50 mm Tris–HCl, pH 8.0, 0.1 mm (buffer A). Protein concentration was measured as described in Bradford (1976). Sterile filtration of the protein solution was performed using a 0.45-mm filter (Schleicher & Schuell, Keene, NH). Subsequently, the filtrate was loaded onto a 1-ml Resource Q column (Amersham-PharmaciaBiotech) equilibrated with 10-column bed volumes of buffer A. Total protein content was 5 mg. The column was washed with buffer A until A280 decreased to baseline. Bound proteins were eluted with a 25-ml linear gradient from 0–750 mm of NaCl in buffer A at a flow rate of 2 ml/min. Fractions of 1 ml were collected (25 fractions). At this stage of purification, fractions were dialyzed overnight at 4° against 50 mm ammonium bicarbonate. Prior to digestion, 2-ml samples were concentrated in a speed vac.

UltraHPLC/MS/MS analysis for protein identification:

Prior to analysis, protein fractions were digested as previously described (Wienkoop et al. 2004). For identification of high-abundance proteins of the Chlamydomonas proteome, a 1D nano flow ultraHPLC system with precolumn (UPLC, Waters, Germany) was used. A C18 column (Waters, Germany) of 25 cm length and an ID of 75 μm was coupled to an Orbitrap LTQ XL mass spectrometer (Thermo Electron, Bremen, Germany). Peptides were eluted during a 100-min gradient from 5% acetonitril (ACN)/0.1% formic acid (FA) to 40% ACN/0.1% FA followed by an additional 5 min to 80% ACN/0.1% FA with a controlled flow rate of 300 nl/min. Specific tune settings for the mass spectroscopy (MS) were as follows: spray voltage was set to 1.8 kV and temperature of the heated transfer capillary was set to 150°.

Protein libraries and databases:

After MS analysis, DTA files were created from raw files and searched against the following sets: (A) the JGI database Chlre 3.1 protein set containing the 15,143 nuclear-encoded proteins augmented by 68 proteins from the chloroplast (cp) genome and 8 proteins from the mitochondrial (mt) genome (Chlre 3.1 set); (B) the larger set of gene models and associated protein sequences comprising a total of 147,924 protein sequences available from JGI (allProteins set); and (C) a database including all known 167,641 ESTs and genomic scaffold sequences translated in all six reading frames, using Bioworks 3.3 (see next section). The intersection of sets A and B contained 14,304 sequences. Clustering the union of sets A and B at a 95% sequence identity level yielded 33,526 clusters using CD-HIT (Li and Godzik 2006).

Using the databases Bioworks 3.3 (Thermofisher) and DTASelect (Tabb et al. 2002), a list of identified proteins was obtained using the following criteria: a peptide precursor mass accuracy of 5 ppm and Xcorr of -1 2.2, -2 2.4, -3 3.5 for hits with at least two different peptides. All spectra have been uploaded and can be found in the ProMEX database system (http://promex.mpimp-golm.mpg.de/cgi-bin/peplib.pl) (Hummel et al. 2007).

Metabolite profiling:

Metabolites were analyzed using a GCxGC TOF mass spectrometer (Pegasus IV) from Leco. Samples of Chlamydomonas were prepared as previously described (Bolling and Fiehn 2005). Extracts for metabolites and starch were prepared as described in Kempa et al. (2007). Samples were injected in the temperature-controlled CIS4 injector (Gerstel), applying a temperature program starting from 75° and reaching 280° using a baffled liner. For first-dimension separation, standard settings were used (Erban et al. 2007). The samples were measured applying a 4-sec separation time on the second dimension using a VF17-MS column 0.1 mm ID and 10 μm film thickness (Varian). The chromatograms were analyzed using ChromaTOF 3.25 software. For peak identification, a customized mass spectral and retention-time index library of ~1000 nonredundant entries, which currently includes 360 identified metabolic components of plant, microbial, and animal origin, was used (Kopka et al. 2005).

EST coverage of proteins:

The available 167,641 EST sequences (downloaded from PlantGDB; Dong et al. 2005) were mapped to the 15,143 protein amino acid sequences from JGI version 3.1 using blastx. ESTs mapping to proteins were identified by alignments with an E-value of <1e − 10 and >95% sequence identity. Applying these thresholds, a total of 83,154 ESTs were found to map to 8081 unique proteins corresponding to 53% of the total protein set.

Functional annotation:

Kyoto encyclopedia of genes and genomes annotation:

For mapping Chlamydomonas genes onto the Kyoto encyclopedia of genes and genomes (KEGG) (Kanehisa et al. 2006) pathway annotation, we used a strategy similar to the KAAS method (KEGG Automatic Annotation Server) (Moriya et al. 2007), which is based on reciprocally best blast similarity hits against all KEGG orthology (KO) groups of functionally related genes assigned in the KEGG GENES database. Since the Chlamydomonas genome is still only partially annotated, we also allowed one-directional best blast hits with significant E-values (<1e − 10) to annotate additional sequences.

A downloadable KEGG annotation is available via the JGI Chlamydomonas website. In comparison, the JGI-KEGG annotation for the allProteins set of 38,012 different gene models provides for 731 different EC numbers on 114 KEGG pathways. For the 15,143 Chlre 3.1 protein set, it comprises annotations for 552 different EC numbers in 111 KEGG pathways.

MapMan annotation:

To assign predicted Chlamydomonas proteins to MapMan categories, all proteins were used in a blast search (NCBI Blast version 2.2.16) against plant proteins, which had previously been classified using the MapMan classification system (Thimm et al. 2004). Here, all blast-derived hits with bit scores of ≤50 were excluded from further analysis. Furthermore, all sequences were scanned for known motifs and/or families using Interproscan. The results were combined to provide a draft classification of the Chlamydomonas nuclear-encoded proteins. The 76 proteins known to be organelle encoded were classified manually on the basis of their gene name and available literature information as well as by using the above-mentioned combination of automated searches.

Inferring missing metabolic reactions from mathematical models:

We analyzed the metabolic draft network by applying the method of network expansion (Handorf et al. 2005). This method determines which metabolites are, in principle, producible if an organism is provided with a certain combination of external resources. The available substances are called the seed and the set of producible metabolites is termed the scope of the seed. In experiments performed in well-defined growth media, the identified metabolites are necessarily metabolic products generated by biochemical activities of the biological system under investigation from the nutrients within the growth medium. We calculated the scope of the growth medium for the draft network and compared the result with the observed metabolites.

The reference network containing the reactions on which the assigned KO numbers of the Chlamydomonas draft network were mapped was retrieved from the KEGG database. The complete list of reactions was curated by removing reactions with erroneous stoichiometries or ambiguous structure information, such as unspecified residues or chains of chemical groups of variable length. Furthermore, we omitted reactions involved in glycan synthesis because here our focus lies on the interconversion of small molecules. The curation process is described in detail in Handorf and Ebenhoh (2007).

Web services:

Identified and manually curated peptides have been uploaded to the JGI-Chlamydomonas resource (http://genome.jgi-psf.org/Chlre3/Chlre3.home.html) and are available via the PMap2 annotation track in the genome viewer. Furthermore, all recorded mass spectra can be text searched and visualized in ProMEX, a mass spectral reference library for plant proteomics (http://promex.mpimp-golm.mpg.de/home.shtml). The functional MapMan classification of Chlamydomonas proteins was made available as a web service using the Perl BioMoby API (Wilkinson and Links 2002) on a standard server running SUSE Linux. Functional MapMan classification can be performed via the web (http://mapman.mpimp-golm.mpg.de/general/ora/ora.shtml). The MapMan software, including visualization of Chlamydomonas experiments, is available from http://gabi.rzpd.de/projects/MapMan/. Functional classifications of MapMan proteins can also be accessed using the Biomoby framework. KEGG mappings used in this study and further material, including the lists of all identified metabolites and proteins, are provided as supplemental material.


Draft metabolic network of C. reinhardtii based on genome annotation and MapMan annotation categories:

Using the 15,143 proteins contained in the JGI version 3.1, together with 76 organellar proteins, 3307 protein sequences from the Chlre 3.1 set and 58 sequences from the organellar genomes were mapped onto the KO annotation. KO assignment has been shown to be useful as a standard controlled vocabulary for genome annotation (Mao et al. 2005). The resulting draft metabolic network derived from the total of 3365 annotated Chlamydomonas sequences comprises 198 KEGG pathways, 7330 KEGG reactions, and 713 enzyme classifications. All annotations are available as supplemental material.

Taking all predicted proteins in Chlamydomonas and mapping them onto MapMan classification bins yielded >5000 hits to nontrivial classifications covering about one-third of the predicted proteins in Chlamydomonas (see also http://gabi.rzpd.de/projects/MapMan/). Comparing the relative distribution of all major MapMan protein classes with the distribution in Arabidopsis, it became immediately evident that only a few proteins were identified within the cell-wall section, as, unlike higher plants, Chlamydomonas cell walls do not contain cellulose or other polysaccharides, but consist of hydroxyproline-rich glycoproteins (HRGPs) (Goodenough et al. 1986; Ferris et al. 2001). Manual inspection revealed that the biosynthetic machinery to synthesize NDP sugars, the precursors for cell-wall synthesis, had almost been completely classified automatically, whereas very few glycosyltransferases or cell-wall-modifying enzymes were found (Figure 1, top left). Furthermore, compared to the Arabidopsis protein set, a depletion of proteins associated with the classes “secondary metabolism” as well as “hormones” was evident.

Figure 1.
Schematic of major pathways and processes using the MapMan visualization platform. All squares represent Chlamydomonas gene models, which have been assigned to the various metabolic pathways depicted in the diagram. Red indicates that matching peptides ...

We analyzed the coverage of the recently introduced GreenCut and CiliaCut protein sets (Merchant et al. 2007) by the set of experimentally determined proteins (see below) and MapMan ontology (Tables 1 and and2).2). The GreenCut comprises 349 Chlamydomonas proteins with orthologs in other Viridiplantae, but not in nonphotosynthetic organisms. Using the automated MapMan annotation dataflow, we classified 195 proteins into MapMan bins. Fifty-seven of these proteins were identified using the proteomics techniques as described above. The CiliaCut contains 195 proteins related to motile and nonmotile cilia. Of those, MapMan classification was possible for 53 proteins of which 15 were also identified using proteomics techniques. A complete and detailed list of the annotated proteins can be found in the supplemental material.

Identification and classification of GreenCut proteins by MapMan and by proteomics analysis
Identification and classification of CiliaCut proteins by MapMan and proteomics analysis

Identification of 1069 proteins in Chlamydomonas:

To achieve a broad proteome coverage, we used a protein prefractionation method recently established for Arabidopsis thaliana (Wienkoop et al. 2004). We analyzed 13 anion exchange chromatographic protein fractions for three technical replicates. In total, we submitted 585,000 mass spectra for protein database searches against the sets: Chlre 3.1 (15,219 proteins) and allProteins (147,924 proteins) using Bioworks 3.3 (Thermo/Fisher). Using stringent search criteria, we found 4202 unique MS-peptide sequences in the Chlre 3.1 data set of which 3890 could also be identified against the allProteins set. The 4202 peptides map to 1069 proteins from the Chlre 3.1 set. A total of 1109 peptide sequences were uniquely identified in the larger allProteins data set (Figure 2). Combined, we have identified 5311 peptides mapping to 24,326 protein sequences from the allProteins set representing 3600 protein clusters at 95% sequence identity (data available as supplemental material).

Figure 2.
Venn diagram of unique MS-peptide sequence identifications in the protein sets Chlre 3.1 (15,219 proteins) and allProteins (147,924 proteins). In total, we found 4202 unique MS-peptide sequences in the Chlre 3.1 data set of which 3890 could also be identified ...

All spectra can be text searched and visualized using the plant proteomics mass spectral reference library ProMEX (http://promex.mpimp-golm.mpg.de/home.shtml). Furthermore, an experimental description of the growth conditions and the mass spectrometric analysis can be found for each spectrum.

All unique peptide sequences matching to proteins are made publicly available on the JGI Chlamydomonas genome resource web service (http://genome.jgi-psf.org/Chlre3/Chlre3.home.html).

Identified proteins show higher EST counts and support in silico gene models:

Proteins that have been identified experimentally in this study were found to produce significantly more hits to the available set of EST sequences than proteins that have not been detected. While a median number of 11 EST reads mapped to measured proteins, only 3 reads (median) mapped to proteins that were not contained in this set (P [double less-than sign] 0.01; Figure 3). As EST counts can be viewed as a semiquantitative measure of transcript abundance—especially when obtained from non-normalized libraries—this result suggests that the observed proteins correspond to genes with high expression levels and therefore are themselves relatively more abundant than the undetected proteins, assuming that the transcript level is indicative of the protein level. Thus, the set of detected proteins may represent the most abundant, constitutively expressed proteins and may correspond to housekeeping functions or other functions that require high protein levels. However, among the 1069 detected proteins, 204 currently do not have EST sequence support (as defined by blast alignments with E-value <1e − 10 and percentage identity >95%). Thus, using proteomics data, a significant portion of the measured peptides provides evidence for the validity of in silico gene models.

Figure 3.
EST coverage of detected vs. undetected proteins. Relative frequency of the number of EST reads mapping to proteins on the basis of blastx alignments in a semilogarithmic plot.

MapMan-based functional assignments for the identified protein set:

We then asked what functional categories are overrepresented among the found peptides and corresponding proteins. We compared the found peptides to the genetic background using the online classification for MapMan categories (Usadel et al. 2006). Indeed, we found that major biological processes, like photosysnthesis, protein synthesis, proteasome-dependent degradation, TCA cycle, and nucleotide metabolism were highly enriched, whereas unknown/unclassified proteins were significantly less represented in our list of identified peptides (P < 0.01 in all cases; Figure 4). Similarly, we asked how many of the named and annotated proteins in the Chlamydomonas JGI v3.1 release were represented in the list of found peptides. Indeed, of the ~3600 named and annotated proteins, we were able to identify nearly 600 by our proteomics approach; thus annotated genes are highly enriched (P < 0.01).

Figure 4.
Classification of the 1069 identified proteins according to the MapMan categories. The identified proteins were classified according to the previously defined MapMan categories by using the online MapMan protein classifier. 1, PS; 2, major CHO metabolism; ...

Proteomics-guided gene annotation:

We generated a FASTA file for mass spectral peptide identification search, including all known 167,641 ESTs and genomic scaffold sequences translated in all six reading frames. Almost all proteins identified on the basis of the Chlre 3.1 and allProteins databases were found in this search. However, several examples demonstrate how the combined EST/proteomics data can help in gene model definitions. Figure 5A shows a currently unannotated EST, i.e., not associated with any transcript/protein model, which is, however, supported by MS peptides, lending support to the validity of the EST as a protein-coding transcript. Figure 5B shows a translated EST sequence that is well covered by MS peptides. However, one MS peptide shows a diverging sequence from the proposed gene model in one segment while well anchored to the existing gene model in the second part, thus indicating the presence of an alternative transcript/protein model.

Figure 5. Figure 5.
Peptides supporting the validity of candidate genes or alternative gene models. (A) A currently unannotated EST, i.e., not associated with any transcript/protein model, which is, however, supported by two MS peptides. (B) A translated EST sequence that ...

Metabolite profiling in Chlamydomonas and integration with proteomics data:

Using GCxGC-MS, we identified 159 known metabolites in Chlamydomonas falling into different classes and covering a major portion of the central pathways in Chlamydomonas. Because of the approximately sixfold increase in signal-to-noise ratio of GCxGC MS compared to conventional GC-MS, and by using a cold injection system, we were able to almost double the set of detected metabolites compared to recent studies (Bolling and Fiehn 2005). These metabolites can be considered abundant, thus representing essential constituents of the metabolic repertoire of Chlamydomonas with the caveat that only GC-MS-compatible metabolites can be found. An integrative view of both metabolites and detected protein complement is shown in the MapMan schematic (Figure 1). Although the coverage of metabolic pathways is incomplete, a wide spectrum of pathways and regulatory regions in Chlamydomonas is covered.

Computational analysis of the reconstructed draft metabolic network of Chlamydomonas and the experimentally proposed draft metabolic network:

Applying the method of network expansion, we calculated which metabolites are producible by the draft metabolic network (see materials and methods). Of the 159 experimentally observed metabolites, 127 metabolites are represented by the KEGG-based metabolic network, of which 70 are contained in the metabolic scope, indicating that the draft network already contains a comprehensive model of the central metabolic pathways.

For the remaining 57 metabolites, the draft network derived from the annotated proteins does not include a production pathway. The reason for this insufficient functionality may directly result either from gaps in the current genomic sequence information—i.e., the apparently missing enzymes may be located in unsequenced portions of the genome—or from an incomplete annotation, in which some sequence homologies have not been identified correctly. Alternatively, the gaps might be a consequence of incomplete pathways in the KEGG database, which was used for the construction of the draft network. Which of the two reasons is responsible for the observed gaps must be decided by case-by-case inspection.

Here, we outline a systematic strategy to computationally infer candidates for such missing reactions. Our approach is based on the idea to identify minimal extensions of the draft network which allow for the synthesis of the observed metabolic products. These extensions recruit their reactions from a much larger reference network comprising all reactions (>6000) found in the KEGG database (Kanehisa et al. 2006). For each of the 57 measured metabolites not producible by the draft network, we identify minimal extensions by applying the following greedy algorithm: Initially, the draft network is extended by all reactions from the reference network. The extension is minimized by removing each reaction one by one. If the removal results in the loss of the capacity to produce the metabolite, the reaction is necessary for the production and is therefore included in the minimal extension; otherwise it is removed permanently. Since the result of this algorithm generally depends on the order of removal, we perform this calculation a large number of times with different orderings and compare the results. If for a particular metabolite one or several reactions are present in each of the calculated minimal extensions, this is a strong indication that these reactions are necessary for a functional production pathway. Such a finding thus leads to the hypothesis that the genes coding for the corresponding enzymes must exist within the genome.

For most of the 57 observed metabolites that are not contained in the scope, no reaction appeared in any minimal extensions. For a selected set of key metabolites, we searched all available genomic sequence information. For example, sucrose was detected in GCxGC-MS, pointing to a corresponding metabolic pathway. An obvious candidate for the sucrose phosphate synthase (SPS) was not contained in the JGI Chlre 3.1 protein set. However, comparing Arabidopsis SPS to alternative gene models (allProteins set), the protein model ID 176209 was identified as a likely SPS candidate (666 amino acids), and therefore we propose to include this model as a bona fide gene model in the next Chlamydomonas release. A putative SPS is in agreement with the computational pathway extension approach described above. A first minimal extension run proposed sucrose synthesis or degradation to be mediated by invertases annotated in the Chlamydomonas genome. Because of the thermodynamic irreversibility of this reaction, we omitted this step, and, alternatively, SPS was suggested by minimal extensions after several rounds of calculation.

The metabolite galactinol, also found by Bolling and Fiehn (2005), is not part of the scope of the current metabolic pathway. However, raffinose synthase, catalyzing the subsequent enzymatic step in the pathway, is annotated in the genome (122516), suggesting the presence of galactinol synthase and thus representing the most parsimonious way to close this pathway gap.

The phenylpropanoid caffeate was detected in Chlamydomonas, but could not be produced by the draft Chlamydomonas network. The initial enzyme for phenylpropanoid biosynthesis, phenylalanine ammonium lyase, is present neither in the Chlre 3.1 nor in the allProteins set on the basis of sequence homology searches. Interestingly, an alternative pathway has already been proposed by Birch et al. (1953).

These examples illustrate how metabolic profiling combined with pathway inspection may lead to targeted gene or even alternative pathway discovery.


In this article, we combine multi-level profiling methods with bioinformatic and theoretical modeling approaches to characterize the molecular repertoire of C. reinhardtii under reference conditions. We analyzed and integrated (i) a combination of database resources, such as existing genome annotations from JGI v3.1, EST collections, six-frame translation of the genomic sequence, protein domain scanning, and pathway annotation information; (ii) systematic high-resolution shotgun proteomics for high-throughput protein identification; (iii) systematic metabolite profiling and projection of identified metabolites to the reconstructed metabolic draft network in Chlamydomonas on the basis of existing gene annotation; and (iv) structural modeling of the reconstructed metabolic network to identify minimum extension pathways on the basis of the presence of identified metabolites.

MapMan classification of the predicted Chlamydomonas protein set and comparison with other organisms yielded information for a smaller portion of all proteins than typically found (about one-half) in higher plant species. However, given that MapMan was developed using higher plants and that more annotation is available for these species, this discrepancy is not surprising. Accordingly, more than half of the proteins predicted to belong to the plant and plastid lineages (GreenCut; Merchant et al. 2007; Table 1) were classifiable by our automated annotation dataflow using the MapMan categories, whereas proteins that probably are not associated with higher plant lineages were annotated at a much lower percentage [for example, only ~25% for proteins in the CiliaCut set (Merchant et al. 2007; Table 2) were categorized into MapMan bins]. The ongoing development of MapMan (Rotter et al. 2007) will allow capturing protein classes not yet included in the current annotation scheme, further strengthening MapMan's utility as a comparative visualization and annotation system.

High-throughput, high-mass-accuracy shotgun proteomics (for review see Weckwerth 2008 and Allmer et al. 2004, 2006) was applied to characterize an initial set of abundant proteins in the Chlamydomonas proteome. For the analysis, we used a standard fractionation protocol established for A. thaliana and adapted to C. reinhardtii to increase the number of detected proteins (Wienkoop et al. 2004). To assess whether the identified proteins are indeed abundant, we systematically matched available EST sequences to the annotated proteins. A comparison revealed a significantly higher EST count for the identified 1069 unique proteins than for the unidentified proteins, which agrees with the notion that higher or more frequent transcript abundance correlates also with increased protein abundance. Projection of these proteins to the MapMan annotation ontology revealed a high coverage of almost all known pathways with representative protein candidates (see Figure 1). All gathered proteomics data are available in the plant proteomics mass spectral reference library ProMEX (Hummel et al. 2007).

By combining EST with proteomics data and in silico gene models we demonstrated that proteomics can help to improve genome annotation, as also shown by Allmer et al. (2004, 2006). Therefore, we assume that proteomics data repositories such as ProMEX will contribute greatly to improving gene predictions and gene annotations.

The metabolite repertoire is another important complement of genome annotation. Integration of metabolomics data in the draft metabolic network of Chlamydomonas helps to identify as-of-yet-missing reactions in the network.

In our experiments sucrose was found to be produced by Chlamydomonas, similar to the findings reported by Klein (1987). Minimal extension network analysis of the Chlamydomonas draft metabolic network revealed different putative pathways leading to sucrose. However, a thermodynamically feasible pathway was predicted via sucrose–phosphate synthase, which is indeed annotated in the alternative gene models (allProteins set). An important subsequent step for sucrose synthesis is the reaction of sucrose-6-phosphate to sucrose catalyzed by sucrose–phosphate–phosphatase (SPP) (Lunn 2003; Lunn et al. 2003). A similarity search against the allProteins set revealed a likely candidate for SPP (149366).

Despite the fact that galactinol synthase has not been identified yet, the presence of galactinol was reported in previous studies (Bolling and Fiehn 2005) and was confirmed by our experiments. Interestingly, raffinose synthase (RS) is predicted in the Chlamydomonas genome (122516). However, while raffinose has not yet been detected in the metabolome of Chlamydomonas, the presence of galactinol and RS suggests that raffinose oligosaccharides can be produced.

Also, the phenylpropanoid caffeate was detected in Chlamydomonas. Furthermore, genes of the phenylpropanoid and flavonoid biosynthesis pathway are annotated or show high similarity in the current JGI annotation [CAD (191379), FCoAS (113763), and IFR (150866, 132437, 192854)]. The existence of caffeate as an intermediate of the flavonoid pathway has also been reported previously (Birch et al. 1953). Thus, our studies provide further experimental evidence for the existence of phenylpropanoid metabolism in Chlamydomonas.

We have shown that metabolic modeling approaches to studying the metabolism of an organism are already useful even when only a draft network exists. By comparing computational predictions on the producibility of metabolites with observations from metabolomics measurements, it can be determined in which synthesis pathways enzymes are not yet annotated. Considering the fraction of metabolites whose presence cannot yet be explained by the draft network, it can be estimated how incomplete the network actually still is. Moreover, and more importantly, our model supports the annotation process by predicting missing enzymes that must be encoded in the genome, but for which no gene has been identified so far. The knowledge that a particular enzyme should be encoded somewhere in the genome will enhance the efficiency of homology searches to identify the coding genes. Subsequently, the products of the candidate genes can be isolated and their predicted function can be validated by in vitro experiments. With the presented strategy to closely interlink experiments, bioinformatics, and modeling, hypothesis generation about gene existence and improvement of genome annotation is demonstrated. Furthermore, the results will contribute to an improvement of metabolic databases. Presently, our methods are limited to one selected large-scale metabolic network analysis technique, the method of network expansion. Further, we can predict strictly necessary reactions only by considering those found in all minimal extensions. In principle, a similar strategy can be based on flux balance analysis and we expect that the comparison of the two large-scale network analysis methods will reveal further insight into the metabolism of Chlamydomonas. We further plan to improve the algorithm for identifying minimal network extensions with the goal of predicting not only essential reactions, but also alternative pathways, which might lead to the production of a certain metabolite. This will result in alternative hypotheses of synthesis, which then may be validated experimentally.


We thank John Lunn and Elspeth MacRae for discussing their unpublished results with us. Financial support was provided by a Forschungszenten Systembiologie Bundesministerium für Bildeung und Forschung grant (http://www.goforsys.de/).


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