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Research

Integrated Proteomics and Genomics Strategies Bring New Insight into Candida albicans Response upon Macrophage Interaction^*,S

Elena Fernández-Arenas, Virginia Cabezón, Clara Bermejo, Javier Arroyo, César Nombela, Rosalía Diez-Orejas^¶ and Concha Gil

From the Departamento de Microbiología II, Facultad de Farmacia, Universidad Complutense, 28040 Madrid, Spain

	ABSTRACT

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
The interaction of Candida albicans with macrophages is considered a crucial step in the development of an adequate immune response in systemic candidiasis. An in vitro model of phagocytosis that includes a differential staining procedure to discriminate between internalized and non-internalized yeast was developed. Upon optimization of a protocol to obtain an enriched population of ingested yeasts, a thorough genomics and proteomics analysis was carried out on these cells. Both proteins and mRNA were obtained from the same sample and analyzed in parallel. The combination of two-dimensional PAGE with MS revealed a total of 132 differentially expressed yeast protein species upon macrophage interaction. Among these species, 67 unique proteins were identified. This is the first time that a proteomics approach has been used to study C. albicans-macrophage interaction. We provide evidence of a rapid protein response of the fungus to adapt to the new environment inside the phagosome by changing the expression of proteins belonging to different pathways. The clear down-regulation of the carbon-compound metabolism, plus the up-regulation of lipid, fatty acid, glyoxylate, and tricarboxylic acid cycles, indicates that yeast shifts to a starvation mode. There is an important activation of the degradation and detoxification protein machinery. The complementary genomics approach led to the detection of specific pathways related to the virulence of Candida. Network analyses allowed us to generate a hypothetical model of Candida cell death after macrophage interaction, highlighting the interconnection between actin cytoskeleton, mitochondria, and autophagy in the regulation of apoptosis. In conclusion, the combination of genomics, proteomics, and network analyses is a powerful strategy to better understand the complex host-pathogen interactions.

These infections are the result of a coordinated battle between the fungus and its host. The mammalian immune system is a powerful barrier to Candida infections, and phagocytic cells, such as macrophages and neutrophils, are a primary line of defense against microbial infections. Macrophages also communicate with the adaptative immune system as antigen-presenting cells, capturing and processing foreign antigens for presentation to T cells, enabling immunological memory (7). When a phagocyte recognizes the presence of a foreign cell, it engulfs the microorganism by a process called phagocytosis by which its membrane forms a phagosome (8). This phagosome is a hostile environment (low nutrients, acidic pH, and hydrolytic enzymes) for the fungus. Although tuned host responses are critical in controlling fungal infections, pathogens like Candida have evolved mechanisms to overcome the damage caused by the macrophage. Among the most important of these mechanisms are the morphological transition and inhibition or neutralization of toxic compounds (9). In this study, we suggest that the induction of different pathways leading to active self-destruction (i.e. programmed cell death (PCD)¹), originally described in mammals, could be a yeast survival mechanism (10, 11).

The ready availability of the C. albicans genome sequence has led to genome-wide transcription analysis in C. albicans (12–14). Genome-wide mRNA expression profiling by DNA microarrays has proven to be a powerful tool in characterizing the changes in biological processes (15). Different genomics approaches to studying C. albicans response after phagocyte interaction have been performed, showing the potential of these strategies to analyze the host-pathogen interplay (16–18). Nevertheless most biological functions are executed by the proteins. As a result, proteomics-based approaches, which examine the level of protein expression of a tissue or cell type, complement the genome initiatives and are increasingly being used to address biomedical questions. Thus, proteomics could substantially complement the existing molecular understanding of host-pathogen interaction, giving new insight at a higher level (19). Proteomics is based on highly efficient methods of separation and analysis of proteins in living systems, providing exhaustive information on biochemical properties such as the level of protein expression, post-translational modifications, protein-protein interactions, etc.

A thorough proteomics analysis of the C. albicans response upon macrophage interaction could provide a large amount of information, such as putative new virulence factors required for future drug discovery and vaccine development (20, 21). In this work, we used an in vitro system of phagocytoses by utilizing the murine macrophage cell line RAW 264.7 and the wild-type yeast strain SC5314. An exhaustive protocol of yeast isolation after the encounter with macrophages was developed. Differential C. albicans protein expression in these conditions was studied by two-dimensional (2D) PAGE. Differentially expressed proteins were identified by peptide mass fingerprinting and fragmentation using a MALDI-TOF/TOF mass spectrometer. In addition, the differential gene expression using microarrays was also analyzed. Proteomics information, when combined with mRNA expression data, has provided a more detailed picture of the C. albicans-macrophage interaction.

This integrated approach has led to the establishment of correlations in "specific" cellular pathways that reflect a global view of the molecular fungal phenotype in this biological context. Results from the network visualizing system led us to hypothesize that Candida cells could possibly trigger different cell death pathways after macrophage contact. Furthermore this apoptotic yeast cell death is connected with mitochondria and actin cytoskeleton.

	EXPERIMENTAL PROCEDURES

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
Strains, Media, and Cell Culture Conditions—
The C. albicans strain used in this study was SC5314 (a clinical isolate) (22). For phagocytic experiments, this strain was grown on solid YED medium (1% D-glucose, 1% Difco yeast extract, and 2% agar), incubated at 30 °C for at least 2 days, and then collected and diluted to the desired cell dilution in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum (Invitrogen).

RAW 264.7 NO^– murine macrophages were obtained from the American Type Culture Collection (Manassas, VA) and maintained in complete culture medium (RPMI 1640 medium containing 10% heat-inactivated fetal bovine serum, 2 mM L-glutamine, 100 units/ml penicillin, and 100 µg/ml streptomycin (Invitrogen) at 37 °C in a humidified atmosphere with 5% CO₂. Cells were split after reaching the confluent state, usually every 2–3 days. RAW cells were plated at the indicated density 18–24 h prior to the start of the experiments (23).

Phagocytosis Quantification—
To select the optimal conditions for subsequent genomics and proteomics analyses, a differential staining protocol was set up to quantify the phagocytic process. C. albicans yeasts were prelabeled with Oregon Green 488 (1 µM) (Molecular Probes). Labeling was performed in the dark with gentle shaking (30 °C) for 1 h. Dye uptake and cell viability in these conditions were confirmed by visualization of green fluorescence using a FITC filter. Cells were washed twice with PBS, 100 mM glycine and resuspended in PBS at the desired density. RAW 264.7 macrophages (5 x 10⁵ cells/well) were allowed to ingest Oregon Green-labeled yeast on sterile glass coverslips for 1.5 and 3 h using a fungus-macrophage ratio of 1:1. Cells were then washed with ice-cold PBS and fixed in 4% paraformaldehyde for 30 min. To distinguish between internalized and attached/non-ingested yeast, C. albicans cells were counterstained with calcofluor white M2R (Sigma) (2.5 µM) for 15 min in the dark. CWH, a fungus-specific stain, binds specifically to the yeast cell wall chitin and does not enter into macrophages. After several washes, coverslips were mounted with specific mounting medium (DakoCytomation Denmark A/S). The number of ingested cells (containing green fluorescence) and/or adhered/non-ingested (calcofluor white blue fluorescence) were quantified by phase-contrast and fluorescence microscopy using a Carl Zeiss Axioplan-2 microscope (Carl Zeiss AG) with a mercury HBO/100-watt lamp fitted with FITC (excitation/emission BP 480/30 and BP 535/40, respectively) and UV filters (4',6-diamidino-2-phenylindole, dihydrochloride excitation/emission BP 365/12 and long pass 397, respectively) and equipped with a digital camera, Spot-2 (Diagnostic Instruments). Metamorph 5.0 (Universal Imaging Corp.) and ImageJ version 1.35h (available at rsb.info.nih.gov/ij/index.html) softwares were used to analyze images. Three different slides were prepared for each time point, and at least four experiments were done. No less than 600 C. albicans cells were scored per slide, and data were expressed as the percentage of cells internalized by macrophages. To follow the interaction between C. albicans and macrophages (fungus-macrophage ratio, 1:1), 3-h time course video microscopy was carried out. Images were captured every 30 s with a Nikon Eclipse TE2000-U microscope (using contrast phase lenses) equipped with a thermostatic plate and connected to a high resolution Hamamatsu ORCA-ER camera. The information was processed using Aquacosmos 2.0 software.

Isolation of Ingested/Attached Yeasts after Macrophage Interaction—
On day –1 (18–24 h prior to the start of the co-culture experiments), macrophages were collected and counted with a Neubauer chamber (hemocytometer). A total of 3.5 x 10⁷ cells were plated in complete culture medium in a 750-ml cell culture flask and grown overnight at 37 °C in a humidified atmosphere with 5% CO₂. C. albicans strain SC5314 was grown overnight at 30 °C on solid YED medium (to maintain cells in the yeast form). On day 1, these yeast cells were harvested, washed twice with PBS, counted, and diluted to the desired density in 50 ml of complete culture medium. A total of 5 x 10⁷ yeast cells were added per flask to obtain a fungus-macrophage ratio of 1:1 (because repeated cell counts of the overnight macrophage cultures indicated a growth rate of 1.4) and incubated for 1.5 h (for microarray experiments) and 3 h (for both proteomics and microarray experiments) at 37 °C and 5% CO₂. Non-ingested and unbound Candida cells were removed by washing three times with ice-cold PBS. The macrophages plus bound/ingested yeast cells were dislodged by scraping the flask with rubber scrapers in ice-cold water and pooled by centrifugation for 10 min at 4000 rpm (Kubota 2010, Kubota Corp.). The resulting cell pellet was resuspended in a 0.25% Triton X-114 solution (50 mM Tris-HCl, 2 mM EDTA, pH 7.5), vortexed three times, and chilled on ice for 30 min to eliminate most of the lysed macrophages. Subsequently another Triton X-114 treatment was performed for 5 min. Next the sample was washed four to six times with ice-cold MilliQ water to eliminate any trace of the detergent used. Control C. albicans cells were collected, washed, and treated in the same way as above but in the absence of macrophages.

Generation of Candida Protein Extracts for Proteomics Analysis—
Cytoplasmic extracts were obtained according to Ref. 24 with some modifications. Briefly the cell pellet was resuspended in 3 ml of lysis buffer (50 mM Tris-HCl, pH 7.5, 1 mM EDTA, 150 mM NaCl, 1 mM DTT, 0.5 mM PMSF, and a mixture of protease inhibitors (Complete Mini, EDTA-free protease inhibitor mixture tablets, Roche Diagnostics). An equal volume of 0.4–0.6-mm-diameter glass beads was added. Cells were disrupted in a FastPrep cell breaker (Bio 101, Inc.) for 20 s and cooled on ice for 5 min (this procedure was repeated until at least 80% of the cells had been lysed as determined by phase-contrast microscopic examination). Cell extracts were separated from glass beads and cell debris, collected in a new tube through refrigerated centrifugation, further clarified at 13,000 rpm for 15 min, and stored at –80 °C. Protein quantification was performed using the Bradford assay (Bio-Rad).

2D PAGE—
Two-dimensional gel electrophoresis was performed as reported previously (25) with some modifications. Samples containing 100 µg (analytical gels) or 0.5–1 mg (preparative gels) of protein were solubilized in a rehydration buffer (7 M urea, 2 M thiourea, 4% CHAPS, 2% Pharmalyte pH 3–10, 1% Destreak (GE Healthcare), and bromphenol blue (Sigma)) and were then applied onto 18-cm ready made IPG strips with non-linear pH 3–10 gradient (GE Healthcare). IEF was performed using an IPGphor® focusing unit (GE Healthcare) at 15 °C: for analytical gels, 500 V for 1 h, 500–2000 V in 1 h, and 8000 V for 5.5 h; for preparative gels, 30 V (active rehydration) for at least 13 h, 500 V for 1 h, 1000 V for 1 h, 2000 for 1 h, 2000–5000 V in 3 h, and 8000 V for 11 h. After this, IPG strips were reduced (2% dithioerythritol) and then alkylated (2.5% iodoacetamide) in equilibration buffer (6 M urea, 50 mM Tris-HCl, pH 6.8, 30% glycerol, 2% SDS). The second dimension separation by molecular weight was carried out on homogenous 10% T, 1.6% C (piperazine diacrylamide as a cross-linker) in polyacrylamide gels (1.5 mm thick). Electrophoresis was conducted at 40 mA/gel constant current for 6 h in a Protean II gel tank (Bio-Rad). Analytical gels were silver-stained according to Bjellqvist et al. (26) with few modifications, and preparative gels were stained using R-250 Coomassie Brilliant Blue (Sigma) and silver staining compatible with MS analyses (27).

Analysis of Differential Protein Expression—
2D images were captured by scanning stained gels using a GS-800 imaging densitometer (Bio-Rad), digitalized with Multi-Analyst software (Bio-Rad), and analyzed with the Image-Master 2D-Platinum computer software (GE Healthcare). Six gels of each sample (control and treated), obtained from three different assays, were analyzed to guarantee representative results and for future comparative studies. After automated spot detection, spots were checked manually to eliminate any possible artifacts such as background noise or streaks. The patterns of each sample were overlapped and matched, using the selection of 28 common spots present in both images as landmarks, to detect potential differentially expressed proteins. Spot normalization, as an internal calibration to make the data independent of experimental variations between gels, was made using relative volumes (%Vol) to quantify and compare the gel spots; %Vol corresponds to the volume of each spot divided by the total volume of all the spots in the gel. Synthetic gels from both treated and control yeasts were obtained from the six gels analyzed previously.

Protein Identification by MALDI-TOF MS or MS/MS—
The protein spots of interest were manually excised from preparative silver-stained gels by biopsy punches, placed in an Eppendorf tube, and washed twice with double distilled water. Proteins for analysis were in-gel reduced, alkylated, and digested with bovine trypsin (12.5 ng/µl, sequencing grade; Roche Applied Science) according to the procedure published by Sechi and Chait (28). After digestion, the supernatant was collected, and 1 µl was spotted on a MALDI target plate and allowed to air dry for 10 min at room temperature. Subsequently 0.4 µl of matrix (3 mg/ml of -cyano-4-hydroxy-trans-cinnamic acid (Sigma) diluted in 0.1% TFA-ACN/H₂O (1:1, v/v)) were added to the dried peptide digest spots and allowed to air dry for another 5 min at room temperature. The samples were analyzed with a 4700 Proteomics Analyzer MALDI-TOF/TOF mass spectrometer (Applied Biosystems, Framingham, MA). This MALDI-TOF/TOF instrument consists of a MALDI source with a 200-Hz neodynium YAG (yttrium aluminium garnet) laser operating at 355 nm and operated in positive ion reflector mode with an accelerating voltage of 20,000 V. All MS spectra were internally calibrated using peptides from the autodigestion of trypsin. The analysis by MALDI-TOF mass spectrometry produced peptide mass fingerprints, and the peptides observed can be collated and represented as a list of monoisotopic molecular weights. For MS analyses, a monoisotopic peak is selected, and all known contaminant ions were excluded during the process. The parameters used to analyze the data were: a signal to noise threshold of 20 and a resolution higher than 10,000 with a mass accuracy of 20 ppm. Proteins ambiguously identified by peptide mass fingerprints were subjected to MS/MS sequencing analyses using the 4700 Proteomics Analyzer (Applied Biosystems). Hence from the MS spectra, suitable precursors were selected for MS/MS analyses with CID (atmospheric gas was used) in 1-kV ion reflector mode and precursor mass windows of ±10 Da. The plate model and default calibration were optimized for the MS/MS spectra processing. The parameters used to analyze the data were: a signal to noise threshold of 10 and a resolution higher than 6000.

For the protein identification, both MS and MS/MS spectra were automatically searched using a Local license of Mascot 1.9 from Matrix Science through the Protein Global Server (GPS) from Applied Biosystems. The search parameters for peptide mass fingerprints and tandem MS spectra obtained were set as follows: two sequence databases were used, Swiss-Prot/TrEMBL non-redundant protein database (www.expasy.ch/sprot) as well as CandidaDB (genolist.Pasteur.fr/CandidaDB) (14) and CGD (www.candidagenome.org) (12, 13). Fixed and variable modifications were considered (Cys as S-carbamidomethyl derivative and Met as oxidized methionine), allowing for one missed cleavage site; precursor tolerance was 50–100 ppm and MS/MS fragment tolerance was 0.3 Da; a restriction was placed on isoelectric point (pI 3–10); and a protein mass range from 10 to 100 kDa was accepted. In all protein identifications, peptide mass fingerprint, or MS/MS, the probability scores were greater than the score fixed as significant with a p value <0.05.

RNA Isolation, Microarray Hybridization, and Data Analysis—
All these processes were basically carried out following the protocols described by Garcia et al. (29) and Eurogentec (manufacturer) with some modifications. Briefly total RNA was isolated using the RNeasy MIDI kit (Qiagen). RNA concentrations were determined by measuring absorbance at 260 nm. RNA purity and integrity were assessed using RNA Nano Labchips in an Agilent 2100B Bioanalyzer (Agilent Technologies, Palo Alto, CA) following the manufacturer’s instructions. cDNA was synthesized from 20–30 µg of total RNA (control and ingested yeast) by reverse transcription using the CyScribe^TM Post-Labeling kit, incorporating Cy3-dUTP or Cy5-dUTP (GE Healthcare) into the cDNA corresponding to each sample to be compared. The amount of cDNA, as well as the incorporation of Cy3 and Cy5 dyes into cDNA targets, was quantified on an Ultrospec 3300 Pro UV/visible spectrophotometer (GE Healthcare) by measuring the absorbance of each sample at 260, 550, and 660 nm, respectively. Both labeled cDNA populations were combined, dried in a vacuum trap, and used as a hybridization probe after resuspension in 55 µl of EGT hybridization solution (Eurogentec, Seraing, Belgium) and 100 µg of salmon sperm (Invitrogen) ml^–1. The printed microarrays including the complete set of 6039 ORFs coded by the C. albicans SC5314 genome and 27 control genes (spotted in duplicate) used in this study were provided by Eurogentec. Information about C. albicans gene annotations was obtained from CandidaDB, MycoPathPD (Proteome Bioknowledge Library, www.proteome.com/control/tools/proteome), and CGD. Slides were hybridized overnight with the labeled probe at 42 °C in a water bath. Before scanning, the chips were washed and dried following the manufacturer’s instructions.

For each condition tested, the total RNA from at least two different experiments was analyzed, and for each RNA sample at least two different hybridizations were performed. Expression ratios were obtained from the average of four different microarray experiments using two biological samples and dye swapping. To allow a direct comparison of the data obtained from each experiment, labeled cDNA corresponding to RNA from C. albicans upon macrophage interaction was co-hybridized with RNA isolated from control C. albicans cells. Microarrays were scanned with a GenePix 4000B scanner (Axon Instruments, Union City, CA) at a resolution of 5 µm (photomultiplier values ranging from 550 to 700 and laser power at 100%). GenePix Pro 4.0 analysis software (Axon Instruments) was used to locate spots in the microarray with the appropriate grid and to obtain the two image TIFF files. All images were further processed using GenePix 4.0 software according to the manufacturer’s instructions. Flagged spots and spots with an average intensity minus background below the mean of the background (considering this as the median of the local background for each spot) for all the non-flagged spots in any of the channels (Cy3 or Cy5) were not retained for further analysis. Within this group, the spots showing in one channel a value of intensity minus background higher than 5 times the mean of the background for all spots in the microarray for that channel were recovered. The reproducibility of replicates in each microarray (two spots per ORF) was analyzed by creating a normal distribution log₂ (RA x 1/RB) where RA and RB are the ratios of replicated spots after background subtraction and normalization. Replicates that exceeded the average by more than ±3 standard deviation were discarded. Data analysis was accomplished using the GeneSight 4.0 software package (BioDiscovery Inc., El Segundo, CA). Locally weighed linear regression (LOWESS) analysis was performed as a normalization method to remove the intensity-dependent deviation in the log₂ (ratio) values. Significance analysis of the results was conducted using a Student’s t test (GeneSight). Genes with p <0.05 were considered to be significantly differentially expressed with respect to the treatment condition; and genes with p values between 0.05 and 0.1 were considered to probably be significantly differentially expressed with respect to the treatment condition.

The microarray data described here follow the MIAME (minimum information about a microarray experiment) recommendations and have been deposited at the National Center for Biotechnology Information gene expression and hybridization array data repository (Gene Expression Omnibus (GEO), www.ncbi.nlm.nih.gov/geo/) with the following accession number: GSE4794.

Functional Classification—
Functional categories (pathways) for C. albicans genes/proteins were assigned using CandidaDB, MycoPathPD, and CGD available from the Pasteur Institute, the Proteome Bioknowledge Library, and the Candida Genome Consortium and on the basis of the Saccharomyces Genome Database (www.yeastgenome.org/) and MIPS (mips.gsf.de/proj/yeast/CYGD/db/index.html) functional assignments for Saccharomyces cerevisiae homologs (30).

The FunSpec (an acronym for "Functional Specification") web-based tool was used for statistical evaluation of groups of genes and proteins with respect to existing annotations, including GO terms or parents of the GO terms (31). Statistical testing for enrichment of functional categories among the set of identified proteins and genes was based on a hypergeometric distribution model using the method of Hughes and co-workers (31). This method gives the probability (p value) that the intersection of a given list with any given functional category occurs by chance. A threshold cutoff p value of p <0.05 was used as a final selection criterion to highlight statistically significant, potentially biologically interesting clusters.

Visualization, Integration, and Manipulation of Interaction Networks—
The Osprey version 1.2.0 software platform was used for visualization and manipulation of complex interaction networks (32). This network visualization system not only represents interactions in a flexible and rapidly expandable graphical format but also provides options for functional comparisons between datasets. Furthermore the built data-rich graphical representations are color-coded for gene function and experimental interaction data. Osprey is dynamically linked to the BioGRID (General Repository for Interaction Datasets) database (33), which in turn compiles gene annotations provided by the Saccharomyces Genome Database.

	RESULTS

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

 
Yeast Macrophage Co-culture
To set up the optimal conditions to study proteins and genes of C. albicans differentially expressed upon macrophage interaction, co-culture assays were performed as described in detail under "Experimental Procedures" using a macrophage-Candida ratio of 1:1. Cells ingested or associated with macrophages were discriminated using differential fluorescent labeling with Oregon Green and CWH (Fig. 1A). The percentage of ingested cells was determined at different time points. As shown in Fig. 1B the phagocytic percentage increased over the time from 1 to 3 h; it was 80% at the later time point. To monitor this process in vivo, 3-h time course video microscopy experiments were carried out. Yeast internalization occurred as early as 15–20 min. After 1 h of interaction, the fungus started to germinate in an attempt to avoid being engulfed by the macrophage within 3 h (Supplemental Fig. 1).

View larger version (27K): [in this window] [in a new window]   FIG. 1. A, RAW 264.7 macrophages exposed to labeled C. albicans wild-type strain SC5314 (Oregon Green 488) for 1.5 h (left column) and 3 h (central and right columns). Intracellular and external/adhered C. albicans cells were distinguished based on fluorescence after co-staining with CWH, which does not enter or stain macrophages. CWH stains the cell wall of external/adhered fungal cells blue so that green cells have an external rim of blue fluorescence. (Fluorescence and phase-contrast micrographs were acquired using a 40x oil objective.) B, phagocytosis of C. albicans yeasts was quantified at the indicated periods of time (x axis). The number of ingested yeast (percentage) expressing Oregon Green 488 fluorescence (y axis) is shown. Results shown are derived from triplicate measurements in one experiment, which was representative of four independent experiments (±S.D.) as described under "Experimental Procedures."

View larger version (32K): [in this window] [in a new window]   FIG. 2. Strategy followed to obtain an enriched-ingested C. albicans fraction upon macrophage interaction for both proteomics and genomics approaches. M, macrophage(s); FBS, fetal bovine serum.

View larger version (109K): [in this window] [in a new window]   FIG. 3. Representative silver-stained 2D map of total yeast extracts after macrophage interaction. Yeast proteins were separated by two-dimensional gel electrophoresis (x axis, first dimension, 18-cm pH 3–10 non-linear IPG strips; y axis, second dimension, 10% SDS-PAGE gels using the Protean II system) and visualized by silver staining. Mr and pI calibrations were based on calculated molecular masses. Labeled spots indicate 132 statistically significant protein species that show differential changes in yeast protein levels after macrophage exposure in all three independent experiments (two gels per experiment). Down-regulated spots (53 spots) are indicated by circles, and up-regulated spots (79 spots) are indicated by arrows. 100 µg of protein sample were loaded. Magnified regions highlight examples of differentially expressed yeast proteins upon macrophage interaction; examples of over- (Gim5p, YKR049p, Sod1p, Rbp1p, Gpx1p, and Cit1p) and underexpressed proteins (Tdh3p) are shown.

View larger version (108K): [in this window] [in a new window]   FIG. 4. Two-dimensional gel electrophoresis silver-stained gel of differentially expressed C. albicans proteins after macrophage interaction identified by mass spectrometry (MALDI-TOF/TOF). 67 identified proteins (40 over- and 27 underexpressed) were annotated in the gel by their protein names according to MycoPathPD (Proteome Bioknowledge Library, www.proteome.com/control/tools/proteome) database. More information can be found at COMPLUYEAST-2DPAGE site (babbage.csc.ucm.es/2d/2d.html).

Genomics Analysis
Differential transcriptional analysis of C. albicans response after 1.5- and 3-h exposure to murine macrophages was carried out to complement proteomics. Whole-genome microarrays representing almost the complete genome of C. albicans (microarrays contained 6039 Candida genes) were used. A total of 239 genes were found to be significantly differentially expressed (1.75-fold up- or down-regulated). Of these, 120 and 119 genes were found to be up- and down-regulated, respectively. Although 75 genes were consistently induced at 1.5 h, 45 were induced after 3 h. In addition, 60 and 59 genes were found to be repressed after 1.5 and 3 h, respectively (Supplemental Table III and Table II). Entire datasets of global expression profiles can be found at Gene Expression Omnibus as described under "Experimental Procedures."

A comparative analysis of the expression profile (number of up- and down-regulated genes) from both times analyzed, classified according to their biological function, is shown in Supplemental Fig. 3. The number of modulated genes is greater at the earlier time point, both in the up- and down-regulation response. Only protein and cell fate, as well as protein with binding function, exhibit a higher number of genes after 3 h of interaction. The high number of experimentally uncharacterized genes both over- and underexpressed should be noted.

Annotation and Statistical Significance of the Functional Categories
To obtain an integral view of the yeast biological processes affected after macrophages interaction, functional annotation was used for both proteomics and transcriptomics data analyses. Due to the low number of C. albicans genes annotated in the CGD (www.geneontology.org/GO.current.annotations.shtml) (50 and 15.5% of the genes/proteins examined in this study, respectively) and the high homology of this organism with the model yeast S. cerevisiae, we used S. cerevisiae orthologs as the input gene/protein list for FunSpec analysis (see "Experimental Procedures" for further details). An issue to be taken into account is that S. cerevisiae is not a pathogenic fungus; thus some biological functions may not be present in the output summary of functional classes (i.e. pathogenesis).

To interpret the data generated by the groups of related fungal genes/proteins differentially expressed after macrophage interaction, we used a web-based tool called FunSpec, which uses information from public databases (i.e. MIPS and GO). We can look in the input list of yeast genes/proteins to determine whether they are enriched for particular attributes (i.e. functional roles, biochemical properties, and localization) using a well accepted statistical model. Supplemental Table IV shows a summary of the statistically significant (p < 0.05) evidence of functional enrichment in the genomics and proteomics datasets.

An important fraction of the overexpressed proteins identified by 2D PAGE-MS were found to be involved in tricarboxylic acid pathway, cell rescue, defense and virulence, protein fate, glyoxylate cycle, and actin polymerization and/or depolymerization. Regarding the underexpressed proteins, biological enrichment was found for metabolism (glycolysis, gluconeogenesis, and fermentation) and for protein folding and stabilization.

Additionally gene transcripts that were up-regulated were enriched for transport facilitation, metal ion homeostasis, lipid and fatty acid metabolism, cell wall, cell-cell adhesion, and detoxification. Meanwhile gene transcripts that were down-regulated were enriched for metabolism (C-compounds and carbohydrates), response to stress, cell fate, and protein folding. Hence by using this statistical model we were able to reinforce the significance of the differentially expressed proteins/genes on the biological functions of the cell as well as the importance of certain pathways considerably altered in the fungus after encountering the macrophage.

Genetic Network Analysis of Unknown Genes
Because the unknown function category is the one that contains the highest number of modulated genes we used a network visualization system (Osprey) to extract biological meaning and, when possible, to formulate testable hypotheses from the data generated in this work. We used S. cerevisiae orthologs as the input gene list as was done before with FunSpec (50% of the unknown genes did not present homolog). Maps of physical and genetic interactions of induced and repressed uncharacterized genes are shown in Supplemental Fig. 4, A and B, respectively. The network generated using the up-regulated unknown set of genes as input file shows significant interactions with genes related to cell organization and biogenesis (mainly associated with cell wall and nuclear pore), DNA repair and DNA damage response, protein biosynthesis, and, surprisingly, autophagy (Supplemental Fig. 4A). On the other hand, when the down-regulated unknown genes were used as the input list, interactions associated to metabolism, cell organization and biogenesis (largely linked to mitochondrion and actin cytoskeleton biogenesis), and transport routes and other uncharacterized genes were observed (Supplemental Fig. 4B). It is worth mentioning that the gene up-regulated at 1.5 h, SGE1 (crystal violet resistance protein), interacts with SLN1 that has high similarity to mammalian BAG1 gene that negatively regulates apoptosis.

Interaction Network Analyses of Cytoskeleton-, Mitochondria-, Autophagy-, and Apoptosis-related Genes/Proteins
As described in the above genetic network analysis, we detected some interactions between unknown genes and genes involved in autophagy and apoptosis. Reports that connect apoptosis with actin dynamics, mitochondrial dysfunction, and autophagy have been described recently (35–37). Thus, we decided to search in our datasets for proteins/genes belonging to these biological functions to carry out a new interaction network analysis. A total of 32 proteins/genes, enclosed in those functions, were detected. A significant repression in genes related to the actin cytoskeleton and mitochondria was noted, pointing toward a potential reduction in actin and mitochondrial dynamics. Only three proteins of 22 proteins/genes that belong to the above functional groups were overexpressed. Concerning the apoptosis-related genes, induction of four genes and repression of two genes were observed. Finally two differentially regulated autophagic genes were observed. More detailed information about these data can be found in the hierarchical cluster illustrated in Fig. 5A. An interaction network analysis revealed a highly complex regulatory map in which all these proteins/genes are deeply interconnected (Supplemental Fig. 5, A, B, C, and D), highlighting the relevant role of these categories in the dynamics of the fungal response and cell death upon macrophage interaction.

View larger version (44K): [in this window] [in a new window]   FIG. 5. A, hierarchical clustering of selected C. albicans protein/gene expression profiles after macrophage interaction (1.5-fold up- or down-regulated). This visualization shows records in a dendrogram (a tree graph) based on the similarity between them. The clustering method used was unweighted pair group method with arithmetric mean (UPGMA) (unweighted average), and the similarity measure was done based on the Euclidean distance. Each column represents interaction times (1.5 and 3 h), and each row represents the expression ratios (log2) for a particular gene upon macrophage contact. Clustering was obtained by using the software Spotfire DecisionSite. Red represents expression ratios higher than 1, and green indicates ratios lower than 1. The degree of color saturation represents the amount of the expression ratio as indicated by the scale bar. Gray denotes missing values. Asterisks indicate genes differentially expressed after macrophage interaction with a ratio between 1.35- and 1.5-fold up- or down-regulated, and underlined genes represent RT-PCR values (data not shown). B, hypothetical model of yeast cell death after macrophage interaction. Different cell death pathways and their interdependency based on mammalian and S. cerevisiae knowledge are shown here. Genes marked in black correspond to C. albicans, and genes in green/blue correspond to S. cerevisiae/human orthologs, respectively. Blue boxes and arrows point out those pathways detected in mammals, whereas those in black indicate pathways detected in S. cerevisiae. Dotted arrows indicate hypothetical interrelations (for further details see the text). PKA, cAMP-dependent protein kinase.

	DISCUSSION

TOP ABSTRACT EXPERIMENTAL PROCEDURES RESULTS DISCUSSION REFERENCES

Proteomics studies enabled us to describe a total of 132 statistically differentially expressed protein species. Of these, 74 over- and underexpressed protein species were identified by MS, corresponding to 67 unique proteins (Fig. 4 and Table I) classified according to their biological function. Some of these differentially expressed proteins are present in a 2D reference hyphal map and are also recognized by serum from mice and patients with disseminated candidiasis, highlighting the importance of these proteins in the virulence of Candida (25, 46–49). It should be noted that certain proteins could not be represented due to a range of 2D PAGE technical limitations (i.e. molecular weight and proteins either too acidic, too basic, or too hydrophobic) and also due to the relative abundance of proteins (50, 51). To overcome these restrictions, more sensitive techniques such as 2D DIGE (52, 53) or non-gel proteomics could be used. Due to technical limitations of generating large proteome datasets, only a limited number of studies comparing differential gene and protein expression have been performed; a major obstacle is a lack of scalable database systems and computational tools (54). Integrating the information from selected pathways has provided us with a global view of the yeast response. Thus, a close correlation between genomics and proteomics datasets may indicate that genes for certain specific biological processes may require better transcriptional and translational synchronization for synergistic purposes and that the response may be more specific. The most relevant pathways related to this hostile environment are discussed below (Tables I and II and Supplemental Table III).

Metabolism and Energy—
The metabolic changes detected in C. albicans protein/gene expression levels belonging to different functional groups reflect how the yeast cells sense this hostile environment. As described previously by other genomics studies, the glyoxylate cycle enables microorganisms to grow on acetate or fatty acids as the sole carbon sources, reflecting the important adaptation of pathogenic microorganisms such as Mycobacterium tuberculosis and C. albicans (16, 55). Microorganisms that are able to achieve this cycle can produce energy and grow in lipid-rich environments such as host cells. A similar biological response was detected here using a proteomics strategy. Proteins belonging to the glyoxylate and tricarboxylic acid cycles were overexpressed indicating a dynamic yeast response to nutrient starvation inside the phagosome (56). The C. albicans proteomic response showed a clear down-regulation in the glycolytic route, indicating that the yeast surroundings are deficient in glucose. Moreover other up-regulated proteins may reflect an increase in the lipid and fatty acid ß oxidation metabolism. These results are in agreement with previous reports that have suggested that various lipids are released during the Candida-macrophage interaction (57, 58). Similar results were exhibited in our genomics study as well as by studies of Candida-phagocytes interaction using genomics approaches (16, 18).

Protein Fate—
From the proteomics results we can see a general overexpression of the proteins belonging to this pathway. The yeast proteasome is a multicatalytic/multifunctional proteinase complex necessary for the degradation of short lived, misfolded, ubiquitinated, and oxidized proteins (59, 60). Thus, the possible existence of non-functional/toxic proteins that the fungus needs to eliminate for its own survival could explain the greatly increased expression of the proteasomal proteins. This biological situation may be similar to other biochemical and physiological stimuli, such as perturbation in calcium homeostasis, redox status, or sugar/glucose deprivation that can disrupt endoplasmic reticulum homeostasis, impose stress to the endoplasmic reticulum, and subsequently lead to accumulation of unfolded or misfolded proteins (61). Together with the induction of these systems, an important number of folding proteins were also up-regulated. On the other hand, genomics reflects a different situation. The vast majority of the genes related to protein folding and stabilization, protein targeting, sorting, modification, and degradation as well as assembly of protein complexes were repressed at both time points. This discrepancy in the expression of genes/proteins belonging to this particular pathway may be explained due to a very early yeast transcript response and high stability of these proteins, taking into account their function stabilizing other proteins and their importance for the cell viability. These latter hypotheses also reinforce the utility of both genomics and proteomics approaches to discern a more complete biological sense of these experimental results.

Cellular Transport—
Proteomics studies reflect a combined response of over- and underexpressed mitochondrial transporters. The role of Por1p in apoptosis will be discussed later on. Genes corresponding to this nutrient acquisition pathway were found to be predominantly induced; briefly amino acid permeases (62), lipid transporters, and ion and oxygen transporters as well as permeases for carbon compounds (63) were found to be up-regulated. Few genes belonging to these permeases were found to be repressed, possibly reflecting a strict regulatory specialization, thus permitting the cell to respond to different biological conditions.

Cell Rescue, Defense, and Virulence—
As deduced from genomics and proteomics results, this pathway showed an upward tendency, indicating its important role in the yeast response after macrophage interaction. Different functional groups, such as oxidative stress and detoxification, DNA damage repair, and pathogenesis are included in this pathway. Reactive oxygen intermediates (ROI) and reactive nitrogen intermediates (RNI), generated by professional phagocytes as part of the antimicrobial burst, are thought to play a critical role in resistance to mucosal and systemic candidiasis (64, 65). Nevertheless microorganisms can sometimes overcome the production of ROI and RNI and avoid being killed by suppressing production of ROI and RNI (9, 66). The strategies used by C. albicans for the evasion of ROI and RNI are similar to those described for other microorganisms; they include enzymatic detoxification of reactive species, scavenging of species to remove them, iron sequestration, stress responses, and damage repair (67–69).

From our proteomics data sheet, several proteins that play an important role as antioxidants and detoxifying enzymes were identified. In contrast, C. albicans genes with antioxidant functions were found to be mostly down-regulated at 1.5 h. These different transcriptional responses may depend on the levels of the reactive species (70). In addition, the detoxification machinery of Candida is up-regulated at both time points but predominantly at the earlier time point, indicating the attempt of the yeast to neutralize and repair the oxidative damage caused by the reactive oxygen species of the macrophage.

Other important functional groups were only detected by our genomics approach. Among them, several genes related to DNA repair mechanisms were highly induced, predominantly at the earlier time point. A notable induction of genes belonging to pathogenesis was also appreciable at both time points.

Biogenesis of the Cell Wall and Fungal Cell Type Differentiation—
The yeast-to-hypha transition, hypha-associated factors, cell wall proteins, such as glycosylphosphatidylinositol (GPI)-anchored proteins, and hydrolytic enzymes have been described as factors involved in host-pathogen interactions (i.e. full morphogenesis, virulence, survival, and resistance to macrophages) (71–74). During this interaction, the yeast population has switched to hyphal forms at 1.5 h, increasing in amount over time. A number of genes known to be hypha-associated genes were found to be up-regulated as were a large number of genes associated with cell wall biogenesis and involved in virulence and invasive growth. Nevertheless in this hostile environment, the yeast could somehow be damaged by macrophages and could in some way arrest its growth and inhibit the filamentous formation as well as impair its survival and reduce its virulence as can be deduced by the repression of some genes belonging to fungal cell type differentiation and cell wall biogenesis.

Metal Ion Homeostasis—
The regulation of metal homeostasis, such as iron, may be of great importance to the ability of the microorganism to deal with oxidative stress because hydrogen peroxide can break down to form the highly reactive hydroxyl radical in the presence of a transition metal catalyst (69). Moreover iron acquisition is recognized to be a fundamental step in the infection process by pathogens because this essential nutrient is tightly sequestered by high affinity iron-binding proteins and, therefore, not readily available in mammalian hosts (75, 76). C. albicans is no exception and has evolved different mechanisms (secreted siderophores and/or high affinity uptake systems) to acquire iron from the host tissues (77, 78). Two homologous transmembrane ferric reductases were found to be chiefly induced at 1.5 h of interaction. In addition, the elevated expression of the copper homeostasis gene SLF1 is in agreement with the recent finding of the requirement of copper for high affinity iron import (79).

Experimentally Uncharacterized Genes—
The largest category of modulated genes corresponds to those without known homology that might play an important role in vivo and would be modulated (induced or repressed) under conditions relevant to pathogenesis. The genomics results showed a significant representation of unknown genes, 28% (67 of 239 genes), the majority of which are, at present, unique to C. albicans. A detailed study of these genes will be very interesting due to their possible role in Candida pathogenesis. Network analysis of unknown genes with S. cerevisiae orthologs (50%) revealed functional relationships between and within important regulatory modules (Supplemental Fig. 4, A and B).

Interconnection between Actin Cytoskeleton, Mitochondria, Autophagy, and Apoptosis in Candida during Phagocytosis—
After a thorough network analysis, our observations suggest the occurrence of several mechanisms that could lead to programmed cell death in Candida after phagocytosis (Supplemental Fig. 5A). We speculate that Candida cells could activate different cell death pathways after macrophage contact, similar to other external stimuli (nutrient limitation, heat, oxygen peroxide, and drug treatment among others). To establish this hypothesis, different proteins/genes implicated in PCD will be discussed using as reference the model yeast S. cerevisiae as well as the mammalian system. On this basis, we propose a hypothetical model of yeast cell death after macrophage interaction, summarized in Fig. 5B. Interactions between mitochondria and the actin cytoskeleton in budding yeast are critical for normal mitochondrial morphology, respiratory activity, motility, and inheritance (36). We observed a significant repression in genes related to the actin cytoskeleton and mitochondrial function, indicating a potential reduction in actin dynamics and altered mitochondrial gene/protein turnover (Supplemental Fig. 5, B and C). Two S. cerevisiae actin-regulatory genes, SLA1 and END3, closely interact with most of the actin-related genes detected in this study. A recent report indicates that cells expressing a mutated form of Sla1p or lacking End3p display markers of apoptosis, suggesting the relevant role of these interactions in the possible induction of apoptosis (80). Additionally overexpression of Cofilin 1 (Cof1p) in the context of reduced actin dynamics may have an important function during the initiation phase of apoptosis as described in mammals (81). The actin network diagram (Supplemental Fig. 5B) shows interconnection between Cofilin 1 and adenylate cyclase (CYR1), which participates in the Ras-cAMP signaling pathway. In yeast, this pathway plays a role in resistance to nutrient limitations and promotes cell aging (82, 83). In this work, two components of this pathway were detected as differentially expressed: FGR38 (putative ortholog of CYR1) was up-regulated, whereas PDE1 (low affinity cyclic nucleotide phosphodiesterase) was repressed. This suggests that a constitutive activation of the Ras-cAMP pathway may exist, leading to elevated cAMP levels inside the fungus, and that this accumulation may accelerate Candida cell death (84). Moreover various direct and indirect physical interactions observed in the genetic map between cAMP signaling and actin-related genes hint at a substantial link between both functional groups and may control Candida apoptosis as described in S. cerevisiae (85). Surprisingly and in relation to the findings mentioned above, we observed overexpression of the yeast voltage-dependent anion-selective channel (Por1p); this protein has been described as an inducer of apoptotic cell death (86). On the basis of all this evidence, we highlight the relevant link between actin cytoskeleton-mitochondria and apoptosis because a reduction in actin dynamics can lead to reduced mitochondrial membrane potential (_m) and to an open state of the mitochondrial membrane pore (Por1p), allowing the release of apoptogenic proteins from the mitochondria (35).

In regard to apoptotic signals, differences over time were detected, reflecting the importance of selecting several time points. At early time points, proapoptotic genes (IPF12606.3eoc, LCB1, and IPF12676) were induced with the exception of the metacaspase MCA1. The antiapoptotic gene BIR1 (baculovirus inhibitor of apoptosis) was induced, whereas CAP1 (key transcriptional regulator in the oxygen stress response and aging) was repressed (Fig. 5, A and B). This apparent contradictory result can be explained by Bir1p, a substrate for Nma111p (S. cerevisiae ortholog for IPF12606.3eoc): the antiapoptotic effect of Bir1p is antagonized by overexpression of Nma111p (87). In addition, repression of CAP1 plus the induction of LCB1, whose human ortholog is said to control de novo ceramide synthesis triggering apoptosis (88), may suggest a proapoptotic process. In contrast, also at early time points, down-regulation of the yeast MCA1, involved in the initiation of apoptosis, reflects a negative regulation of PCD (89). In conclusion, a dual yeast behavior (pro- and antiapoptotic signals) can be detected at 1.5 h, whereas at the later time only proapoptotic signals are identified, reflecting the aggressive environment that surrounds them.

Lastly induction of autophagic cell death and a co-regulation of autophagy and apoptosis may be suggested by induction of UTH1 (S. cerevisiae ortholog for SUN42) and Beclin 1 (BECN1, human ortholog for VPS30), indicating a high degree of flexibility in the response of a cell to changes in environmental conditions (37, 90, 91) (Supplemental Fig. 5D). In conclusion, all the proteomics data combined with the genomics information described in this work reflect the biological situation of the fungus inside macrophages, which depends on the expression of a specific group of protein/genes, which in turn triggers the adaptation to this environment.

These results provide evidence of the importance of certain fungal pathways: metabolism and energy, protein fate, cellular transport, cell rescue, defense and virulence, biogenesis of the cell wall and fungal cell type differentiation, metal homeostasis, and uncharacterized genes/proteins. Network analyses have led us to suggest a hypothetical model of Candida cell death after macrophage interaction. Although further studies are needed to reveal the mechanistic basis of cell death decisions in this organism after macrophage contact, the specific role of actin and mitochondria interactions in the possible activation of different cell death pathways in Candida during phagocytosis are currently under investigation.

	ACKNOWLEDGMENTS

 
We thank M. L. Hernáez, M. D. Gutierrez, and J. García (from Centro de Genómica y Proteómica, Universidad Complutense and Parque Científico de Madrid) and L. M. Alonso (from Centro de Microscopía y Citometría) for excellent technical support. We also thank L. Monteoliva, R. García, L. Castillo, and E. Valentín for helpful and fruitful discussions regarding microarray experiments.

	FOOTNOTES

 
Received, June 5, 2006, and in revised form, November 1, 2006.

Published, MCP Papers in Press, December 12, 2006, DOI 10.1074/mcp.M600210-MCP200

¹ The abbreviations used are: PCD, programmed cell death; 2D, two-dimensional; BP, band pass; CGD, Candida Genome Database; CWH, calcofluor white M2R; FunSpec, Functional Specification; NO^–, nitric oxide-deficient; GO, Gene Ontology; MIPS, Munich Information Center for Protein Sequences; RNI, reactive nitrogen intermediates; ROI, reactive oxygen intermediates; GPI, glycosylphosphatidylinositol; C-compound, carbon-compound.

^* This work was supported in part by Grant BIO-2003-00030 from Comisión Interministerial de Ciencia y Tecnología and Grant MRTN CT-2004-512481 from the European Union and the Fundación Ramón Areces of Spain. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

^S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.

The data discussed in this publication have been deposited in NCBIs Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession number GSE4794.

Recipient of a fellowship from the Ministerio de Educación y Ciencia.

Director of the Merck Sharp and Dohme Special Chair in Genomics and Proteomics.

^¶ To whom correspondence should be addressed. Tel.: 34-91-394-1744; Fax: 34-91-394-1745; E-mail: rosaliad{at}farm.ucm.es

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