Comparative Proteomic Analysis of Candida albicans and Candida glabrata
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Candida albicans and Candida glabrata are the two most common opportunistic pathogens which are part of the normal flora in humans. Clinical diagnosis of infection by these organisms is still largely based on culturing of these organisms. In order to identify species-specific protein expression patterns, we carried out a comparative proteomic analysis of C. albicans and C. glabrata.
We used “isobaric tag for relative and absolute quantitation” (iTRAQ) labeling of cell homogenates of C. albicans and C. glabrata followed by LC-MS/MS analysis using a quadrupole time-of-flight mass spectrometer. The MS/MS data was searched against a protein database comprised of known and predicted proteins reported from these two organisms. Subsequently, we carried out a bioinformatics analysis to group orthologous proteins across C. albicans and C. glabrata and calculated protein abundance changes between the two species.
Results and Conclusions
We identified 500 proteins from these organisms, the large majority of which corresponded to predicted transcripts. A number of proteins were observed to be significantly differentially expressed between the two species including enolase (Eno1), fructose-bisphosphate aldolase (Fba1), CCT ring complex subunit (Cct2), pyruvate kinase (Cdc19), and pyruvate carboxylase (Pyc2). This study illustrates a strategy for investigating protein expression patterns across closely related organisms by combining orthology information with quantitative proteomics.
KeywordsBiomarker Candidiasis Candidemia Medical mycology Molecular diagnostics Fungal infection Quantitative proteomics
Microbial type culture collection
Open reading frame
Candida albicans and Candida glabrata are the commonest opportunistic pathogens of human mucosa and blood [1, 2, 3]. Clinical disorders with immunosuppression including those with HIV and patients on chemotherapy provide an opportunity for the otherwise harmless C. glabrata to cause candidiasis. As a result, C. glabrata is now second to C. albicans among the various Candida species isolated from patients with candidiasis [4, 5]. High prevalence of C. glabrata in the oral flora of patients with diabetes mellitus, advanced cancers, and HIV infection has been reported in several studies [6, 7, 8, 9, 10, 11]. Often, high mortalities are associated with C. glabrata infections especially among cancer patients and bone marrow transplant patients [12, 13, 14, 15, 16]. The majority of C. glabrata isolates are reported to be resistant to both fluconazole and itraconazole , whereas most isolates of C. albicans are sensitive to these antifungal agents [9, 10]. Thus, it is even more important to diagnose C. glabrata infections early and accurately so that the appropriate therapy can be initiated [17, 18].
Although C. albicans and C. glabrata are often co-isolated from mucosal lesions, not many investigations have focused to study these organisms together. When together, they exhibit more severe symptoms and pose a greater difficulty in treatment [19, 20]. Molecular diagnostic procedures described thus far to distinguish between these two species cannot easily be incorporated as routine diagnostics as they require specialized equipment and expertise [21, 22, 23, 24, 25]. Although these organisms cannot be distinguished based on the clinical presentation of the patients, they differ in many aspects of genetic and physiological attributes. C. albicans thrives as a diploid [26, 27] and exhibits considerable heterozygosity while C. glabrata exists as a haploid genome . Unlike C. albicans, C. glabrata cannot form true hyphae . C. glabrata does not produce extracellular proteinases including secreted aspartyl proteinases  which are considered as virulent factors of Candida species [31, 32, 33, 34, 35]. C. albicans lacking superoxide dismutases are unable to thrive in the presence of macrophages . Phospholipases that have been implicated in the virulence of C. albicans [37, 38, 39, 40, 41] also do not seem to play a major role in C. glabrata infection [42, 43]. C. albicans and C. glabrata are also known to differ in their host response profiles. Infection by C. glabrata induces expression of high levels of granulocyte monocyte colony-stimulating factor in the cells of oral mucosa, while C. albicans is not known to elicit such a response. On the other hand, C. albicans infection produces a strong IL-8 response, whereas it is elicited only weakly in the case of C. glabrata-infected cells [44, 45, 46]. C. glabrata is often resistant to several antimicrobial proteins secreted by the host such as beta defensins [47, 48, 49], histatins, and magainins [50, 51, 52]. A substantial variation among gene families involved in cell wall formation, transport, and sexual reproduction has been reported by comparing genomes of different pathogenic species of Candida and related yeasts . Interestingly, a phylogenetic comparison of sequenced yeast genomes showed that C. glabrata belongs to “Saccharomyces clade” while most other Candida species including C. albicans belong to “Candida clade” . The existence of distinct variation in diverse clinical features indicates the probable differences in the mechanisms of infection and adaptations to overcome host defense mechanisms and antifungal drugs. The availability of genome sequence information for both C. albicans  and C. glabrata  now provides a scaffold for proteomic investigations to understand distinct biological traits of these organisms at the molecular level. A comparative proteomic profile of C. albicans and C. glabrata should also provide information on differentially expressed proteins in these organisms that could serve as candidate molecules to distinguish these two species by protein-based tests such as ELISA. We describe a quantitative proteomic investigation using isobaric tag for relative and absolute quantitation (iTRAQ) reagents and mass spectrometry to identify the differentially expressed proteins in C. albicans and C. glabrata.
Materials and Methods
Culturing of C. albicans and C. glabrata
C. albicans (MTCC 1637) and C. glabrata (MTCC 6507) cultures were obtained from the Microbial Type Culture Collection & Gene Bank resource in Chandigarh, India. The cells were cultured in identical conditions in 2% YPD broth at 30°C with shaking for 6 h. The cells were harvested at the same density by centrifuging at 2,000×g for 10 min, and pellets were washed using sterile distilled water. Cell pellets were stored at −80°C until further analysis.
iTRAQ Labeling and Strong Cation Exchange Fractionation
Mass Spectrometry and Data Analysis
Mass spectrometry analysis was carried out using a reversed-phase liquid chromatography system (RP-LC) interfaced with a quadrupole time-of-flight mass spectrometer (QSTAR/Pulsar, Applied Biosystems). MS/MS data was acquired by online analysis of peptides eluted using 5–40% acetonitrile in 0.1% formic acid for 30 min with a flow rate of 300 nl/min. The MS/MS spectra were acquired in a data-dependent manner from m/z 350 to 1,200 Da targeting three most abundant ions in every survey scan for MS/MS with a dynamic exclusion time of 45 s. Twenty percent higher collision energy was applied during MS/MS scan with a charge state-dependent collision energy selection criteria.
We created a custom protein database of non-redundant protein sequences of C. albicans and C. glabrata by combining ORFs of C. albicans from the Candida Genome Database (http://candidagenome.org/), ORFs of C. glabrata from databases of Genolevures (http://genolevures.org/download.html), and protein sequences of these organisms from NCBI RefSeq database. This customized database contained 39,196 protein sequences with 28,152 protein sequences from C. albicans and 11,044 from C. glabrata. We used ProteinPilot software (V 3.0 Applied Biosystems) for data analysis, which includes Paragon algorithm for peptide identification and Pro Group algorithm for summarizing proteins. Search parameters included iTRAQ labeling at N-terminus and lysine residues, cysteine modification by MMTS as fixed modifications, and trypsin as a protease. Proteins identified with >95% confidence or Protscore >1.3 as determined by Pro Group algorithm were used for further analysis. We used false discovery rate analysis by PSPEP software that is in-built into ProteinPilot 3.0. The data generated by LC-MS/MS analysis of 30 SCX fractions were searched against the custom protein database, which includes protein sequences that belong to C. albicans and C. glabrata filtered from RefSeq, Candida Genome Database and ORF database of Genolvures using ProteinPilot 3.0 software. Peptides identified in this study are catalogued in Supplementary Table 1. A detected protein threshold of 1.3 which corresponds to a confidence of 95% was used in identification and quantitation of proteins.
Results and Discussion
Substantial differences exist between the genomes and proteomes of C. albicans and C. glabrata, which influences protein identification in these two species. C. albicans is diploid with considerable heterozygosity, whereas C. glabrata thrives as a simple haploid. C. albicans has 12,015 genes encoded by its heterozygous diploid genome including alleles as described by genome sequencing consortium [26, 27], whereas C. glabrata has only over 5,000 genes as described by Dujon et al. . It was also observed that the proteome diversity of C. albicans is further enhanced by ambiguous usage of CUG codon to randomly incorporate either amino acid leucine or serine in the proteome, whereas such codon alternation is not reported in C. glabrata [55, 56, 57].
A partial list of proteins differentially expressed in Candida albicans
Relative expression (C. glabrata/C. albicans)
Common tryptic peptides (≤95% confidence)
Common tryptic peptides (≥95% confidence)
Translation elongation factor Eft2 [C. albicans]
MVPTSDK, NMSVIAHVDHGK, AYLPVNESFGFTGELR, AVQYLNEIK, AGIISAAK
KFGVDK, KIWCFGPDGNGPNLVVDQTK, FYAFGRVFAGTVK
Hypothetical protein CAGL0A03234g [C. glabrata]
Enolase Eno1 [C. albicans]
Hypothetical protein CAGL0I02486g [C. glabrata]
Pyruvate kinase Cdc19 [C. albicans]
GVNLPGTDVDLPALSEK, VHMIFASFIR, GRPLAIALDTK, AEVSDVGNAILDGADCVMLSGETAK
Hypothetical protein CAGL0E05610g [C. glabrata]
Fructose-bisphosphate aldolase Fba1 [C. albicans]
Hypothetical protein CAGL0L02497g [C. glabrata]
Fatty acid synthase alpha subunit CaO19.13370 [C. albicans]
Hypothetical protein CAGL0E06138g [C. glabrata]
Likely cytosolic ribosomal protein Rps24 [C. albicans]
Hypothetical protein CAGL0J03234g [C. glabrata]
Cytoplasmic threonyl-tRNA synthetase Ths1 (Cao19.5685) [C. albicans]
Hypothetical protein CAGL0M12991g [C. glabrata]
RAN-like GTP binding protein Gsp1 [C. albicans]
VCENIPIVLCGNK, NLQYYDISAK, LVLVGDGGTGK, FDVWDTAGQEK
Hypothetical protein CAGL0I00594g [C. glabrata]
Histone H2B Htb1 [C. albicans]
LILPGELAK, AMSIMNSFVNDIFER, QTHPDTGISQK
Hypothetical protein CAGL0K11462g [C. glabrata]
Dihydroxyacid dehydratase Ilv3 (cao19.4040) [C. albicans]
Hypothetical protein CAGL0B03993g [C. glabrata]
A partial list of proteins with higher levels of expression in Candida glabrata
Relative expression (C. glabrata/C. albicans)
Common tryptic peptides (≤95% confidence)
Common tryptic peptides (≥95% confidence)
Likely cobalamin-independent methionine synthase Met6 [C. albicans]
Hypothetical protein CAGL0I04994g [C. glabrata]
Likely adenylylsulfate kinase Met14 [C. albicans]
Hypothetical protein CAGL0L02321g [C. glabrata]
Adenosine triphosphatase Pma1 [C. albicans]
GYLVAMTGDGVNDAPSLK, SAADIVFLAPGLSAIIDALK, GAPLFVLK, KQAIVQK
Hypothetical protein CAGL0A00495g [C. glabrata]
Likely 26 S proteasome regulatory particle ATPase Rpt1 [C. albicans]
Hypothetical protein CAGL0E06490g [C. glabrata]
Mitochondrial ketol-acid reductoisomerase Ilv5 [C. albicans]
TLYFSHGFSPVFK, DNGLNVIIGVR, YGMDYMYDACSTTAR
Hypothetical protein CAGL0B03047g [C. glabrata]
Histone H3 Hht2 [C. albicans]
QTARKSTGGK, KSTGGK, STGGKAPR, KQLASK, KLPFQR
Hypothetical protein CAGL0M06655g [C. glabrata]
Heat shock protein Hsp90 [C. albicans]
EDQLEYLEEK, TKPLWTR, KNNIK
Hypothetical protein CAGL0L00495g [C. glabrata]
Cytosolic NADP-specific isocitrate dehydrogenase Idp2 [C. albicans]
NILGGTVFR, CATITPDEAR, LIDDMVAQMLK
Hypothetical protein CAGL0B04917g [C. glabrata]
Mitochondrial NADP-specific isocitrate dehydrogenase Idp1 [C. albicans]
Hypothetical protein CAGL0D00770g [C. glabrata]
Beta-tubulin Tub2 [C. albicans]
Hypothetical protein CAGL0K12650g [C. glabrata]
The peptides/proteins which are unique to one organism and/or differentially expressed between two organisms should be attractive candidates as potential biomarkers for the diagnosis and monitoring of treatment. Development of similar rapid diagnostic methods have been investigated in order to identify infections by Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, and Plasmodium malariae using antibodies generated against merozoite surface protein 1, which are highly expressed among these pathogens [61, 62]. Similarly, histidine-rich protein-2 (HRP-2) of P. falciparum and lactate dehydrogenase (Pv-pLDH) of P. vivax have been targeted in the FK80 kit (Standard Diagnostics, Korea) to differentially diagnose malaria infection caused by P. falciparum and P. vivax . Another successful attempt has been the development of ELISA-based antigen capture assays targeting non-structural protein (NS1) for laboratory detection of acute primary and secondary dengue [61, 64, 65]. Our study provides a list of proteins which are uniquely or differentially expressed in C. albicans and C. glabrata which can be used in the development of such diagnostic tests. This study also demonstrates a comparative proteomics platform which will facilitate the discovery of several such candidate target molecules with diagnostic potential in many other human diseases with multiple etiologies.
Generally, quantitative proteomic studies are carried out to compare the proteomes of the same species under more than one condition. Often, there is a need to compare two distinct but related proteomes. The orthology-based approach has been used to profile proteomes of organisms with unsequenced genomes , but not for differential profiling of proteomes between two closely related organisms. Our study outlines a strategy to investigate protein expression patterns across closely related organisms, by combining orthology information with quantitative proteomics. Such studies should help identify mechanisms responsible for distinct biological features of clinical importance among closely related organisms as exemplified in C. albicans and C. glabrata. As discussed previously, although C. albicans and C. glabrata cause similar infections, they exhibit distinct biological features. As most proteins from C. albicans and C. glabrata are yet to be investigated in detail for their role in various biological processes, our study provides the basis to guide such investigations in the future.
We thank the Department of Biotechnology of the Government of India for research support to the Institute of Bioinformatics, Bangalore. TSKP is supported by research grants including a Young Investigator award from the Department of Biotechnology, India. We thank the Council for Scientific and Industrial Research (CSIR), India for the research support to HKCJ, HP, and NP and the University Grants Commission (UGC), India for the research support to SR.
Conflict of Interest
The authors have declared no conflict of interest.