Abstract
Introduction
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.
Methods
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.
Similar content being viewed by others
Introduction
Candida albicans and Candida glabrata are the commonest opportunistic pathogens of human mucosa and blood [1–3]. 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 [6–11]. Often, high mortalities are associated with C. glabrata infections especially among cancer patients and bone marrow transplant patients [12–16]. The majority of C. glabrata isolates are reported to be resistant to both fluconazole and itraconazole [11], 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 [21–25]. 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 [28]. Unlike C. albicans, C. glabrata cannot form true hyphae [29]. C. glabrata does not produce extracellular proteinases including secreted aspartyl proteinases [30] which are considered as virulent factors of Candida species [31–35]. C. albicans lacking superoxide dismutases are unable to thrive in the presence of macrophages [36]. Phospholipases that have been implicated in the virulence of C. albicans [37–41] 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 [44–46]. C. glabrata is often resistant to several antimicrobial proteins secreted by the host such as beta defensins [47–49], histatins, and magainins [50–52]. 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 [53]. 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” [53]. 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 [26] and C. glabrata [28] 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
Cell pellets were dissolved in 0.5% SDS and homogenized. The samples were subsequently sonicated using ultrasonicator (Branson Ultrasonics) and centrifuged at 10,000×g for 10 min at 4°C. Clear supernatant was collected in fresh vials. Protein estimation was carried out using Lowry’s assay. Further, two samples were normalized based on total protein amount. Normalization was verified by SDS-PAGE. Protein samples were reduced, cysteine-blocked, and digested with trypsin. iTRAQ (Applied Biosystems Catalog No. 4352135) labeling of peptides was carried out according to manufacturer’s protocol essentially as described earlier [54]. Briefly, 80 μg of protein extract from each sample was treated with 2 μl of reducing agent (tris(2-carboxyethyl) phosphine) at 60°C for 1 h and alkylated with 1 μl of methyl methanethiosulfonate (MMTS)) for 10 min at room temperature. Proteins were then digested using sequencing grade trypsin (Promega) (1:10) overnight at 37°C. Peptides in duplicate from C. albicans sample were labeled using iTRAQ reagents containing reporters 114 and 115 and peptides in duplicate from C. glabrata sample were labeled using iTRAQ reagents containing reporters 116 and 117 (Fig. 1). Labeling was carried out for 2 h at room temperature. After labeling, the peptides from the two samples were pooled and fractionated using strong cation exchange chromatography on Polysulfoethyl A column (PolyLC, Columbia, MD) (100 × 2.1 mm, 5 μm particles with 300 Å pores) using an LC Packing HPLC system connected to a Probot fraction collector. Thirty SCX fractions were collected at a flow rate of 0.2 ml/min using a 70-min gradient of KCl from 0 to 350 mM concentration in 10 mM potassium phosphate buffer, 25% acetonitrile (pH 2.85). The fractions were dried and reconstituted in 10 μl of 2% trifluoroacetic acid before mass spectrometric analysis.
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
In this study, we applied a quantitative proteomic approach to identify differentially expressed proteins in C. albicans and C. glabrata. Cell lysates of C. albicans and C. glabrata were labeled with iTRAQ reagents as shown in Fig. 1. We prepared technical replicates by labeling peptides derived from C. albicans with iTRAQ reagents giving rise to reporter ions at m/z of 114 and 115 and C. glabrata derived peptides with those yielding reporter ions at m/z of 116 and 117. From a total of 24,196 MS/MS spectra, using a ProtScore >1.3 (95% confidence) cutoff, we identified 500 proteins among these organisms, 386 of which belonged to C. albicans and 114 proteins belonged to C. glabrata (Supplementary Tables 2 and 3). As tryptic peptide profile was different for individual proteins among protein homolog pairs of these two species, the ProteinPilot 3.0 software could not cluster most of protein orthologs as “pairs”. In this study, it clustered only 24 protein orthologs among the 500 identified proteins belonging to both species. Thus, quantitation data determined by the ProteinPilot 3.0 software were biologically relevant for only these 24 ortholog proteins. Therefore, we performed in-house bioinformatics analysis to determine protein pairs and their relative expression levels based on the relative expression profile of common tryptic peptides as follows. For each protein identified, we determined its counterpart in other species under investigation using a reciprocal BLAST analysis. We identified another set of 35 ortholog proteins in addition to 24 ortholog proteins clustered by ProteinPilot 3.0. At least one common tryptic peptide was observed for these 59 ortholog pairs identified in this study. We calculated weighted average ratio for relative expression level of peptides for these 59 protein pairs using the formula used by ProteinPilot 3.0 software. The values of reporter ion ratio 115:114 for all of these protein pairs were found to be near 1 as expected with an average of 1.05 ± 0.08. Also, values of (116:114)/(117:114) were also expected to be near 1 and found with an average of 0.93 ± 0.1. MS/MS spectra of representative peptides belonging to four differentially expressed protein pairs are given in Fig. 2. The relative intensities of reporter ions from these spectra are shown in the insets. As shown in the Fig. 2, reporter ion intensities of individual common peptides belonging to same ortholog protein pairs were found to be in agreement with each other. Intensities of these reporter ions among technical replicates were also found to be in the same range. The mass spectrometry data generated in this study has been submitted to Peptidome resource (http://www.ncbi.nlm.nih.gov/pepdome).
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. [28]. 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 [55–57].
This protein pair analysis identified several proteins which are expressed only in either C. albicans or C. glabrata. MS/MS spectra of these peptides showed only reporter ions belonging to either C. albicans or C. glabrata (Fig. 3). Proteins which are found to be expressed only in C. albicans included 3-phosphoglycerate kinase (Pgk1), pyruvate decarboxylase (CaO19.2877), immunogenic alcohol dehydrogenase (Adh1), and heat shock protein 60 (Hsp60). It is interesting to note that several proteins involved in glucose metabolism including gluconeogenesis are highly expressed in C. albicans. There is also a marked difference in the expression of serine-, lysyl-, leucyl-, and valyl-tRNA synthetases and several ribosomal subunits, which is significant in the context of differential usage of specific codons in C. albicans or C. glabrata [53]. Proteins expressed unique to C. glabrata included hypothetical proteins CAGL0I06600g (similar to ubiquitin-specific proteinase Ubp3), CAGL0I01496g (similar to heat shock protein Ssc1), CAGL0E03916g (similar to glycerol kinase Gut1), and CAGL0L12364g (similar to acetyl-CoA C-acetyltransferaseYPL028w).
Analysis of expression levels of orthologous proteins showed that 19 proteins of C. albicans were found to be higher (>1.5-fold) in abundance when compared to their corresponding orthologs in C. glabrata (Table 1). Conversely, expression levels of 12 C. glabrata proteins were found to be higher when compared to their counterparts in C. albicans (Table 2). The relative protein expression ratios (i.e., 116:114 or 117:114) for the remaining 28 protein pairs ranged from 0.75 to 1.5 (Fig. 4). Proteins with higher expression levels in C. albicans included enolase (Eno1), fructose-bisphosphate aldolase (Fba1), dihydroxyacid dehydratase (Ilv3), glutathione oxidoreductase (Glr1), ubiquitin-activating enzyme (Uba1), and chaperonin CCT ring complex subunit (Cct2). Interestingly, fructose-bisphosphate aldolase has already been investigated as a target in C. albicans using a methionine/cysteine-conditional mutant [58]. Proteins expressed higher in C. glabrata include several hypothetical proteins CAGL0F06941g (similar to pyruvate carboxylase fragment Pyc2), CAGL0L00495g (similar to heat shock protein Hsp90), CAGL0A00495g (similar to adenosine triphosphatase Pma1), CAGL0L02321g (likely adenylylsulfate kinase Met14), CAGL0B04917g (similar to cytosolic NADP-specific isocitrate dehydrogenase Idp2), and CAGL0D00770g (similar to mitochondrial NADP-specific isocitrate dehydrogenase Idp1). Overexpression of Hsp90 in C. glabrata is interesting in the light of its established role in the emergence of azole drug resistance. It was shown that loss of Hsp90 function in yeast and C. albicans resulted in the decreased resistance to antifungal drugs including echinocandins [59, 60].
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 [63]. 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.
Conclusions
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 [66], 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.
Abbreviations
- MTCC:
-
Microbial type culture collection
- ORF:
-
Open reading frame
References
Pfaller MA, Diekema DJ. Rare and emerging opportunistic fungal pathogens: concern for resistance beyond Candida albicans and Aspergillus fumigatus. J Clin Microbiol. 2004;42(10):4419–31.
Vazquez JA. Options for the management of mucosal candidiasis in patients with AIDS and HIV infection. Pharmacotherapy. 1999;19(1):76–87.
Pfaller MA. Nosocomial candidiasis: emerging species, reservoirs, and modes of transmission. Clin Infect Dis. 1996;22 Suppl 2:S89–94.
Wingard JR. Importance of Candida species other than Candida albicans as pathogens in oncology patients. Clin Infect Dis. 1995;20(1):115–25.
Fidel Jr PL, Vazquez JA, Sobel JD. Candida glabrata: review of epidemiology, pathogenesis, and clinical disease with comparison to C. albicans. Clin Microbiol Rev. 1999;12(1):80–96.
Kadir T, Pisiriciler R, Akyuz S, Yarat A, Emekli N, Ipbuker A. Mycological and cytological examination of oral candidal carriage in diabetic patients and non-diabetic control subjects: thorough analysis of local aetiologic and systemic factors. J Oral Rehabil. 2002;29(5):452–7.
Canuto MM, Rodero FG, Ducasse VOD, et al. Determinants for the development of oropharyngeal colonization or infection by fluconazole-resistant Candida strains in HIV-infected patients. Eur J Clin Microbiol Infect Dis. 2000;19(8):593–601.
Fongsmut T, Deerochanawong C, Prachyabrued W. Intraoral Candida in Thai diabetes patients. J Med Assoc Thai. 1998;81(6):449–53.
Redding SW, Kirkpatrick WR, Coco BJ, et al. Candida glabrata oropharyngeal candidiasis in patients receiving radiation treatment for head and neck cancer. J Clin Microbiol. 2002;40(5):1879–81.
Redding SW, Dahiya MC, Kirkpatrick WR, et al. Candida glabrata is an emerging cause of oropharyngeal candidiasis in patients receiving radiation for head and neck cancer. Oral Surg Oral Med Oral Pathol Oral Radiol Endo. 2004;97(1):47–52.
Bagg J, Sweeney MP, Lewis MA, et al. High prevalence of non-albicans yeasts and detection of anti-fungal resistance in the oral flora of patients with advanced cancer. Palliat Med. 2003;17(6):477–81.
Clark TA, Hajjeh RA. Recent trends in the epidemiology of invasive mycoses. Curr Opin Infect Dis. 2002;15(6):569–74.
Anaissie EJ, Vartivarian SE, Abi-Said D, et al. Fluconazole versus amphotericin B in the treatment of hematogenous candidiasis: a matched cohort study. Am J Med. 1996;101(2):170–6.
Goodman JL, Winston DJ, Greenfield RA, et al. A controlled trial of fluconazole to prevent fungal infections in patients undergoing bone marrow transplantation. N Engl J Med. 1992;326(13):845–51.
Krcmery Jr V, Oravcova E, Spanik S, et al. Nosocomial breakthrough fungaemia during antifungal prophylaxis or empirical antifungal therapy in 41 cancer patients receiving antineoplastic chemotherapy: analysis of aetiology risk factors and outcome. J Antimicrob Chemother. 1998;41(3):373–80.
Hajjeh RA, Sofair AN, Harrison LH, et al. Incidence of bloodstream infections due to Candida species and in vitro susceptibilities of isolates collected from 1998 to 2000 in a population-based active surveillance program. J Clin Microbiol. 2004;42(4):1519–27.
Willocks L, Leen CL, Brettle RP, Urquhart D, Russell TB, Milne LJ. Fluconazole resistance in AIDS patients. J Antimicrob Chemother. 1991;28(6):937–9.
Hitchcock CA, Pye GW, Troke PF, Johnson EM, Warnock DW. Fluconazole resistance in Candida glabrata. Antimicrob Agents Chemother. 1993;37(9):1962–5.
Redding SW, Zellars RC, Kirkpatrick WR, et al. Epidemiology of oropharyngeal Candida colonization and infection in patients receiving radiation for head and neck cancer. J Clin Microbiol. 1999;37(12):3896–900.
Redding SW. The role of yeasts other than Candida albicans in oropharyngeal candidiasis. Curr Opin Infect Dis. 2001;14(6):673–7.
Panackal AA, Gribskov JL, Staab JF, Kirby KA, Rinaldi M, Marr KA. Clinical significance of azole antifungal drug cross-resistance in Candida glabrata. J Clin Microbiol. 2006;44(5):1740–3.
Valerio HM, Weikert-Oliveira Rde C, Resende MA. Differentiation of Candida species obtained from nosocomial candidemia using RAPD-PCR technique. Rev Soc Bras Med Trop. 2006;39(2):174–8.
Santos MA, el-Adlouni C, Cox AD, Luz JM, Keith G, Tuite MF. Transfer RNA profiling: a new method for the identification of pathogenic Candida species. Yeast. 1994;10(5):625–36.
Essendoubi M, Toubas D, Bouzaggou M, Pinon JM, Manfait M, Sockalingum GD. Rapid identification of Candida species by FT-IR microspectroscopy. Biochim Biophys Acta. 2005;1724(3):239–47.
Gupta N, Haque A, Lattif AA, Narayan RP, Mukhopadhyay G, Prasad R. Epidemiology and molecular typing of Candida isolates from burn patients. Mycopathologia. 2004;158(4):397–405.
Jones T, Federspiel NA, Chibana H, et al. The diploid genome sequence of Candida albicans. Proc Natl Acad Sci USA. 2004;101(19):7329–34.
van het Hoog M, Rast TJ, Martchenko M, et al. Assembly of the Candida albicans genome into sixteen supercontigs aligned on the eight chromosomes. Genome Biol. 2007;8(4):R52.
Dujon B, Sherman D, Fischer G, et al. Genome evolution in yeasts. Nature. 2004;430(6995):35–44.
Lachke SA, Joly S, Daniels K, Soll DR. Phenotypic switching and filamentation in Candida glabrata. Microbiology. 2002;148(Pt 9):2661–74.
Chakrabarti A, Nayak N, Talwar P. In vitro proteinase production by Candida species. Mycopathologia. 1991;114(3):163–8.
Magee BB, Hube B, Wright RJ, Sullivan PJ, Magee PT. The genes encoding the secreted aspartyl proteinases of Candida albicans constitute a family with at least three members. Infect Immun. 1993;61(8):3240–3.
Monod M, Togni G, Hube B, Sanglard D. Multiplicity of genes encoding secreted aspartic proteinases in Candida species. Mol Microbiol. 1994;13(2):357–68.
Gilfillan GD, Sullivan DJ, Haynes K, Parkinson T, Coleman DC, Gow NA. Candida dubliniensis: phylogeny and putative virulence factors. Microbiology. 1998;144(Pt 4):829–38.
Zaugg C, Borg-Von Zepelin M, Reichard U, Sanglard D, Monod M. Secreted aspartic proteinase family of Candida tropicalis. Infect Immun. 2001;69(1):405–12.
Hube B, Naglik J. Candida albicans proteinases: resolving the mystery of a gene family. Microbiology. 2001;147(Pt 8):1997–2005.
Frohner IE, Bourgeois C, Yatsyk K, Majer O, Kuchler K. Candida albicans cell surface superoxide dismutases degrade host-derived reactive oxygen species to escape innate immune surveillance. Mol Microbiol. 2009;71(1):240–52.
Barrett-Bee K, Hayes Y, Wilson RG, Ryley JF. A comparison of phospholipase activity, cellular adherence and pathogenicity of yeasts. J Gen Microbiol. 1985;131(5):1217–21.
Banno Y, Yamada T, Nozawa Y. Secreted phospholipases of the dimorphic fungus, Candida albicans; separation of three enzymes and some biological properties. Sabouraudia. 1985;23(1):47–54.
Cole GT, Lynn KT, Seshan KR. An animal model for oropharyngeal, esophageal and gastric candidosis. Mycoses. 1990;33(1):7–19.
Ibrahim AS, Mirbod F, Filler SG, et al. Evidence implicating phospholipase as a virulence factor of Candida albicans. Infect Immun. 1995;63(5):1993–8.
Takahashi M, Banno Y, Nozawa Y. Secreted Candida albicans phospholipases: purification and characterization of two forms of lysophospholipase-transacylase. J Med Vet Mycol. 1991;29(3):193–204.
Samaranayake YH, MacFarlane TW, Samaranayake LP, Aitchison T. The in vitro proteolytic and saccharolytic activity of Candida species cultured in human saliva. Oral Microbiol Immunol. 1994;9(4):229–35.
Al-Rawi N, Kavanagh K. Characterisation of yeasts implicated in vulvovaginal candidosis in Irish women. Br J Biomed Sci. 1999;56(2):99–104.
Schaller M, Mailhammer R, Grassl G, Sander CA, Hube B, Korting HC. Infection of human oral epithelia with Candida species induces cytokine expression correlated to the degree of virulence. J Invest Dermatol. 2002;118(4):652–7.
Li L, Dongari-Bagtzoglou A. Oral epithelium–Candida glabrata interactions in vitro. Oral Microbiol Immunol. 2007;22(3):182–7.
Li L, Kashleva H, Dongari-Bagtzoglou A. Cytotoxic and cytokine-inducing properties of Candida glabrata in single and mixed oral infection models. Microb Pathog. 2007;42(4):138–47.
Abiko Y, Saitoh M, Nishimura M, Yamazaki M, Sawamura D, Kaku T. Role of beta-defensins in oral epithelial health and disease. Med Mol Morphol. 2007;40(4):179–84.
Feng Z, Jiang B, Chandra J, Ghannoum M, Nelson S, Weinberg A. Human beta-defensins: differential activity against candidal species and regulation by Candida albicans. J Dent Res. 2005;84(5):445–50.
Vylkova S, Nayyar N, Li W, Edgerton M. Human beta-defensins kill Candida albicans in an energy-dependent and salt-sensitive manner without causing membrane disruption. Antimicrob Agents Chemother. 2007;51(1):154–61.
Helmerhorst EJ, Venuleo C, Beri A, Oppenheim FG. Candida glabrata is unusual with respect to its resistance to cationic antifungal proteins. Yeast. 2005;22(9):705–14.
Helmerhorst EJ, Venuleo C, Sanglard D, Oppenheim FG. Roles of cellular respiration, CgCDR1, and CgCDR2 in Candida glabrata resistance to histatin 5. Antimicrob Agents Chemother. 2006;50(3):1100–3.
Beckloff N, Laube D, Castro T, et al. Activity of an antimicrobial peptide mimetic against planktonic and biofilm cultures of oral pathogens. Antimicrob Agents Chemother. 2007;51(11):4125–32.
Butler G, Rasmussen MD, Lin MF, et al. Evolution of pathogenicity and sexual reproduction in eight Candida genomes. Nature. 2009;459(7247):657–62.
Chaerkady R, Harsha HC, Nalli A, et al. A quantitative proteomic approach for identification of potential biomarkers in hepatocellular carcinoma. J Proteome Res. 2008;7(10):4289–98.
Miranda I, Rocha R, Santos MC, et al. A genetic code alteration is a phenotype diversity generator in the human pathogen Candida albicans. PLoS ONE. 2007;2(10):e996.
Silva RM, Paredes JA, Moura GR, et al. Critical roles for a genetic code alteration in the evolution of the genus Candida. EMBO J. 2007;26(21):4555–65.
Gomes AC, Miranda I, Silva RM, et al. A genetic code alteration generates a proteome of high diversity in the human pathogen Candida albicans. Genome Biol. 2007;8(10):R206.
Rodaki A, Young T, Brown AJ. Effects of depleting the essential central metabolic enzyme fructose-1, 6-bisphosphate aldolase on the growth and viability of Candida albicans: implications for antifungal drug target discovery. Eukaryot Cell. 2006;5(8):1371–7.
Cowen LE. Hsp90 orchestrates stress response signaling governing fungal drug resistance. PLoS Pathog. 2009;5(8):e1000471.
Singh SD, Robbins N, Zaas AK, Schell WA, Perfect JR, Cowen LE. Hsp90 governs echinocandin resistance in the pathogenic yeast Candida albicans via calcineurin. PLoS Pathog. 2009;5(7):e1000532.
Muerhoff AS, Birkenmeyer LG, Coffey R, et al. Detection of Plasmodium falciparum, vivax, ovale and malariae MSP1-p19 antibodies in human malaria patients and experimentally infected non-human primates. Clin Vaccine Immunol. 2010. doi:10.1128/CVI.00196-10
Birkenmeyer L, Muerhoff AS, Dawson GJ, Desai SM. Isolation and characterization of the MSP1 genes from Plasmodium malariae and Plasmodium ovale. Am J Trop Med Hyg. 2010;82(6):996–1003.
Gillet P, van Dijk DP, Bottieau E, Cnops L, Van Esbroeck M, Jacobs J. Test characteristics of the SD FK80 Plasmodium falciparum/Plasmodium vivax malaria rapid diagnostic test in a non-endemic setting. Malar J. 2009;8:262.
Lima Mda R, Nogueira RM, Schatzmayr HG, dos Santos FB. Comparison of three commercially available dengue NS1 antigen capture assays for acute diagnosis of dengue in Brazil. PLoS Negl Trop Dis. 2010;4(7):e738.
Qiu LW, Di B, Wen K, et al. Development of an antigen capture immunoassay based on monoclonal antibodies specific for dengue virus serotype 2 nonstructural protein 1 for early and rapid identification of dengue virus serotype 2 infections. Clin Vaccine Immunol. 2009;16(1):88–95.
Junqueira M, Spirin V, Balbuena TS, et al. Protein identification pipeline for the homology-driven proteomics. J Proteomics. 2008;71(3):346–56.
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Supporting Information Available
Below is the link to the electronic supplementary material.
Supplementary Table 1
List of peptides identified from Candida albicans and Candida glabrata cell lysates (XLS 4061 kb)
Supplementary Table 2
Proteins from C. albicans identified in this study (XLS 131 kb)
Supplementary Table 3
Proteins from C. glabrata identified in this study (XLS 53 kb)
Rights and permissions
Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Prasad, T.S.K., Keerthikumar, S., Chaerkady, R. et al. Comparative Proteomic Analysis of Candida albicans and Candida glabrata. Clin Proteom 6, 163–173 (2010). https://doi.org/10.1007/s12014-010-9057-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12014-010-9057-9