Dataset Information

Quantitative proteomic analysis in Candida albicans using SILAC-based mass spectrometry

ABSTRACT: Stable isotope labelling by amino acids in cell culture (SILAC) in conjunction with mass spectrometry analysis is a sensitive and reliable technique for quantifying relative differences in protein abundance and post-translational modifications between cell populations. We have developed and utilised SILAC-MS workflows for quantitative proteomics in the fungal pathogen Candida albicans. Arginine metabolism provides important cues for escaping host defences during pathogenesis, which limits the use of auxotrophs in Candida research. Our strategy eliminates the need for engineering arginine auxotrophs for SILAC experiments and allows the use of ARG4 as selectable marker during strain construction. Cells that were auxotrophic for lysine were successfully labelled with both lysine and arginine stable isotopes. We found that prototrophic C. albicans preferentially uses exogenous arginine and downregulates internal production, which allowed it to achieve high incorporation rates. However, similar to other yeast, C. albicans was able to metabolise heavy arginine to heavy proline, which compromised the accuracy of protein quantification. A computational method was developed to correct for the incorporation of heavy proline. In addition, we utilised the developed SILAC labelling in Candida albicans for the global quantitative proteomic analysis of a strain expressing a phosphatase-dead mutant Cdc14PD.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Candida Albicans (yeast)

TISSUE(S): Fungal Cell

SUBMITTER: Iliyana Kaneva

LAB HEAD: Mark Dickman

PROVIDER: PXD007825 | Pride | 2018-04-17

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20151016_Cdc14C275S_YL6_1.raw	Raw
	20151016_Cdc14C275S_YL6_2.raw	Raw
	20151016_Cdc14C275S_YL6_3.raw	Raw
	20151016_Cdc14C275S_YL6_4.raw	Raw
	20151016_Cdc14C275S_YL6_5.raw	Raw

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Publications

Quantitative Proteomic Analysis in Candida albicans Using SILAC-Based Mass Spectrometry.

Kaneva Iliyana N IN Longworth Joseph J Sudbery Peter E PE Dickman Mark J MJ

Proteomics 20180220 5-6

Stable isotope labelling by amino acids in cell culture (SILAC) in conjunction with MS analysis is a sensitive and reliable technique for quantifying relative differences in protein abundance and posttranslational modifications between cell populations. We develop and utilise SILAC-MS workflows for quantitative proteomics in the fungal pathogen Candida albicans. Arginine metabolism provides important cues for escaping host defences during pathogenesis, which limits the use of auxotrophs in Candi ...[more]

PMID: 29280593

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Project description:Recent studies have shown that the transcriptional landscape of the pleiomorphic fungus Candida albicans is highly dependent upon growth conditions. Here using a dual RNA-seq approach we identified 299 C. albicans and 72 Streptococcus gordonii genes that were either up- or down-regulated specifically as a result of co-culturing these human oral cavity microorganisms. Seventy five C. albicans genes involved in responses to chemical stimuli, regulation, homeostasis, protein modification and cell cycle were statistically (P ≤0.05) upregulated, while 36 genes mainly involved in transport and translation were down-regulated. Upregulation of filamentation-associated TEC1 and FGR42 genes, and of ALS1 adhesin gene, concurred with previous evidence that the C. albicans yeast to hypha transition is promoted by S. gordonii. Increased expression of genes required for arginine biosynthesis in C. albicans was potentially indicative of a novel oxidative stress response. The transcriptional response of S. gordonii to C. albicans was less dramatic, with only eight S. gordonii genes significantly (P ≤0.05) up-regulated ≥ twofold (glpK, rplO, celB, rplN, rplB, rpsE, ciaR, and gat). The expression patterns suggest that signals from S. gordonii cause a positive filamentation response in C. albicans, while S. gordonii appears to be transcriptionally less influenced by C. albicans. Five Samples; Sample 1 - Candida albicans cells grown in hypha inducing conditions for two hours; Sample 2 - Candida albicans cells grown in hypha-inducing conditions for two hours before co-culture with Streptococcus gordonii cells for one hour in a 2:1 rato; Sample 3 - Candida albicans cells grown in hypha-inducing conditions for two hours before culture in Streptococcus gordonii media for one hour; Sample 4 - Candida albicans cells grown in hypha inducing conditions for two hours, filtered to remove Candida albicans cells and media added to Streptococcus gordonii cells for one hour; Sample 5 - Streptococcus gordonii cells alone for one hour. All samples extracted and sequenced in biological triplicate using Illumina HiSeq2500. Samples 1, 2 and 3 aligned to the reference genome for Candida albicans and Samples 2, 4 and 5 aligned to the reference genome for Streptococcus gordonii.

Project description:Metabolic labeling with stable isotopes is a prominent technique for comparative quantitative proteomics and stable isotope labeling with amino acids in cell culture (SILAC) is the most commonly used approach. SILAC is, however, traditionally limited to simple tissue culture regiments and only rarely employed in the context of complex culturing conditions as those required for human embryonic stem cells (hESCs). Classic hESC culture is based on the use of mouse embryonic fibroblasts (MEFs) as a feeder layer and as a result possible xenogeneic contamination, contribution of unlabeled amino acids by the feeders, inter-laboratory variability of MEF preparation and the overall complexity of the culture system are all of concern in conjunction with SILAC. We demonstrate a feeder-free SILAC culture system based on a customised version of a commonly used chemically defined hESC medium developed by Ludwig et al. (2006) and commercially available as mTeSR1 . This medium, together with adjustments to the culturing protocol, facilitates reproducible labeling which is easily scalable to the protein amounts required by proteomic work flows. It greatly enhances the usability of quantitative proteomics as a tool for the study of mechanisms underlying hESCs differentiation and self-renewal. Mass spectrometric data was analysed using the MaxQuant suite of algorithms [version 1.3.0.5]. The data was searched against the Homo sapiens UniProtKB protein sequence database (downloaded on 10/24/2012, 68,108 entries including canonical and isoform sequence data). Enzyme specificity was set to trypsin, allowing for cleavage N-terminal to proline and between aspartic acid and proline. Carbamidomethylcysteine was set as a fixed modification, and oxidized methionine and N-acetylation were set as variable modifications. Labeled Arg and Lys were used as peptide modifications for quantitation. MaxQuant’s requantitation option was not used where incorporation efficiency and arginine to proline conversion were analyzed. The maximum mass deviation allowed was set at 20 ppm for the initial search, and 6 ppm for the main search for monoisotopic precursor ions; the mass error tolerance was set at 20 ppm for MS/MS peaks. A maximum of two missed cleavages and three labeled amino acids (arginine and lysine) were allowed. The required false discovery rates were set to 1% at the peptide and protein level (the protein level filter was disabled for incorporation efficiency and arginine to proline conversion testing), and the minimum required peptide length was set to 6 amino acids. Statistical analysis and plotting were performed using the R Statistical Programming Environment. The analyses performed are documented in an R package provided in the proteomeXchange repository and based on a publicly available collection of tools (RCFPD, URL: http://sourceforge.net/projects/rcfpd/).