- Research article
- Open Access
Proteome remodelling during development from blood to insect-form Trypanosoma brucei quantified by SILAC and mass spectrometry
© Gunasekera et al.; licensee BioMed Central Ltd. 2012
- Received: 27 July 2012
- Accepted: 11 October 2012
- Published: 16 October 2012
Trypanosoma brucei is the causative agent of human African sleeping sickness and Nagana in cattle. In addition to being an important pathogen T. brucei has developed into a model system in cell biology.
Using Stable Isotope Labelling of Amino acids in Cell culture (SILAC) in combination with mass spectrometry we determined the abundance of >1600 proteins in the long slender (LS), short stumpy (SS) mammalian bloodstream form stages relative to the procyclic (PC) insect-form stage. In total we identified 2645 proteins, corresponding to ~30% of the total proteome and for the first time present a comprehensive overview of relative protein levels in three life stages of the parasite.
We can show the extent of pre-adaptation in the SS cells, especially at the level of the mitochondrial proteome. The comparison to a previously published report on monomorphic in vitro grown bloodstream and procyclic T. brucei indicates a loss of stringent regulation particularly of mitochondrial proteins in these cells when compared to the pleomorphic in vivo situation. In order to better understand the different levels of gene expression regulation in this organism we compared mRNA steady state abundance with the relative protein abundance-changes and detected moderate but significant correlation indicating that trypanosomes possess a significant repertoire of translational and posttranslational mechanisms to regulate protein abundance.
- Life Cycle Stage
- Bloodstream Form
- Dialyze Fetal Bovine Serum
- Stable Isotope Label Amino Acid
- Trypanosome Alternative Oxidase
T. brucei sspp are single celled, flagellated protozoan parasites that cause human African sleeping sickness and Nagana in cattle. The organisms follow a complex life cycle alternating between the mammalian and insect hosts. T. brucei sspp replicate by binary fission and, in the infected mammals, long-slender bloodstream forms use antigenic variation of their surface protein coat to effectively evade elimination of the population by the host immune system. In order to efficiently infect the insect vector (tsetse; Glossina sp.) the LS cells (pleomorphic cell lines) transform into quiescent “short stumpy” (SS) cells that are pre-adapted to the life in the midgut of the tsetse. This transition occurs in response to a parasite derived unidentified “stumpy inducing factor” (SIF) in a cell density dependent manner. The further transition from the quiescent SS stage to the procyclic (PC) insect stage occurs in the tsetse midgut. In vitro this situation can be mimicked through a temperature drop (from 37°C to 27°C) and citrate or cis-aconitate addition to the medium. The mechanism of the in vitro differentiation involves the stage-specific expression of carboxylate transport proteins and a phosphatase cascade that eventually relays the differentiation signal to the glycosomes. Glycosomes are peroxisome-derived trypanosome-specific organelles harbouring more than 200 proteins involved in a number of different pathways including Glycolysis and the beta-oxidation of fatty acids[4, 5]. How the glycosomes further promote differentiation remains elusive.
During the life cycle T. brucei undergoes dramatic changes including the surface proteome, changes in overall size, shape and motility as well as intracellular changes most prominently seen in the mitochondrion. The organelle transforms from an acristate tubular structure devoid of cytochromes, oxidative phosphorylation and tricarboylic acid “cycle” (TCA) activity to a compartment capable of the bona fide mitochondrial activities. The morphological and metabolic changes occurring during the life cycle are accompanied by major changes in gene expression at the mRNA level[7–12]. Trypanosomes regulate steady state mRNA abundance mainly at the posttranscriptional level likely through differential stability of the mRNA molecules as has been shown for a number of transcripts[14, 15]. Additionally, a number of individual studies including work on the procyclic surface proteins, the glycosomal aconitase, the mitochondrial cytochrome c reductase and several cytochrome oxidase subunits, have demonstrated that trypanosomes use translational and posttranslational mechanisms to regulate gene expression, however the genome-wide scale of these mechanisms remains unknown. The majority of proteomic studies in T. brucei have focused on individual parts/compartments of the cell including the mitochondrion, the plasma membrane, the flagellum[22, 23] or applied semi-quantitative strategies in two life stages (LS and PC;[24, 25]). No comprehensive proteomics study of the three life cycle stages have been done and/or compared with the corresponding transcriptome. Thus the importance and scale of translational regulation and protein stability as means of gene expression regulation in T. brucei remain elusive.
In recent years SILAC approaches have become the method of choice for the quantitation of proteomes in numerous organisms ranging from yeast to plants and humans[26–28]. Here we present the first quantitative proteomics study of three life stages of T. brucei using SILAC and compare it with recently published transcriptome data and proteome data.
Insect-form T. brucei cells grew at very similar rates irrespective of the medium (SDM80 or 79), dialyzed/regular fetal bovine serum (FCS) and labeled or unlabeled amino acids (Additional file1: Figure S1). We could show that after 11 cell division cycles in medium containing heavy isotope labeled Arg/Lys the mean ratio of heavy over light isotopes in the detected peptides changed more than 20 fold from 0.51 to 11.4 indicating that the majority of proteins had incorporated the heavy isotope amino acids (Additional file1: Figure S2A). This is also supported by the inspection of individual peptides, where the incorporation of heavy isotopes can be followed from 0, 2, 11 cell cycle divisions (Additional file1: Figure S2B). For the comparison of relative protein abundance we used in vitro grown PC (Antat 1.1) and the mammalian LS or SS parasites (pleomorphic Antat 1.1) from infected animals (Wistar rats). LS parasites were harvested from an early day three infection at a density of 5 × 107 cells/ml, while the SS cells were harvested at a density of 3-5 × 108/ml on day six from immunosuppressed animals. Parasites grew at similar rates in immunosuppressed and untreated animals (data not shown). We purified the bloodstream form trypanosomes using anion exchange chromatography as described previously. 5 × 106 insect-form and bloodstream form cells (LS or SS) were lysed using SDS PAGE buffer and subsequently mixed. The total cell extracts were separated on SDS PAGE in triplicate. Each lane was cut into 10 slices, trypsinized and the resulting peptides analyzed by LC mass spectrometry.
Overall proteome changes during development
RNA-Seq and MS/MS overlap(proteins)
N-terminal extensions and alternative splicing to diversify protein production
Although we have no additional experimental verification the mass spectrometry data together with the alternative splicing profile and our previous results for the tRNA synthetase suggest that from the transcripts described above two or more proteins differing in the N-terminus are potentially translated. These findings support the hypothesis that alternative trans-splicing is a significant mechanism for the diversification of the information encoded in the T. brucei genome.
Novel transcripts expressed
Kolev and co-workers recently showed the expression of 1011 previously not annotated genes in the T. brucei genome at the RNA level. We detected peptides for 31 or 3% of these 1011 genes (Additional file12: Table S11), while we could show the expression of about 30% of the remaining genes in the T. brucei genome. Four of the 31 detected proteins encoded by novel transcripts belong to the retrotransposon hot spot protein family, while the remaining, including a 41 amino acid peptide, are proteins of unknown function with no significant similarities to other proteins outside the group of trypanosomes.
This is the first study demonstrating the usefulness of the SILAC approach in the comparison of the total proteome of three T. brucei life cycle stages. We could show that fly transmissable insect-form T. brucei (Antat1.1;) grows well in SILAC heavy isotope-adapted SDM80 medium in vitro with little to no Arg-13C6 to Pro-13C5 conversion detectable. This is in good agreement with the lack of the ornithine aminotransferase in the T.brucei genome, an enzyme essential for the conversion Arg to Pro in other systems (pers. communication F. Bringaud). Interestingly the closely related parasite Leishmania contains all enzymes necessary for the Arg to Pro conversion and thus might be less suited for a SILAC approach with labelled Arg. As expected for a gel-based approach detection was slightly biased against very small proteins, proteins with >1 trans-membrane domain and very basic proteins, the latter possibly due to trypsin based cleavage of these proteins in very small peptides that are subsequently missed in the LC mass spectrometry. However, with more than 2500 different proteins detected we achieved very good coverage of the T. brucei proteome (Figure1D). The comparison with a recently published study by Urbaniak and coworkers showed overall excellent correlation of protein abundance-changes between blood (LS) and insect (PC) form cells (rho=0.83, p-value < 2.2e-16; Figure4B) despite the fact that the growth conditions (in vitro vs. in vivo for the bloodstream cells) and the cell lines (monomorphic vs pleomorphic) were quite different. Interestingly, many mitochondrial proteins showed a decrease in fold-change between the blood and insect-form in the monomorphic when compared to the pleomorphic cell line. At this point we cannot distinguish if this is due to an increase of mitochondrial proteins in the bloodstream form, a decrease in the insect-form or a combination of both. However, it is well established that in vitro grown monomorphic bloodstream form cells differ in shape and motility from their in vivo grown pleomorphic counterparts (reviewed in). Furthermore it has been speculated that the monomorphic cells are more similar to the intermediate forms of the pleomorphic cells, a differentiation step in between the LS and SS forms. When we compared our results to a recent study of the related parasite T. congolense we also detected strong positive correlation of protein changes during development (LS and PC), however less strong than when compared to the monomorphic T. brucei strain. Similar to T. brucei, the T. congolense parasite is transmitted by the Tsetse and infects a variety of mammalian hosts. However, there are a number of differences in the mammalian host pathogenesis between the two parasites including the localization in the host, suggesting that there are likely differences in the underlying protein expression in the mammalian blood stage. These differences in biology could explain the discrepancies we find between the two studies, especially the significant number of proteins that are regulated in opposing directions. Additionally, the differences in the expression profiles could also be a result of the techniques applied in the two studies. Isobaric tags for relative and absolute quantitation (iTraq), which was used by Eyford and co-workers requires that the samples are individually processed, protease treated and labelled with the isobaric tags, while SILAC allowes the combined processing of the samples after growth in the heavy and light isotope media. We feel that the combined processing is an advantage of SILAC over iTraq since no variability in processing is introduced between two samples. iTraq on the other hand offers the advantage to measure up to eight samples together and thus save mass spectrometry resources and costs (for a review on iTraq see).
Analyzing the changes during development we could show that about 45% of the proteins change in abundance in each of the comparisons, while more than 50% remained unchanged (Figure1D, Figure3A). Among the unchanged protein groups we detected 102 ribosomal proteins with mean and median fold-change of close to one in each of the life-stage comparisons (Figure3B and Additional file6: Table S5). This is not unexpected since ribosomal proteins are part of the cell’s core machinery and as such unlikely to undergo large changes in abundance. Among the proteins that change abundance during differentiation we found very good correlation to previously published data of a number of proteins including the alternative terminal oxidase (TAO; Tb927.10.7090;) a glycosomally targeted phosphatase (TbPIP39; Tb09.160.4460,) and the surface transporter family proteins associated with differentiation (PAD1; Tb927.7.5930 and PAD2; Tb927.7.5940,). The overall increase in protein abundance during the transition from the LS to the SS cells is explained by the specific requirements for this intermediate life cycle stage. The SS cell is adapted to the nutrients, pH and temperature of the mammalian host, while at the same time it is pre-adapted to the conditions in the tsetse midgut as particularly through the increase in abundance of mitochondrial proteins. It is well established that the bloodstream form trypanosomes are devoid of cytochromes and consequently lack oxidative phosphorylation activity. The data presented here now suggests that one of the final activation steps of energy production in the mitochondrion rests in the production and assembly of complexes III and IV of the oxidative phosphorylation chain, while complexes I, II and V are already present in the quiescent SS cells. This is in good agreement with the abundance of the trypanosome alternative oxidase (TAO) in the bloodstream form parasites. TAO is the terminal electron acceptor in the bloodstream cells necessary to re-oxidize the reduction equivalents produced during Glycolysis, essentially substituting complex IV. Only after the transition to the procyclic form and the activation of the regular oxidative phosphorylation chain including complex III and IV the abundance of the TAO protein decreased. At first glance the presence of many of the procyclic-specific enzymes in the SS stage is seemingly at odds with some of the metabolome data. The major end product of glucose metabolism in the SS cells, for example, has been shown to be pyruvate and not acetate as it is found in the PC cells. This poses the question as to why the excess pyruvate is not converted to acetate if the enzymes of the corresponding pathway (PDH, ASCT, SCoAS) are present. We identified a potential pyruvate dehydrogenase phosphatase (Tb927.5.1660), a homolog of the PDH activating enzyme in other systems, which is upregulated at the protein level only upon transition to the insect-form. The developmental regulation of the phosphatase thus would explain the lack of ASCT activity due to a lack of acetyl CoA substrate (Additional file1: Figure S5). Clearly this is only a hypothesis and requires experimental verification, but it demonstrates the power of the SILAC comparative approach to study the specific biology of trypanosomes. Furthermore we would like to remind the reader that the in vitro grown insect-form cells, although fly transmissible, are possibly different from the trypanosomes in the in vivo situation in the tsetse.
The recent discovery of a large number of novel transcripts prompted us to specifically search for corresponding protein products, however we only detected peptides for 3% of these transcripts. Many of the novel transcripts contain very small open reading frames (<25aa) and thus there is the possibility that these small proteins were missed on SDS PAGE, although we detected peptides belonging to a very small protein (41 aa) encoded by a novel transcript. Overall we think that a majority of the novel transcripts, although poly adenylated and capped are likely non-coding and thus present a large repertoire of potentially regulatory RNA species in the T. brucei genome as has recently been demonstrated for two of the novel transcripts by Michaeli and co-workers.
One of the enigmas of gene expression regulation is the apparent lack of correlation between the changes at the mRNA level and the protein level in some systems. A recent study, for example, described the concordance of mouse proteome and transcriptome from different mouse strains to be at a modest level (rho=0.27; p<0.05) with about 40% of the detected proteins not correlating to the changes in mRNA level. On the other hand, a study in yeast showed that correlation of protein and mRNA levels following a perturbation in osmolarity was strong for the genes up-regulated at the mRNA level, however there was no correlation for mRNAs down-regulated during this process. The authors propose a model in which decrease in mRNA abundance serves purposes different from the down regulation of protein abundance during osmolarity stress. In our study the comparison of LS/PC showed a similar behaviour. RNA/protein abundance-changes correlated much better when RNA was increased at least two fold (Spearman, up r=0.36; p<0.0001) than if RNA was decreased at least two fold (down, r=0.11; p<0.02), however this phenomenon was reversed when PC was compared to the SS form (up r=0.07, p<0.1; down r=0.24, p<0.001). In order to test if the lack of correlation between RNA and protein is due to biological variability at the RNA level we restricted the set to transcripts with good correlation between a previous microarray study of bloodstream and procyclic T. brucei and our SLT data and compared it to the protein data (Additional file1: Figure S6). Of the 3451 genes with a robust regulation pattern at the RNA level 677 were represented in our mass spectrometry data. The correlation coefficient of the restricted dataset (rho=0.35; p<2.2e-16) was only marginally higher than the entire dataset (rho=0.28; p<2.2e-16) thus even for transcripts with a robust expression pattern, protein abundance-changes do not correlate very well. Although we cannot entirely exclude that the difference in regulation at the RNA and protein level is due to the variability in biological replicates it seems likely that a significant portion of the protein abundance-changes are due to regulation of translation or protein stability and thus are not reflected at the RNA level.
In summary we show (i) the effective use of SILAC for the analysis of whole proteome changes during the life cycle in T. brucei, especially also to uncover potential regulatory proteins that control the differentiation (ii) the likely N-termini of 109 proteins and that number of them possibly produce several N-terminal isoforms; (iii) a surprising difference between the protein and RNA changes during the life cycle, that are possibly explained by a significant level of translational/posttranslational regulation that has yet to be explored, (iv) that the massive changes, especially in the mitochondrial proteome of the quiescent SS form support its status as a distinctly differentiated subpopulation of cells committed to life in the fly.
Cells and growth conditions
Procyclic Antat 1.1 cells were pre-adapted to SDM 80 with 10% dialyzed fetal bovine serum (FCS; 10,000 molecular weight cut-off; Amimed) for seven days at 27°C; cells were then washed with PBS and transferred into 10 ml SDM 80 with 10% dialyzed fetal bovine serum (FCS) and the stable isotope labelled amino acids Arginine (1,1 mM; 13C6, 99%, CIL) and Lysine (0,4 mM; 13C6, 99%, CIL) that replaced the non labelled amino acids. Cells were harvested after 0, 2 and 11 cell division cycles (doubling time 8 hours) and the incorporation of stable isotope labelled amino acids was monitored by mass spectrometry. For the final experiments cells were harvested 5 × 107 cells (5 × 106 cells/ml) after 11 cell division cycles in SDM with stable isotope labelled amino acids. Cells were washed with PBS and lysed directly in Laemmli buffer at a concentration of 5*105 cells/μl. Lysates were stored at −80°C.
Bloodstream form cells were grown in rats (Wistar). Long slender cells were harvested at day three post-intraperitoneal injection of 5 × 106 cells at a density of 5 × 107 cells/ml blood. For short stumpy cells rats were immunosuppressed with 200 mg/kg cyclophosphoamide (Sigma) 24 hours prior to intraperitoneal injection of 1 × 106 cells. Short stumpy cells were harvested at day five from immunosuppressed rats at a density of 5-8 × 108 cells/ml blood. 85% of cells harvested at day five displayed the typical short stumpy appearance with the flagellum significantly shortened, while >90% of cells harvested at day three showed a long slender appearance with the flagellum extending anterior to the cell body, as seen by light microscopy after methanol fixation. Bloodstream form cells were purified from whole rat blood using DE-52 anion exchange resin (Whatman) equilibrated to pH 8 with a bicine glucose buffer (50 mM bicine, 50 mM NaCl, 5 mM KCl, 50 mM glucose).
Two technical replicates were done each using 5 × 106 procyclic cells that were mixed with 5 × 106 LS or SS cells in Laemmli buffer, boiled at 95°C for 5 min and resolved on a 10% acryl amide gel (1,0 mm). Gels were stained with standard Coomassie Brilliant Blue G-250 solution (2.5 g Coomassie Brilliant Blue G-250 from Applichem in 450 ml methanol, 100 ml acetic acid and 400 ml water) and destained in the same solution without Coomassie. Subsequently gels were stored in 20% (v/v) ethanol at 4°C until MS analysis that was done in duplicate on two SDSPAGE lanes (within 2 days). For this, each gel lane was cut into ten bands. Each band was cut into several little cubes that were transferred to a low-binding reagent tube (Sarstedt, Nümbrecht, Germany). Gel slices were washed with 50 mM Tris/HCl pH 8 (Tris buffer) and Tris buffer/acetonitrile (LC-MS grade, Fluka, Buchs, Switzerland) 50/50 before protein reduction with 50 mM DTT (Fluka, Buchs, Switzerland) in Tris buffer for 30 min at 37°C, and alkylation with 50 mM iodoacetamide (Fluka, Buchs, Switzerland) in Tris buffer for 30 min at 37°C in the dark. After washing with Tris buffer and dehydration with acetonitrile the gel cubes were soaked with trypsin solution composed of 10 ng/ml trypsin (Promega) in 20 mM Tris/HCl pH 8, 0.01% ProteaseMax (Promega) for 30 min on ice. Gel cubes were covered by addition of 5–10 ml 20 mM Tris/HCl before digestion for 60 min at 50°C. The supernatant liquid was combined with a single gel extract performed with 20 ml 20% (v/v) formic acid (Merck) in polypropylene HPLC vials. An aliquot of 10 ml from each digest was loaded onto a self-made pre-column (Magic C18, 5 mm, 300 Å, 0.15 mm i.d. x 30 mm length) at a flow rate of ~5 ml/min with solvent A (0.1% formic acid in water/acetonitrile 98:2). After loading, peptides were eluted in back flush mode onto the analytical nano-column (Magic C18, 5 mm, 100 Å, 0.075 mm i.d. × 75 mm length) using an acetonitrile gradient of 5% to 40% solvent B (0.1% formic acid in water/acetonitrile 4.9:95) in 60 min at a flow rate of ~400 nl/min. The column effluent was directly coupled to an LTQ-orbitrap XL mass spectrometer (ThermoFisher, Bremen, Germany) via a nanospray ESI source operated at 1700 V. Data acquisition was made in data dependent mode with precursor ion scans recorded in the Fourier transform detector (FT) with resolution of 60’000 (@ m/z =400) parallel to five fragment spectra of the most intense precursor ions in the linear iontrap. CID mode settings were: Wideband activation on; precursor ion selection between m/z range 360–1400; intensity threshold at 500; precursors excluded for 15 sec. Further tune parameter settings were: Max. injection time LTQ MS2 = 200 ms, orbitrap MS 500 ms; automatic gain control orbitrap = 5 × 105, LTQ MS = 3 × 104, MS2 = 1 × 104.
Mass Spectrometry data and SILAC ratio interpretation was made with MaxQuant version 220.127.116.11 run under Windows7 against a T. brucei sequence database (version available in May 2011) from the Wellcome Trust Sanger Institute Pathogen Sequencing Unit. The default contamination database in Andromeda (MaxQuant) was searched together with the target database. We applied the following MaxQuant default settings: for precursor masses in the first search (+/− 20ppm), in the second search (re-calibrated mass values; +/− 6ppm). For fragment spectra default was set to +/− 0.5 Da and only the top 6 peaks per mass interval of 100 were kept (peak filtering;). Other Maxquant parameters were: Peptide and protein FDR set at 1%; carbamidomethyl-cystein set as fixed modification; allowed dynamic modifications were Met oxidation, protein N-terminal acetylation, Phosphorylation on Ser/Thr/Tyr. For SILAC ratio at least two unique or razor peptides without modification were required. A strict trypsin cleavage rule was applied i.e. cleavage c-terminal after K or R, no cleavage if a P follows R or K!. The normalisation was done using the default settings in MaxQuant.
For western blotting total cell protein from 1 × 107 cells of bloodstream (long slender, short stumpy) and procyclic cells was resolved on 10% SDSPAGE, transferred to a PVDF membrane (Immobilon FL, Millipore) and probed with antibodies shown in Additional file3: Table S1. Signals were quantified using the Odyssey Infrared Imaging System (LI-COR).
Analysing MSMS data
Perl (perl, v5.10.1), unix (in Ubuntu 10.04.3 LTS (Lucid Lynx)) and R (R version 2.14.1 (2011-12-22)) scripts were used to analyse RNA and protein data. The statistical analyses were performed using R scripts. The scripts will be provided upon request. All plots except the circular visualisation of protein abundance along the chromosomes of T. brucei, which was produced using Circos software were produced using R and supportive packages.
Detecting novel transcripts and genes with N-terminal extensions
Peptide coverage for novel transcripts and n-terminal extensions was detected using Trans Proteomic Pipeline (TPP,). The raw files containing MS/MS spectra were converted into mzXML files and subsequently files were used for database searches. The database searches were done using the X!Tandem search-engine. Protein databases were created based on DNA sequences of the novel transcripts and genes with N-terminal extensions for the database search. Precursor mass tolerance was set to −2 and +4 Da. We used this mass tolerance window together with the other default parameters for the X!Tandem database search. The output files of X!Tandem search engine were converted into pepXML (XML format) files, which were used for subsequent peptide level analysis. Validation of peptide spectrum assignment was done using PeptideProphet with the “accurate mass binning” option enabled. The minimum peptide length considered in the analysis is seven amino acids. The results were filtered to a 1% FDR at the peptide and protein levels. Finally, the protein identifications were converted into protXML files, which contain the information for protein level analysis.
Principal component analysis
Where is the correlation matrix, i.e. square symmetric and λ is the eigenvalue and is the eigenvector. The size of the eigenvectors is equal to number of variables and the eigenvectors are orthogonal to each other, i.e.
We have used R, rgl and lattice packages to compute the matrix of variable loadings, rotated data and to plot rotated variables.
This research was funded by the Swiss National Science Foundation (31003A-125194). We thank A. Schneider, I. Roditi, C. Clayton and J. Lukes for antibodies.
- Vassella E, Reuner B, Yutzy B, Boshart M: Differentiation of African trypanosomes is controlled by a density sensing mechanism which signals cell cycle arrest via the cAMP pathway. J Cell Sci. 1997, 110 (Pt 21): 2661-2671.PubMedGoogle Scholar
- Dean S, Marchetti R, Kirk K, Matthews KR: A surface transporter family conveys the trypanosome differentiation signal. Nature. 2009, 459 (7244): 213-217.PubMed CentralView ArticlePubMedGoogle Scholar
- Szoor B, Ruberto I, Burchmore R, Matthews KR: A novel phosphatase cascade regulates differentiation in Trypanosoma brucei via a glycosomal signaling pathway. Genes Dev. 2010, 24 (12): 1306-1316.PubMed CentralView ArticlePubMedGoogle Scholar
- Opperdoes FR, Szikora JP: In silico prediction of the glycosomal enzymes of Leishmania major and trypanosomes. Mol Biochem Parasitol. 2006, 147 (2): 193-206.View ArticlePubMedGoogle Scholar
- Parsons M: Glycosomes: parasites and the divergence of peroxisomal purpose. Mol Microbiol. 2004, 53 (3): 717-724.View ArticlePubMedGoogle Scholar
- Priest JW, Hajduk SL: Developmental regulation of mitochondrial biogenesis in Trypanosoma brucei. J Bioenerg Biomembr. 1994, 26 (2): 179-191.View ArticlePubMedGoogle Scholar
- Kolev NG, Franklin JB, Carmi S, Shi H, Michaeli S, Tschudi C: The transcriptome of the human pathogen Trypanosoma brucei at single-nucleotide resolution. PLoS Pathog. 2010, 6 (9): e1001090-PubMed CentralView ArticlePubMedGoogle Scholar
- Nilsson D, Gunasekera K, Mani J, Osteras M, Farinelli L, Baerlocher L, Roditi I, Ochsenreiter T: Spliced leader trapping reveals widespread alternative splicing patterns in the highly dynamic transcriptome of Trypanosoma brucei. PLoS Pathog. 2010, 6 (8): e1001037-PubMed CentralView ArticlePubMedGoogle Scholar
- Siegel TN, Hekstra DR, Wang X, Dewell S, Cross GA: Genome-wide analysis of mRNA abundance in two life-cycle stages of Trypanosoma brucei and identification of splicing and polyadenylation sites. Nucleic Acids Res. 2010, 38 (15): 4946-4957.PubMed CentralView ArticlePubMedGoogle Scholar
- Jensen BC, Sivam D, Kifer CT, Myler PJ, Parsons M: Widespread variation in transcript abundance within and across developmental stages of Trypanosoma brucei. BMC Genomics. 2009, 10 (1): 482-PubMed CentralView ArticlePubMedGoogle Scholar
- Kabani S, Fenn K, Ross A, Ivens A, Smith TK, Ghazal P, Matthews K: Genome-wide expression profiling of in vivo-derived bloodstream parasite stages and dynamic analysis of mRNA alterations during synchronous differentiation in Trypanosoma brucei. BMC Genomics. 2009, 10 (1): 427-PubMed CentralView ArticlePubMedGoogle Scholar
- Koumandou VL, Natesan SK, Sergeenko T, Field MC: The trypanosome transcriptome is remodelled during differentiation but displays limited responsiveness within life stages. BMC Genomics. 2008, 9: 298-PubMed CentralView ArticlePubMedGoogle Scholar
- Webb H, Burns R, Ellis L, Kimblin N, Carrington M: Developmentally regulated instability of the GPI-PLC mRNA is dependent on a short-lived protein factor. Nucleic Acids Res. 2005, 33 (5): 1503-1512.PubMed CentralView ArticlePubMedGoogle Scholar
- Berberof M, Vanhamme L, Tebabi P, Pays A, Jefferies D, Welburn S, Pays E: The 3'-terminal region of the mRNAs for VSG and procyclin can confer stage specificity to gene expression in Trypanosoma brucei. EMBO J. 1995, 14 (12): 2925-2934.PubMed CentralPubMedGoogle Scholar
- Furger A, Schurch N, Kurath U, Roditi I: Elements in the 3' untranslated region of procyclin mRNA regulate expression in insect forms of Trypanosoma brucei by modulating RNA stability and translation. Mol Cell Biol. 1997, 17 (8): 4372-4380.PubMed CentralView ArticlePubMedGoogle Scholar
- Hehl A, Vassella E, Braun R, Roditi I: A conserved stem-loop structure in the 3' untranslated region of procyclin mRNAs regulates expression in Trypanosoma brucei. Proc Natl Acad Sci U S A. 1994, 91 (1): 370-374.PubMed CentralView ArticlePubMedGoogle Scholar
- Saas J, Ziegelbauer K, von Haeseler A, Fast B, Boshart M: A developmentally regulated aconitase related to iron-regulatory protein-1 is localized in the cytoplasm and in the mitochondrion of Trypanosoma brucei. J Biol Chem. 2000, 275 (4): 2745-2755.View ArticlePubMedGoogle Scholar
- Priest JW, Hajduk SL: Developmental regulation of Trypanosoma brucei cytochrome c reductase during bloodstream to procyclic differentiation. Mol Biochem Parasitol. 1994, 65 (2): 291-304.View ArticlePubMedGoogle Scholar
- Mayho M, Fenn K, Craddy P, Crosthwaite S, Matthews K: Post-transcriptional control of nuclear-encoded cytochrome oxidase subunits in Trypanosoma brucei: evidence for genome-wide conservation of life-cycle stage-specific regulatory elements. Nucleic Acids Res. 2006, 34 (18): 5312-5324.PubMed CentralView ArticlePubMedGoogle Scholar
- Panigrahi AK, Ogata Y, Zikova A, Anupama A, Dalley RA, Acestor N, Myler PJ, Stuart KD: A comprehensive analysis of Trypanosoma brucei mitochondrial proteome. Proteomics. 2009, 9 (2): 434-450.PubMed CentralView ArticlePubMedGoogle Scholar
- Bridges DJ, Pitt AR, Hanrahan O, Brennan K, Voorheis HP, Herzyk P, de Koning HP, Burchmore RJ: Characterisation of the plasma membrane subproteome of bloodstream form Trypanosoma brucei. Proteomics. 2008, 8 (1): 83-99.View ArticlePubMedGoogle Scholar
- Oberholzer M, Langousis G, Nguyen HT, Saada EA, Shimogawa MM, Jonsson ZO, Nguyen SM, Wohlschlegel JA, Hill KL: Independent analysis of the flagellum surface and matrix proteomes provides insight into flagellum signaling in mammalian-infectious Trypanosoma brucei. Mol Cell Proteomics. 2011, 10 (10): M111 010538-PubMed CentralView ArticlePubMedGoogle Scholar
- Broadhead R, Dawe HR, Farr H, Griffiths S, Hart SR, Portman N, Shaw MK, Ginger ML, Gaskell SJ, McKean PG, et al: Flagellar motility is required for the viability of the bloodstream trypanosome. Nature. 2006, 440 (7081): 224-227.View ArticlePubMedGoogle Scholar
- Vertommen D, Van Roy J, Szikora JP, Rider MH, Michels PA, Opperdoes FR: Differential expression of glycosomal and mitochondrial proteins in the two major life-cycle stages of Trypanosoma brucei. Mol Biochem Parasitol. 2008, 158 (2): 189-201.View ArticlePubMedGoogle Scholar
- Colasante C, Ellis M, Ruppert T, Voncken F: Comparative proteomics of glycosomes from bloodstream form and procyclic culture form Trypanosoma brucei brucei. Proteomics. 2006, 6 (11): 3275-3293.View ArticlePubMedGoogle Scholar
- Gruhler A, Schulze WX, Matthiesen R, Mann M, Jensen ON: Stable isotope labeling of Arabidopsis thaliana cells and quantitative proteomics by mass spectrometry. Mol Cell Proteomics. 2005, 4 (11): 1697-1709.View ArticlePubMedGoogle Scholar
- Walther DM, Mann M: Accurate quantification of more than 4000 mouse tissue proteins reveals minimal proteome changes during aging. Mol Cell Proteomics. 2010, 10 (2): M110 004523-PubMed CentralView ArticlePubMedGoogle Scholar
- de Godoy LM, Olsen JV, Cox J, Nielsen ML, Hubner NC, Frohlich F, Walther TC, Mann M: Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature. 2008, 455 (7217): 1251-1254.View ArticlePubMedGoogle Scholar
- Lanham SM, Godfrey DG: Isolation of salivarian trypanosomes from man and other mammals using DEAE-cellulose. Exp Parasitol. 1970, 28 (3): 521-534.View ArticlePubMedGoogle Scholar
- Urbaniak MD, Guther ML, Ferguson MA: Comparative SILAC proteomic analysis of Trypanosoma brucei bloodstream and procyclic lifecycle stages. PLoS One. 2012, 7 (5): e36619-PubMed CentralView ArticlePubMedGoogle Scholar
- Eyford BA, Sakurai T, Smith D, Loveless B, Hertz-Fowler C, Donelson JE, Inoue N, Pearson TW: Differential protein expression throughout the life cycle of Trypanosoma congolense, a major parasite of cattle in Africa. Mol Biochem Parasitol. 2011, 177 (2): 116-125.PubMed CentralView ArticlePubMedGoogle Scholar
- Rettig J, Wang Y, Schneider A, Ochsenreiter T: Dual targeting of Isoleucyl tRNA synthetase in Trypanosoma brucei is mediated through alternative trans-splicing. Nucleic Acids Res. 2011Google Scholar
- Zhang X, Cui J, Nilsson D, Gunasekera K, Chanfon A, Song X, Wang H, Xu Y, Ochsenreiter T: The Trypanosoma brucei MitoCarta and its regulation and splicing pattern during development. Nucleic Acids Res. 2010, 38 (21): 7378-7387.PubMed CentralView ArticlePubMedGoogle Scholar
- Vassella E, Oberle M, Urwyler S, Renggli CK, Studer E, Hemphill A, Fragoso C, Butikofer P, Brun R, Roditi I: Major surface glycoproteins of insect forms of Trypanosoma brucei are not essential for cyclical transmission by tsetse. PLoS One. 2009, 4 (2): e4493-PubMed CentralView ArticlePubMedGoogle Scholar
- Matthews KR, Ellis JR, Paterou A: Molecular regulation of the life cycle of African trypanosomes. Trends Parasitol. 2004, 20 (1): 40-47.View ArticlePubMedGoogle Scholar
- Luckins AG, Gray AR: An extravascular site of development of Trypanosoma congolense. Nature. 1978, 272 (5654): 613-614.View ArticlePubMedGoogle Scholar
- Christoforou AL, Lilley KS: Isobaric tagging approaches in quantitative proteomics: the ups and downs. Anal Bioanal Chem. 2012, 404 (4): 1029-1037.View ArticlePubMedGoogle Scholar
- Chaudhuri M, Ajayi W, Temple S, Hill GC: Identification and partial purification of a stage-specific 33 kDa mitochondrial protein as the alternative oxidase of the Trypanosoma brucei brucei bloodstream trypomastigotes. J Eukaryot Microbiol. 1995, 42 (5): 467-472.View ArticlePubMedGoogle Scholar
- Tyler KM, Matthews KR, Gull K: The bloodstream differentiation-division of Trypanosoma brucei studied using mitochondrial markers. Proc Biol Sci. 1997, 264 (1387): 1481-1490.PubMed CentralView ArticlePubMedGoogle Scholar
- van Grinsven KW, Van Den Abbeele J, Van den Bossche P, van Hellemond JJ, Tielens AG: Adaptations in the glucose metabolism of procyclic Trypanosoma brucei isolates from tsetse flies and during differentiation of bloodstream forms. Eukaryot Cell. 2009, 8 (8): 1307-1311.PubMed CentralView ArticlePubMedGoogle Scholar
- Michaeli S, Doniger T, Gupta SK, Wurtzel O, Romano M, Visnovezky D, Sorek R, Unger R, Ullu E: RNA-seq analysis of small RNPs in Trypanosoma brucei reveals a rich repertoire of non-coding RNAs. Nucleic Acids Res. 2012, 40 (3): 1282-1298.PubMed CentralView ArticlePubMedGoogle Scholar
- Ghazalpour A, Bennett B, Petyuk VA, Orozco L, Hagopian R, Mungrue IN, Farber CR, Sinsheimer J, Kang HM, Furlotte N, et al: Comparative analysis of proteome and transcriptome variation in mouse. PLoS Genet. 2011, 7 (6): e1001393-PubMed CentralView ArticlePubMedGoogle Scholar
- Lu P, Vogel C, Wang R, Yao X, Marcotte EM: Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol. 2007, 25 (1): 117-124.View ArticlePubMedGoogle Scholar
- Le Ray D, Barry JD, Easton C, Vickerman K: First tsetse fly transmission of the "AnTat" serodeme of Trypanosoma brucei. Ann Soc Belg Med Trop. 1977, 57 (4–5): 369-381.PubMedGoogle Scholar
- Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M: Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011, 10 (4): 1794-1805.View ArticlePubMedGoogle Scholar
- Cox J, Mann M: MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008, 26 (12): 1367-1372.View ArticlePubMedGoogle Scholar
- Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA: Circos: an information aesthetic for comparative genomics. Genome Res. 2009, 19 (9): 1639-1645.PubMed CentralView ArticlePubMedGoogle Scholar
- Keller A, Eng J, Zhang N, Li XJ, Aebersold R: A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol. 2005, 1: 2005 0017-PubMed CentralView ArticlePubMedGoogle Scholar
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