Open Access

Codon usage suggests that translational selection has a major impact on protein expression in trypanosomatids

BMC Genomics20089:2

DOI: 10.1186/1471-2164-9-2

Received: 22 August 2007

Accepted: 03 January 2008

Published: 03 January 2008

Abstract

Background

Different proteins are required in widely different quantities to build a living cell. In most organisms, transcription control makes a major contribution to differential expression. This is not the case in trypanosomatids where most genes are transcribed at an equivalent rate within large polycistronic clusters. Thus, trypanosomatids must use post-transcriptional control mechanisms to balance gene expression requirements.

Results

Here, the evidence for translational selection, the enrichment of 'favoured' codons in more highly expressed genes, is explored. A set of highly expressed, tandem-repeated genes display codon bias in Trypanosoma cruzi, Trypanosoma brucei and Leishmania major. The tRNA complement reveals forty-five of the sixty-one possible anticodons indicating widespread use of 'wobble' tRNAs. Consistent with translational selection, cognate tRNA genes for favoured codons are over-represented. Importantly, codon usage (Codon Adaptation Index) correlates with predicted and observed expression level. In addition, relative codon bias is broadly conserved among syntenic genes from different trypanosomatids.

Conclusion

Synonymous codon bias is correlated with tRNA gene copy number and with protein expression level in trypanosomatids. Taken together, the results suggest that translational selection is the dominant mechanism underlying the control of differential protein expression in these organisms. The findings reveal how trypanosomatids may compensate for a paucity of canonical Pol II promoters and subsequent widespread constitutive RNA polymerase II transcription.

Background

Trypanosomatids have a devastating impact on the world's poor, causing African trypanosomiasis, Chagas disease and leishmaniasis [1]. The consequences of this range of human and animal diseases are hundreds of thousands of deaths each year, ~1.5 million cases a year of the disfiguring lesions associated with cutaneous leishmaniasis and severely curtailed agricultural development throughout sub-Saharan Africa. The African trypanosome also causes Nagana disease in cattle, rendering 10 million square kilometres of land unsuitable for livestock.

The protozoan parasites responsible branched early from the eukaryotic lineage and display a range of unusual molecular features. RNA polymerase II transcription of protein coding genes is polycistronic and constitutive and all mature mRNAs are trans-spliced to an identical leader sequence [2]. Genome sequencing revealed remarkably conserved gene order or synteny across the genomes of the African trypanosome, Trypanosoma brucei, the South American trypanosome, Trypanosoma cruzi, and Leishmania major [3]. These trypanosomatids cause distinct diseases, are spread by different insect vectors and are thought to have diverged from a common ancestor several hundred million years ago. Trypanosomatids display a unique paucity of conventional RNA polymerase II promoters and transcriptional control is extremely limited compared to any other organism studied in any detail. Widespread constitutive and polycistronic transcription places considerable emphasis on post-transcriptional control since genes in the same transcriptional cluster function in unrelated pathways and are expressed at widely different levels [4]. Thus, trypanosomatids present a unique opportunity to study post-transcriptional control of gene expression.

Cells must express different proteins over an enormous abundance-range, from fewer than 50 to more than a million molecules per cell reported in Saccharomyces cerevisiae [5]. Efficiently translated mRNA species are translated several thousand times, with translation initiating up to once every 2 s, providing substantial scope for differential control at the level of translation. Translational selection has been reported in a range of species, whereby more frequent synonymous codons correspond to more abundant cognate tRNAs, with the correspondence being more pronounced for highly expressed genes [68]. Synonymous codon bias has also been reported in trypanosomatids [912]. There is a good correlation between mRNA levels and protein levels in yeast with 73% of variance in protein abundance explained by mRNA abundance [13]. In contrast, microarray analysis reveals modest differences in mRNA abundance in trypanosomatids and proteome analysis suggests substantial differential control at the level of translation or protein turnover [1417]. Evidence for translational selection is explored here using trypanosomatid genome sequence data [1820] and whole-cell proteome data [21]. The findings suggest that translational selection is the dominant mechanism underlying the control of differential protein expression in trypanosomatids.

Results

Tandem genes are highly expressed and display codon bias

Most trypanosomatid genes are 'single copy' (trypanosomatid genomes are typically diploid) but tandem gene amplification is thought to contribute to increased expression [20, 22]. Thus, tandem amplified genes may be among the most highly expressed and, if translational selection operates, may be an excellent source of favoured codons. To begin to explore evidence for translational selection in trypanosomatids, codon bias was assessed in tandem amplified genes. Whole-cell proteome data has been derived from T. cruzi [21] but the genome assembly is incomplete due to sequence complexity; the strain used for the sequencing project is a hybrid of two genotypes with multiple distinct alleles for most genes [3]. Since the T. brucei assembly is excellent and most trypanosomatid genes share orthologues accessible through the GeneDB interface, the T. brucei genome was scanned for tandem duplicated protein-coding genes. Consistent with the first prediction above, sixty-four tandem-amplified genes in T. brucei encode proteins with orthologues among the 243 proteins over-represented (≥ 10 mass spectra) in the non-redundant T. cruzi proteome set [21]. This includes the histones, ribosomal proteins, chaperones, tubulins and enzymes of carbohydrate metabolism. Tandem arrayed genes display little or no sequence divergence so a single copy from each tandem was selected for further analysis (see Table 1 in Additional file 1).

Codon usage was analysed for this tandem gene set from T. brucei and for the orthologous sets from T. cruzi and L. major, >60,000 codons in total. Consistent with previous reports [9, 11, 12], this revealed codon bias in all three trypanosomatids (Table 1). An extreme example is the gene encoding the highly abundant α-tubulin gene in L. major which uses only 40 of the 61 available codons. Figure 1 illustrates this bias across all synonymous codons for T. brucei and L. major. What is clear from Figure 1 is that codon bias is more pronounced in L. major. This is likely explained by the higher 'background' GC-content; the intergenic and protein-coding GC-contents are 41% and 50.9% in T. brucei [18], 47% and 53.4% in T. cruzi [19] and 57.3% and 62.5% in L. major [20] respectively. Since RNA sequences probably compete for access to the translation machinery and most GC3-codons (codons with G or C at the third position) are favoured, the higher GC-content may have driven the increase in codon bias within protein coding regions.
Table 1

Codon usage across the trypanosomatid tandem gene set (see table 1 in the additional data file, >60,000 codons) and tRNA gene copy number (see table 2 in the additional data file) are shown. Overall favoured codons and all sixteen CU3 'wobble tRNA pairs' are indicated in bold text. T. cruzi has additional tRNA genes in many cases because the strain used for the sequencing project is a hybrid of two genotypes [19].

Am Acid

Codon

T. brucei Number

Fraction

tRNAs

T. cruzi Number

Fraction

tRNAs

L. major Number

Fraction

tRNAs

Ala

GCG

452

0.24

2

685

0.36

2

925

0.49

2

Ala

GCA

406

0.21

1

283

0.15

1

79

0.04

1

Ala

GCU

526

0.28

2

325

0.17

2

299

0.16

2

Ala

GCC

516

0.27

0

629

0.33

0

593

0.31

0

Arg

AGG

93

0.07

1

95

0.07

2

45

0.04

1

Arg

AGA

29

0.02

1

25

0.02

2

15

0.01

1

Arg

CGG

117

0.09

1

103

0.08

2

86

0.07

1

Arg

CGA

82

0.06

1

55

0.04

2

21

0.02

1

Arg

CGU

415

0.31

3

295

0.23

4

148

0.12

4

Arg

CGC

592

0.45

0

730

0.56

0

922

0.75

0

Asn

AAU

248

0.29

0

178

0.23

0

64

0.08

0

Asn

AAC

596

0.71

2

597

0.77

4

716

0.92

3

Asp

GAU

501

0.44

0

293

0.27

0

216

0.21

0

Asp

GAC

639

0.56

2

795

0.73

2

804

0.79

3

Cys

UGU

112

0.33

0

69

0.21

0

40

0.12

0

Cys

UGC

231

0.67

2

260

0.79

2

296

0.88

1

Gln

CAG

508

0.71

2

604

0.86

4

653

0.95

3

Gln

CAA

209

0.29

1

102

0.14

2

35

0.05

1

Glu

GAG

893

0.67

2

1084

0.8

4

1183

0.93

2

Glu

GAA

441

0.33

1

266

0.2

1

93

0.07

1

Gly

GGG

176

0.11

1

177

0.12

2

111

0.08

1

Gly

GGA

246

0.15

1

163

0.11

2

43

0.03

1

Gly

GGU

750

0.46

0

362

0.24

0

304

0.21

0

Gly

GGC

469

0.29

3

829

0.54

4

997

0.69

4

His

CAU

111

0.28

0

73

0.18

0

52

0.13

0

His

CAC

292

0.72

1

344

0.82

4

349

0.87

2

Ile

AUA

82

0.08

1

37

0.04

3

20

0.02

1

Ile

AUU

430

0.42

2

350

0.38

4

165

0.18

3

Ile

AUC

516

0.5

0

533

0.58

0

753

0.8

0

Leu

UUG

243

0.14

0

154

0.09

2

72

0.05

1

Leu

UUA

61

0.04

1

16

0.01

2

6

0

1

Leu

CUG

526

0.31

1

764

0.46

2

970

0.64

2

Leu

CUA

107

0.06

1

39

0.02

2

29

0.02

1

Leu

CUU

391

0.23

2

343

0.21

4

138

0.09

3

Leu

CUC

375

0.22

0

336

0.2

0

289

0.19

0

Lys

AAG

1248

0.78

3

1458

0.9

4

1457

0.96

3

Lys

AAA

350

0.22

1

158

0.1

2

66

0.04

1

Met

AUG

571

1

3

558

1

6

596

1

4

Phe

UUU

261

0.35

0

327

0.43

0

111

0.16

0

Phe

UUC

479

0.65

2

426

0.57

4

579

0.84

2

Pro

CCG

191

0.22

1

290

0.33

2

469

0.59

2

Pro

CCA

190

0.22

1

138

0.16

2

63

0.08

1

Pro

CCU

185

0.22

1

152

0.18

2

75

0.09

2

Pro

CCC

288

0.34

0

287

0.33

0

187

0.24

0

Ser

AGU

137

0.11

0

77

0.06

0

39

0.03

0

Ser

AGC

234

0.19

2

299

0.25

2

330

0.28

2

Ser

UCG

210

0.17

1

302

0.25

2

384

0.32

1

Ser

UCA

180

0.14

1

85

0.07

2

35

0.03

1

Ser

UCU

237

0.19

1

158

0.13

2

132

0.11

1

Ser

UCC

262

0.21

0

274

0.23

0

276

0.23

0

Thr

ACG

368

0.29

1

553

0.47

2

622

0.58

2

Thr

ACA

328

0.26

1

182

0.16

2

82

0.08

1

Thr

ACU

280

0.22

1

141

0.12

2

74

0.07

3

Thr

ACC

276

0.22

0

292

0.25

0

294

0.27

0

Trp

UGG

194

1

1

204

1

2

167

1

1

Tyr

UAU

177

0.31

0

83

0.15

0

39

0.07

0

Tyr

UAC

391

0.69

1

466

0.85

2

510

0.93

3

Val

GUG

805

0.47

1

1022

0.6

4

1115

0.69

2

Val

GUA

200

0.12

1

60

0.04

2

46

0.03

1

Val

GUU

449

0.26

2

318

0.19

2

141

0.09

2

Val

GUC

250

0.15

0

290

0.17

0

322

0.2

0

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-2/MediaObjects/12864_2007_Article_1196_Fig1_HTML.jpg
Figure 1

Relative frequency of synonymous codon usage in the tandem, high expression gene sets from T. brucei and L. major; codon usage patterns were broadly similar in T. brucei and T. cruzi (data not shown). Tandem amplified genes were considered highly expressed if represented by ≥ 10 mass spectra from whole-cell proteome analysis of four life-cycle stages of T. cruzi [21].

Although all two-fold degenerate codons show preference for GC3, this feature is not universal throughout the high-expression gene sets. T. brucei shows little bias for Ala, Pro, Ser or Thr codons and GGGGly, AGGArg and CGGArg codons are more than two-fold under-represented in all three trypanosomatids (Fig. 1 and Table 1).

'Favoured' codons correspond to over-represented cognate tRNAs

Biased codons likely favour translation if cognate tRNAs are more abundant [7, 23]. Previous analysis indicated that T. brucei tRNA genes are organised into clusters spread over several chromosomes and that relative tRNA abundance correlates with codon usage but not with gene copy number [24]. It was noted in that study, however, that tRNA nucleotide modification may lead to under-estimation of tRNA abundance in some cases. The tRNA gene complement was analysed in all three trypanosomatids in order to explore the relationship between codon usage and tRNA gene copy number.

The GeneDB database revealed a total of 261 annotated tRNAs among the three trypanosomatids. The universal genetic code comprises 61 codons for 20 amino acids but some tRNAs decode multiple codons, a phenomenon known as wobble [25]. The trypanosomatid tRNA complement (see table 2 in the additional data file) represents 45 anticodons, a tRNA gene distribution that suggests that sixteen CU3 codons are decoded by wobble tRNAs (see Table 1). Eight U3 codons are likely decoded by anticodons with guanosine while another eight C3 codons are likely decoded by anticodons with inosine (deaminated adenosine) in the wobble position respectively [25, 26]. All but one of these 'wobble pairs' were predicted previously in T. brucei [24]. Codon bias is seen among wobble pairs in the high-expression gene set; Asn, Asp, Cys, His, Phe and Tyr for example (Table 1), possibly reflecting translational selection based on differential stability of codon-anticodon interaction. In addition, some wobble codon pairs and putative cognate tRNAs are biased and over-represented respectively; CGU/CArg-ACGtRNA and GGU/CGly-GCCtRNA for example (Table 1).

To test the idea that tRNA abundance is related to gene copy number in trypanosmatids, amino acid frequency in the tandem, high-expression protein set was plotted against tRNA gene copy number (Fig. 2A). The positive correlation strongly supports the idea that tRNA gene copy number determines relative tRNA abundance. Having established this relationship, the four GA3 synonymous codon-pairs with >30% bias in all three trypanosomatids were analysed (Fig. 2B). The results show a positive correlation between tRNA gene copy number and codon usage bias providing further evidence for translational selection; only one T. brucei tRNA gene fails to display a matching bias in copy number. The correlation is more striking in L. major, the trypanosomatid that displays more pronounced codon bias. In this case, eight of nine GA3 pairs that display substantial codon bias also display a corresponding tRNA bias (Table 1; those shown in Fig. 2b plus GCG/AAla, CUG/ALeu, CCG/APro and ACG/AThr). Indeed, there are several examples in Leishmania where tRNA genes that recognise favoured codons appear to have been specifically amplified since divergence from the trypanosomes (CAGGln, CUGLeu, CCGPro, ACGThr and favoured 'wobble' codons; CGCArg, GGCGly, AUCIle, CUCLeu, CCCPro and ACCThr; see Table 1), perhaps to counter the high background GC-content. Thus, there is a correspondence between numbers of cognate tRNAs, a likely measure of tRNA abundance, and preferred codons in the high expression gene sets in all three trypanosomatid genomes.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-2/MediaObjects/12864_2007_Article_1196_Fig2_HTML.jpg
Figure 2

Correspondence between codon-usage in highly expressed genes and tRNA gene copy number. (a) Correspondence between amino acid frequency and cognate tRNA gene copy number in T. brucei; patterns were broadly similar in T. cruzi and L. major (data not shown). (b) Correspondence between synonymous codon usage (lower charts with black bars) and cognate tRNA gene copy number (upper charts with grey bars). The GA3 codon-pairs with >30% bias in all three trypanosomatids are shown.

Codon usage correlates with expression level

The codon adaptation index (CAI) is used to measure synonymous codon usage bias and can predict gene expression level if translational selection operates [27]. Whole-cell proteome data are available for T. cruzi [18] and the number of mass-spectra matched to individual genes provides an indication of relative expression level. Thus, proteome data provide an opportunity to test for a correlation between codon usage and expression. For this analysis, T. cruzi sequences were divided into four categories expected to have progressively higher CAI scores if translational selection operates. The categories were as follows: (a) intergenic regions (translated in all six reading-frames); (b) 'Single-copy' genes; (c) 'Single-copy' genes detected through whole-cell proteome analysis (≥ 5 mass spectra) and (d) Tandem-arrayed genes detected through whole-cell proteome analysis (≥ 10 mass spectra). The analysis shown in Figure 3 indicates progressively increasing CAI scores from (a) through (d). Protein coding sequences appear to have evolved to optimise translation while intergenic sequences may have evolved to counter translation.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-2/MediaObjects/12864_2007_Article_1196_Fig3_HTML.jpg
Figure 3

Codon usage correlates with expression level. T. cruzi sequences were divided into four categories and CAI scores were calculated. The distribution of scores is indicated for each category. (a) intergenic regions (translated in all six reading frames). (b) 'Single-copy' genes (see table 4 in the additional data file). (c) 'Single-copy' genes detected (≥ 5 mass spectra) through whole-cell proteome analysis (see table 3 in the additional data file). (d) Tandem-arrayed genes detected (≥ 10 mass spectra) through whole-cell proteome analysis (see table 1 in the additional data file). The average score +/- standard deviation is indicated for each category.

Codon usage can predict expression level for individual proteins

A more rigorous test of the contribution of translational selection to gene expression is whether expression level can be predicted for individual genes exclusively based on codon usage. Single-copy genes represented in the T. cruzi proteome data [21] were used for this analysis (see table 3 in the additional data file); tandem array genes were not suitable because gene copy number, thought to contribute to expression, is highly variable and unknown in most cases. CAI scores were calculated for each gene and plotted against the gene length-adjusted number of cognate mass-spectra, a measure of relative expression. A positive correlation emerges and the relationship between protein abundance and CAI is log-linear (Fig. 4). The results indicate that T. cruzi protein-coding sequences can predict relative steady-state protein expression level. The trend is remarkable because of the range of other parameters, both alternative modes of expression control and experimental sampling that could impact on the outcome. The findings are consistent with the idea that codon bias has a major impact on steady state protein levels in trypanosomatids.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-2/MediaObjects/12864_2007_Article_1196_Fig4_HTML.jpg
Figure 4

Codon usage is predictive of expression level for individual genes. CAI scores were calculated for single-copy genes (see Fig. 3c and table 3 in the additional data file). The number of mass spectra, corrected for gene length, provides a measure of relative abundance. Only genes represented by ≥ 5 mass spectra from T. cruzi proteome analysis [21] were analysed to minimise sampling errors.

Relative codon bias is conserved among trypanosomatids

Current, high throughput technologies fail to detect and/or quantify the expression levels of less abundant proteins [28]. An interesting question is whether CAI can predict expression level across the genome. Although certain proteins will be required in substantially different quantities in the different trypanosomatids, the majority are expected to be expressed at similar relative levels. Thus, relative CAI scores are expected to be broadly conserved if translational selection impacts upon global gene expression. CAI scores were calculated for 'single-copy' genes from syntenic, polycistronic gene clusters on three different chromosomes (see table 4 in the additional data file). The different gene clusters from each trypanosomatid showed similar CAI distribution so the data were pooled. CAI scores were first compared in the trypanosomes, T. brucei and T. cruzi and the analysis indicates that relative scores are indeed conserved (Fig. 5A). A more rigorous test was then carried out, between T. brucei and L. major, thought to have diverged around 250 Mya. Despite the substantially higher GC content in L. major, relative scores remain broadly conserved (Fig. 5B). The results are consistent with the idea that codon bias predicts translation efficiency for any mRNA in trypanosomatids.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-9-2/MediaObjects/12864_2007_Article_1196_Fig5_HTML.jpg
Figure 5

Relative codon bias is conserved among trypanosomatids. CAI scores were calculated for single-copy, protein-coding sequences. (a) Relative scores for T. brucei and T. cruzi. (b) Relative scores for T. brucei and L. major. The syntenic, polycistronic clusters analysed were from, T. brucei chromosomes 1, 6 and 10 and from the orthologous genes in T. cruzi and on L. major chromosomes 12, 30 and 3 (see table 4 in the additional data file). The T. cruzi set is the same as that presented in Figure 3b; the chromosome numbers have not been determined in T. cruzi due to problems with sequence assembly.

Discussion

In trypanosomatids, bias in codon usage correlates with tRNA gene copy number and with expression level. This provides strong evidence for a major impact of translational selection on gene expression. Thus, translational selection facilitates the generation of differential protein abundance from genes embedded within polycistrons. Since translation rates are likely retarded by codons with low-abundance cognate tRNAs, natural selection of tRNA gene numbers and codon bias allows optimization of translation rate and efficiency across the genome. Many of the most highly expressed genes use a dual strategy to enhance expression; increased gene dosage combined with a high proportion of codons with more abundant cognate tRNAs. This dual strategy allows for an increase in overall transcription and translation.

In S. cerevisiae, although the value of codon bias as a predictor of protein levels is disputed, proteins encoded by genes with low bias are not detected on two-dimensional gels and protein abundance does correlate when only genes with high bias are considered [29, 30]. Thus, translational selection may be a pervasive mechanism in the control of gene expression but its impact may be obscured in many cell-types due to the impact of other regulatory mechanisms. I propose that translational selection makes a more substantial contribution to gene expression control in trypanosomatids due to the paucity of regulated transcription. Initial ribosome assembly on mRNA may also be largely unregulated since trans-splicing leads to the attachment of an identical spliced-leader sequence to every mRNA [4]. Thus, differential translation efficiency may be the dominant level of gene expression control in trypanosomatids. Translational selection may have emerged in primitive cells that lacked mechanisms for differential mRNA expression and the emergence of differential transcription in other cell types may have obscured or partially replaced this mode of control.

Many trypanosomatid proteins are differentially expressed during the cell-cycle and the life-cycle and additional controls must clearly determine such differential expression. A number of mRNA un-translated regions, particularly at the 3' end, may modulate mRNA maturation, transport, turnover and translation for example and protein turnover may also vary [4]. When these additional controls operate, codon bias should fail to predict expression level. Prominent examples of differential regulation include the variant surface glycoprotein gene, abundantly expressed in bloodstream form T. brucei, and procyclins, expressed in insect stage T. brucei. Expression of these proteins is regulated using an unusual mechanism involving differential transcription by RNA polymerase I which is restricted to RRNA genes in other eukaryotes [31]. mRNA turnover [32] and protein turnover [33] also contribute to controlling variant surface glycoprotein expression and, as expected, codon bias fails to predict relative expression when these controls operate (CAI for variant surface glycoprotein genes = 0.54 +/-0.01. n = 4). Thus, codon analysis in combination with high-throughput proteome analysis may allow identification of proteins subject to the alternative expression control strategies described above. In addition, orthologous genes that show poor correspondence in relative codon bias among trypanosomatids may be those that display species-specific expression differences. If this is the case, genome-wide codon-usage analysis will facilitate the identification of these genes.

Protein coding sequences are relatively easy to predict in trypanosomatids due to high density, intron poverty and organisation into directional clusters. New annotation tools are under development, however [34], and gene annotation could be refined. The findings reported here indicate that algorithms incorporating codon sampling could facilitate the annotation of current and future trypanosomatid genome sequence data.

Conclusion

Constitutive RNA polymerase II transcription is widespread in trypanosomatids so differential gene expression must be controlled post-transcription. Research in this area has focussed on un-translated mRNA regulatory sequences typically found within 3' un-translated regions. As reported here, analysis of synonymous codon bias indicated pronounced bias in highly expressed genes and this bias correlates with tRNA gene copy number and with gene expression level. In addition, relative codon bias is conserved among orthologous genes from divergent trypanosomatids, even in genes thought to be expressed at low level. Taken together, the results suggest that control at the level of translation, translational selection, is the dominant mechanism underlying differential protein expression in these organisms.

Methods

Analysis of sequence and expression data

Annotated trypanosomatid genome sequence data were browsed and analysed using the GeneDB interface [35] hosted by the Wellcome Trust Sanger Institute [36]. T. cruzi proteome expression data [21] were obtained from Supporting Online Material available through the Science website [37]. Codon usage was determined using the Codon Usage feature [38] within The Sequence Manipulation Suite [39]. CodonW [40] was used to generate codon usage tables for each trypanosomatid and to calculate CAI scores. Codon usage tables were assembled using fifty genes from each tandem, high expression gene set (Table 1 in the additional data file). Data were manipulated and analysed using Excel (Microsoft).

References for additional file

1. GeneDB http://www.genedb.org.

Table S1 and Table S3

2. Atwood JA, 3rd, Weatherly DB, Minning TA, Bundy B, Cavola C, Opperdoes FR, Orlando R, Tarleton RL: The Trypanosoma cruzi proteome. Science 2005, 309(5733):473–476.

Table S1

3. Mottram JC, Murphy WJ, Agabian N: A transcriptional analysis of the Trypanosoma brucei hsp83 gene cluster. Mol Biochem Parasitol 1989, 37(1):115–127.

4. Marchand M, Poliszczak A, Gibson WC, Wierenga RK, Opperdoes FR, Michels PA: Characterization of the genes for fructose-bisphosphate aldolase in Trypanosoma brucei. Mol Biochem Parasitol 1988, 29(1):65–75.

List of abbreviations

CAI: 

Codon Adaptation Index.

Declarations

Acknowledgements

Genome sequence data were produced by the Trypanosomatid Sequencing Groups at the Wellcome Trust Sanger Institute, Pathogen Sequencing Unit; The Institute for Genome Research (Now the J. Craig Venter Institute); The Seattle Biomedical Research Institute and The Karolinska Institute. Sequence data were obtained through the Wellcome Trust Sanger Institute's interface, GeneDB [36]. I thank Sam Alsford for help with statistical analysis and also John Kelly and Michael Gaunt for comments on the findings. Research in my laboratory is funded by The Wellcome Trust.

Authors’ Affiliations

(1)
London School of Hygiene & Tropical Medicine

References

  1. World Health Organisation - Tropical Disease Research . [http://www.who.int/tdr/diseases/]
  2. Palenchar JB, Bellofatto V: Gene transcription in trypanosomes. Mol Biochem Parasitol. 2006, 146 (2): 135-141. 10.1016/j.molbiopara.2005.12.008.PubMedView ArticleGoogle Scholar
  3. El-Sayed NM, Myler PJ, Blandin G, Berriman M, Crabtree J, Aggarwal G, Caler E, Renauld H, Worthey EA, Hertz-Fowler C, Ghedin E, Peacock C, Bartholomeu DC, Haas BJ, Tran AN, Wortman JR, Alsmark UC, Angiuoli S, Anupama A, Badger J, Bringaud F, Cadag E, Carlton JM, Cerqueira GC, Creasy T, Delcher AL, Djikeng A, Embley TM, Hauser C, Ivens AC, Kummerfeld SK, Pereira-Leal JB, Nilsson D, Peterson J, Salzberg SL, Shallom J, Silva JC, Sundaram J, Westenberger S, White O, Melville SE, Donelson JE, Andersson B, Stuart KD, Hall N: Comparative genomics of trypanosomatid parasitic protozoa. Science. 2005, 309 (5733): 404-409. 10.1126/science.1112181.PubMedView ArticleGoogle Scholar
  4. Clayton CE: Life without transcriptional control? From fly to man and back again. Embo J. 2002, 21 (8): 1881-1888. 10.1093/emboj/21.8.1881.PubMedPubMed CentralView ArticleGoogle Scholar
  5. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, Dephoure N, O'Shea EK, Weissman JS: Global analysis of protein expression in yeast. Nature. 2003, 425 (6959): 737-741. 10.1038/nature02046.PubMedView ArticleGoogle Scholar
  6. Akashi H, Eyre-Walker A: Translational selection and molecular evolution. Curr Opin Genet Dev. 1998, 8 (6): 688-693. 10.1016/S0959-437X(98)80038-5.PubMedView ArticleGoogle Scholar
  7. Duret L: tRNA gene number and codon usage in the C. elegans genome are co-adapted for optimal translation of highly expressed genes. Trends Genet. 2000, 16 (7): 287-289. 10.1016/S0168-9525(00)02041-2.PubMedView ArticleGoogle Scholar
  8. Sharp PM, Averof M, Lloyd AT, Matassi G, Peden JF: DNA sequence evolution: the sounds of silence. Philos Trans R Soc Lond B Biol Sci. 1995, 349 (1329): 241-247. 10.1098/rstb.1995.0108.PubMedView ArticleGoogle Scholar
  9. Alvarez F, Robello C, Vignali M: Evolution of codon usage and base contents in kinetoplastid protozoans. Mol Biol Evol. 1994, 11 (5): 790-802.PubMedGoogle Scholar
  10. Michels PA: Evolutionary aspects of trypanosomes: analysis of genes. J Mol Evol. 1986, 24 (1-2): 45-52. 10.1007/BF02099950.PubMedView ArticleGoogle Scholar
  11. Necsulea A, Lobry JR: Revisiting the directional mutation pressure theory: the analysis of a particular genomic structure in Leishmania major. Gene. 2006, 385: 28-40. 10.1016/j.gene.2006.04.031.PubMedView ArticleGoogle Scholar
  12. Parsons M, Stuart K, Smiley BL: Trypanosoma brucei: analysis of codon usage and nucleotide composition of nuclear genes. Exp Parasitol. 1991, 73 (1): 101-105. 10.1016/0014-4894(91)90012-L.PubMedView ArticleGoogle Scholar
  13. 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. 10.1038/nbt1270.PubMedView ArticleGoogle Scholar
  14. Cohen-Freue G, Holzer TR, Forney JD, McMaster WR: Global gene expression in Leishmania. Int J Parasitol. 2007, 37 (10): 1077-1086. 10.1016/j.ijpara.2007.04.011.PubMedView ArticleGoogle Scholar
  15. Duncan R: DNA microarray analysis of protozoan parasite gene expression: outcomes correlate with mechanisms of regulation. Trends Parasitol. 2004, 20 (5): 211-215. 10.1016/j.pt.2004.02.008.PubMedView ArticleGoogle Scholar
  16. Leifso K, Cohen-Freue G, Dogra N, Murray A, McMaster WR: Genomic and proteomic expression analysis of Leishmania promastigote and amastigote life stages: the Leishmania genome is constitutively expressed. Mol Biochem Parasitol. 2007, 152 (1): 35-46. 10.1016/j.molbiopara.2006.11.009.PubMedView ArticleGoogle Scholar
  17. McNicoll F, Drummelsmith J, Muller M, Madore E, Boilard N, Ouellette M, Papadopoulou B: A combined proteomic and transcriptomic approach to the study of stage differentiation in Leishmania infantum. Proteomics. 2006, 6 (12): 3567-3581. 10.1002/pmic.200500853.PubMedView ArticleGoogle Scholar
  18. Berriman M, Ghedin E, Hertz-Fowler C, Blandin G, Renauld H, Bartholomeu DC, Lennard NJ, Caler E, Hamlin NE, Haas B, Bohme U, Hannick L, Aslett MA, Shallom J, Marcello L, Hou L, Wickstead B, Alsmark UC, Arrowsmith C, Atkin RJ, Barron AJ, Bringaud F, Brooks K, Carrington M, Cherevach I, Chillingworth TJ, Churcher C, Clark LN, Corton CH, Cronin A, Davies RM, Doggett J, Djikeng A, Feldblyum T, Field MC, Fraser A, Goodhead I, Hance Z, Harper D, Harris BR, Hauser H, Hostetler J, Ivens A, Jagels K, Johnson D, Johnson J, Jones K, Kerhornou AX, Koo H, Larke N, Landfear S, Larkin C, Leech V, Line A, Lord A, Macleod A, Mooney PJ, Moule S, Martin DM, Morgan GW, Mungall K, Norbertczak H, Ormond D, Pai G, Peacock CS, Peterson J, Quail MA, Rabbinowitsch E, Rajandream MA, Reitter C, Salzberg SL, Sanders M, Schobel S, Sharp S, Simmonds M, Simpson AJ, Tallon L, Turner CM, Tait A, Tivey AR, Van Aken S, Walker D, Wanless D, Wang S, White B, White O, Whitehead S, Woodward J, Wortman J, Adams MD, Embley TM, Gull K, Ullu E, Barry JD, Fairlamb AH, Opperdoes F, Barrell BG, Donelson JE, Hall N, Fraser CM, Melville SE, El-Sayed NM: The genome of the African trypanosome Trypanosoma brucei. Science. 2005, 309 (5733): 416-422. 10.1126/science.1112642.PubMedView ArticleGoogle Scholar
  19. El-Sayed NM, Myler PJ, Bartholomeu DC, Nilsson D, Aggarwal G, Tran AN, Ghedin E, Worthey EA, Delcher AL, Blandin G, Westenberger SJ, Caler E, Cerqueira GC, Branche C, Haas B, Anupama A, Arner E, Aslund L, Attipoe P, Bontempi E, Bringaud F, Burton P, Cadag E, Campbell DA, Carrington M, Crabtree J, Darban H, da Silveira JF, de Jong P, Edwards K, Englund PT, Fazelina G, Feldblyum T, Ferella M, Frasch AC, Gull K, Horn D, Hou L, Huang Y, Kindlund E, Klingbeil M, Kluge S, Koo H, Lacerda D, Levin MJ, Lorenzi H, Louie T, Machado CR, McCulloch R, McKenna A, Mizuno Y, Mottram JC, Nelson S, Ochaya S, Osoegawa K, Pai G, Parsons M, Pentony M, Pettersson U, Pop M, Ramirez JL, Rinta J, Robertson L, Salzberg SL, Sanchez DO, Seyler A, Sharma R, Shetty J, Simpson AJ, Sisk E, Tammi MT, Tarleton R, Teixeira S, Van Aken S, Vogt C, Ward PN, Wickstead B, Wortman J, White O, Fraser CM, Stuart KD, Andersson B: The genome sequence of Trypanosoma cruzi, etiologic agent of Chagas disease. Science. 2005, 309 (5733): 409-415. 10.1126/science.1112631.PubMedView ArticleGoogle Scholar
  20. Ivens AC, Peacock CS, Worthey EA, Murphy L, Aggarwal G, Berriman M, Sisk E, Rajandream MA, Adlem E, Aert R, Anupama A, Apostolou Z, Attipoe P, Bason N, Bauser C, Beck A, Beverley SM, Bianchettin G, Borzym K, Bothe G, Bruschi CV, Collins M, Cadag E, Ciarloni L, Clayton C, Coulson RM, Cronin A, Cruz AK, Davies RM, De Gaudenzi J, Dobson DE, Duesterhoeft A, Fazelina G, Fosker N, Frasch AC, Fraser A, Fuchs M, Gabel C, Goble A, Goffeau A, Harris D, Hertz-Fowler C, Hilbert H, Horn D, Huang Y, Klages S, Knights A, Kube M, Larke N, Litvin L, Lord A, Louie T, Marra M, Masuy D, Matthews K, Michaeli S, Mottram JC, Muller-Auer S, Munden H, Nelson S, Norbertczak H, Oliver K, O'Neil S, Pentony M, Pohl TM, Price C, Purnelle B, Quail MA, Rabbinowitsch E, Reinhardt R, Rieger M, Rinta J, Robben J, Robertson L, Ruiz JC, Rutter S, Saunders D, Schafer M, Schein J, Schwartz DC, Seeger K, Seyler A, Sharp S, Shin H, Sivam D, Squares R, Squares S, Tosato V, Vogt C, Volckaert G, Wambutt R, Warren T, Wedler H, Woodward J, Zhou S, Zimmermann W, Smith DF, Blackwell JM, Stuart KD, Barrell B, Myler PJ: The genome of the kinetoplastid parasite, Leishmania major. Science. 2005, 309 (5733): 436-442. 10.1126/science.1112680.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Atwood JA, Weatherly DB, Minning TA, Bundy B, Cavola C, Opperdoes FR, Orlando R, Tarleton RL: The Trypanosoma cruzi proteome. Science. 2005, 309 (5733): 473-476. 10.1126/science.1110289.PubMedView ArticleGoogle Scholar
  22. Jackson AP: Tandem gene arrays in Trypanosoma brucei: Comparative phylogenomic analysis of duplicate sequence variation. BMC Evol Biol. 2007, 7 (1): 54-10.1186/1471-2148-7-54.PubMedPubMed CentralView ArticleGoogle Scholar
  23. Kanaya S, Yamada Y, Kudo Y, Ikemura T: Studies of codon usage and tRNA genes of 18 unicellular organisms and quantification of Bacillus subtilis tRNAs: gene expression level and species-specific diversity of codon usage based on multivariate analysis. Gene. 1999, 238 (1): 143-155. 10.1016/S0378-1119(99)00225-5.PubMedView ArticleGoogle Scholar
  24. Tan TH, Pach R, Crausaz A, Ivens A, Schneider A: tRNAs in Trypanosoma brucei: genomic organization, expression, and mitochondrial import. Mol Cell Biol. 2002, 22 (11): 3707-3717. 10.1128/MCB.22.11.3707-3716.2002.PubMedPubMed CentralView ArticleGoogle Scholar
  25. Agris PF, Vendeix FA, Graham WD: tRNA's wobble decoding of the genome: 40 years of modification. J Mol Biol. 2007, 366 (1): 1-13. 10.1016/j.jmb.2006.11.046.PubMedView ArticleGoogle Scholar
  26. Rubio MA, Pastar I, Gaston KW, Ragone FL, Janzen CJ, Cross GA, Papavasiliou FN, Alfonzo JD: An adenosine-to-inosine tRNA-editing enzyme that can perform C-to-U deamination of DNA. Proc Natl Acad Sci U S A. 2007, 104 (19): 7821-7826. 10.1073/pnas.0702394104.PubMedPubMed CentralView ArticleGoogle Scholar
  27. Sharp PM, Li WH: The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 1987, 15 (3): 1281-1295. 10.1093/nar/15.3.1281.PubMedPubMed CentralView ArticleGoogle Scholar
  28. Gygi SP, Corthals GL, Zhang Y, Rochon Y, Aebersold R: Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci U S A. 2000, 97 (17): 9390-9395. 10.1073/pnas.160270797.PubMedPubMed CentralView ArticleGoogle Scholar
  29. Futcher B, Latter GI, Monardo P, McLaughlin CS, Garrels JI: A sampling of the yeast proteome. Mol Cell Biol. 1999, 19 (11): 7357-7368.PubMedPubMed CentralView ArticleGoogle Scholar
  30. Gygi SP, Rochon Y, Franza BR, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol Cell Biol. 1999, 19 (3): 1720-1730.PubMedPubMed CentralView ArticleGoogle Scholar
  31. Gunzl A, Bruderer T, Laufer G, Schimanski B, Tu LC, Chung HM, Lee PT, Lee MG: RNA polymerase I transcribes procyclin genes and variant surface glycoprotein gene expression sites in Trypanosoma brucei. Eukaryot Cell. 2003, 2 (3): 542-551. 10.1128/EC.2.3.542-551.2003.PubMedPubMed CentralView ArticleGoogle Scholar
  32. 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.PubMedPubMed CentralGoogle Scholar
  33. Seyfang A, Mecke D, Duszenko M: Degradation, recycling, and shedding of Trypanosoma brucei variant surface glycoprotein. J Protozool. 1990, 37 (6): 546-552.PubMedView ArticleGoogle Scholar
  34. Gopal S, Awadalla S, Gaasterland T, Cross GA: A computational investigation of kinetoplastid trans-splicing. Genome Biol. 2005, 6 (11): R95-10.1186/gb-2005-6-11-r95.PubMedPubMed CentralView ArticleGoogle Scholar
  35. Hertz-Fowler C, Peacock CS, Wood V, Aslett M, Kerhornou A, Mooney P, Tivey A, Berriman M, Hall N, Rutherford K, Parkhill J, Ivens AC, Rajandream MA, Barrell B: GeneDB: a resource for prokaryotic and eukaryotic organisms. Nucleic Acids Res. 2004, 32: D339-43. 10.1093/nar/gkh007.PubMedPubMed CentralView ArticleGoogle Scholar
  36. GeneDB . [http://www.genedb.org]
  37. Science . [http://www.sciencemag.org]
  38. Sequence Manipulation Suite . [http://www.bioinformatics.org/sms2]
  39. Stothard P: The sequence manipulation suite: JavaScript programs for analyzing and formatting protein and DNA sequences. Biotechniques. 2000, 28 (6): 1102, 1104-PubMedGoogle Scholar
  40. codonw: Correspondence Analysis of Codon Usage. [http://bioweb.pasteur.fr/seqanal/interfaces/codonw.html]
  41. Mottram JC, Murphy WJ, Agabian N: A transcriptional analysis of the Trypanosoma brucei hsp83 gene cluster. Mol Biochem Parasitol. 1989, 37 (1): 115-127. 10.1016/0166-6851(89)90108-4.PubMedView ArticleGoogle Scholar
  42. Marchand M, Poliszczak A, Gibson WC, Wierenga RK, Opperdoes FR, Michels PA: Characterization of the genes for fructose-bisphosphate aldolase in Trypanosoma brucei. Mol Biochem Parasitol. 1988, 29 (1): 65-75. 10.1016/0166-6851(88)90121-1.PubMedView ArticleGoogle Scholar

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