Open Access

Protein-based signatures of functional evolution in Plasmodium falciparum

  • Kate B Gardner1, 2,
  • Ipsita Sinha1,
  • Leyla Y Bustamante3,
  • Nicholas PJ Day1,
  • Nicholas J White1 and
  • Charles J Woodrow1Email author
BMC Evolutionary Biology201111:257

DOI: 10.1186/1471-2148-11-257

Received: 24 March 2011

Accepted: 14 September 2011

Published: 14 September 2011

Abstract

Background

It has been known for over a decade that Plasmodium falciparum proteins are enriched in non-globular domains of unknown function. The potential for these regions of protein sequence to undergo high levels of genetic drift provides a fundamental challenge to attempts to identify the molecular basis of adaptive change in malaria parasites.

Results

Evolutionary comparisons were undertaken using a set of forty P. falciparum metabolic enzyme genes, both within the hominid malaria clade (P. reichenowi) and across the genus (P. chabaudi). All genes contained coding elements highly conserved across the genus, but there were also a large number of regions of weakly or non-aligning coding sequence. These displayed remarkable levels of non-synonymous fixed differences within the hominid malaria clade indicating near complete release from purifying selection (dN/dS ratio at residues non-aligning across genus: 0.64, dN/dS ratio at residues identical across genus: 0.03). Regions of low conservation also possessed high levels of hydrophilicity, a marker of non-globularity. The propensity for such regions to act as potent sources of non-synonymous genetic drift within extant P. falciparum isolates was confirmed at chromosomal regions containing genes known to mediate drug resistance in field isolates, where 150 of 153 amino acid variants were located in poorly conserved regions. In contrast, all 22 amino acid variants associated with drug resistance were restricted to highly conserved regions. Additional mutations associated with laboratory-selected drug resistance, such as those in PfATPase4 selected by spiroindolone, were similarly restricted while mutations in another calcium ATPase (PfSERCA, a gene proposed to mediate artemisinin resistance) that reach significant frequencies in field isolates were located exclusively in poorly conserved regions consistent with genetic drift.

Conclusion

Coding sequences of malaria parasites contain prospectively definable domains subject to neutral or nearly neutral evolution on a scale that appears unrivalled in biology. This distinct evolutionary landscape has potential to confound analytical methods developed for other genera. Against this tide of genetic drift, polymorphisms mediating functional change stand out to such an extent that evolutionary context provides a useful signal for identifying the molecular basis of drug resistance in malaria parasites, a finding that is of relevance to both genome-wide and candidate gene studies in this genus.

Background

Identifying the molecular basis of disease-causing traits is one of the major justifications for the recent expansion in genomic data covering a wide range of taxa. Nowhere is this goal more clearly defined than in the case of malaria, where adaptive evolution in the form of drug resistance continues to undermine efforts to control human disease caused by P. falciparum [1] and P. vivax [2]. Understanding the molecular basis of resistance phenotypes is of great operational importance as markers can be used to monitor spread and alternative therapeutic strategies can be designed. Establishment of a genetic cross has proven a fruitful starting point for determination of the genotypic basis of drug resistance [35], but is difficult, expensive and hence rarely achieved in practice. Genomic epidemiological approaches currently represent a promising route forward allowing detection of signatures of selection associated with drug resistance [6], although this approach relies on identification of linkage disequilibrium which is known to be of variable strength [7]. Candidate gene approaches based on other forms of data analysis may also generate hypotheses suitable for further testing [8, 9].

Despite the power of these approaches for the discovery of new drug-resistance genotypes, a decade has passed since the description of the last confirmed resistance gene for a widely used antimalarial [4]. It remains exceptional for mutations hypothesized as being involved in drug-resistance to be validated by transfection and phenotypic testing in P. falciparum, steps that allow polymorphisms of true adaptive value to be discriminated from those that are present simply as a result of genetic drift.

A specific phenomenon that may complicate studies in this genus relates to the remarkable degree to which Plasmodium proteins are enriched in non-globular domains [10]. Since their first systematic description more than a decade ago, the function of these domains has remained unresolved [1113] with one possibility being that they represent downstream consequences of events at the DNA level (i.e. broadly neutral in functional terms). If this were to be the case, they would represent potent generators of neutral or near neutral non-synonymous polymorphism in line with central concepts of modern evolutionary biology [1416] that will inevitably confound both genome-wide and candidate approaches to identifying the molecular basis of resistance. Notably, minimal attempts have been made to determine systematically the degree to which specific areas of coding sequence have become released from negative selective in the long-term, and by inference unlikely to mediate functional change in the short-term. Measurement of selective forces is still generally undertaken at a whole-gene level, an approach that falls down if pressures vary considerably within individual genes, preventing determination of the true baseline for variation which is neutral evolution within and between malaria species.

We reasoned that application of two basic biological concepts could improve significantly the accuracy of approaches designed to identify the genetic basis of drug-resistance. Firstly, drug resistance, by necessity, requires a significant functional change in the parasite, and hence a functional change in one or more proteins. Secondly, functionally important regions of proteins are generally conserved across wide distances of evolution, and can therefore be discriminated in orthologous sequence alignments from sequences under minimal constraint [17].

We undertook a systematic study of evolutionary processes in Plasmodium coding sequences, revealing widespread, dynamic variation in selection pressures within individual coding sequences consistent with neutral theories of evolution. The use of underlying conservation score as an independent means of identifying functional variation against the backdrop of genetic drift was validated and applied by examining the molecular basis of drug-resistance, a phenotype that, by necessity, requires a significant functional change in the parasite, and hence frequently a functional change in one or more proteins.

Results

Polymorphism and divergence: whole-gene level

In order to study short-term evolutionary processes within the hominid malaria clade a reference set of 40 P. falciparum genes (Table 1) was identified, consisting of sequences well covered by genome sequencing of the closely related primate parasite P. reichenowi [18]. Single-nucleotide changes in these genes associated with divergence (between P. falciparum 3D7 strain and P. reichenowi) and intra-species polymorphism (within sequenced P. falciparum isolates) were quantified. The 40-gene reference set contained 1270 fixed synonymous differences between P. falciparum and P. reichenowi indicating a synonymous pairwise divergence (dS) of 6.05%, a figure consistent with previous estimates [19]. There were 795 non-synonymous fixed differences (dN = 0.92%) indicating an overall dN/dS ratio of 0.17, consistent with broad negative selection across these genes. This compares to a genome-wide assessment of fixed differences between P. falciparum and P. reichenowi, dN/dS = 0.21, and indicates that the sequences chosen broadly capture the range of dN/dS ratios encountered in the P. falciparum genome [18]. Within sequenced P. falciparum isolates there were 83 synonymous and 102 non-synonymous polymorphisms (dN/dS ratio 0.31). Non-synonymous changes hence constitute 55.1% of intraspecies polymorphism but only 38.5% of interspecies divergence, suggesting that approximately one-third of observed amino acid variation within sequenced P. falciparum isolates is deleterious and would naturally undergo purifying selection over a longer period.
Table 1

Reference set of genes

P. falciparum gene

Other identifier

Product

Pf amino acids

P. chabaudi gene

Pc amino acids

ATPases

     

PFA0300c

MAL1P1.52

Vacuolar ATPase-coupled proton transport

383

PCHAS_020560

383

PFC0840w

PfATPase7

Phospholipid transporting ATPase

1864

PCHAS_080610

1773

PFE0195w

PfATPase3

Cation transporting ATPase

2393

PCHAS_110330

1904

PF07_0047

None

Cell-division cycle ATPase

1229

PCHAS_122230

1106

PF08_0113

None

Vacuolar proton transporting ATPase

1053

PCHAS_122440

957

PFI0240c

None

Cu2+- transporting ATPase

2563

PCHAS_041740

2128

PFL0590c

PfATPase4

Non-SERCA type Ca2+-transporting ATPase

1208

PCHAS_020540

1468

PFL1125w

MAL12P1.225

Phospholipid transporting ATPase

1618

PCHAS_144030

1601

Transporters

     

PFB0210c

PfHT1

Hexose transporter

504

PCHAS_030470

505

PFB0465c

PF02_0097

Monocarboxylate transporter

457

PCHAS_030940

456

PFE1185w

MAL5P1.237

Metal transporter

684

PCHAS_123900

704

PFF0450c

MAL6P1.94

Zn2+ or Fe2+ transporter

361

PCHAS_010830

338

PFF0170w

MAL6P1.38

Calcium antiporter

441

PCHAS_010300

440

PFF1430c

MAL6P1.133

Amino acid transporter

606

PCHAS_112780

617

PFI1295c

None

Monocarboxylate transporter

529

PCHAS_082700

507

PF11_0210

None

Mg2+, Co2+ & Ni2+ channel

529

PCHAS_091640

435

PF11_0338

PfAQP

Aquaglyceroporin

258

PCHAS_093040

258

MAL13P1.23

None

CorA-like Mg2+ transporter

468

PCHAS_140460

491

PF13_0252

PfNT1

Nucleoside transporter

422

PCHAS_136470

414

PF14_0679

None

Inorganic anion antiporter

664

PCHAS_133900

700

Glycolytic

     

PFF1155w

MAL6P1.189

Hexokinase

493

PCHAS_112240

494

PF14_0341

None

Glucose-6-phosphate isomerase

579

PCHAS_100870

578

PFI0755c

None

6-Phosphofructokinase

1418

PCHAS_081620

1306

PF14_0425

None

Fructose-bisphosphate aldolase

369

PCHAS_131180

369

PF14_0378

None

Triosephosphate isomerase

248

PCHAS_130700

248

PFI1105w

PGK

Phosphoglycerate kinase

416

PCHAS_082320

416

PF11_0208

None

Phosphoglycerate mutase

250

PCHAS_091620

250

PF10_0155

None

Enolase

446

PCHAS_121500

446

PFF1300w

MAL6P1.160

Pyruvate kinase

511

PCHAS_112510

511

PF13_0141

PfLDH

L-lactate dehydrogenase

316

PCHAS_134470

316

DNA/RNA

     

PFB0730w

PF02_0151

DEAD/DEAH box helicase

1997

PCHAS_031480

1404

PFC0805w

MAL3P6.20

DNA-directed RNA polymerase II

2457

PCHAS_080680

2307

PFE0465c

MAL5P1.95

RNA polymerase I

2914

PCHAS_110870

2570

PFE0715w

MAL5P1.144

Asparagine-tRNA ligase

1128

PCHAS_111360

1084

PFF1095w

MAL6P1.201

Leucyl tRNA synthase

1447

PCHAS_112120

1251

MAL7P1.145

None

Mismatch repair protein pms1

1330

PCHAS_020880

1094

PF13_0251

None

DNA topoisomerase III

710

PCHAS_136460

676

PF14_0234

None

DNA-directed DNA polymerase

1236

PCHAS_101890

989

PF14_0316

None

DNA topoisomerase II

1472

PCHAS_101120

1460

PF14_0695

None

DNA-directed RNA polymerase

861

PCHAS_134050

796

Conservation across the Plasmodiumgenus

Study of conservation across the Plasmodium genus was undertaken using orthologous protein sequences from the rodent parasite P. chabaudi, as genome sequencing of P. chabaudi was at a more advanced stage at the time of the studies than other available rodent species. P. falciparum (3D7) protein sequences were compared with P. chabaudi; an average score for all hominid malaria variants would strictly be more accurate, although this would only influence approximately 1% of residues. The mean level of protein conservation per gene ranged widely across the reference set of 40 housekeeping genes (mean +4.88 to -0.47 based on the BLOSUM62 matrix). It was also noted that overall 20.1% of P. falciparum residues were non-aligned, compared to 13.3% for P. chabaudi, consistent with expansion of non-aligned sequence in the hominid malaria lineage since the last common ancestor of these species.

Correlation between short and long-term conservation

At the whole-gene level the dN/dS ratio for P. falciparum - P. reichenowi divergence strongly correlated with the mean cross-genus conservation score (Figure 1a, r2 = 0.69, p < 0.0001); as expected dS showed no such relationship. For polymorphism within P. falciparum (where interpretation of dN/dS is less straightforward [20]), non-significant trends of the same form were seen (Figure 1b). Gene function also appeared to be associated with the degree of purifying selection, with glycolytic enzymes showing lower dN/dS values than the other groups (Figure 1c) consistent with previous work [18]. We compared our dN/dS scores for P. falciparum/reichenowi divergence (Nei-Gojobori method with Jukes-Cantor correction) with those previously reported for the same 40 genes using maximum likelihood for calculation of dN/dS [18]. The two sets of results were well correlated (Spearman r = 0.96) indicating that in relative terms dN/dS is very similar for the differing methods; it also confirms that the fixed difference datasets between the two studies are highly comparable. The slope of the line of correlation between our results and those of Jeffares et al. [18] was 0.81 suggesting that the Nei-Gojobori analysis overestimates dN/dS to a small degree compared to PAML.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-257/MediaObjects/12862_2011_Article_1879_Fig1_HTML.jpg
Figure 1

Hominid malaria clade divergence and conservation across the Plasmodium genus. For the forty individual genes of the reference set, dN/dS and dS are plotted against mean conservation score for each gene. (a) Fixed differences between P. falciparum and P. reichenowi. (b) Polymorphisms within P. falciparum. (c) dN/dS ratios for fixed differences between P. falciparum and P. reichenowi according to functional group.

A stratified analysis was undertaken in which each amino acid residue was assigned to one of four levels of cross-genus evolutionary conservation according to P. falciparum - P. chabaudi CLUSTALW alignment. Residues were defined as non-aligned, non-conserved (-4 to -1), semi-conserved (0 to +3) and identical (+4 and above) on the basis of the BLOSUM62 matrix. Synonymous substitution rates within the hominid malaria clade were similar across all levels of conservation (Figure 2a) but non-synonymous substitution rates were strongly influenced by long-term evolutionary context (Figure 2b). The dN/dS ratio for divergence was approximately 20-fold higher at non-aligning residues (0.64) compared to residues identical across the genus (0.034; Table 2). Chi-squared testing comparing divergence with polymorphism (McDonald-Kreitman test) within each conservation stratum showed that strong purifying selection was largely confined to residues identical across the genus (Table 2), with minimal evidence for purifying selection in non-aligned sequence regions.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-257/MediaObjects/12862_2011_Article_1879_Fig2_HTML.jpg
Figure 2

Mutation rates within the hominid malaria clade stratified by conservation level across the Plasmodium genus. (a) Synonymous substitution rates. (b) dN/dS ratios. Data are based on 40 reference genes. Fixed differences between P. falciparum and P. reichenowi are indicated by filled grey bars; polymorphism within P. falciparum is indicated by empty bars. Error bars represent 95% confidence intervals. Conservation levels were based on BLOSUM62 score at individual amino acids in P. falciparum - P. chabaudi alignments; identical (+4 and above), semi-conserved (0 to +3), non-conserved (-4 to -1) and non-aligned.

Table 2

Divergence and polymorphism in reference genes assessed by McDonald-Kreitman test, stratified by level of cross-genus conservation

Conservation level

dN/dS

Adjusted dN/dS

 

Synonymous

Non-synonymous

Chi-squared

Neutrality Index

Identical

0.030

0.034

D

839

101

8.3 × 10-15

5.6

   

P

52

35

  

Semi-conserved

0.21

0.24

D

186

174

0.22

1.6

   

P

12

18

  

Non-conserved

0.35

0.39

D

90

118

0.38

1.5

   

P

7

14

  

Non-aligned

0.55

0.64

D

155

402

0.74

1.1

   

P

12

35

  

Total

0.15

0.17

D

1270

795

9.5 × 10-6

2.0

   

P

83

102

  

dN/dS ratios refer to P. falciparum /P. reichenowi divergence. Conservation levels were based on BLOSUM62 score in P. falciparum - P. chabaudi alignments; identical (+4 and above), semi-conserved (0 to +3), non-conserved (-4 to -1) and non-aligned. Adjusted dN/dS ratios were derived based on the transition: transversion ratio measured at 4-fold degenerate sites (see Table 4). D = divergent mutations, P = polymorphisms. Chi-squared and Neutrality Index are the outputs of the McDonald-Kreitman test; neutrality index indicates the extent to which the levels of variation depart from the expected in the neutral model with values greater than 1 indicating an excess of polymorphism and hence negative selection.

Relationship between evolutionary conservation, hydrophilicity and informational complexity

Given the lack of a gold-standard informatic approach to defining non-globular regions, the relationship between conservation and non-globularity was explored via one purely structural marker of non-globularity (Kyte-Doolittle hydrophilicity) as well as a purely informational one (low-complexity regions defined by the SEG algorithm). These studies were undertaken in the context of an approach to cross-genus conservation measurement designed for high throughput use, involving a sliding window of 9 amino acid residues.

Decreasing cross-genus conservation was linked to both a shift to unusually high hydrophilicity scores, with a clear inflexion point present between conservation scores of 2 and 3 (Figure 3), and a reduction in strength of negative selection within the hominid malaria clade (Figure 4). A minority (31%) of sequence with conservation score less than 2.5 was responsible for 77% of non-synonymous differences between P. falciparum and P. reichenowi, including 87% of radical substitutions (Figure 4c). Low-complexity regions defined by the SEG algorithm (default parameters) made up 11.9% of the sequence studied (missing many poorly conserved regions of sequence and including some highly conserved areas), and contained only 19.0% of non-synonymous differences between P. falciparum and P. reichenowi. Taken together these observations indicate that a measurement of cross-genus evolutionary conservation identifies highly hydrophilic sequences that show relaxed purifying selection in the short-term, features of non-globular domains, and that this method appears superior to an approach based purely on informational complexity.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-257/MediaObjects/12862_2011_Article_1879_Fig3_HTML.jpg
Figure 3

Density plot of conservation vs. hydrophilicity for amino acid residues of 40 reference genes. (a) Number of occurrences at each co-ordinate (using bins of 0.1 on both × and Y-axes). (b) Average hydrophilicity (conservation score bins of 0.2). Conservation levels in P. falciparum - P. chabaudi alignments were based on BLOSUM62 score using a sliding, overlapping window of 9 residues; hydrophilicity was based on the Kyte-Doolittle index (sliding, overlapping window of 14).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-257/MediaObjects/12862_2011_Article_1879_Fig4_HTML.jpg
Figure 4

Conservation-hydrophilicity at sites of amino acid variants in 40 reference genes. (a) Conservative amino acid variants between P. falciparum and P. reichenowi. (b) Radical amino acid variants between P. falciparum and P. reichenowi. (c) Proportion of total sequence, conservative amino acid variants and radical amino acid variants according to conservation level (conservation score bins of 1.0). Conservation and hydrophilicity were derived as for Figure 3.

Chromosomal regions containing genes responsible for drug resistance in field isolates

In order to test the utility of this approach for detecting functional variation in P. falciparum, we examined three chromosomal regions containing PfCRT, PfDHFR and PfDHPS, testing polymorphisms between two pairs of sensitive and resistant parasite isolates in each case. As observed with the reference genes, there were wide ranging levels of long-term conservation (Figure 5), and a similar shift to very high levels of hydrophilicity at conservation scores below 3 in all three cases (Figure 6). Indeed, the proportion of sequence falling below a conservation threshold of 2.5 was even greater than in the reference gene set (56% vs. 31%) indicating that at a genomic level non-globular domains may be responsible for an even higher proportion of non-synonymous polymorphism than in our reference genes. Consistent with this, amino acid variants in genes not involved in drug resistance fell almost exclusively in less conserved regions with 150 of 153 such amino acid variants below the 2.5 conservation threshold (Figure 5g, Chi-squared test for proportion of mutations vs. proportion of total sequence at each conservation level: p = 9 × 10-23). In contrast 22 amino acid variants within known drug-resistance genes were located in more conserved regions (Chi-squared test for proportion of mutations vs. proportion of total sequence at each conservation level: p = 3.8 × 10-7), with all mutations being located at positions with relatively high conservation scores (> 3.0). Further there was no overlap in conservation scores between amino acid variants in the drug-resistance gene and those within other genes within any single alignment. These results indicate that the position of mutations within a long-term and measurable evolutionary landscape provides a robust signature that can aid identification of functionally important amino acid variants.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-257/MediaObjects/12862_2011_Article_1879_Fig5_HTML.jpg
Figure 5

Relationship between Plasmodium genus conservation score and amino acid variation in P. falciparum drug-resistance regions. For each of three drug-resistance regions, two comparisons between resistant and sensitive isolates were made. (a, b) CRT resistance locus on chromosome 7. (c, d) DHFR resistance locus on chromosome 4. (e, f) DHPS locus on chromosome 8. Mutations in known drug-resistance genes are shown as filled circles; other mutations are shown as open circles. A threshold conservation score of 2.5 is indicated by the dotted line. Only coding regions are displayed, with gene designations and colours used for orientation purposes only. (g) Proportion of total sequence, amino acid variation in drug-resistance genes and amino acid variation in other genes according to conservation level (based on a sliding window of 9 residues, with conservation scores shown within bins of 1.0).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-257/MediaObjects/12862_2011_Article_1879_Fig6_HTML.jpg
Figure 6

Density plot of conservation vs. hydrophilicity for 3 drug resistance regions. Left: Number of occurrences at each co-ordinate (using bins of 0.1 on both × and Y-axes). Right: Mean hydrophilicity by conservation score (bins of 0.2). Conservation and hydrophilicity are derived as for Figure 3.

Laboratory-selected drug resistance

Fourteen amino acid variants associated with selection of atovaquone, azithromycin and spiroindolone resistance in vitro (involving mitochondrial, apicoplast and nuclear genomes respectively [2123]) conformed to the same pattern (Figure 7). Combining these with field resistance mutations produced a set of 36 non-synonymous polymorphisms associated with drug resistance. Notably, among these mutations the nature of the amino acid variants itself (radical vs. conservative) provided no discriminatory value, indicating that it is primarily the location of the protein change that distinguishes functional adaptation.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-257/MediaObjects/12862_2011_Article_1879_Fig7_HTML.jpg
Figure 7

Conservation-hydrophilicity plot for amino acid variants associated with drug resistance or within drug-resistance regions. All mutations shown to be involved in drug resistance by transfection (22 nSNPs in CRT, DHFR and DHPS) or drug pressure studies (14nSNPs in cytochrome b, rpl4 and PfATPase4) are shown along with variants in chromosomal regions around CRT, DHFR and DHPS (see Figure 5).

Population data from P. falciparumisolates

Evolutionary analysis of long-term conservation is also relevant to population studies on large numbers of field samples since the neutral theory of evolution describes a strong relationship between selective pressure and allele frequency [24]. Sequences of PfSERCA, a postulated target of artemisinin, have been determined for thousands of isolates across the world (See Table 3 for data sources). Consistent with neutral evolution, maximum allele frequencies for amino acid variants in PfSERCA were linked closely to cross-genus conservation level. All mutations reaching a prevalence of more than 25% were located in regions of low conservation (Figure 8a), matching the pattern of fixed differences between P. falciparum and P. reichenowi SERCAs. The opposite pattern was seen with amino acid variants associated with spiroindolone resistance in PfATPase4, which stand out distinctly against the flow of corresponding amino acid variants between P. falciparum and P. reichenowi ATPase4 sequences (Figure 8b).
Table 3

Maximum field allele frequencies for non-synonymous mutations in PfSERCA (PFA0310c, PfATP6)

Mutation

Frequency (%)

Location

Reference

R37K

100

Venezuela

[54]

K67R

3

Ghana

[54]

K88E

1

Tanzania

[54]

I89T

38

Philippines

[54]

S176N

3

Malawi

[54]

V229I

< 1

Zanzibar

[55]

I230T

3

Iran

[54]

H243Y

5

Malawi

[54]

A338V

1

Tanzania

[54]

L402V

28

Brazil

[56]

D405N

1

Tanzania

[54]

E431K

58

Ethiopia

[57]

E432K

36

Cameroon

[58]

D436N

3

Tanzania

[54]

A438D

18

Vanuatu

[54]

N462D

10

Venezuela

[54]

N463S

3

Vietnam

[59]

464 del

2

Cambodia

[60]

N465S

16

Thailand

[1]

S466N

35

Peru

[61]

S529I

3

Tanzania

[54]

K561N

1

Niger

[62]

N569K

36

Zanzibar

[55]

G585D

< 1

Zanzibar

[55]

T602I

< 1

Zanzibar

[55]

L610I

1

Tanzania

[54]

K611N

< 1

Zanzibar

[55]

N612D

< 1

Zanzibar

[55]

A621D

< 1

Zanzibar

[55]

A623E

4

Ethiopia

[57]

A630S

58

Brazil

[56]

G632E

3

Malawi

[54]

T635K

16

Madagascar

[54]

E637G

< 1

Zanzibar

[55]

G639D

90

Venezuela

[54]

S641G

< 1

Zanzibar

[55]

E643Q

1

Zanzibar

[55]

N644I

1

Zanzibar

[55]

K649E

< 1

Zanzibar

[55]

L650W

2

Bangladesh

[54]

T657I

3

Malawi

[54]

N683K

9

Bangladesh

[54]

I723V

3

Tanzania

[54]

H747Y

11

Madagascar

[54]

S769N

4

Ethiopia

[57]

K771A

4

Angola

[63]

K776N

1

Angola

[63]

K783E

1

Tanzania

[54]

G805E

3

Malawi

[54]

R809G

< 1

Zanzibar

[55]

844 del

77

Peru

[61]

D845N

3

Tanzania

[54]

E847K

2

Thailand

[1]

T857S

1

Tanzania

[54]

A1115G

4

Papua New Guinea

[54]

V1169I

24

Brazil

[54]

S1187T

3

Ghana

[54]

D1228E

3

Ghana

[54]

3D7 sequence was used as reference. Only studies where alleles were determined for at least ten samples were included. Samples with mixed infections were included in each numerator.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-257/MediaObjects/12862_2011_Article_1879_Fig8_HTML.jpg
Figure 8

Conservation-hydrophilicity plots and single-nucleotide changes in the hominid malaria clade in calcium-ATPase genes. (a) PfSERCA (PFA0310c) showing amino acid variants between P. falciparum and P. reichenowi and field amino acid variants (see Table 3); symbol size indicates maximum allele frequency on a countrywide basis. (b) PfATPase4 (PFL0590c), showing amino acid variants between P. falciparum and P. reichenowi as well as the mutations associated with spiroindolone selection (see text). Conservation and hydrophilicity are derived as for Figure 3.

Discussion

Non-adaptive evolution in Plasmodium

We show that genes that maintain synteny and clearly defined orthologous relationships across the genus also contain poorly conserved domains that occupy approximately half of the total coding sequence when analysed at the chromosomal level. In other words, in Plasmodium species, negative (purifying) selective force is unusually chimeric in nature, being alternately strong and weak within the same gene. Plasmodium chabaudi has approximately two-thirds as much non-aligned sequence as P. falciparum, while other work on a genome-wide scale indicates that P. vivax has approximately 40% [25]. This indicates that the phenomenon is genus-wide, being particularly marked in the hominid malaria clade. The reason why internal proteins mediating core biological functions possess such widespread areas of non-globular domain experiencing minimal purifying selection remains obscure but our data are certainly consistent with these sequences representing a downstream consequence of genetic processes [13, 26] such as replication slippage and unequal crossing over [27]. The dN/dS ratio of 0.64 in non-aligned regions suggests that these sequences are under very weak negative selective pressure, with purifying selection perhaps acting only to maintain non-globular structure.

Applications

Improved understanding of these issues has major implications for the study of all Plasmodium biology that relates to evolution. The findings are most immediately relevant to the goal of understanding molecular mechanisms of drug resistance, since this represents a classical example of evolution in action. As predicted, we were able to show that mutations selected by drug pressure in the field or laboratory are located in conserved protein sequences that have remained largely unchanged for millions of years (being conserved across the Plasmodium genus) by virtue of their functional importance, as already noted by other authors [22, 2830]. What has not been considered is that the gradient in purifying selective force across the genus is strong enough to allow drug-resistance mutations to be discriminated accurately from those that are likely to non-adaptive in nature. Prediction of the functional consequences of mutations based on long-term evolutionary conservation has been described in the context of human genetic studies [3133] but there is even greater scope for this approach to assist studies in P. falciparum, where the contribution of non-adaptive change appears to be log-orders of magnitude higher.

The potential for non-adaptive evolution to confound studies of genotype-phenotype relationships is clear in several areas. Candidate gene surveys based on sequencing of field isolates, where few or no phenotypic data are available, provide an obvious example; the majority of polymorphisms found in PfSERCA are seen to be located in poorly conserved regions, including all those reaching frequencies of more than 25% in field isolates. This finding indicates that polymorphism in this gene is dominated by non-adaptive evolution, and the temptation to invoke positive selection [34] should be avoided. Use of dN/dS to infer positive selection in whole genome analyses of P. vivax [35] may also be prone to the same issue; although measures of dN/dS across a gene tend to be conservative due to purifying selection, it is still likely on statistical grounds that across a large number of genes, a number of high dN/dS ratios (greater than one) will be generated simply by the action of non-adaptive evolution.

Understanding of the degree to which non-adaptive evolution occurs is also relevant to the identification of drug-resistance genes in a chromosomal context. In our analyses of regions known to contain drug-resistance genes, application of evolutionary conservation added considerable specificity with more than 95% of mutations outside drug-resistance genes falling into poorly conserved sequence. Nevertheless the accuracy of the evolutionary approach as it stands is unlikely to be sufficient for its use in isolation, but rather as a method for pinpointing polymorphisms within regions identified by other means, particularly genome-wide association studies using population genetic tests [6, 36]. Genetic cross experiments are also a robust starting point; for example, the complex polymorphisms in the cg1 and cg2 genes seen at the chloroquine resistance locus identified by the cross of HB3 and DD2 [37] would be defined as being of low priority for further study by the approach we describe. Further work is in progress to examine systematically the evolutionary properties of other regions already identified as being of phenotypic importance by experimental means [38], with the aim of prioritizing polymorphisms for more detailed studies.

Wider relevance

Awareness of the chimeric selection landscape of Plasmodium coding sequences is also relevant to the study of the history of Plasmodium species. A higher dN/dS at the whole gene level for hominid parasites P. falciparum and P. reichenowi compared to rodent malaria parasites has been suggested to provide evidence for small population size during the P. falciparum-reichenowi divergence [39]. However, based on the set of forty reference genes we studied, hominid malaria proteins contain significantly more non-aligned sequence than do rodents, likely leading to higher dN/dS ratios at the whole gene level. In fact in sequence regions conserved across the Plasmodium genus the average dN/dS ratio for the P. falciparum-P. reichenowi divergence was 0.034, indicating strong purifying selection and implying a historically large population size following the lateral transfer of P. falciparum to humans from bonobos [40] or gorillas [41].

Conclusion

The coding sequences of Plasmodium species exemplify the theories of neutral evolution [14, 42] and the ensuing nearly neutral theory [15], operating on a grand scale that to our knowledge is unrivalled in genome biology. Failing to take into account the effect of this in any work that relates to evolution of coding sequence represents a missed opportunity to distinguish adaptive from non-adaptive polymorphism. For example neither genome-wide or candidate gene association studies into antimalarial resistance currently take into account the fact that most polymorphism is non-adaptive in nature. The approach we describe takes advantage of protein sequence data, but does not require any knowledge of protein function, a major advantage since the elucidation of function for the majority of Plasmodium genes remains a distant goal. Application of this method on a genome-wide scale, where it can be integrated into DNA-based methods as well as candidate gene approaches, offers a powerful approach for future studies in the field of antimalarial resistance, as well as other areas of research that are linked to evolutionary biology in this important genus.

Methods

Comparisons between P. falciparum and P. chabaudiorthologs

The rodent malaria species Plasmodium chabaudi was used as comparator species given its status in terms of genome completion. Protein and DNA sequences (Plasmodb version: 2009.03.24) were obtained from Plasmodb.org. Forty orthologous pairs of sequences were chosen, fulfilling the following criteria; no known or hypothesised role in drug resistance or host interaction, syntenic relationship between P. falciparum and P. chabaudi and > 75% coverage in the P. reichenowi ortholog [18] (used for measurement of divergence within hominid malaria parasites). Since sexual-stage genes are released from purifying selection in asexual culture (experienced by several of the isolates under study) [43] genes with no evidence of asexual expression in transcriptomic surveys [44, 45] were also excluded. In order to reflect the types of genes which are implicated in drug resistance, as well as to obtain a range of conservation levels across this reference set, the reference genes consisted of ATPases (8), secondarily active transporters (12), glycolytic enzymes (10) and enzymes involved in DNA and RNA processing (10)(Table 1). The Plasmodb gene model for PfL0590c (PfATPase4) was found to be inconsistent with published data based on cDNA; the latter were used as coding sequence [23].

CLUSTALW alignments of orthologous protein sequences from P. falciparum and P. chabaudi were performed using the default settings of BioEdit. BLOSUM62 scores (reflecting conservation) were then calculated for each P. falciparum residue. Regions that could not be aligned between P. falciparum and P. chabaudi orthologs were defined as the gaps in BLASTP alignments of P. falciparum and P. chabaudi orthologs (BLOSUM62 matrix, gap penalties: existence 9, extension 2) A manual check of the protein alignment in BioEdit was also performed and on rare occasions where short alignments had been excluded by the BLASTP search these were retained. For residues where there was no aligned P. chabaudi residue, a conservation score of -5 was applied. Relatively small regions with no P. reichenowi coverage were also removed from analysis to ensure comparable denominators for inter- and intra-species comparison.

Annotation of polymorphisms and fixed differences

We analysed single-nucleotide polymorphisms (SNPs) derived from Plasmodb, based on available sequence for various P. falciparum strains from around the world generated by the Broad Institute [46], Wellcome Trust Sanger Institute [18] and NIH [47]. Fixed differences between P. falciparum 3D7 strain and P. reichenowi (Oscar strain) were also obtained from Plasmodb [18]. Radical amino acid substitutions were defined as those with BLOSUM62 matrix score < 0.

The challenge of identifying single nucleotide changes within a sequence that is undergoing frequent insertion-deletion polymorphism has been described [48]. In addition, we noted that although complex polymorphisms were said to be excluded in publications, the Plasmodb lists of SNPs within P. falciparum and fixed differences between P. falciparum 3D7 strain and P. reichenowi sometimes contained repetitive mutations within tandem repeats (confirmed by Pustell protein matrix, MacVector) that were clearly part of complex indel polymorphisms and hence not genuine SNPs. These regions contributed 27.5% of all SNPs among P. falciparum isolates and 9.4% of the interspecies divergence, and were excluded from both polymorphism and divergence analyses.

Calculation of positions and transitions

Calculation of synonymous and non-synonymous positions was undertaken for each P. falciparum ortholog using a standard substitution matrix (assuming equal mutation rates) with Jukes-Cantor correction [49]. Confidence intervals for dS were determined assuming a continuous distribution. Confidence intervals for dN/dS were determined using the delta method. Transitional bias was determined by studying synonymous fixed differences between P. falciparum and P. reichenowi orthologs occurring at amino acids encoded by four codons. Consistent with previous measurements on a chromosomal scale [50], synonymous sites made up 20.1% of all sites within the reference genes (See Table 4) with 8.9% of sites being 4-fold degenerate sites (nucleotide positions at which all mutations are synonymous). For synonymous differences at 4-fold degenerate sites, which we assume are selectively neutral, transitions made up 41.4% of changes and transversions 58.6%, consistent with a moderate transitional bias that would produce falsely low dN/dS ratios [51] (since transitional mutations are associated with degeneracy at many 2-fold degenerate sites). Taking this factor into account led to upwards revision of dN/dS ratios for divergence of between 12 and 16% according to the level of conservation; the effect was greatest for non-aligned sequence where the adjusted dN/dS ratio rose to 0.64 (Table 1).
Table 4

Synonymous sites in reference set of genes

Conservation level

All

4-fold

Transitional sites

Transversional sites

Adjusted S

Identical

14127 (20.8)

6384 (9.4)

6389 (9.4)

1354 (2.0)

15537

Semi-conserved

2889 (19.1)

1249 (8.3)

1319 (8.7)

321 (2.1)

3176

Non-conserved

1609 (21.5)

811 (10.9)

672 (9.0)

126 (1.7)

1759

Non-aligned

3240 (17.7)

1187 (6.5)

1819 (10.0)

234 (1.3)

3658

Total

21866 (20.1)

9631 (8.9)

10200 (9.4)

2036 (1.9)

24131

Transitional sites refer to 2-fold synonymous codons by virtue of transitional substitution; transversional sites to 2-fold synonymous codons by virtue of transversional substitution. Numbers in parentheses indicate proportion (%) of total positions.

Studies of hydrophilicity and complexity

Hydrophilicity scores were measured by the Kyte-Doolittle index (window = 14). Low-complexity regions were defined using the SEG algorithm at its default parameters [52].

Drug-resistance chromosomal regions

P. chabaudi orthologs were again used to generate the conservation score. In the case of one gene (MAL8P1.111) there was no rodent malaria parasite ortholog as previously reported [53] and in consequence the syntenic P. vivax ortholog was used for comparison. For the single apicoplast gene rpl4 the partial P. chabaudi sequence PC103611.00.0 was available for generation of the cross-genus conservation score at sites of mutation. For all studies at drug-resistance regions a neighbourhood conservation score was used (averaging the individual conservation scores across a sliding, overlapping window of 9 residues). This allows for the possibility that drug-resistance mutations may occur at residues that have previously undergone conservative change within a wider area of conservation, thereby reducing stochastic loss of sensitivity. This also obviated the need for a specific step to identify non-aligned regions at genomic regions. Non-synonymous SNPs (nSNPs) between sensitive and resistant parasites were studied at each locus, spanning the drug-resistance gene in each case and extending outwards symmetrically until 10 nSNPs outside the drug-resistance gene itself had been documented in at least one pairwise comparison. All residues known to be intrinsic to resistance haplotypes, whether or not each individual residue has been shown to cause drug-resistance independently, were included. Chi-squared testing was undertaken testing whether the distribution of amino acid variants in terms of conservation level was the same as the distribution of total sequence, using 13 conservation levels (bins of 1), and hence 12 degrees of freedom. The test was performed first for drug-resistance genes and mutations, and then for the other genes and the mutations within them.

Authors' information

CW, ND and NW are clinicians based at the Mahidol-Oxford Tropical Medicine Research Unit and are engaged in tackling the problem of multi-drug resistant malaria in Southeast Asia.

Declarations

Acknowledgements

CW and KG were funded by an MRC Clinician Scientist Fellowship to CW. NW is a Wellcome Trust Principal Fellow. The authors thank Arjen Dondorp, Mallika Imwong, Olivo Miotto, Chris Newbold and Sue Lee for helpful discussions. No funding body had any role in study design, collection, analysis, and interpretation of data, the writing of the manuscript or the decision to submit the manuscript for publication.

Authors’ Affiliations

(1)
Wellcome Trust Mahidol University-Oxford Tropical Medicine Research Unit (MORU)
(2)
Centre for Infection, St. George's, University of London
(3)
Sanger Malaria Programme, Wellcome Trust Sanger Institute

References

  1. Dondorp AM, Nosten F, Yi P, Das D, Phyo AP, Tarning J, Lwin KM, Ariey F, Hanpithakpong W, Lee SJ, Ringwald P, Silamut K, Imwong M, Chotivanich K, Lim P, Herdman T, An SS, Yeung S, Singhasivanon P, Day NP, Lindegardh N, Socheat D, White NJ: Artemisinin resistance in Plasmodium falciparum malaria. N Engl J Med. 2009, 361 (5): 455-467. 10.1056/NEJMoa0808859.View ArticlePubMedPubMed Central
  2. Baird JK: Resistance to therapies for infection by Plasmodium vivax. Clin Microbiol Rev. 2009, 22 (3): 508-534. 10.1128/CMR.00008-09.View ArticlePubMedPubMed Central
  3. Wellems TE, Panton LJ, Gluzman IY, do Rosario VE, Gwadz RW, Walker-Jonah A, Krogstad DJ: Chloroquine resistance not linked to mdr-like genes in a Plasmodium falciparum cross. Nature. 1990, 345 (6272): 253-255. 10.1038/345253a0.View ArticlePubMed
  4. Fidock DA, Nomura T, Talley AK, Cooper RA, Dzekunov SM, Ferdig MT, Ursos LM, Sidhu AB, Naude B, Deitsch KW, Su XZ, Wootton JC, Roepe PD, Wellems TE: Mutations in the P. falciparum digestive vacuole transmembrane protein PfCRT and evidence for their role in chloroquine resistance. Mol Cell. 2000, 6 (4): 861-871. 10.1016/S1097-2765(05)00077-8.View ArticlePubMedPubMed Central
  5. Sa JM, Twu O, Hayton K, Reyes S, Fay MP, Ringwald P, Wellems TE: Geographic patterns of Plasmodium falciparum drug resistance distinguished by differential responses to amodiaquine and chloroquine. Proc Natl Acad Sci USA. 2009, 106 (45): 18883-18889. 10.1073/pnas.0911317106.View ArticlePubMedPubMed Central
  6. Mu J, Myers RA, Jiang H, Liu S, Ricklefs S, Waisberg M, Chotivanich K, Wilairatana P, Krudsood S, White NJ, Udomsangpetch R, Cui L, Ho M, Ou F, Li H, Song J, Li G, Wang X, Seila S, Sokunthea S, Socheat D, Sturdevant DE, Porcella SF, Fairhurst RM, Wellems TE, Awadalla P, Su XZ: Plasmodium falciparum genome-wide scans for positive selection, recombination hot spots and resistance to antimalarial drugs. Nat Genet. 2010, 42 (3): 268-271. 10.1038/ng.528.View ArticlePubMedPubMed Central
  7. MalariaGEN: A global network for investigating the genomic epidemiology of malaria. Nature. 2008, 456 (7223): 732-737. 10.1038/nature07632.View Article
  8. Mu J, Ferdig MT, Feng X, Joy DA, Duan J, Furuya T, Subramanian G, Aravind L, Cooper RA, Wootton JC, Xiong M, Su XZ: Multiple transporters associated with malaria parasite responses to chloroquine and quinine. Mol Microbiol. 2003, 49 (4): 977-989. 10.1046/j.1365-2958.2003.03627.x.View ArticlePubMed
  9. Anderson TJ, Nair S, Qin H, Singlam S, Brockman A, Paiphun L, Nosten F: Are transporter genes other than the chloroquine resistance locus (pfcrt) and multidrug resistance gene (pfmdr) associated with antimalarial drug resistance?. Antimicrob Agents Chemother. 2005, 49 (6): 2180-2188. 10.1128/AAC.49.6.2180-2188.2005.View ArticlePubMedPubMed Central
  10. Gardner MJ, Tettelin H, Carucci DJ, Cummings LM, Aravind L, Koonin EV, Shallom S, Mason T, Yu K, Fujii C, Pederson J, Shen K, Jing J, Aston C, Lai Z, Schwartz DC, Pertea M, Salzberg S, Zhou L, Sutton GG, Clayton R, White O, Smith HO, Fraser CM, Adams MD, Venter JC, Hoffman SL: Chromosome 2 sequence of the human malaria parasite Plasmodium falciparum. Science. 1998, 282 (5391): 1126-1132.View ArticlePubMed
  11. Pizzi E, Frontali C: Low-complexity regions in Plasmodium falciparum proteins. Genome Res. 2001, 11 (2): 218-229. 10.1101/gr.GR-1522R.View ArticlePubMedPubMed Central
  12. Brocchieri L: Low-complexity regions in Plasmodium proteins: in search of a function. Genome Res. 2001, 11 (2): 195-197. 10.1101/gr.176401.View ArticlePubMed
  13. Xue HY, Forsdyke DR: Low-complexity segments in Plasmodium falciparum proteins are primarily nucleic acid level adaptations. Mol Biochem Parasitol. 2003, 128 (1): 21-32. 10.1016/S0166-6851(03)00039-2.View ArticlePubMed
  14. Kimura M: Evolutionary rate at the molecular level. Nature. 1968, 217 (5129): 624-626. 10.1038/217624a0.View ArticlePubMed
  15. Ohta T: Slightly deleterious mutant substitutions in evolution. Nature. 1973, 246 (5428): 96-98. 10.1038/246096a0.View ArticlePubMed
  16. Hughes AL: Near neutrality: leading edge of the neutral theory of molecular evolution. Ann N Y Acad Sci. 2008, 1133: 162-179. 10.1196/annals.1438.001.View ArticlePubMedPubMed Central
  17. Koonin EV: Darwinian evolution in the light of genomics. Nucleic Acids Res. 2009, 37 (4): 1011-1034.View ArticlePubMedPubMed Central
  18. Jeffares DC, Pain A, Berry A, Cox AV, Stalker J, Ingle CE, Thomas A, Quail MA, Siebenthall K, Uhlemann AC, Kyes S, Krishna S, Newbold C, Dermitzakis ET, Berriman M: Genome variation and evolution of the malaria parasite Plasmodium falciparum. Nat Genet. 2007, 39 (1): 120-125. 10.1038/ng1931.View ArticlePubMedPubMed Central
  19. Rich SM, Leendertz FH, Xu G, LeBreton M, Djoko CF, Aminake MN, Takang EE, Diffo JL, Pike BL, Rosenthal BM, Formenty P, Boesch C, Ayala FJ, Wolfe ND: The origin of malignant malaria. Proc Natl Acad Sci USA. 2009, 106 (35): 14902-14907. 10.1073/pnas.0907740106.View ArticlePubMedPubMed Central
  20. Kryazhimskiy S, Plotkin JB: The population genetics of dN/dS. PLoS Genet. 2008, 4 (12): e1000304-10.1371/journal.pgen.1000304.View ArticlePubMedPubMed Central
  21. Korsinczky M, Chen N, Kotecka B, Saul A, Rieckmann K, Cheng Q: Mutations in Plasmodium falciparum cytochrome b that are associated with atovaquone resistance are located at a putative drug-binding site. Antimicrob Agents Chemother. 2000, 44 (8): 2100-2108. 10.1128/AAC.44.8.2100-2108.2000.View ArticlePubMedPubMed Central
  22. Sidhu AB, Sun Q, Nkrumah LJ, Dunne MW, Sacchettini JC, Fidock DA: In vitro efficacy, resistance selection, and structural modeling studies implicate the malarial parasite apicoplast as the target of azithromycin. J Biol Chem. 2007, 282 (4): 2494-2504.View ArticlePubMed
  23. Rottmann M, McNamara C, Yeung BK, Lee MC, Zou B, Russell B, Seitz P, Plouffe DM, Dharia NV, Tan J, Cohen SB, Spencer KR, Gonzalez-Paez GE, Lakshminarayana SB, Goh A, Suwanarusk R, Jegla T, Schmitt EK, Beck HP, Brun R, Nosten F, Renia L, Dartois V, Keller TH, Fidock DA, Winzeler EA, Diagana TT: Spiroindolones, a potent compound class for the treatment of malaria. Science. 2010, 329 (5996): 1175-1180. 10.1126/science.1193225.View ArticlePubMedPubMed Central
  24. Kimura M: Average time until fixation of a mutant allele in a finite population under continued mutation pressure: Studies by analytical, numerical, and pseudo-sampling methods. Proc Natl Acad Sci USA. 1980, 77 (1): 522-526. 10.1073/pnas.77.1.522.View ArticlePubMedPubMed Central
  25. Carlton JM, Adams JH, Silva JC, Bidwell SL, Lorenzi H, Caler E, Crabtree J, Angiuoli SV, Merino EF, Amedeo P, Cheng Q, Coulson RM, Crabb BS, Del Portillo HA, Essien K, Feldblyum TV, Fernandez-Becerra C, Gilson PR, Gueye AH, Guo X, Kang'a S, Kooij TW, Korsinczky M, Meyer EV, Nene V, Paulsen I, White O, Ralph SA, Ren Q, Sargeant TJ, et al: Comparative genomics of the neglected human malaria parasite Plasmodium vivax. Nature. 2008, 455 (7214): 757-763. 10.1038/nature07327.View ArticlePubMedPubMed Central
  26. DePristo MA, Zilversmit MM, Hartl DL: On the abundance, amino acid composition, and evolutionary dynamics of low-complexity regions in proteins. Gene. 2006, 378: 19-30.View ArticlePubMed
  27. Zilversmit MM, Volkman SK, Depristo MA, Wirth DF, Awadalla P, Hartl DL: Low-Complexity Regions in Plasmodium falciparum: Missing Links in the Evolution of an Extreme Genome. Mol Biol Evol. 2010
  28. Nomura T, Carlton JM, Baird JK, del Portillo HA, Fryauff DJ, Rathore D, Fidock DA, Su X, Collins WE, McCutchan TF, Wootton JC, Wellems TE: Evidence for different mechanisms of chloroquine resistance in 2 Plasmodium species that cause human malaria. J Infect Dis. 2001, 183 (11): 1653-1661. 10.1086/320707.View ArticlePubMed
  29. Cowman AF, Morry MJ, Biggs BA, Cross GA, Foote SJ: Amino acid changes linked to pyrimethamine resistance in the dihydrofolate reductase-thymidylate synthase gene of Plasmodium falciparum. Proc Natl Acad Sci USA. 1988, 85 (23): 9109-9113. 10.1073/pnas.85.23.9109.View ArticlePubMedPubMed Central
  30. Triglia T, Cowman AF: Primary structure and expression of the dihydropteroate synthetase gene of Plasmodium falciparum. Proc Natl Acad Sci USA. 1994, 91 (15): 7149-7153. 10.1073/pnas.91.15.7149.View ArticlePubMedPubMed Central
  31. Ng PC, Henikoff S: Predicting deleterious amino acid substitutions. Genome Res. 2001, 11 (5): 863-874. 10.1101/gr.176601.View ArticlePubMedPubMed Central
  32. Sunyaev S, Ramensky V, Koch I, Lathe W, Kondrashov AS, Bork P: Prediction of deleterious human alleles. Hum Mol Genet. 2001, 10 (6): 591-597. 10.1093/hmg/10.6.591.View ArticlePubMed
  33. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A: PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003, 13 (9): 2129-2141. 10.1101/gr.772403.View ArticlePubMedPubMed Central
  34. Jambou R, Martinelli A, Pinto J, Gribaldo S, Legrand E, Niang M, Kim N, Pharath L, Volnay B, Ekala MT, Bouchier C, Fandeur T, Berzosa P, Benito A, Ferreira ID, Ferreira C, Vieira PP, Alecrim MG, Mercereau-Puijalon O, Cravo P: Geographic structuring of the Plasmodium falciparum sarco(endo)plasmic reticulum Ca2+ ATPase (PfSERCA) gene diversity. PLoS One. 2010, 5 (2): e9424-10.1371/journal.pone.0009424.View ArticlePubMedPubMed Central
  35. Dharia NV, Bright AT, Westenberger SJ, Barnes SW, Batalov S, Kuhen K, Borboa R, Federe GC, McClean CM, Vinetz JM, Neyra V, Llanos-Cuentas A, Barnwell JW, Walker JR, Winzeler EA: Whole-genome sequencing and microarray analysis of ex vivo Plasmodium vivax reveal selective pressure on putative drug resistance genes. Proc Natl Acad Sci USA. 2010, 107 (46): 20045-20050. 10.1073/pnas.1003776107.View ArticlePubMedPubMed Central
  36. Weedall GD, Conway DJ: Detecting signatures of balancing selection to identify targets of anti-parasite immunity. Trends Parasitol. 2010, 26 (7): 363-369. 10.1016/j.pt.2010.04.002.View ArticlePubMed
  37. Su X, Kirkman LA, Fujioka H, Wellems TE: Complex polymorphisms in an approximately 330 kDa protein are linked to chloroquine-resistant P. falciparum in Southeast Asia and Africa. Cell. 1997, 91 (5): 593-603. 10.1016/S0092-8674(00)80447-X.View ArticlePubMed
  38. Ferdig MT, Cooper RA, Mu J, Deng B, Joy DA, Su XZ, Wellems TE: Dissecting the loci of low-level quinine resistance in malaria parasites. Mol Microbiol. 2004, 52 (4): 985-997. 10.1111/j.1365-2958.2004.04035.x.View ArticlePubMed
  39. Prugnolle F, McGee K, Keebler J, Awadalla P: Selection shapes malaria genomes and drives divergence between pathogens infecting hominids versus rodents. BMC Evol Biol. 2008, 8: 223-10.1186/1471-2148-8-223.View ArticlePubMedPubMed Central
  40. Krief S, Escalante AA, Pacheco MA, Mugisha L, Andre C, Halbwax M, Fischer A, Krief JM, Kasenene JM, Crandfield M, Cornejo OE, Chavatte JM, Lin C, Letourneur F, Gruner AC, McCutchan TF, Renia L, Snounou G: On the diversity of malaria parasites in African apes and the origin of Plasmodium falciparum from Bonobos. PLoS Pathog. 2010, 6 (2): e1000765-10.1371/journal.ppat.1000765.View ArticlePubMedPubMed Central
  41. Liu W, Li Y, Learn GH, Rudicell RS, Robertson JD, Keele BF, Ndjango JB, Sanz CM, Morgan DB, Locatelli S, Gonder MK, Kranzusch PJ, Walsh PD, Delaporte E, Mpoudi-Ngole E, Georgiev AV, Muller MN, Shaw GM, Peeters M, Sharp PM, Rayner JC, Hahn BH: Origin of the human malaria parasite Plasmodium falciparum in gorillas. Nature. 2010, 467 (7314): 420-425. 10.1038/nature09442.View ArticlePubMedPubMed Central
  42. King JL, Jukes TH: Non-Darwinian evolution. Science. 1969, 164 (881): 788-798. 10.1126/science.164.3881.788.View ArticlePubMed
  43. Scherf A, Petersen C, Carter R, Alano P, Nelson R, Aikawa M, Mattei D, da Silva LP, Leech J: Characterization of a Plasmodium falciparium mutant that has deleted the majority of the gametocyte-specific Pf11-1 locus. Mem Inst Oswaldo Cruz. 1992, 87 (Suppl 3): 91-94.View ArticlePubMed
  44. Bozdech Z, Llinas M, Pulliam BL, Wong ED, Zhu J, DeRisi JL: The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum. PLoS Biol. 2003, 1 (1): E5-View ArticlePubMedPubMed Central
  45. Le Roch KG, Zhou Y, Blair PL, Grainger M, Moch JK, Haynes JD, De La Vega P, Holder AA, Batalov S, Carucci DJ, Winzeler EA: Discovery of gene function by expression profiling of the malaria parasite life cycle. Science. 2003, 301 (5639): 1503-1508. 10.1126/science.1087025.View ArticlePubMed
  46. Volkman SK, Sabeti PC, DeCaprio D, Neafsey DE, Schaffner SF, Milner DA, Daily JP, Sarr O, Ndiaye D, Ndir O, Mboup S, Duraisingh MT, Lukens A, Derr A, Stange-Thomann N, Waggoner S, Onofrio R, Ziaugra L, Mauceli E, Gnerre S, Jaffe DB, Zainoun J, Wiegand RC, Birren BW, Hartl DL, Galagan JE, Lander ES, Wirth DF: A genome-wide map of diversity in Plasmodium falciparum. Nat Genet. 2007, 39 (1): 113-119. 10.1038/ng1930.View ArticlePubMed
  47. Mu J, Awadalla P, Duan J, McGee KM, Keebler J, Seydel K, McVean GA, Su XZ: Genome-wide variation and identification of vaccine targets in the Plasmodium falciparum genome. Nat Genet. 2007, 39 (1): 126-130. 10.1038/ng1924.View ArticlePubMed
  48. Hartl DL, Volkman SK, Nielsen KM, Barry AE, Day KP, Wirth DF, Winzeler EA: The paradoxical population genetics of Plasmodium falciparum. Trends Parasitol. 2002, 18 (6): 266-272. 10.1016/S1471-4922(02)02268-7.View ArticlePubMed
  49. Nei M, Gojobori T: Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol. 1986, 3 (5): 418-426.PubMed
  50. Mu J, Duan J, Makova KD, Joy DA, Huynh CQ, Branch OH, Li WH, Su XZ: Chromosome-wide SNPs reveal an ancient origin for Plasmodium falciparum. Nature. 2002, 418 (6895): 323-326. 10.1038/nature00836.View ArticlePubMed
  51. Li WH: Unbiased estimation of the rates of synonymous and nonsynonymous substitution. J Mol Evol. 1993, 36 (1): 96-99. 10.1007/BF02407308.View ArticlePubMed
  52. Wootton JC: Non-globular domains in protein sequences: automated segmentation using complexity measures. Comput Chem. 1994, 18 (3): 269-285. 10.1016/0097-8485(94)85023-2.View ArticlePubMed
  53. Hall N, Karras M, Raine JD, Carlton JM, Kooij TW, Berriman M, Florens L, Janssen CS, Pain A, Christophides GK, James K, Rutherford K, Harris B, Harris D, Churcher C, Quail MA, Ormond D, Doggett J, Trueman HE, Mendoza J, Bidwell SL, Rajandream MA, Carucci DJ, Yates JR, Kafatos FC, Janse CJ, Barrell B, Turner CM, Waters AP, Sinden RE: A comprehensive survey of the Plasmodium life cycle by genomic, transcriptomic, and proteomic analyses. Science. 2005, 307 (5706): 82-86. 10.1126/science.1103717.View ArticlePubMed
  54. Tanabe K, Zakeri S, Palacpac NM, Afsharpad M, Randrianarivelojosia M, Kaneko A, Marma AS, Horii T, Mita T: Spontaneous mutations in the Plasmodium falciparum sarcoplasmic/endoplasmic reticulum Ca2+-ATPase (PfATP6) gene among geographically widespread parasite populations unexposed to artemisinin-based combination therapies. Antimicrob Agents Chemother. 2011, 55 (1): 94-100. 10.1128/AAC.01156-10.View ArticlePubMedPubMed Central
  55. Dahlstrom S, Veiga MI, Ferreira P, Martensson A, Kaneko A, Andersson B, Bjorkman A, Gil JP: Diversity of the sarco/endoplasmic reticulum Ca(2+)-ATPase orthologue of Plasmodium falciparum (PfATP6). Infect Genet Evol. 2008, 8 (3): 340-345. 10.1016/j.meegid.2008.02.002.View ArticlePubMed
  56. Gama BE, de Oliveira NK, de Souza JM, Santos F, de Carvalho LJ, Melo YF, Rosenthal PJ, Daniel-Ribeiro CT, Ferreira-da-Cruz Mde F: Brazilian Plasmodium falciparum isolates: investigation of candidate polymorphisms for artemisinin resistance before introduction of artemisinin-based combination therapy. Malar J. 2010, 9: 355-10.1186/1475-2875-9-355.View ArticlePubMedPubMed Central
  57. Eshetu T, Berens-Riha N, Fekadu S, Tadesse Z, Gurkov R, Holscher M, Loscher T, Miranda IB: Different mutation patterns of Plasmodium falciparum among patients in Jimma University Hospital, Ethiopia. Malar J. 2010, 9: 226-10.1186/1475-2875-9-226.View ArticlePubMedPubMed Central
  58. Tahar R, Ringwald P, Basco LK: Molecular epidemiology of malaria in Cameroon. XXVIII. In vitro activity of dihydroartemisinin against clinical isolates of Plasmodium falciparum and sequence analysis of the P. falciparum ATPase 6 gene. Am J Trop Med Hyg. 2009, 81 (1): 13-18.PubMed
  59. Bertaux L, Quang le H, Sinou V, Thanh NX, Parzy D: New PfATP6 mutations found in Plasmodium falciparum isolates from Vietnam. Antimicrob Agents Chemother. 2009, 53 (10): 4570-4571. 10.1128/AAC.00684-09.View ArticlePubMedPubMed Central
  60. Imwong M, Dondorp AM, Nosten F, Yi P, Mungthin M, Hanchana S, Das D, Phyo AP, Lwin KM, Pukrittayakamee S, Lee SJ, Saisung S, Koecharoen K, Nguon C, Day NP, Socheat D, White NJ: Exploring the contribution of candidate genes to artemisinin resistance in Plasmodium falciparum. Antimicrob Agents Chemother. 2010, 54 (7): 2886-2892. 10.1128/AAC.00032-10.View ArticlePubMedPubMed Central
  61. Bacon DJ, McCollum AM, Griffing SM, Salas C, Soberon V, Santolalla M, Haley R, Tsukayama P, Lucas C, Escalante AA, Udhayakumar V: Dynamics of malaria drug resistance patterns in the Amazon basin region following changes in Peruvian national treatment policy for uncomplicated malaria. Antimicrob Agents Chemother. 2009, 53 (5): 2042-2051. 10.1128/AAC.01677-08.View ArticlePubMedPubMed Central
  62. Ibrahim ML, Khim N, Adam HH, Ariey F, Duchemin JB: Polymorphism of PfATPase in Niger: detection of three new point mutations. Malar J. 2009, 8: 28-10.1186/1475-2875-8-28.View ArticlePubMedPubMed Central
  63. Menegon M, Sannella AR, Majori G, Severini C: Detection of novel point mutations in the Plasmodium falciparum ATPase6 candidate gene for resistance to artemisinins. Parasitol Int. 2008, 57 (2): 233-235. 10.1016/j.parint.2007.12.004.View ArticlePubMed

Copyright

© Gardner et al; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.