The antigenic switching network of Plasmodium falciparum and its implications for the immuno-epidemiology of malaria

  1. Robert Noble
  2. Zóe Christodoulou
  3. Sue Kyes
  4. Robert Pinches
  5. Chris I Newbold  Is a corresponding author
  6. Mario Recker  Is a corresponding author
  1. University of Oxford, United Kingdom
  2. Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, United Kingdom
6 figures and 3 tables

Figures

Figure 1 with 2 supplements
Proportional var transcript levels for six in vitro cultures.

The parasite cultures initially expressed a variety of dominant ‘starter genes’, which switched off at notably different rates. Nevertheless, most cultures converged towards high level transcription of two centrally located genes var27 and var29 (red and blue lines, respectively), whereas most other gene transcripts (grey lines) remained at relatively low levels.

https://doi.org/10.7554/eLife.01074.004
Figure 1—figure supplement 1
Proportional var transcript levels and model output for cultures 1–6.

The relative transcript levels of activated var genes were measured by qRT-PCR at various time points during in vitro culture for 11 clones with different starter genes, plus the parent culture. Columns one and two show the measured transcript levels at linear and logarithmic scale, respectively; columns three and four show the model fit. Error bars show approximate 95% confidence intervals estimated from qRT-PCR replicates.

https://doi.org/10.7554/eLife.01074.005
Figure 1—figure supplement 2
Proportional var transcript levels and model output for cultures 7–11.

The relative transcript levels of activated var genes were measured by qRT-PCR at various time points during in vitro culture for 11 clones with different starter genes, plus the parent culture. Columns one and two show the measured transcript levels at linear and logarithmic scale, respectively; columns three and four show the model fit. Error bars show approximate 95% confidence intervals estimated from qRT-PCR replicates.

https://doi.org/10.7554/eLife.01074.006
Figure 2 with 1 supplement
Estimation of switch parameters of highly transcribed genes.

Parameter estimations for the 16 most transcribed var genes are represented as a switch matrix and an off-rate vector. The diameter of a circle in the ith row and jth column of the matrix is proportional to the switch bias βij from gene i to gene j, and the diameter of a circle in the off-rate vector is proportional to the rate ωi at which gene i becomes silenced. The fuzziness indicates uncertainty in the estimate, such that the darkness of each concentric ring is proportional to the likelihood that the parameter is within the corresponding range (Noble and Recker, 2012). Starter gene parameters, which are more precisely estimated, are in red, and genes are arranged from left to right, and top to bottom, in order of average transcript level.

https://doi.org/10.7554/eLife.01074.007
Figure 2—source data 1

Estimated switch biases and 95% credible intervals (CI).

Mean switch biases from gene i (row) to gene j (column) for the 16 most transcribed var genes, together with the 95% credible intervals.

https://doi.org/10.7554/eLife.01074.008
Figure 2—figure supplement 1
Validation of estimated switching parameters.

Our method was able to estimate switching parameters not only for starter genes, which were initially transcribed in our cultures, but also for other var genes. To test the accuracy of the latter estimates, we used a cross-validation technique. For example, to generate an alternative set of parameter estimates for var29, we ran the MCMC algorithm without the data for culture #8, which began as a clone transcribing var29. The matrix shown here is a composite of estimates derived in this way. The similarity to Figure 2 confirms that our method was reliable.

https://doi.org/10.7554/eLife.01074.009
Off-rate estimates show strong dependency on chromosomal location.

The estimated rates at which active var genes become silenced are significantly lower for centrally located var genes than for those in subtelomeric locations. There was no significant effect of upstream promoter type when differentially testing off-rates of genes in subtelomeric location (UpsA vs non-UpsA) or in central location (UpsB vs UpsC).

https://doi.org/10.7554/eLife.01074.011
Associations between activation biases and genetic attributes.

The mean activation bias is higher for centrally located var genes. Within the set of centrally located genes, those encoding only four binding domains have higher activation biases than longer genes. Together, short, central genes have activation biases ≈ 10 times higher than the rest of the repertoire.

https://doi.org/10.7554/eLife.01074.012
Variation in switch biases (deviation from the mean).

Antigenic switching between var genes is more frequent between genes with matching chromosomal locations, for example from central to central or from subtelomeric to subtelomeric. No significant associations between switch bias and matching Ups type or gene length are found.

https://doi.org/10.7554/eLife.01074.013
Figure 6 with 1 supplement
Effect of var gene activation on sequence evolution.

(A) Var gene activation biases are significantly correlated with each gene’s relatedness to the rest of the HB3 repertoire, as measured by the gene’s centrality within a shared homology block network, independently of chromosomal location or gene length. Trend lines show the fit of a linear regression model. (B) Simulation of var gene evolution by gene conversion, whereby homology blocks are swapped among pairs of genes chosen at random according to their activation biases, shows a similar correlation between the genes’ activation biases and their relatedness to the rest of the repertoire. The red points show the outcome of one simulation, and the dashed line is the smoothed average of 50 runs. (C) Activation biases are negatively correlated with the gene’s average domain sequence conservation, as a measure of population-level diversity, independently of chromosomal location and gene length. Trend lines show the fit of a linear regression model.

https://doi.org/10.7554/eLife.01074.014
Figure 6—figure supplement 1
Relatedness network of the HB3 repertoire.

Each node represents a var gene, colour coded according to the chromosomal location and the number of binding domains. Node size indicates estimated activation bias. The strength of a connection between two nodes is proportional to the number of shared homology blocks in the DBL1 and CIDR1 domains. Stronger connections are shown thicker and darker, and the layout is force-directed.

https://doi.org/10.7554/eLife.01074.015

Tables

Table 1

Parasite culture and RNA sampling information

https://doi.org/10.7554/eLife.01074.003
CultureStarter geneObserved generations
1var1129, 48, 59, 69, 89
2var1329, 48, 69, 89, 99, 108
3var1330, 49, 70, 90, 100, 109
4var530, 39, 49, 60, 70, 90
5var2730, 49, 70, 90
6var2729, 38, 48, 69, 89
7var2833, 52, 63, 73, 93
8var2930, 49, 70, 80
9var3029, 48, 59, 69, 89
10var3129, 48, 59, 69, 89, 99, 108
11var3532, 51, 65, 72, 92
Parent0, 9, 19, 40, 60, 70, 79
Table 2

Parameter estimates for the HB3 var repertoire with 95% credible intervals

https://doi.org/10.7554/eLife.01074.010
GeneLocationDomains*UpsOff-rate (95% CI)Activation bias (95% CI)
var29Central4C0.3% (0.1, 0.5)27% (22, 32)
var27Central4B0.8% (0.5, 1.2)23% (19, 28)
var35Central4C3.3% (2.2, 4.7)8.0% (5.8, 11)
var28Central4C2.2% (1.4, 3.1)6.9% (5.2, 9.1)
var32Central4C4.7% (2.5, 5.9)2.5% (1.5, 3.3)
var36Central4C1.8% (0.2, 4.2)2.5% (1.5, 4.1)
var31Central4C1.4% (0.9, 2.0)2.2% (1.7, 2.9)
var34Central6C1.3% (0.1, 3.4)2.1% (1.4, 3.6)
var13Subtelomeric4B1.9% (1.5, 2.4)2.1% (1.6, 2.8)
var30Central4C1.9% (1.3, 2.7)1.9% (1.4, 2.5)
var10Subtelomeric5B3.4% (1.1, 5.7)1.7% (0.94, 2.7)
var17Central6B3.0% (0.8, 5.6)1.7% (0.90, 2.8)
var24Central6B1.9% (0.1, 5.2)1.6% (0.85, 3.1)
var7Subtelomeric7B2.2% (0.3, 5.1)1.5% (0.83, 2.7)
var19Central4B1.3% (0.1, 3.2)1.4% (0.93, 2.5)
var26Central4B1.5% (0.2, 3.4)1.4% (0.90, 2.3)
var25Central6B2.7% (0.4, 5.6)1.2% (0.60, 2.1)
var1csaSubtelomeric8A5.5% (4.4, 6.0)1.2% (0.89, 1.5)
var33Central4C2.7% (0.5, 5.8)1.1% (0.57, 1.9)
var16Subtelomeric4B2.8% (1.0, 5.3)0.91% (0.52, 1.5)
var4Subtelomeric8A4.3% (2.1, 5.9)0.86% (0.52, 1.3)
var14Subtelomeric4B4.0% (2.2, 5.9)0.73% (0.45, 1.1)
var22Central7B1.9% (0.2, 5.1)0.72% (0.42, 1.3)
var21Central7B4.7% (2.7, 5.9)0.70% (0.46, 0.94)
var8Subtelomeric7B3.7% (1.8, 5.8)0.66% (0.39, 1.0)
var11Subtelomeric5B1.5% (0.9, 2.2)0.60% (0.45, 0.80)
var18Subtelomeric4B5.2% (3.8, 6.0)0.60% (0.42, 0.79)
var5Subtelomeric6A3.5% (2.7, 4.3)0.53% (0.39, 0.69)
var39pSubtelomeric2B4.7% (2.7, 6.0)0.48% (0.30, 0.66)
var50ΨCentral6C4.4% (2.0, 5.9)0.46% (0.26, 0.67)
var23Central6B0.6% (0.0, 2.0)0.38% (0.26, 0.55)
var9Subtelomeric6B2.8% (0.7, 5.3)0.36% (0.20, 0.58)
var2csaSubtelomeric6E4.7% (2.3, 5.9)0.28% (0.16, 0.40)
var2Subtelomeric6A1.4% (0.2, 4.2)0.27% (0.17, 0.50)
var12Subtelomeric4B3.8% (1.5, 5.8)0.26% (0.15, 0.40)
var20Subtelomeric4B4.1% (1.8, 5.9)0.25% (0.14, 0.37)
var1Subtelomeric7A5.5% (4.2, 6.0)0.024% (0.018, 0.031)
var6Central8A2.9% (0.4, 5.9)0.018% (0.0086, 0.032)
  1. *

    Number of encoded DBL (Duffy binding-like) and CIDR (Cys rich inter-domain region).

  2. Partial gene.

  3. Psuedogene.

Table 3

HB3 qPCR primers and cross-referenced var gene identifiers

https://doi.org/10.7554/eLife.01074.016
Gene nameContig nameBroad locus nameF oligonucleotideR oligonucleotide
HB3 var16HB3-1000-1PFHG_03232.1ccctgtccacaaccatcagccgtcgtcgtcatcagtgtcc
HB3 var1HB3-1000-2PFHG_03234.1ccaaaggagaaggcaccaccacctatggcacccctctcac
HB3 var12HB3-1040PFHG_03416.1gatgctacaaccaccccaccgtgttaccactcgcccactc
HB3 var27HB3-1704_1PFHG_03476.1gctcccaaccaccacgttccgcttcctgctggtggctgtc
HB3 var28HB3-1074-2PFHG_03478.1gatggcacaaaagttggcggtgttctgggtcgacctcctc
HB3 var29HB3-1074-3PFHG_03480.1aagaagatggcgacgaaggctccggtgatccctcttctgg
HB3 var13HB3-1107PFHG_03516.1tggtaaatgcaagggtgatacaggtgcatcgttatcactcaccagc
HB3 var1csaHB3-1108PFHG_03521.1cgcaatatgcaactaatgacacttggcaatattctgaacg
HB3 var2HB3-1210PFHG_03840.1cgaggacaccacggaggaggttggtgctgctggttgtggc
HB3 var5HB3-1235PFHG_03671.1aggtctgctccttcagatgcgtgtgttttccctaccatgacaaggatgcc
HB3 var36HB3-1296PFHG_04012.1atggacaaatgatggtaaggtaggagtaggtgttgcgttc
HB3 var17HB3-1296-2PFHG_04014.1agatggcgacaaaggccaagttgggtttggcaccactagc
HB3 var35HB3-1296-3PFHG_04015.1aaacggaaaacctggcctcctcgtcttggcctttggcttc
HB3 var14HB3-1308PFHG_04035.1ggtggtggtgccgatcccgcctgtgacgcctccgtcttagtggccc
HB3 var8HB3-1334PFHG_04081.1ggcggtgtctgtattccaccgctgcctcaccacctgttag
HB3 var9HB3-1408PFHG_04057.1tgctatgacgtgtaatgccccacttacatgagtcccatctggtg
HB3 var32HB3-1459Fbadfggaaaccgcggtggactcacacttgtgggtgctttggggc
HB3 var11HB3-1499PFHG_04491.1attggatgatgcctgtcgccggcacctggtttagtggtgg
HB3 var19HB3-1514PFHG_04620.1aaactgacaatggccccgacgttgttgagggggtcttcgg
HB3 var18HB3-1523PFHG_04593.1gcggctcacccgacatcttcgccgcctcgtcttcttcgtc
HB3 var10HB3-1587PFHG_04749.1accactcgtgccaccacctcgagtttgtacctggcaccccc
HB3 var7HB3-1604-1PFHG_04769.1agcgagtggtactcaggagggatggaccacgagatgtgcc
HB3 var20HB3-1604-2PFHG_04770.1acgaagaagacgatgccaccgaagtcttcggagcgaccac
HB3 var4HB3-1703PFHG_04861.1tggtgccaaagacccctcccggccactcgctgtgtctgtg
HB3 var2csaAHB3-1727PFHG_05046.1gggggaaatgtggggtgccggggggataccccacactcattaccag
HB3 var3HB3-1737PFHG_05052.1aaagtgcgaagcacctcccccgccactgcagggattagctg
HB3 var2csaBHB3-1817PFHG_05155.1tggtacagctgatggtggtacttccgtgtgcccgctttacggtttcg
HB3 var39p*HB3-2007efbeagccattacgtgcgaagctggagcggcacatcggcatttttg
HB3 var34HB3-209PFHG_00592.1agtggtgctgtagagccaaaagaccctgcggcggtgctgtaagg
HB3 var23HB3-699-1PFHG_02272.1gaaccccttgacgacgacacctcaacacacgtcaaaggcg
HB3 var30HB3-699-2PFHG_02273.1aagacgacaaacctggcaccgtcgttgcttttggcttcgg
HB3 var6HB3-699-3PFHG_02274.1attcacagcactgaaagtcctcacaatcattaaaagcatcc
HB3 var26HB3-699-4PFHG_02276.1aagcagctgatggaacggactggttgttgtgggtcttggc
HB3 var31HB3-699-5PFHG_02277.1cgcgaagacgaaaacgtcacgtttcatccggaccgtcctc
HB3 var50ΨHB3-752-1PFHG_02419.1tggtaatgatgaagatgacggaattggcttcactttgttc
HB3 var24HB3-752-2PFHG_02421.1gctcgctctttaccacccgcttccgtctcctccttcgccg
HB3 var25HB3-752-3PFHG_02423.1agtggtgccaaaactgtcggaccacaaaagtcgcttcccc
HB3 var22HB3-752-4PFHG_02425.1ccaccacaaaacccctccagtccgcttgtggttcgtcttc
HB3 var33HB3-752-5PFHG_02429.1acagaaagttggacaggatgatggttgttttgagaattgc
  1. *

    Partial gene.

  2. Pseudogene.

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  1. Robert Noble
  2. Zóe Christodoulou
  3. Sue Kyes
  4. Robert Pinches
  5. Chris I Newbold
  6. Mario Recker
(2013)
The antigenic switching network of Plasmodium falciparum and its implications for the immuno-epidemiology of malaria
eLife 2:e01074.
https://doi.org/10.7554/eLife.01074