Supplementary information
Cyt b diversity from nested PCR
Sequences obtained from the nested PCR contained 12 unique Plasmodium cyt b lineages and
3 unique Haemoproteus lineages. Four Plasmodium lineages were common in this dataset, in
line with previous findings (Wood et al., 2007); among single lineage infections, pSGS1 was
most abundant (45% infections), followed by pTURDUS1 (36%), pBT7 (14%) and pGRW11
(3%). The other previously documented lineages found were pBLUTI1 (n=3 samples),
pBLUTI2 (n=3), pBLUTI3 (n=1), pBLUTI4 (n=1) and pBLUTI5 (n=1), hBLUTI1 (n=1) and
H. majoris (n=1) Four previously unreported cyt b lineages were found, in one sample each:
three Plasmodium lineages (pBLUTI6, pBLUTI7, pBLUTI8) and one Haemoproteus lineage
(hBLUTI2). These sequences have been deposited in Genbank (Accession numbers
HQ123330 to HQ123333) and the MalAvi database (Bensch et al. 2009).
Species diagnosis by qPCR
Based on published comparisons of morphological and molecular data, we denoted pSGS1
and pGRW11 as belonging to P. relictum, and pTURDUS1 and pBT7 as belonging to P.
circumflexum (Palinauskas et al. 2007). Agreement in species diagnosis by qPCR and
sequence analysis was high; in 96% of infections for which nested PCR gave a single lineage
diagnosis (n=302), the species assigned by cyt b sequence and qPCR product melting
temperature was identical. In the twelve cases of disagreement, 10 involved assignment of P.
circumflexum by qPCR where nested PCR assigned P. relictum, and 2 involved the reverse
situation. Thus there is a small degree of error in species-assignment by this method (4%
infections, assuming nested PCR assignment is 100% accurate). However, it has the benefit
of allowing many samples for which a sequence is difficult to obtain because of low
parasitaemia to be assigned to species.
Three of four samples diagnosed as containing Haemoproteus by nested PCR yielded qPCR
products, suggesting the assay may also detect this parasite. These samples were excluded
from analysis of Plasmodium prevalence and parasitaemia. Of the samples known to contain
a Plasmodium lineage other than pSGS1, pGRW11, pTURDUS1 or pBT7, only two samples
with lineage pBLUTI2 produced a qPCR signal, and their melting temperatures clustered
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with P. circumflexum, as is expected from their high sequence similarity to pTURDUS1 and
pBT7 (Wood et al. 2007).
Comparative sensitivity of qPCR and nested PCR
Infection prevalence by qPCR was significantly higher than by nested PCR, for all
Plasmodium infections combined and for P. relictum, but not P. circumflexum (Table S1),
indicating that sensitivity of the qPCR assay is considerably higher than that of nested PCR.
However, since during nested PCR each sample was tested only once whereas in qPCR each
sample was tested in triplicate we also compared sensitivity of these two methods when only
one replicate was considered. When the number of replicates is held constant there is little
evidence for a difference in sensitivity of the two assays, with a tendency towards greater
detection by nested PCR (χ21=2.99, p=0.084, n=342). Since increased detection by qPCR is
apparently due to greater replication during screening, we examined among samples tested
twice by this method (and thus for which 6 replicates were available), how prevalence
changed as the number of replicates increased. For both Plasmodium species, prevalence
increased dramatically from one to three replicates and began to plateau after this (Fig. S1).
However, the total percentage increase in prevalence from a single replicate to six was
strikingly large (with several clean negative controls per plate), between 59-67% depending
on the species, which highlights the difficulty of detecting chronic Plasmodium infections
even with sensitive molecular diagnostics. qPCR appeared to be less sensitive at detecting
mixed-species infections than nested PCR. Of the 7% (23/334) infections identified as
containing both P. relictum and P. circumflexum by clear double peaks on electropherograms,
only 3 contained two appropriate double melting peaks in the qPCR, and such double melting
peaks were also observed in 6 infections not diagnosed as mixed by sequence analysis.
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Table S1: Comparison of Plasmodium detection by the nested PCR method of Waldenström
et al. (2004), and a novel quantitative PCR assay, among samples tested by both methods.
Prevalence estimates and 95% confidence intervals are shown.
All Plasmodium
Year
N
2005
P. relictum
P. circumflexum
nested PCR
qPCR
nested PCR
qPCR
nested PCR
qPCR
405
0.264
(0.223, 0.309)
0.483
(0.434, 0.531)
0.117
(0.089, 0.152)
0.296
(0.253, 0.342)
0.162
(0.129, 0.201)
0.211
(0.174, 0.254)
2006
416
0.351
(0.307, 0.398)
0.394
(0.348, 0.442)
0.173
(0.140, 0.212)
0.188
(0.153, 0.228)
0.200
(0.164, 0.241)
0.204
(0.168, 0.246)
2007
436
0.314
(0.272, 0.359)
0.443
(0.397, 0.490)
0.195
(0.160, 0.235)
0.296
(0.255, 0.340)
0.152
(0.121, 0.189)
0.133
(0.104, 0.168)
All
years
1254
0.310
(0.285, 0.336)
0.439
(0.412, 0.467)
0.163
(0.143, 0.184)
0.260
(0.236, 0.285)
0.171
(0.151, 0.193)
0.182
(0.161, 0.204)
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Table S2: Results from GLMM modelling of Plasmodium infection status (prevalence)
variation across the Wytham blue tit population. All variables included in the starting model
are shown, with effects in the minimal model in bold. For significant terms (p<0.05) statistics
are from the minimal model; for non-significant terms, statistics are those at the point that
factor left the model. Effect sizes (Pearson’s r) are given for all effects with a single degree of
freedom.
Predictor
Minimum age
Minimum age2
Year
x
y
x*y
Sex (F)
Species (P. relictum)
x*Species
y*Species
Sex*Species
Minimum age*Species
Minimum age2 *Species
Year*Species
x*y*Species
†Denominator
df
Den df†
F
p
r
1
1
3
1
1
1
1
1
1
1
1
1
1
3
1
1824
1824
1824
1824
1824
1824
1824
1824
1824
1824
1824
1822
1821
1824
1823
10.37
4.59
5.83
12.17
8.35
2.17
0.21
39.64
10.44
116.44
6.45
3.24
0.66
18.55
3.56
0.001
0.032
<0.001
<0.001
0.004
0.141
0.646
<0.001
0.001
<0.001
0.011
0.072
0.416
<0.001
0.059
0.075
-0.050
-0.081
0.068
-0.035
-0.011
0.146
0.075
0.245
0.059
0.042
0.019
0.044
degrees of freedom
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Fig. S1: Increase in cumulative Plasmodium prevalence as more qPCR tests are performed.
Means and 95% confidence intervals are shown.
Analysis of age-related changes in parasitaemia
Mixed models are commonly used to test for within-individual changes in traits with age.
However, a significant age effect in such a model does not definitively indicate withinindividual effects when datasets contain missing values, i.e. where not all individuals are
present at all ages, as in this study. In this case, selective appearance or disappearance of
individuals can also contribute towards the age effect. We used the mixed model approach of
van de Pol & Verhulst (2006) to distinguish age-related within-individual increases in
parasitaemia from selective appearance or disappearance of individuals with either low or
high parasitaemia with age. To do this we modelled parasitaemia among individuals infected
in more than one year, including fixed effects of minimum age, minimum age at which an
individual was first present in the dataset (first age), minimum age at which an individual was
last present in the dataset (last age) and year. Individual identity was included as a random
effect. Here minimum age represents the within-individual effect, whilst selective appearance
and disappearance are tested for by including first age and last age. We found no evidence for
selective appearance or disappearance of individuals with respect to parasitaemia (first age:
F1,94=0.47, p=0.497; last age: F1,94=0.01, p=0.914). Of these two effects, only the very weak
negative effect of last age is in a direction that could contribute to the overall positive
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relationship between age and parasitaemia detected at the population level. Whilst controlling
for these two effects, there was a non-significant effect of minimum host age in the expected
direction (parasitaemia increasing within individuals as they age; F1,169=3.00, p=0.085). When
first age and last age were sequentially excluded from the model, the effect of minimum age
became significant (F=1,121=4.21, p=0.043). This increase in significance is likely because
age, first age and last age are to an extent collinear.
References
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Palinauskas, V., Kosarev, V., Shapoval, A., Bensch, S., & Valkiūnas, G. (2007) Comparison of mitochondrial
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