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SUPPLEMENTARY ONLINE MATERIAL: BONNEAUD ET AL.
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Experimental evidence for distinct costs of pathogenesis and immunity against a natural
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pathogen in a wild bird
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Biology of MG pathogenecity
In house finches, MG causes respiratory tract and conjunctivitis infections. Studies on poultry
show that MG pathogenecity is in large part a consequence of MG’s capacity to manipulate the
immune system of its host (Ganapathy & Bradbury 2003; Mohammed et al. 2007; Naylor et al.
1992). Indeed, at the onset of infection and during colonization of the conjunctivae and trachea,
MG induces a damaging inflammatory response in susceptible hosts, which triggers the
recruitment and activation of large numbers of inflammatory cells to the mucosal tissues, giving
rise to host lesions (Gaunson et al. 2000; Gaunson et al. 2006). Inflammation is not always
beneficial to hosts and manipulation of host inflammatory responses can be an effective bacterial
strategy that facilitates colonization and infection. Indeed, the infiltration of immune cells that
follows the activation of an inflammatory response can disrupt the mucosal layer, causing the
rupture and inflammatory destruction of the epithelial barrier and allowing the colonization and
systemic dissemination of microbes (d'Hauteville et al. 2002; Hornef et al. 2002). Hence
triggering inflammation in the host may be beneficial to pathogens under some conditions. MG
infections are also associated with the suppression of components of the immune system that are
more pathogen-specific. For example, chicken (Gallus domesticus) infected with MG displayed
decreased T-cell activity two weeks post-infection (Ganapathy & Bradbury 2003; Gaunson et al.
2000; Mohammed et al. 2007). In addition, mixed infections with MG and Haemophilus
gallinarum in chicken (Matsuo et al. 1978), and with MG and avian pneumovirus in turkeys
(Naylor et al. 1992), resulted in a lowered humoral antibody response to both H. gallinarum and
pneumovirus.
Population differences are explained by adaptive evolution of eastern US finches to MG
We believe that any potential population-differences in responses to MG-infection can only be
explained by differences in the history of exposure to MG rather than genetic drift or ecological
differences, for the following reasons. First, all birds were acclimatized in the same novel captive
environment with ad lib food and water for at least three months prior to the experiment, thus
reducing the risk of obtaining spurious results due, for e.g., to background differences in
condition or gene expression. Second, previous comparisons of gene expression patterns
between finches from Arizona (2007) and from Alabama early (i.e., in 2000) and later (i.e., in
2007) in the MG epizootic, revealed that, as resistance to MG evolved in the eastern populations,
expression patterns of Alabama finches became less similar to those patterns found in finches
from unexposed Arizona populations (Bonneaud et al. 2011). This means that genetic drift or
unknown ecological differences between birds from the two populations are unlikely to mediate
SOM - Cost of immunity vs. pathogenesis
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inter-population differences in responses to MG infection. Third, Alabama finches in 2007
harbored lower bacterial loads post-infection and up-regulated immune genes than finches from
Arizona, which showed evidence of being immuno-suppressed (Bonneaud et al. 2011). These
observations support the interpretation that population differences in responses to MG between
Arizona and Alabama finches arose as a consequence of different histories of exposure to the
disease.
Genetic diversity of MG isolated from house finches
A temporal sampling of MG in house finches conducted over the course of the eastern US
epizootic revealed that the total nucleotide diversity of the whole genome of House finch MG is
very low and only 2.3% of that found in poultry MG (Delaney et al. 2012).
Sample size in this study
Sample sizes within groups and the number of groups were kept to a minimum, while still
ensuring sufficient statistical power, to adhere to animal ethics stipulations. In addition, the
protocol of the experimental infection was specifically designed to allow us to address multiple,
orthogonal questions, thereby ensuring that we maximized the use of the data collected
((Bonneaud et al. 2011; Bonneaud et al. 2012; this study).
Association between levels of expression of the 16 house finch genes with MG load and mass loss
(1) The expression value of each of the 16 house finch gene investigated was transformed using a
log or a square root transformation, depending on the distribution of the variable. We
subsequently standardized the expression value of each gene within each individual to
subsequently allow comparisons of expression changes between genes, using the following
equation:
z = ((x – μ)/ σ)
where z is the standardized value, x the raw value, μ the population mean and σ is the standard
deviation (Gelman & Hill 2007). This allows one to statistically compare regression coefficients
(i.e. slope gradients) generated in different analyses (see below).
(2) We then conducted a total of 96 regression analyses. In 32 of these, MG load was fitted as the
response term and the expression levels of each gene in both the spleen and the trachea were
fitted as the explanatory terms in individual regression analyses (i.e. 16 genes x 2 tissues = 32
analyses). Below we show the mean coefficients from each of the 16 genes for the spleen (Figure
S1A) and trachea (Figure S1B). In the other 64 regressions, mass loss was fitted as the response
term and again the same 16 genes expression profiles in each of the two tissues were fitted as the
explanatory terms, but in this case, regressions were considered separately for each of the two
populations (i.e. 16 genes x 2 tissues x 2 populations = 64 analyses). Again, we show the mean
coefficients arising from each analysis for the two tissues, this time split by population (Figure
S2A (spleen) and S2B (trachea)).
The reason for the combining of the sites in the MG load analyses but not the mass loss analyses
was two-fold. First, because we have evidence to show that some birds in Alabama are
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SOM - Cost of immunity vs. pathogenesis
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susceptible to MG, presumably because their ancestors were never infected, while some in
Arizona are resistant, presumably due to standing genetic variation (Bonneaud et al. 2011),
individuals from the two populations overlap in their gene expressions and resistance to MG.
The most powerful way of testing whether gene expression profiles are associated with MG
loads is to combine the data from the two populations. Second, by contrast, because mass loss
reflects the costs of protective immunity in Alabama but the costs of pathogenesis in Arizona, we
would expect the relationship between mass loss and gene expressions to be opposing, not
complementary, in the two populations; thus necessitating the splitting of the populations for
such analyses.
(3) From each regression, we then extracted the coefficient (i.e. the gradient of the regression
slope). In the case of the analyses involving MG load (where populations were not split), these
coefficients were used in one-sample two-tailed t tests designed to investigate whether the
slopes, on averaged, differed from zero in the manner predicted if gene expression profiles were
associated with protective immunity (i.e. lower MG loads). Two analyses were conducted, one
for each of the tissues sampled. In the case of mass loss (where populations were split), the
relevant coefficients were used in two paired t tests (one for each tissue), to investigate whether
slopes, on average, differed between populations in the manner predicted if gene expression
profiles indicative of protective immunity were associated with greater mass loss.
In the majority of occasions, the directions of the slopes from the above coefficients were
maintained in the t’ tests, i.e. a positive slope had a positive coefficient in the analysis and (vice
versa). However, for two genes, the slopes were reversed (see below). Predictions regarding the
direction of the slope between gene expression and MG load (or mass loss) are based on a prior
investigation of gene expression differences between MG-infected vs control house finches from
Alabama and from Arizona using a microarray (Bonneaud et al. 2011). We previously showed
that finches from Alabama had evolved resistance to MG in 12 yrs of exposure to the bacterium,
while finches from Arizona which had never been exposed, were more susceptible (Bonneaud et
al. 2011). Hence, expression changes measured in Alabama finches post-infection, which
showed an up-regulation of immune genes, were considered to be associated with greater
protective immune activity, while the expression changes detected in Arizona finches postinfection, which revealed a down-regulation of immune genes did not. We therefore expected
here a negative coefficient for the regression of MG load on the expression value for genes
whose expression increased in Alabama finches post-infection in the microarray study, and a
positive coefficient for the regression of MG load on the expression value for genes whose
expression decreased in Alabama finches post-infection in the microarray study. Only two genes
included in the multiplex qRT-PCR were found to be down-regulated post-infection in finches
from Alabama: hsp90 and eukaryotic translation initiation factor (tif); for those two genes only,
we therefore expected the regression coefficient of MG load on gene expression to be positive.
Although the figures below show that not all genes go in the direction predicted, it is generally
the case that they do.
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SOM - Cost of immunity vs. pathogenesis
Ubc
Txn
Tcrβ
Sec61ɣ
RhoA
Ptms
Psap
-0.7
Nadh4
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-0.6
Nabp
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-0.5
MhcIi
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-0.4
Ick
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-0.3
Lcp
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-0.2
IgJ
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-0.1
Ig4A
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0
Hsp90*
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A.
eIF4E*
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Figure S1: Regression coefficient of MG load (mean ± 1S.E) on each of the 16 house finch
genes (A) in the spleen, and (B) in the trachea. For 14 genes, we would expect the relationship
between gene expression and MG load to be negative (i.e. increase expression is associated with
reduced MG load). The two exceptional genes are the first two and are marked with * following
their name; for each we would expect reduced expression to be associated with reduced MG
load. The 16 genes were as follows: eukaryotic translation initiation factor eIF4E (eIF4E), heat
shock protein 90 (Hsp90), immunoglobulin superfamily member 4A isoform a (Ig4a),
immunoglobulin J (IgJ), lymphocyte cytosolic protein (Lcp), MAK-like kinase (Ick), MHC class
II-associate invariant chain Ii (MhcIi), nucleic acid binding protein RY-1 variant 3 (Nabp),
NADH dehydrogenase subunit 4 (Nadh4), prosaposin (Psap), parathymosin (Ptms), RhoA
GTPase (RhoA), SEC61 gamma subunit (Sec61ɣ), TCR beta chain (Tcrβ), thioredoxin (Txn),
ubiquitin c (Ubc).
coefficient (gradient) of the regression of MG load on
gene expression in the spleen
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House finch genes
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Ubc
Txn
Tcrβ
Sec61ɣ
RhoA
Ptms
Psap
Nadh4
Nabp
MhcIi
Ick
Lcp
IgJ
Ig4A
Hsp90*
B.
eIF4E*
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coefficient (gradient) of the regression of MG load on
gene expression in the trachea
SOM - Cost of immunity vs. pathogenesis
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
House finch genes
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SOM - Cost of immunity vs. pathogenesis
Figure S2: Regression coefficient of mass loss (mean ± 1S.E) on each of the 16 house finch
genes (A) in the spleen, and (B) in the trachea. House finches from Alabama (black bars) and
Arizona (white bars) had to be considered separately. Again, 14 of the 16 genes would expected
to show negative slopes under evidence of protective immunity, with the exception of the first
two genes marked with * following their names.
-0.1
-0.15
-0.2
Txn
Ubc
Txn
Ubc
Tcrβ
Sec61ɣ
RhoA
Ptms
AZ
0.2
0.1
0
-0.1
-0.2
-0.3
Tcrβ
Sec61ɣ
RhoA
Ptms
Psap
Nadh4
Nabp
MhcIi
Ick
Lcp
IgJ
Ig4A
-0.4
Hsp90*
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0.3
eIF4E*
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(B)
coefficient (gradient) of the regression of mass
loss on gene expression in the trachea
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AL
House finch genes
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Psap
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Nadh4
-0.25
eIF4E*
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-0.05
Nabp
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0
MhcIi
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0.05
Ick
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0.1
Lcp
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AZ
0.15
IgJ
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AL
0.2
Ig4A
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0.25
Hsp90*
(A)
coefficient (gradient) of the regression of mass
loss on gene expression in the spleen
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House finch genes
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SOM - Cost of immunity vs. pathogenesis
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References
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