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  • Research article
  • Open Access

Immunogenetic response of the bananaquit in the face of malarial parasites

BMC Evolutionary Biology201919:107

https://doi.org/10.1186/s12862-019-1435-y

  • Received: 3 February 2019
  • Accepted: 8 May 2019
  • Published:

Abstract

Background

In the arms race between hosts and parasites, genes involved in the immune response are targets for natural selection. Toll-Like Receptor (TLR) genes play a role in parasite detection as part of the innate immune system whereas Major Histocompatibility Complex (MHC) genes encode proteins that display antigens as part of the vertebrate adaptive immune system. Thus, both gene families are under selection pressure from pathogens. The bananaquit (Coereba flaveola) is a passerine bird that is a common host of avian malarial parasites (Plasmodium sp. and Haemoproteus sp.). We assessed molecular variation of TLR and MHC genes in a wild population of bananaquits and identified allelic associations with resistance/susceptibility to parasitic infection to address hypotheses of avian immune response to haemosporidian parasites.

Results

We found that allele frequencies are associated with infection status at the immune loci studied. A consistent general trend showed the infected groups possessed more alleles at lower frequencies, and exhibited unique alleles, compared to the uninfected group.

Conclusions

Our results support the theory of natural selection favoring particular alleles for resistance while maintaining overall genetic diversity in the population, a mechanism which has been demonstrated in some systems in MHC previously but understudied in TLRs.

Keywords

  • Major histocompatibility complex
  • Toll-like receptor
  • Haemosporidian parasites
  • Avian malaria

Background

The survival of any multicellular organism is dependent upon an effective immune response to ward off invaders. The genes involved in this defense are thus targets for natural selection, and multifaceted mechanisms underlie vertebrate immune gene evolution driven under selective pressure from parasites [13].

Two major immune gene families in the vertebrate immune system are the Major Histocompatibility Complex (MHC) of the adaptive immune system and the Toll-like Receptor (TLR) family of the innate immune system. The MHC is a genetically diverse multigene family that plays a vital role: the host’s MHC receptor molecules bind peptides (antigens) produced by pathogens. MHC molecules then display the antigen on the cell’s surface for recognition by T-cells and subsequent attack on the foreign invader [4]. Therefore, an individual’s MHC genotype governs its ability to detect particular pathogens, affecting its susceptibility to parasitic infection and specific diseases [5, 6].

The MHC has long been the subject for the study of maintenance of genetic diversity by balancing selection. Balancing selection may be driven by negative frequency-dependent selection, in which rare alleles confer a selective advantage, and/or heterozygote advantage, in which heterozygotes are more fit than homozygotes [7, 8]. Additionally selection may fluctuate over space and time [9]. Heterozygote advantage implies that the highest number of MHC alleles would confer the highest fitness by allowing for recognition of the largest diversity of pathogens. However, too much MHC variability can result in removal of T-cells capable of distinguishing “self” from “non-self” and increase the chance of autoimmune disease, potentially conferring higher fitness to individuals with intermediate MHC diversity (the “optimality” hypothesis) [10, 11].

While studies of the impacts of immunogenetic variation on a host’s response to pathogens have mainly focused on MHC genes, genetic variability in other immune loci such as TLRs may also play an important role [12]. TLRs recognize conserved pathogen-associated molecular patterns (PAMPs) derived from different classes of microbes, and upon binding a foreign ligand, induce a signal cascade for the inflammatory response [13]. Evidence suggests TLRs are not as polymorphic as MHC as they are dominated by stabilizing or purifying selection due to functional constraints, but positive selection has been shown in putative ligand-binding regions of TLRs [1416]. TLRs are therefore under similar host-parasite selective pressures as MHC, so genetic variability at these loci likely affect a host’s resistance, and TLR diversity of avian species of conservation concern has begun to be explored [17, 18].

In an attempt to further address associations between host immune gene diversity and susceptibility to parasites, we studied a wild population of bananaquits (Coereba flaveola). The bananaquit, a non-migratory songbird commonly found in the Caribbean and parts of South America and Mexico, lives in a variety of habitats, including forests, shrublands, and human environments such as parks. It shares the characteristics of many “finch-like” tanagers, such as a small body size, colorful plumage, and has a primarily nectivorous diet [19]. The bananaquit is a common host of Haemosporidian parasites (Plasmodium spp., and Haemoproteus spp.), blood-borne pathogens vectored by dipteran insects. These parasites cause avian malaria, which affects a wide range of birds worldwide and can impact the host’s fecundity, lifespan, and survivorship [2022]. One would expect the genetic makeup of MHC and TLR genes in a bananaquit population to evolve in concert in the face of these parasites. In particular, any adaptation in TLR genes would likely be evidenced in the variable extracellular leucine-rich repeat region (LRR) associated with detection of the particular PAMPs. In MHC genes, we would expect evolution of exons 2 and 3 in MHC Class I genes and exon 3 in MHC Class II genes, which encode the peptide-binding regions.

Using a population of bananaquits with different infection statuses sampled from.

Guanica Forest in Puerto Rico, we attempted to identify TLR and MHC allelic associations with resistance or susceptibility to parasitic infection. This population was shown to be subject to infection by three genetically distinct lineages of Haemoproteus, the host specialist LA07 and host generalists OZ02 and OZ21, and the overall parasite prevalence of the population over the time of the sampling period averaged 51% [23, 24]. We grouped these individuals by infection status and use a pooled amplicon sequencing approach to identify alleles present at MHC and TLR loci. We expected to find different allele frequencies and sequence divergence among the groups, and looked for supporting evidence of mechanisms of selection. If at a particular locus, uninfected bananaquits exhibit higher allelic diversity compared to infected bananaquits, the mechanism of heterozygote advantage would be supported for that locus. Alternatively, if uninfected bananaquits exhibit lower allelic diversity, frequency-dependent selection or positive selection for particular genes that confer resistance is more likely explanation.

Results

Sample collection & processing

A total of 99 bananaquits were collected from Guanica Forest and used in this study. Of those, 45 were uninfected, 41 were infected with the LA07 strain of Haemoproteus sp., and 13 were infected with various strains of Haemoproteus sp. (either OZ02 or OZ21 lineages) (Additional file 1: Table S1).

Pooled Sequencing & Sequence Processing

Ultimately, we successfully amplified, indexed, equimolarly pooled, sequenced, and called alleles for 5 immune loci in the same 99 bananaquit individuals:45 in the uninfected group (called UNI) 41 in the group infected with host-specialist LA07 (called LA07), and 13 in the group infected with either OZ02 or OZ21 (called INF). (UNI = uninfected, LA07 = infected with host-specialist LA07 strain, or INF = infected with OZ02 and OZ21). The 5 immune loci included two portions of MHC1-UAA gene (referred to here as MHC1-UAA-1 and MHC1-UAA-2) and 3 TLRs (TLR1A, TLR2B, and TLR7–1).

Upon sequencing, 15,845,214 raw reads were produced. After merging sequence reads and filtering by quality and length, 6,093,555 reads were retained (mean of 1,218,711 reads/locus; mean of 12,310 reads/locus/individual).

Allele characterization

All of the five loci were polymorphic, with a range of 3–14 alleles (Table 1). In the UNI group 2–5 alleles were seen among the loci, the LA07 group showed between 1 and 7 alleles per locus, and the INF group showed 3–7 alleles.
Table 1

Summary of loci/amplicons sequenced

   

GROUP

   

UNI

LA07

INF

   

n = 90 (45 individuals)

n = 82 (41 individuals)

n = 26 (13 individuals)

  

Total # Alleles

# Alleles

# Unique Alleles

Amplicon Depth (# reads)

Avg. Allele Depth

# Alleles

# Unique Alleles

Amplicon Depth (# reads)

Avg. Allele Depth

# Alleles

# Unique Alleles

Amplicon Depth (# reads)

Avg. Allele Depth

LOCUS

TLR1A

7

4

0

3310

828

5

0

1711

342

5

2

2709

542

TLR2B

14

5

0

2467

493

7

2

2387

341

7

7

1465

209

TLR7

7

5

0

3216

643

5

0

3741

748

7

2

3695

528

MHC1-UAA-1

3

3

0

3816

1272

1

0

3442

3442

3

0

3616

1205

MHC1-UAA-2

4

2

0

4382

2191

4

0

3875

969

4

0

2794

699

Loci/amplicons and their presumptive alleles by group (phred score > 20, amplicon depth > 1000, allele depth > 100, minor allele frequency = 4%). UNI Uninfected group, LA07 infection with LA07 strains (host specialist), INF various infection strains other than LA07

Alleles within each locus were the same length and among the loci ranged from 363 to 384 nucleotides. There were between 2 and 16 polymorphic sites in each locus corresponding to a proportion of polymorphic sites of 0.005–0.042 (Table 2).
Table 2

Summary of sequence variation across loci/amplicons

 

# Alleles

Length

# Polymorphisms

# Polymorphisms / Length

π

TLR1A

7

363

5

0.014

0.006

TLR2B

14

384

16

0.042

0.013

TLR7

7

372

8

0.022

0.008

MHC1-UAA-1

3

371

2

0.005

0.004

MHC1-UAA-2

4

364

4

0.011

0.006

Mean over loci

7

371

7

0.019

0.007

St. Dev. over loci

4

8

5

0.014

0.004

Range over loci

3–14

363–384

2–16

0.005–0.042

0.004–0.013

Presumptive alleles detected at each locus/amplicon and their overall genetic variation. π = nucleotide diversity (average pairwise differentiation between unique allele sequences)

A large portion of TLR genes are conserved but the recognition of particular PAMPs of parasites occurs by particular polymorphic amino acids of the extracellular N-terminal domain containing leucine-rich repeats (LRR). Primers were therefore designed for each TLR such that coding regions for a portion of the LRR domain were amplified. In the three TLRs successfully sequenced in this study, TLR1A, TLR2B, and TLR7–1, the levels of polymorphism observed suggest at least a portion of these pathogen recognition areas were amplified, which enhances our chances of detecting signatures of selection.

In MHC Class I genes, the peptide binding groove is formed when the α1 and α2 domains (encoded by exons 2 and 3 of the gene) of the alpha chain fold in to form a pocket where the ligand is recognized. The particular amino acids lining the walls of this groove are highly polymorphic to recognize various ligands, and are encoded by multiple short regions spaced throughout the nucleotide sequence that encodes the α1 and α2 domains. Because the amino acids which line the groove depends on the 3D structure of the protein, those precise nucleotide regions cannot be determined by examination of the genome sequence alone. In the case of the MHC Class I gene that was successfully amplified in this study (MHC1-UAA), the primers were designed such that two separate regions of the genome sequence encoding the α1 and α2 domains would be flanked in an attempt to capture at least a portion of the peptide-binding region. The first locus, MHC1-UAA-1, has a length of 371 nucleotides: the first 333 bp code for 111 amino acids, then there is a stop codon, and then 35 more nucleotides of an intronic region. The second locus, MHC1-UAA-2, has a length of 364 nucleotides: the first 1–312 nucleotides code for 104 amino acids, then there is a stop codon, and then 48 nucleotides of an intronic region. Unfortunately both regions (MHC1-UAA-1 and MHC1-UAA-2) spanned by our primers showed no or little polymorphism in either coding or non-coding (intron) regions.

Statistical analyses

Frequency-based analyses

For each locus, contingency tables were constructed with the frequency of each allele in each of the three groups (UNI, LA07, INF) in order to test for associations between allele frequencies and groups (Chi-square, Fisher’s Exact Test, and Wilk’s G) and the statistical results are given in Additional file 1 (Table S4). In each case the null hypothesis that the allele frequencies and groups are independent was rejected at a significance level of α = 0.05. When comparing UNI vs. COMBO (the weighted average of LA07 and INF), the same premise held true: the allele frequencies are significantly different among groups. Charts comparing allele frequencies can be seen in Fig. 1 for the TLR loci and Fig. 2 for the MHC loci, and individual alleles within each locus and group whose frequencies are significantly higher or lower (i.e. above or below the 95% CI) than the expected frequencies based on the resampling method are indicated. Figure 3 indicates expected heterozygosity for each locus: standard error bars indicate there are differences in mean expected heterozygosity between groups at the TLR2B and MHC1-UAA-1 loci, with uninfected groups showing lower heterozygosity than infected groups.
Fig. 1
Fig. 1

Allele frequency differences at TLR loci between experimental groups. All group/locus correlation/association tests are overall significant at among groups based on Chi-square test and/or Fisher’s exact test. Alleles with significantly higher (green up-arrow) or lower (red down-arrow) frequencies based on the 95% confidence interval of the null distribution upon resampling. UNI Uninfected group, LA07 infected with LA07 strains, INF infected with various strains other than LA07. COMBO represents combined infected groups (the weighted average of LA07 and INF)

Fig. 2
Fig. 2

Allele frequency differences at MHC loci between experimental groups. All group/locus correlation/association tests are overall significant at among groups based on Chi-square test and/or Fisher’s exact test. Alleles with significantly higher (green up-arrow) or lower (red down-arrow) frequencies based on the 95% confidence interval of the null distribution upon resampling. UNI Uninfected group, LA07 infected with LA07 strains, INF infected with various strains other than LA07. COMBO represents combined infected groups (the weighted average of LA07 and INF)

Fig. 3
Fig. 3

Expected heterozygosity for each locus and group. Error bars are +/− one standard error from the mean. UNI Uninfected group, LA07 infected with LA07 strains, INF infected with various strains other than LA07. COMBO represents combined infected groups (the weighted average of LA07 and INF)

Distance-based analyses

The results of the hierarchical AMOVA to determine sources of allele sequence variation are shown in Additional file 1 (Table S5). For all loci, the highest source of variation (77–90%) was within populations (i.e. the groups UNI, LA07, and INF), whereas the source of variation among the populations ranges from 0 to 22%. The source of variation among regions (i.e. the groups UNI and COMBO) ranges from 0 to 13% across loci. Phi-PT values (the sequence-based FST analogue) representing the pairwise differentiation at each locus for each pair of populations range from 0 to 0.299, and are in most cases significant.

Discussion

We found that the allele frequencies at the three TLR and two MHC loci sequenced in this study, as well as the expected heterozygosities, are associated with infection status in the bananaquit. This result is to be expected if particular alleles and/or the overall number of alleles at these loci confer or contribute to pathogen resistance or susceptibility.

Parasite-mediated selection is thought to drive immune genes to evolve primarily under.

balancing selection, in which polymorphisms at these loci are maintained via negative frequency-dependent selection or heterozygote advantage (over-dominance) [25, 26]. Evidence for balancing selection has been shown in studies of MHC diversity in birds and other taxa [2729]. These findings include birds exposed to avian malaria, which suggest that balancing selection is capable of maintaining MHC variation even in populations undergoing a genetic bottleneck [30]. Evidence for mechanisms underlying balancing selection are mixed. For example, in the mouse lemur, particular rare MHC alleles were found to be associated with resistance to nematode infection [31], whereas in the alpine ibex, MHC heterozygosity was associated with resistance to bacterial conjunctivitis [32]. Additionally, support for the “optimality hypothesis” has been shown: in the stickleback an intermediate level of MHC alleles was associated with resistance to tapeworms and parasitic fungi [33]. In the Chinese egret, both a particular MHC allele and an intermediate number of alleles was associated with infection status and burden by parasitic nematodes [34].

At TLR loci, evolution by balancing selection has been implicated in humans [35], and in bank voles both particular alleles of some TLRs and heterozygosity of other TLRs are associated with resistance to the blood pathogen Bartonella [36]. Evidence in other mammalian taxa, however, suggests directional selection is the dominant force [37]. Avian studies have indicated TLR genes evolve primarily under stabilizing or purifying selection attributed to functional constraints, with small portions of the gene under episodic positive selection for amino acid diversification [14, 16]. Studies of TLRs in avian species on a micro-evolutionary scale are rare, but research aimed at detecting balancing selection in populations of conservation concern indicate that genetic drift is the dominant force shaping their genetic variability [38, 39].

We found the presence and frequency of immune alleles at each locus show an overall significant trend in which infected individuals harbor some unique alleles and also have more alleles than uninfected individuals. The pattern is exemplified with TLR2B, in which the UNI group has 5 alleles ranging in frequency from 0.096–0.373, the LA07 group has 7 alleles (five shared with UNI and two additional ones) ranging from 0.008–0.234, and the INF group (OZ strains) has 7 alleles (all unique) ranging from 0.106–0.236.

This supports the idea that particular alleles confer resistance rather than high allelic diversity, and the lower frequency or absence of those alleles confer susceptibility. Among the five loci, the exception to this pattern is one region of the MHC1-UAA gene, MHC1-UAA-1, in which LA07 is fixed for one allele whereas the other groups have three alleles.

An advantage of the pooled sequencing approach is its relatively low cost and high efficiency [40], but it precludes the identification of genotypes within or among loci. Therefore observed zygosity cannot be determined, but the expected heterozygosity at each locus and group may give additional insight into mechanisms that may be operating, such as overdominance. In particular, with TLR2B and MHC1-UAA-2 locus, the mean expected heterozygosity is significantly lower in uninfected groups than in infected groups, which, taken along with the allele frequency data, suggests that heterozygote advantage is not operating to confer resistance at these loci.

The hierarchical AMOVA takes nucleotide sequence distances into consideration to estimate the biggest contributions to the genetic variance present in each locus: within the group itself, among the groups, or between the regions into which the groups fall (i.e. uninfected or infected). Our data supported the hypothesis that host TLR and MHC sequences diverge in the presence of parasites, although they showed different sources for this genetic variance.

Among the TLRs, no source of variation is seen among regions, but there is variation among groups, indicating that sequence variation between uninfected and infected individuals is attributable to the particular infection status (specialist LA07 vs. generalist OZ strains) and not infection as a whole. The highest contribution among groups is at locus TLR2B, where it accounts for 22% of the molecular variance. This can also be seen in the high pairwise differentiation values of UNI vs. INF (Phi-PT = 0.244) and LA07 vs. INF (Phi-PT = 0.184) at that locus. Additionally, the high molecular divergence between INF and the other groups reflects the previous findings of only unique alleles in that group at that locus. The other two TLR loci, TLR1A and TLR7, show among group variation contributions of 6–10% and significant pairwise Phi-PT values ranging from 0.036–0.091. Taken together these data support the idea that TLR sequence evolution occurs in response to selection pressure by pathogens, and different alleles respond to particular strains of Haemoproteus. This might be expected for adaptive immune system genes like MHC which evolve in response to novel pathogens, but innate TLRs are thought to be more general in the PAMPs they recognize (e.g. they are expected to recognize PAMPs conserved among protozoans, but not to differentiate between ligands of lineages within one genus).

Among the MHC-UAA loci, the region (uninfected or infected) accounted for 9–13% of the variation, and the group accounts for 0–14% of the variation. Pairwise Phi-PT values showed differentiation among populations ranging from significant values of 0.126–0.364. These data supported the hypothesis that host MHC sequences divergence is due to general infection status as well as to the infection status by lineage.

Caveats

Some caveats exist regarding interpretation of our data. The underlying assumption to our analyses is the equimolar pooling of individual products for equal representation during sequencing. While our best efforts were made to ensure this is the case, technical error could be introduced during the experiment, such as from variation in fluorometric quantification of DNA or undetected sequencing errors. Therefore, while we expect our results to have biological significance, we cannot rule out potential impacts from technical error on the patterns we observed. Additionally, opportunistic sampling may not precisely reflect the alleles present in the entire population. For example, the striking pattern at the TLR2B locus of no overlap in alleles between the INF group (the smallest group) and the other groups could be impacted in part by technical error or effects of sampling. Additional studies with more sampling and sequencing (at these loci and others) would help to address these potential impacts and further explore the underlying biological mechanisms in the observed patterns.

Caution is to be used regarding the interpretation of the two loci (MHC1-UAA-1 and MHC1-UAA-2) composing regions of the MHC1-UAA gene. Although the vast majority of polymorphisms seen at the two loci in the MHC-UAA gene are contained in noncoding regions, these regions are expected to be in linkage disequilibrium with the rest of the gene. We can say that in general, the MHC data showed the same trend as the TLR data, which supports the hypothesis that particular alleles confer resistance and that heterozygote advantage is not operating. While our results suggest that particular alleles of TLRs confer resistance, rather than heterozygote advantage, they also do not contradict the “optimal” number of alleles theory. The diversity seen in the infected groups may be too high, and the smaller number of alleles in the uninfected groups may be the optimal number for parasite recognition without autoimmune consequences. However, we cannot determine if birds that were infected did not survive and thus were unrepresented in our study.

Additionally, since all bananaquits in this study were collected from the same time and place, we have assumed they were all equally likely to be exposed to hemosporidian parasites at the same rate. Thus our above interpretation of the data is based on the assumption that uninfected group of bananaquits are uninfected because their immunogenetic repertoire allowed them to recognize and clear Haemoproteus infections. Similarly we assume that the LA07 group does not have the immune alleles necessary to recognize or clear the LA07 strain of Haemoproteus, and the INF group’s immune genes do not allow for recognizing or clearing the OZ strains of Haemoproteus. An alternative way of interpreting the data would result if one were to start with the assumption that the uninfected groups are instead those individuals that have not been exposed to parasites (so the results of that group become uninformative for our purposes). In this scenario the infected groups are those that are infected but survive, so presumably have an immunogenetic repertoire that allows the individuals to live with chronic malaria (as oppose to dying during the acute stage). In this case, any increased allelic diversity in these immune genes (relative to birds that died) would result from balancing selection due to heterozygote advantage.

Conclusions

Among TLRs we observed a large amount of alleles maintained overall in the population, but within individuals the presence of certain alleles at each locus seem to confer resistance, as indicated by being present in higher frequencies in the uninfected groups than the infected groups and the sequence divergence between the alleles of the groups. As postulated by the red queen hypothesis, pathogens evolve in response to the most common alleles in the host population [41, 42]. Therefore rare alleles confer a selective advantage to the host, and over time directional selection causes the previously common alleles become less common and the previously rare alleles become more common.

In this study these “rare alleles” are present at a high frequency in the uninfected groups but rarer in the infected groups. While a high amount of polymorphism is required at these loci in the population as a whole (as evident in the many additional alleles in the other groups), within individuals it is particular alleles which matter (those which are still able to recognize and respond to pathogens). This supports the notion of directional or frequency-dependent selection (as opposed to heterozygote advantage) in the individual. To our knowledge, this is the first study to demonstrate associations between TLR alleles and infection statuses in a wild avian population, and our data suggest that certain alleles confer resistance and may differentially recognize haemosporidian pathogens even at the sub-genus level.

Methods

The experimental design for the investigation of immune gene alleles among bananaquits of different infection statuses using pooled sequencing of target amplicons is described below and summarized in Fig. 4.
Fig. 4
Fig. 4

(Left) Overview of experimental design for investigating immune alleles among birds of different infection statuses using pooled sequencing of target amplicons (Right) Schematic of Primer Design

Sample collection

Bananaquit individuals were collected opportunistically along with other local avifauna via mist nets in Guanica Forest of Puerto Rico in 2001 as reported in [24]. Briefly, blood (5-10ul) from the brachial vein was drawn in the field and placed in Puregene cell lysis buffer, and genomic DNA was subsequently extracted by salt precipitation as reported in [24]. Animals were released upon blood collection. All applicable international, national, and institutional guidelines for the care and use of animals were followed: blood samples were collected and transported under the appropriate permits and licenses from local governments following protocols approved by the University of Pennsylvania and the University of Missouri, St Louis [24].

Identification of infection status

In each individual, presence or absence of haemosporidian parasite infection was determined by PCR amplification of a conserved region of ribosomal rRNA as reported in [43]. For those that were infected, the lineage of Plasmodium spp. or Haemoproteus spp. was subsequently determined by amplification and sequencing of the mitochondrial cytochrome b using a variety of primer combinations as reported in [44].

Pooled sequencing

The concentrations of the extracted genomic DNA from these previously sampled individuals with determined infection statuses were measured with a Nanodrop,spectrophotometer, and working solutions of 20 ng/ul were prepared. In order to create a library of pooled amplicons from all individuals at all loci we performed two PCR steps: the first to amplify the loci in all samples and the second to add Illumina flow-cell adapters and group-specific indexes to all samples. First we designed locus-specific primer sequences for portions of 12 immune (MHC and TLR) genes based on the annotated bananaquit genome [45]. For some genes (e.g. the MHC Class I gene MHC1-UAA), two sets of primers flanking different regions within the gene were chosen to increase our changes of capturing the peptide-binding region. The primers were chosen such that they flanked genomic regions containing putative ligand-binding sites and spanned no longer than 390 bp of the locus (to allow room for appending to the sequences for a 500-cycle MiSeq run).

We performed a two-step PCR to append Illumina adapters and unique indices to amplicons (Fig. 4, right panel). Specifically, we tailed the 5′ end of the locus-specific primers with 29–34 bp of the Illumina TruSeq Universal adapter sequences (Additional file 1: Table S2). Thus the first round of PCR amplified the loci (with the individuals in each experimental group in a separate plate) and created sequences which could be recognized by the primers in the next step of PCR. At this locus amplification step, if the locus did not amplify in the 99 individuals upon PCR optimization, that locus was disregarded from the study. The second PCR step incorporated the remaining Illumina Truseq Universal Adapter sequence and group index and includes the flow cell adapter sequence (Additional file 1: Table S2). In this way the locus-specific amplicons were labeled by experimental group (UNI = uninfected, LA07 = infected with host-specialist LA07 strain, or INF = infected with OZ02 and OZ21). See Additional file 1: Table S3 for PCR conditions.

The resulting products were cleaned using Sephadex columns and visualized by agarose gel electrophoresis. Each successful product (representing an amplicon from one locus from one individual) was quantified using an Invitrogen Quant-iT PicoGreen dsDNA assay kit, and read on a Neo Synergy plate fluorometer, in which the standard curve was replicated in triplicate and each plate was read three times and the values averaged.. The products were subsequently pooled equimolarly to a total of 10 nM. This library of pooled products was sequenced for paired-end reads using a 500-cycle Illumina Miseq.

Sequence processing

The raw reads (2x250bp) were merged with BBmap (Bushnell, https://sourceforge.net/projects/bbmap), with an overlap of 12 bases and a minimum length of 30 bases. Cutadapt was used for adapter removal, and Trimmomatic was used to clip lower quality bases (less than Phred-20) from both the 5′ and 3′ [46, 47]. Using a custom script, we sorted the trimmed, merged, and quality-controlled reads into separate fastq files using the experimental group indexes.

Amplicon and allele identification

We used AmpliSAS for amplicon identification and allele identification from the pooled sequencing reads [48]. Reads from each of the three experimental group’s fastq file were first processed using the associated program AmpliCLEAN to remove reads not corresponding to any amplicon (locus) based on locus-specific forward and reverse primer sequences. AmpliSAS was then used for each experimental group to a) de-multiplex reads into amplicons based on locus-specific primers; b) cluster amplicon sequences into potential alleles and artifacts (due to PCR or sequencing errors); and c) filter artifacts and assign true alleles. The amplicon read depths were set at a minimum of 1000 reads and the maximum allowed by AmpliSAS of 5000 reads. The maximum number of alleles to be considered per amplicon was 15. Clustering parameters for alleles and artifacts allowed for 1% substitution errors and 0.01% indel errors. Filtering parameters included a minimum allele frequency of 4%, and sequences that were chimeras from other major sequences were discarded.

We calculated summary statistics for each locus to quantify their overall genetic variation. These included the number of segregating sites, Watterson’s estimator Θ, and nucleotide diversity (π).

Statistical analyses

For all subsequent analyses we compared allele frequency and molecular distance differences within and among the three groups (UNI, LA07, INF). In addition, we created a group named COMBO which combines the two groups with infected individuals into one group (LA07 + INF), using a weighted average to take into account sample size, in order to compare all uninfected with all infected individuals.

Frequency-based analyses

Allele frequencies for each locus and group were calculated from the AmpliSAS output by dividing the number of reads for a particular allele by the total number of reads for that amplicon, such that the frequency of all alleles for a particular locus per group summed to one. Thus the number of reads per amplicon (locus) per treatment group were used as allele frequencies. To test for allelic frequency differences among groups we used XLSTAT to compute several association tests against the null hypothesis of independence between the rows (the frequency of particular alleles) and columns (the experimental group) of the contingency tables at a significance level of α = 0.05. A Chi-square test for independence was performed and the strength of any effect was calculated by the association parameter Cramer’s V. We also performed a Fisher’s Exact Test on contingency tables small enough (< 5 × 3) for an exact p-value, and a Wilks’ G2 Likelihood-Ratio Test for independence.

To robustly determine particular alleles with significantly high or low frequencies, we implemented a randomized resampling technique in which the observed values of each allele for each group was compared to a null distribution of allele frequencies for each allele. The null distribution was created by assuming the weighted average of the three groups represent the allele frequency of the population, based on group sample sizes and parasite prevalence of the overall population. Group sample sizes were calculated as twice the number of diploid individuals in the group (the number of alleles represented in the pool): 2n = 90 for UNI, 2n = 82 for LA07, and 2n = 26 for INF. The parasite prevalence in the overall population during the sampling period averaged 34% for the LA07 strain and 15.75% for OZ strains (the INF group), with the remaining 50.25% of individuals uninfected [24]. We then generated 1000 simulated populations of 100 individuals in which each individual has two alleles at a locus. For each simulated population the expected allele frequency was calculated for each allele, and then a 95% confidence interval (CI) for allele frequency was estimated across all replicates. The CIs were interpreted as the null distribution, to which the observed allele frequencies were compared. If the observed value of an allele was within the CI, it was not significantly different from the null distribution; it was significantly high or low if the observed value was larger or smaller than the CI. Additionally, expected heterozygosity for each locus and group were calculated based on their allele frequencies, with +/− one standard error from the mean.

Distance-based analyses

For each locus we wished to identify molecular variance among the alleles present within and among experimental groups while taking into account the sample sizes of the groups. We again estimated the theoretical number of alleles present for a particular locus and group by multiplying the allele frequency by the sample size for the group (2* the number of diploid individuals in the group). The equimolar pooling of all individuals at all loci prior to sequencing was implemented in an effort to sequence all DNA molecules evenly and distribute noise produced during sequencing evenly among all individuals/groups/loci/alleles. Thus we make the assumption that any over-counting of these theoretical numbers is done so proportionately, and these theoretical numbers of alleles were used as a proxy for individual haplotypes in the population for Analysis of Molecular Variance (AMOVA).

We implemented AMOVA in GenAlEx with a hierarchical structure to represent the presence or absence of infection as well as infection by certain lineages [49, 50]. There were two broad “regions” (UNI and COMBO) and three “populations” (UNI and LA07 and INF). LA07 and INF were populations in the COMBO region, while UNI was the only population in the UNI region. Then we created a haploid distance matrix for calculation of Phi-Statistics (an F-Statistics analog for sequence data), on which AMOVA is based. We also calculated Phi-PT values for pairwise group comparisons based on 999 permutations as a measurement of genetic divergence among groups.

List of abbreviations

AMOVA: 

Analysis of molecular variance

LRR: 

Leucine-rich repeat

MHC: 

Major histocompatibility complex

PAMPs: 

Pathogen-associated molecular patterns

TLR: 

Toll-like receptor

Declarations

Acknowledgements

We thank members of the Ricklefs lab for assistance with providing samples, and J. Willoughby for assistance with statistical analyses. We thank M. Christie, J. Dunning, C. Searle and members of the DeWoody Lab for critical review of a previous version of this manuscript.

Funding

This work was funded by the U.S. National Institute of Food and Agriculture, Purdue’s Department of Forestry & Natural Resources, and the University Faculty Scholar program to JAD. Publication of this article was funded in part by Purdue University Libraries Open Access Publishing Fund. The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

The data sets supporting the results of this article are available in the Data Dryad repository, DOI: https://doi.org/10.5061/dryad.f47d293 [51]. https://datadryad.org/review?doi=doi:10.5061/dryad.f47d293.

Authors’ contributions

Conceptualization and design: JA, RR, and JAD. Funding acquisition: JAD. Investigation: JA, SM, and MS. Resources: JAD and RR. Writing: JA. Revision: JA, SM, and JAD. All authors read and approved the final manuscript.

Ethics approval and consent to participate

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. Samples were collected and transported under the appropriate permits and licenses from local governments following protocols approved by ethics committees of the University of Pennsylvania and the University of Missouri, St Louis, as reported by Fallon et al. (2004).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Forestry and Natural Resources, Purdue University, 715 W. State Street, Pfendler Hall 141, West Lafayette, IN 47907, USA
(2)
Department of Biological Sciences, Purdue University, 915 W. State St, Indiana, USA
(3)
Department of Biology, University of Missouri, 1 University Blvd, St. Louis, MO, USA

References

  1. Best A, White A, Boots M. The implications of Coevolutionary dynamics to host-parasite interactions. Am Nat. 2009;173(6):779–91.View ArticleGoogle Scholar
  2. Tellier A, Brown JKM. Stability of genetic polymorphism in host-parasite interactions. P Roy Soc B-Biol Sci. 2007;274(1611):809–17.View ArticleGoogle Scholar
  3. Gokhale CS, Papkou A, Traulsen A, Schulenburg H. Lotka-Volterra dynamics kills the red queen: population size fluctuations and associated stochasticity dramatically change host-parasite coevolution. BMC Evol Biol. 2013;13.View ArticleGoogle Scholar
  4. Klein J. Natural history of the major histocompatibility complex. New York: Wiley; 1986. xv. p. 775.Google Scholar
  5. Westerdahl H, Waldenström J, Hansson B, Hasselquist D, von Schantz T, Bensch S. Associations between malaria and MHC genes in a migratory songbird. Proc R Soc B Biol Sci. 2005;272(1571):1511–8.View ArticleGoogle Scholar
  6. Sepil I, Lachish S, Hinks AE, Sheldon BC. Mhc supertypes confer both qualitative and quantitative resistance to avian malaria infections in a wild bird population. P Roy Soc B-Biol Sci. 2013;280(1759).View ArticleGoogle Scholar
  7. Borghans JAM, Beltman JB, De Boer RJ. MHC polymorphism under host-pathogen coevolution. Immunogenetics. 2004;55(11):732–9.View ArticleGoogle Scholar
  8. Sommer S. The importance of immune gene variability (MHC) in evolutionary ecology and conservation. Front Zool. 2005;2:16.View ArticleGoogle Scholar
  9. Eizaguirre C, Lenz TL, Kalbe M, Milinski M. Rapid and adaptive evolution of MHC genes under parasite selection in experimental vertebrate populations. Nat Commun. 2012;3:621.View ArticleGoogle Scholar
  10. Nowak MA, Tarczy-Hornoch K, Austyn JM. The optimal number of major histocompatibility complex molecules in an individual. Proc Natl Acad Sci U S A. 1992;89(22):10896–9.View ArticleGoogle Scholar
  11. Woelfing B, Traulsen A, Milinski M, Boehm T. Does intra-individual major histocompatibility complex diversity keep a golden mean. Philos T R Soc B. 2009;364(1513):117–28.View ArticleGoogle Scholar
  12. Acevedo-Whitehouse K, Cunningham AA. Is MHC enough for understanding wildlife immunogenetics? Trends Ecol Evol. 2006;21(8):433–8.View ArticleGoogle Scholar
  13. Kobe B, Kajava AV. The leucine-rich repeat as a protein recognition motif. Curr Opin Struc Biol. 2001;11(6):725–32.View ArticleGoogle Scholar
  14. Alcaide M, Edwards SV. Molecular evolution of the toll-like receptor multigene family in birds. Mol Biol Evol. 2011;28(5):1703–15.View ArticleGoogle Scholar
  15. Downing T, Lloyd AT, O'Farrelly C, Bradley DG. The differential evolutionary dynamics of avian cytokine and TLR gene classes. J Immunol. 2010;184(12):6993–7000.View ArticleGoogle Scholar
  16. Grueber CE, Wallis GP, Jamieson IG. Episodic positive selection in the evolution of avian toll-like receptor innate immunity genes. PLoS One. 2014;9(3).View ArticleGoogle Scholar
  17. Grueber CE, Wallis GP, Jamieson IG. Genetic drift outweighs natural selection at toll-like receptor (TLR) immunity loci in a re-introduced population of a threatened species. Mol Ecol. 2013;22(17):4470–82.View ArticleGoogle Scholar
  18. Dalton DL, Vermaak E, Smit-Robinson HA, Kotze A. Lack of diversity at innate immunity toll-like receptor genes in the critically endangered White-winged Flufftail (Sarothrura ayresi). Sci Rep-Uk. 2016;6.Google Scholar
  19. Online NB. Bananaquit (Coereba flaveola) Ithaca, NY, USA: Cornell Lab of Ornithology; 2019 [Available from: https://neotropical.birds.cornell.edu/Species-Account/nb/species/banana.
  20. Atkinson CT, Dusek RJ, Woods KL, Iko WM. Pathogenicity of avian malaria in experimentally-infected Hawaii Amakihi. J Wildlife Dis. 2000;36(2):197–204.View ArticleGoogle Scholar
  21. Knowles SC, Palinauskas V, Sheldon BC. Chronic malaria infections increase family inequalities and reduce parental fitness: experimental evidence from a wild bird population. J Evol Biol. 2010;23(3):557–69.View ArticleGoogle Scholar
  22. Asghar M, Hasselquist D, Hansson B, Zehtindjiev P, Westerdahl H, Bensch S. Chronic infection. Hidden costs of infection: chronic malaria accelerates telomere degradation and senescence in wild birds. Science. 2015;347(6220):436–8.View ArticleGoogle Scholar
  23. Ricklefs RE, Dodge Gray J, Latta SC, Svensson-Coelho M. Distribution anomalies in avian haemosporidian parasites in the southern Lesser Antilles. J Avian Biol. 2011;42(6):570–84.View ArticleGoogle Scholar
  24. Fallon S, Ricklefs R, Latta S, Bermingham E. Temporal stability of insular avian malarial parasite communities. Proc R Soc Lond Ser B Biol Sci. 2004;271(1538):493–500.View ArticleGoogle Scholar
  25. Gandon S, Michalakis Y. Local adaptation, evolutionary potential and host–parasite coevolution: interactions between migration, mutation, population size and generation time. J Evol Biol. 2002;15(3):451–62.View ArticleGoogle Scholar
  26. May RM, Anderson R. Epidemiology and genetics in the coevolution of parasites and hosts. Proceedings of the royal society of London series B. Biological sciences. 1983;219(1216):281–313.PubMedGoogle Scholar
  27. Bernatchez L, Landry C. MHC studies in nonmodel vertebrates: what have we learned about natural selection in 15 years. J Evol Biol. 2003;16(3):363–77.View ArticleGoogle Scholar
  28. Hedrick PW. Evolutionary genetics of the major histocompatibility complex. Am Nat. 1994;143(6):945–64.View ArticleGoogle Scholar
  29. Spurgin LG, Richardson DS. How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings. P Roy Soc B-Biol Sci. 2010;277(1684):979–88.View ArticleGoogle Scholar
  30. Jarvi SI, Tarr CL, McIntosh CE, Atkinson CT, Fleischer RC. Natural selection of the major histocompatibility complex (Mhc) in Hawaiian honeycreepers (Drepanidinae). Mol Ecol. 2004;13(8):2157–68.View ArticleGoogle Scholar
  31. Schad J, Ganzhorn JU, Sommer S. Parasite burden and constitution of major histocompatibility complex in the malagasy mouse lemur, Microcebus murinus. Evolution. 2005;59(2):439–50.View ArticleGoogle Scholar
  32. Brambilla A, Keller L, Bassano B, Grossen C. Heterozygosity-fitness correlation at the major histocompatibility complex despite low variation in alpine ibex (Capra ibex). Evol Appl. 2018;11(5):631–44.View ArticleGoogle Scholar
  33. Kurtz J, Kalbe M, Aeschlimann PB, Haberli MA, Wegner KM, Reusch TBH, et al. Major histocompatibility complex diversity influences parasite resistance and innate immunity in sticklebacks. P Roy Soc B-Biol Sci. 2004;271(1535):197–204.View ArticleGoogle Scholar
  34. Lei W, Zhou XP, Fang WZ, Lin QX, Chen XL. Major histocompatibility complex class II DAB alleles associated with intestinal parasite load in the vulnerable Chinese egret (Egretta eulophotes). Ecol Evol. 2016;6(13):4421–34.View ArticleGoogle Scholar
  35. Ferrer-Admetlla A, Bosch E, Sikora M, Marques-Bonet T, Ramirez-Soriano A, Muntasell A, et al. Balancing selection is the main force shaping the evolution of innate immunity genes. J Immunol. 2008;181(2):1315–22.View ArticleGoogle Scholar
  36. Kloch A, Wenzel MA, Laetsch DR, Michalski O, Welc-Faleciak R, Piertney SB. Signatures of balancing selection in toll-like receptor (TLRs) genes novel insights from a free-living rodent. Sci Rep-Uk. 2018;8.Google Scholar
  37. Areal H, Abrantes J, Esteves PJ. Signatures of positive selection in toll-like receptor (TLR) genes in mammals. BMC Evol Biol. 2011;11.View ArticleGoogle Scholar
  38. Grueber CE, Knafler GJ, King TM, Senior AM, Grosser S, Robertson B, et al. Toll-like receptor diversity in 10 threatened bird species: relationship with microsatellite heterozygosity. Conserv Genet. 2015;16(3):595–611.View ArticleGoogle Scholar
  39. Gilroy DL, van Oosterhout C, Komdeur J, Richardson DS. Toll-like receptor variation in the bottlenecked population of the endangered Seychelles warbler. Anim Conserv. 2017;20(3):235–50.View ArticleGoogle Scholar
  40. Hoban S, Kelley JL, Lotterhos KE, Antolin MF, Bradburd G, Lowry DB, et al. Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions. Am Nat. 2016;188(4):379–97.View ArticleGoogle Scholar
  41. Hamilton WD. Sex Versus Non-Sex Versus Parasite. Oikos. 1980;35(2):282–90.View ArticleGoogle Scholar
  42. Apanius V, Penn D, Slev PR, Ruff LR, Potts WK. The nature of selection on the major histocompatibility complex. Crit Rev Immunol. 1997;17(2):179–224.View ArticleGoogle Scholar
  43. Fallon SM, Ricklefs RE, Swanson B, Bermingham E. Detecting avian malaria: an improved polymerase chain reaction diagnostic. J Parasitol. 2003;89(5):1044–7.View ArticleGoogle Scholar
  44. Richard FA, Sehgal RNM, Jones HI, Smith TB. A comparative analysis of PCR-based detection methods for avian malaria. J Parasitol. 2002;88(4):819–22.View ArticleGoogle Scholar
  45. Antonides J, Ricklefs R, DeWoody JA. The genome sequence and insights into the immunogenetics of the bananaquit (Passeriformes: Coereba flaveola). Immunogenetics. 2017;69(3):175–86.View ArticleGoogle Scholar
  46. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.View ArticleGoogle Scholar
  47. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011;17(1):10.Google Scholar
  48. Sebastian A, Herdegen M, Migalska M, Radwan J. AMPLISAS: a web server for multilocus genotyping using next-generation amplicon sequencing data. Mol Ecol Resour. 2016;16(2):498–510.View ArticleGoogle Scholar
  49. Excoffier L, Smouse PE, Quattro JM. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992;131(2):479–91.PubMedPubMed CentralGoogle Scholar
  50. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in excel. Population genetic software for teaching and research--an update. Bioinformatics. 2012;28(19):2537–9.View ArticleGoogle Scholar
  51. Antonides J, Mathur S, Sundaram M, Ricklefs R, DeWoody JA. Data from: Immunogenetic response of the bananaquit in the face of malarial parasites. Dryad digital repository; 2019.Google Scholar

Copyright

© The Author(s). 2019

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