Infection, Genetics and Evolution 28 (2014) 317–327

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Major Histocompatibility Complex, demographic, and environmental predictors of antibody presence in a free-ranging mammal María José Ruiz-López a,⇑, Ryan J. Monello a,1, Stephanie G. Schuttler a,b, Stacey L. Lance c, Matthew E. Gompper a, Lori S. Eggert b a b c

Department of Fisheries and Wildlife Sciences, University of Missouri, Columbia, MO 65211, USA Division of Biological Sciences, University of Missouri, Columbia, MO 65211, USA Savannah River Ecology Laboratory, P O Drawer E, Aiken, SC 29802, USA

a r t i c l e

i n f o

Article history: Received 23 June 2014 Received in revised form 30 September 2014 Accepted 16 October 2014 Available online 24 October 2014 Keywords: Major Histocompatibility Complex Raccoon Procyon lotor Canine distemper virus Parvovirus 454 pyrosequencing

a b s t r a c t Major Histocompatibility Complex (MHC) variability plays a key role in pathogen resistance, but its relative importance compared to environmental and demographic factors that also influence resistance is unknown. We analyzed the MHC II DRB exon 2 for 165 raccoons (Procyon lotor) in Missouri (USA). For each animal we also determined the presence of immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to two highly virulent pathogens, canine distemper virus (CDV) and parvovirus. We investigated the role of MHC polymorphism and other demographic and environmental factors previously associated with predicting seroconversion. In addition, using an experimental approach, we studied the relative importance of resource availability and contact rates. We found important associations between IgG antibody presence and several MHC alleles and supertypes but not between IgM antibody presence and MHC. No effect of individual MHC diversity was found. For CDV, supertype S8, one allele within S8 (Prlo-DRB⁄222), and a second allele (Prlo-DRB⁄204) were positively associated with being IgG+, while supertype S4 and one allele within the supertype (Prlo-DRB⁄210) were negatively associated with being IgG+. Age, year, and increased food availability were also positively associated with being IgG+, but allele Prlo-DRB⁄222 was a stronger predictor. For parvovirus, only one MHC allele was negatively associated with being IgG+ and age and site were stronger predictors of seroconversion. Our results show that negative-frequency dependent selection is likely acting on the raccoon MHC and that while the role of MHC in relation to other factors depends on the pathogen of interest, it may be one of the most important factors predicting successful immune response. Ó 2014 Elsevier B.V. All rights reserved.

1. Introduction Among mammalian carnivores, canine distemper virus (CDV) and parvoviruses are important, highly virulent pathogens that are typically enzootic as well as frequently associated with epizootics (Harder and Osterhaus, 1997; Allison et al., 2013). These viruses are present throughout domestic and wild carnivore populations worldwide, and antibody prevalence among susceptible species often exceeds 50% and may approach ubiquity for the oldest age classes (e.g. Gompper et al., 2011). Both pathogens are

⇑ Corresponding author at: Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97401, USA. Tel.: +1 541 346 8879. E-mail addresses: [email protected], [email protected] (M.J. Ruiz-López). 1 Current address: Biological Resource Management Division, National Park Service, Fort Collins, CO 80525, USA. http://dx.doi.org/10.1016/j.meegid.2014.10.015 1567-1348/Ó 2014 Elsevier B.V. All rights reserved.

likely significant causes of mortality in free-ranging populations (Williams et al., 1988; Kennedy et al., 2000; Barker and Parrish, 2001). To better forecast the impact of these pathogens, it is necessary to identify factors that predict host exposure and the subsequent risk of mortality. Recent research has focused on the genetic basis of host susceptibility, and in particular, the role of genes involved in the immune response. Ultimately, these genes contribute to a host’s ability to overcome infection. Many studies of host survival following pathogen exposure have focused on the Major Histocompatibility Complex (MHC), a multi-gene family that encodes more than one hundred proteins involved in the adaptive immune response (Klein et al., 2007). Classical MHC class I and class II genes code for proteins that present pathogen-derived antigens to T-cells and subsequently trigger an adaptive immune response (Hughes and Yeager, 1998). MHC class I and class II genes are among the most polymorphic within the vertebrate genome (Sommer,

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2005). Polymorphism primarily occurs in the antigen binding sites, also referred to as the peptide binding region (PBR), which are directly involved in antigen recognition (Hughes and Nei, 1988). High levels of polymorphism are mainly maintained by pathogen-mediated selection (Bernatchez and Landry, 2003; Piertney and Oliver, 2006) and variation at MHC has been linked to susceptibility (the risk of infection) and resistance (the ability to survive after exposure) for a wide range of pathogenic and host taxa (Hill, 1991; Grimholt et al., 2003; Harf and Sommer, 2005; Kloch et al., 2010; Savage and Zamudio, 2011; Srithayakumar et al., 2011). Prior studies in carnivores suggest that MHC variation plays an important role in overcoming infection by CDV and parvoviruses (Hedrick et al., 2003; Castro-Prieto et al., 2012). However, the role of the MHC relative to other factors that also determine host response to pathogen exposure is unknown. For example, seasonality, habitat quality, and age and sex of an individual have been shown to contribute to, or correlate with, high seroprevalence or epizootics of CDV and parvoviruses in several carnivore species (Roscoe, 1993; Barker and Parrish, 2001; Rossiter et al., 2001; Biek et al., 2006; Åkerstedt et al., 2010; Gompper et al., 2011). Therefore, understanding the role of MHC variation in predicting host immune response requires concurrent examination of non-genetic factors intrinsic and extrinsic to the host. Such approaches facilitate assessing the relative importance of genetic, demographic, and environmental factors underpinning successful immunologic response to challenges. Here we use a multivariate approach to investigate associations between the MHC and the likelihood of host seroconversion (the presence of CDV and parvovirus antibodies as a result of infection) and their importance when placed in the context of demographic and environmental factors. We focus on free-ranging populations of raccoons (Procyon lotor) in Missouri, USA, where virtually all individuals (>80% of adults) are eventually exposed to both CDV and parvovirus (Gompper et al., 2011). We analyzed the MHC II DRB exon 2 because it is highly polymorphic, is subject to natural selection (Castillo et al., 2010), and has been linked to rabies resistance and susceptibility in raccoons (Srithayakumar et al., 2011) and to CDV and canine parvovirus resistance in wolves (Hedrick et al., 2003). We tested whether the probability of individuals mounting a successful immune response (i.e. seroconversion) was associated with either carrying a particular MHC II DRB exon 2 allele or individual MHC diversity measured as the number of alleles. In addition, because the DRB exon 2 in raccoons is highly polymorphic, we clustered alleles with similar amino acid properties that can potentially bind similar antigens into functional supertypes (Doytchinova and Flower, 2005), and tested whether the likelihood of seroconversion was associated with carrying specific MHC supertypes or individual MHC supertype diversity (number of supertypes). Since other (non-MHC) genetic and non-genetic factors are likely to influence the likelihood of exposure and successful seroconversion, we incorporated several additional factors into our analyses. We manipulated several of our populations to alter contact rates and food availability, as both factors have shown an important association with macroparasite infection in raccoons (Monello and Gompper, 2010, 2011) and are likely important in the spread of CDV and parvovirus through populations. In addition, in our models we included factors linked to CDV and parvovirus exposure and epizootics in other populations of raccoons, including the timing of exposure, the age and sex of the hosts, and the neutral genetic diversity of the hosts (Barker and Parrish, 2001; Rossiter et al., 2001; Gompper et al., 2011). Given the strong selective pressure that these viruses may have on carnivore populations, we expected to find important associations between seroconversion and MHC (either specific alleles or MHC diversity). However, we predicted that its relative importance would be small

compared to other environmental and demographic factors as the predictive strength of these latter factors is often relatively robust (Gompper et al., 2011). 2. Methods 2.1. Study populations Raccoons were sampled from 2005 to 2007 at 12 forested sites located at 6 different conservation areas within 60 km of Columbia, Missouri, USA (Ruiz-López et al., 2012). All sites had similar ecological characteristics and raccoon population densities. Details on sites, raccoon populations, associated parasite communities, and host and parasite sampling protocols are reported elsewhere (Monello and Gompper, 2007, 2009, 2010, 2011; Gompper et al., 2011). Briefly, raccoons were live-trapped, anesthetized, eartagged, weighed, sexed, measured, and aged (Grau et al., 1970; Monello and Gompper, 2007) as class I (58 months) individuals. Blood samples were collected for genetic and immunologic analyses. During years 2006 and 2007 contact rates and resource availability were manipulated by providing supplemental food in either a clumped or dispersed fashion. Sites within the six study areas were randomly assigned to a treatment category. The three treatments were: (1) receipt of a permanent feeding station with 35 kg/wk of dried dog food at a single location to aggregate raccoons (n = 5 sites; high contact rates, food supplementation); (2) the same quantity of food, placed at highly dispersed and temporally variable locations to control for the effects of food additions without aggregating hosts (n = 3 sites; low contact rates, food supplementation), or (3) no food supplementation (n = 4 sites; no aggregation, no food supplementation). Research was carried out under Missouri Department of Conservation permit #12869, and University of Missouri Animal Care and Use Protocol #3927. 2.2. Viral seroconversion Exposure to CDV and parvovirus was quantified using the Biogal Immunocomb ‘dot’-Elisa kits for both immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies (Biogal-Galad Labs, Kibbutz Galad, Israel) (Gompper et al. 2011). The titer of each individual was quantified in S unit (range: S0–S6) following manufacturer’s instructions and after which IgG and IgM antibodies were classified as present or absent for each host based on a threshold value of S2 (ca 1:32 IgG or IgM titer using a haemagglutination inhibition test or a serum neutralization test). For individuals that were captured and tested in multiple years, we randomly chose one test result. The presence of IgM antibodies (IgM+) signals a current infection or recent re-exposure. During the course of the infection, IgG levels rise and IgM levels fall. An individual that had IgG antibodies but lacked IgM antibodies (IgM/IgG+) was assumed to have overcome viral exposure. Individuals who were IgM/IgG were assumed not to have been exposed to the virus, as anti-CDV and anti-parvovirus IgG antibodies persist throughout life (Barker and Parrish, 2001; Williams, 2001). 2.3. Genetic analyses Total genomic DNA was extracted from blood samples using DNeasy Blood and Tissue Kits (Qiagen, Valencia, CA, USA) following the manufacturer’s protocol. The genetic analyses included MHC genotyping, as well as microsatellite genotyping to assess neutral genetic diversity.

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We genotyped each individual at 14 unlinked nuclear microsatellite loci developed for raccoons: PLM01, PLM03, PLM05, PLM06, PLM07, PLM08, PLM09, PLM10, PLM11, PLM13, PLM14, PLM15, PLM16 and PLM17 (Siripunkaw et al., 2008); detailed information on genotyping can be found elsewhere (Gompper et al., 2011; Ruiz-López et al., 2012). We calculated individual genetic diversity, measured as internal relatedness (IR; Amos et al., 2001), using the program IRmacroN4 (http://www.zoo.cam.ac.uk/zoostaff/meg/ amos.htm). We identified full sibling and parent offspring associations in the program ML-Relate (Kalinowski et al., 2006) and randomly removed one of each pair from further analyses. MHC genotyping was carried out using 454-pyrosequencing (454 Life Sciences, a Roche company, Branford, CT, USA). We amplified a 237-bp fragment (excluding primer region) of the MHC II DRB exon 2 using primers 10 F (50 -TCGCGTCCCCACAGCACATT-30 ) and 10 R-(50 -GCCGCTGCACACTGAAACTCTCAC-30 ) based on consensus sequences for conserved regions and successful amplification in other carnivore species (Wan et al., 2006). Previous studies have found between one and four alleles per raccoon for MHC DRB exon 2 (Castillo et al., 2010; Srithayakumar et al., 2011), suggesting that it has been duplicated in some populations or in the species as a whole. To allow individual identification after sample pooling, the DRB 10 F primer was modified by adding a 10 bp Multiplex Identifier Tag (MID-Tag). The polymerase chain reaction (PCR) contained 1 ll (10–20 ng) of genomic DNA, 1 PCR Buffer, 2 mM dNTPs, 0.1 lM of each primer and 0.625 U AmpliTaq GoldÒ (Applied BiosystemsÒ, Foster City, CA, USA) and was conducted in 25 ll reactions. The profile consisted of amplification at 95 °C for 10 min, followed by 40 cycles of 95 °C for 1 min, 57 °C for 1 min, 72 °C for 1 min, followed by a final extension step at 72 °C for 7 min to reduce the potential for the formation of chimeras. PCR products were purified using the Agencourt AMPure XP PCR Purification Kit (Beckman Coulter, Brea, CA, USA), concentrations were measured using a QubitÒ Fluorometer (Life Technologies, Grand Island, NY, USA), and equimolar amounts of individual amplicons were pooled. Samples were sequenced in a 454-GSFLX platform (Roche, Branford, CT, USA) using one-quarter of a Pico Titer plate device. Raw reads were filtered for quality using the FASTX-tool kit in Galaxy (https://main.g2.bx.psu.edu/) and we retained the subset of reads in which 90% of the bases had a quality score of Q30 (1/1000 error probability). We extracted all reads containing the 10 F primer and 10 bp of the 10 R primer, assigned reads to individuals, and generated alignments of the variants present in each individual using program jMHC (http://code.google.com/p/jmhc/; (Stuglik et al., 2011). We differentiated true alleles (TA) and false alleles (FA) using a stepwise method. First, we kept only sequences that matched the expected sequence size (237 bp), or had ±3 bp so the reading frame was not shifted. Second we removed all variants that had less than 4 copies in the dataset, and individuals with 2 individuals, a common approach when cloning.

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We then checked the frequency of putative TA in the population. When a putative TA was present in only one individual, it was kept as a putative TA only if it was the most common allele. When putative TAs were present in two individuals, they were kept if they presented >2 reads in at least one sample and >5 in the other. After establishing the pool of putative TA at the population level, we checked each individual and removed variants that had a single copy and variants that had 1 estimated from the data. We used M0 to check that parameter estimates in more complex models were consistent. Models M7 and M8 were compared using a likelihood ratio test (LRT) for positive selection in sites with xs > 1. We identified the positive selected sites (PSS) by calculating the posterior probability that each site falls into the different site classes using the Bayes Empirical Bayes (BEB) approach (Yang et al., 2005). We used two cutoffs (p > 0.95 and p > 0.99) to identify sites under positive selection. The TAs were clustered into supertypes (Doytchinova and Flower, 2005), which are groups of alleles that have similar amino acid properties and can potentially bind similar antigens. This approach is commonly used because it reduces the number of variables to test and can aid in understanding pathogen susceptibility (Huchard et al., 2008; Schwensow et al., 2007; Srithayakumar et al., 2011). We characterized the amino acids in the sequence that were under positive selection with a probability higher than 0.99 by five physicochemical z-descriptor variables: z1 (hydrophobicity), z2 (steric bulk), z3 (polarity), z4 and z5 (electronic effects)

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and translated them into a matrix (Sandberg et al., 1998). We identified supertype clusters using hierarchical clustering based on three similarity distances, Euclidean distance, maximum distance and Manhattan distance, using R version 2.13.1 (R Development Core Team 2011). Sequences were assigned to supertypes if they grouped together in more than one tree (Huchard et al., 2008; Srithayakumar et al., 2011). 2.4. Statistical analyses We studied the association between DRB variation and the immune response for CDV and parvovirus by analyzing the effects of specific supertypes, specific alleles, the number of supertypes in an individual and the number of alleles in an individual on IgM and IgG seroconversion separately for both CDV and parvovirus. Given the large number of alleles and supertypes, and the high differences in frequencies, we limited our study to those alleles and supertypes that were carried by >5% of all individuals to minimize type I statistical error. Analyses were carried out using generalized linear models with a binomial error distribution and a logit link function, and only main factors were included to avoid overparameterization. We ran four models to explore relationships with IgG and IgM antibodies of CDV and parvovirus. In addition to the DRB information, all the models also included the explanatory factors extrinsic to the host (sampling year, food supplementation, aggregation) and intrinsic to the host (age, sex, IR) that have been shown to potentially affect CDV or parvovirus infection. We also included an additional binomial variable that identified individuals that had antibodies to both CDV and parvovirus to determine if successful immune responses were correlated. If this variable was most important relative to other variables it would indicate that some other unidentified factor related to the immune system might be most important. We controlled for the potential effects of the sampling location by including the 6 source areas that collectively included the 12 sites as a covariate in all models. These 12 sites comprise a single genetic population (overall FST = 0.008, Ruiz-López et al., 2012). When we analyzed the association between the number of DRB supertypes/alleles and seroconversion we also included the quadratic term of the number of supertypes/ alleles to control for potential non-linear relationships (Kloch et al., 2010). To compare the effects of neutral genetic diversity and MHC diversity and its quadratic term to the categorical variables we standardized these variables by centering and dividing by two standard deviations (SDs) to force a mean of 0 and a SD of 0.5. We calculated odds ratios (±95% C.I.) and used this as our primary parameter of interest to compare the relative importance of the variables in the model. All analyses were conducted using R.v.2.13.1 and the library MASS for carrying out the generalized linear models. 3. Results 3.1. Antibody seroprevalence Across all populations and years, 55.8% and 47.3% of individuals had been exposed to CDV and parvovirus, respectively, based on IgG seroprevalence. The proportion of individuals that were positive for recent or current exposure based on IgM+ seroprevalence was 17.6% for CDV and 8.5% for parvovirus, respectively. Seroprevalence varied across experimental treatments, especially for CDV IgG which was higher among individuals that were aggregated and that had food supplementation (Fig. 1). In agreement with previous studies (Gompper et al., 2011), we also observed variation in seroprevalence with age, with older individuals having higher seroprevalence (Fig. 2a), and between years, especially for CDV, which increased greatly in 2007 (Fig. 2b).

Fig. 1. Seroprevalence (% individuals exposed as assessed from IgG and IgM antibodies) of CDV and parvovirus among individual raccoons (n = 165) under the 3 different experimental conditions. Black and light gray bars correspond to CDV IgM+ and IgG+, respectively. Dark gray and open bars correspond to parvovirus IgM+ IgG+, respectively.

Fig. 2. Seroprevalence of CDV and parvovirus (based on the presence of IgG and IgM antibodies) for 165 raccoons divided by age class (a) and by sampling year (b). Black and light gray bars correspond to CDV IgM+ and IgG+, respectively. Dark gray and open bar correspond to parvovirus IgM+ IgG+, respectively.

3.2. MHC diversity and selection We genotyped 165 individuals, identifying 52 alleles (GenBank accession #: KP064227 – KP064274 and KP064276 – KP064279). Fifty-nine of the 237 nucleotide positions were polymorphic, with 37 transitions and 36 transversions. Nucleotide diversity was 0.077 (±0.039 SD) and the mean number of pairwise differences between alleles (p) was 18.3 bp (±8.3 SD). Allele frequencies ranged from 0.006 to 0.352 (Table S2 Supporting information) and individuals presented 1–6 alleles, corresponding to 3 putative loci. We were

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98 56

Prlo-DRB*202(S1) Prlo-DRB*203(S1) Prlo-DRB*201(S1) Prlo-DRB*246(S1) Prlo-DRB*209(S1) Prlo-DRB*216(S2) Prlo-DRB*243(S8)

93

Prlo-DRB*222(S8)

S8

Prlo-DRB*238(S12) Prlo-DRB*247(S14) Prlo-DRB*221(S10) Prlo-DRB*239(S13) Prlo-DRB*250(S13) 60

Prlo-DRB*240(S13) Prlo-DRB*213(S13) Prlo-DRB*231(S12) Prlo-DRB*232(S12) Prlo-DRB*211(S12) Prlo-DRB*220(S14)

89

Prlo-DRB*244(S5) Prlo-DRB*229(S5) Prlo-DRB*233(S5)

74

Prlo-DRB*208(S5) 53

Prlo-DRB*248(S5)

95

Prlo-DRB*223(S3)

Prlo-DRB*224(S12) 99

Prlo-DRB*217(S3) Prlo-DRB*237(S3) Prlo-DRB*251(S11) Prlo-DRB*212(S3) 61

Prlo-DRB*236(S11) Prlo-DRB*252(S11)

80

Prlo-DRB*227(S11) Prlo-DRB*228(S6)

63 97

Prlo-DRB*210(S4) Prlo-DRB*226(S4)

67

Prlo-DRB*206(S4) Prlo-DRB*207(S4) Prlo-DRB*241(S6) Prlo-DRB*225(S6) Prlo-DRB*214(S6) 58

Prlo-DRB*205(S7) Prlo-DRB*218(S6) Prlo-DRB*253(S10) Prlo-DRB*245(S7) Prlo-DRB*204(S7)

61

Prlo-DRB*234(S7) Prlo-DRB*219(S7) Prlo-DRB*235(S9) Prlo-DRB*242(S9)

71 67

Prlo-DRB*215(S9)

S9

Prlo-DRB*230(S4) DLA-DRB*AF016912 Fig. 3. Phylogenetic relationships among MHC alleles inferred using maximum likelihood (model GTR + G) in MEGA 5.1; the percentage of replicates in which the associated taxa clustered together in the bootstrap test (>50% in 1000 replicates) is shown next to the branches. The supertype for each allele is shown in parentheses and monophyletic supertypes are highlighted.

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unable to assign alleles to individual loci based on phylogenetic inference (Fig. 3); therefore, analyses were carried out considering all alleles as representative of the DRB Exon 2 locus. There was no evidence of positive selection acting on the entire MHC II DRB exon 2 (Z = 1.125, p-value = 0.131). However, when PBR and non-PBR sites were analyzed separately, we found greater rates of nonsynonymous than synonymous substitutions at the PBR sites, indicating positive selection (PBR Z = 2.587, p-value = 0.011; non PBR Z = 0.391, p-value = 0.697). Results of the random sites model analyses led to rejection of the null model (M7; v2 = 37.74, df = 1, p-value 1) with high posterior probabilities (p > 0.95) and 8 of those had very high probabilities (p > 0.99). Nine of the 13 PSS matched with the PBR (Table 1 and Fig. S3 Supporting information). Those that did not match a PBR site were 1 or 2 positions from an inferred PBR site. Based on the properties of the 8 amino acids under positive selection we clustered the alleles into 14 supertypes which ranged in frequency from 0.042 to 0.527 (Table S2 and Figs. S4–S6 Supporting information). 3.3. Genetic and non-genetic predictors of seroconversion While neither the number of supertypes nor alleles (or its quadratic term) within an individual raccoon was associated with seroconversion, we found several meaningful associations between seroconversion and specific MHC supertypes and alleles. For CDV, IgM antibody presence was only associated with year; no effects of alleles (Table 1) or supertypes (Table S3 Supporting information)

were found. In contrast, two supertypes (S4 and S8) were important to CDV IgG seroconversion (Table 2), although with contrasting patterns. Individuals carrying the S4 supertype had a lower probability (b = 1.45, odds = 0.24) of being IgG+ while individuals carrying supertype S8 had a higher probability of being IgG+ (b = 2.93, odds = 18.64). When specific MHC alleles were analyzed, the results were consistent. Allele Prlo-DRB⁄210, which is clustered within supertype S4, and allele Pro-DRB⁄222, which is clustered within supertype S8, presented associations with seroconversion. Individuals carrying Prlo-DRB⁄210 had a lower probability of being IgG+ (b = 2.13, odds = 0.12), while individuals with Prlo-DRB⁄222 (b = 3.84, odds = 46.42) had higher probability of being IgG+. Individuals carrying allele Prlo-DRB⁄204 (supertype S7) also had a higher probability of being IgG+ (b = 1.65, odds = 5.22), but the odds of seroconversion were much smaller than for individuals carrying Prlo-DRB⁄222 (Table 3). Three non-MHC factors also had consistent associations with CDV IgG seroconversion. Age was one of the most important predictors, with individuals in age class IV having higher odds of being IgG+. However, age had less of a predictive effect on seroconversion than did allele Prlo-DRB⁄222 (Table 3). Year and food supplementation were also predictors of seroconversion. Individuals sampled in year 2007 and individuals inhabiting sites that received food supplementations had higher probabilities of being IgG+. In contrast, sex and sampling area did not have a clear effect. Other factors such as parvovirus exposure history, IR, and whether the host inhabited a population with a high contact rate were not found to be important predictors in CDV IgG seroconversion. For parvovirus, due to the low seroprevalence of IgM+, we focused analyses on IgG seroprevalence. We did not find an effect of supertypes (Table S4 Supporting information), but individuals carrying allele Prlo-DRB⁄201 were less likely to be IgG+

Table 1 Model parameter estimates and associated odds ratios based on a generalized linear model that evaluated the importance of MHC DRB alleles relative to other host and environmental factors on IgM seroconversion to CDV. The effects of sampling location were controlled for by including the 6 study areas as a covariate in all models. Characters in bold show significant factors (p-value

Major Histocompatibility Complex, demographic, and environmental predictors of antibody presence in a free-ranging mammal.

Major Histocompatibility Complex (MHC) variability plays a key role in pathogen resistance, but its relative importance compared to environmental and ...
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