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Major Histocompatibility Complex (MHC) Class II sequence polymorphism in
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long-finned pilot whale (Globicephala melas) from the North Atlantic.
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SÍLVIA S. MONTEIRO 1,2*, JOSÉ V. VINGADA 2,3, ALFREDO LÓPEZ 4,5, GRAHAM J.
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PIERCE 4,6, MARISA FERREIRA 1,2, ANDREW BROWNLOW 7, BJARNI
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MIKKELSEN 8, MISTY NIEMEYER 9, ROBERT J. DEAVILLE 10, CATARINA EIRA
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2,4
, STUART PIERTNEY 11.
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Departamento de Biologia & CBMA, Universidade de Minho, Campus de Gualtar,
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Braga, Portugal
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Biologia, Campus de Gualtar, Braga, Portugal
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Braga, Portugal.
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de Santiago,Aveiro, Portugal
Sociedade Portuguesa de Vida Selvagem, Universidade de Minho, Departamento de
Departamento de Biologia & CESAM, Universidade de Minho, Campus de Gualtar,
Departamento de Biologia & CESAM, Universidade de Aveiro, Campus Universitário
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World headquarters, Yarmouth Port, MA, USA
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10
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of Zoology, Zoological Society of London, London
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Coordinadora para o Estudio dos Mamíferos Mariños, Gondomar, Pontevedra,Spain
Oceanlab, University of Aberdeen, Newburgh, Aberdeenshire, UK
Wildlife Unit, SAC Veterinary Science Division, Inverness, UK
Museum of Natural History, Tórshavn, Faroe Islands
International Fund for Animal Welfare, Marine Mammal Rescue & Research Program,
UK Cetacean Strandings Investigation Programme, The Wellcome Building, Institute
School of Biological Sciences (Zoology), University of Aberdeen, Aberdeen, UK
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*
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Universidade de Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro,
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Portugal. Tel: +351 964192993
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E-mail: [email protected]
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Running head title: MHC polymorphism in pilot whales from North Atlantic
Corresponding author: Silvia S. Monteiro. Departamento de Biologia & CESAM,
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Abstract
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Determining how intra-specific genetic diversity is apportioned among natural
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populations is essential for detecting local adaptation and identifying populations with
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inherently low levels of extant diversity which may become a conservation concern.
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Sequence polymorphism at two adaptive loci (MHC DRA and DQB) was investigated
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in long-finned pilot whales (Globicephala melas) from four regions in the North
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Atlantic and compared with previous data from New Zealand (South Pacific). Three
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alleles were resolved at each locus, with trans-species allele sharing and higher
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levels of non-synonymous to synonymous substitution, especially in DQB locus.
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Overall nucleotide diversities of 0.49 ± 0.38% and 4.60 ± 2.39% were identified for the
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DRA and DQB loci, respectively, which are relatively low for MHC loci in the North
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Atlantic, but comparable to levels previously described in New Zealand (South
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Pacific). There were significant differences in allele frequencies within the North
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Atlantic and between the North Atlantic and New Zealand. Patterns of diversity and
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divergence are consistent with the long-term effects of balancing selection operating
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on the MHC loci, potentially mediated through the effects of host-parasite coevolution.
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Differences in allele frequency may reflect variation in pathogen communities,
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coupled with the effects of differential drift and gene flow.
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Keywords: Adaptive markers, evolution, cetacea
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Introduction
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Determining how intra-specific genetic diversity is spatially and temporally
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apportioned among extant populations is essential for resolving patterns of population
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structure, identifying levels of dispersal and gene flow, detecting local adaptation, and
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identifying populations with inherently low levels of extant diversity which may
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become a conservation concern (e.g. Piertney & Oliver 2006; Witteveen et al. 2011).
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Neutral molecular markers such as mitochondrial DNA (mtDNA) and
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microsatellite polymorphisms have been extensively applied in genetic studies of wild
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populations (Ballard & Whitlock 2004). However, it is appreciated that neutral genetic
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diversity is a poor proxy for levels of adaptively important genetic variation within the
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genome (e.g. Merila & Crnokrak 2001; Holderegger et al. 2006). There is a growing
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emphasis on resolving levels of adaptive genetic diversity and selective processes
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that directly affect traits underpinning individual fitness and population viability (e.g
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Piertney & Oliver 2006; Bos et al. 2008; Spurgin & Richardson 2010; Oliver &
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Piertney 2012). A major focus for examining adaptive genetic diversity has been the
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genes of the Major Histocompatibility Complex (MHC). MHC genes encode proteins
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that recognize and present foreign antigens to the immune system to initiate adaptive
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immune responses (Piertney & Oliver 2006). MHC genes are distributed in two main
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MHC subfamilies (class I and class II), with class I primarily associated with
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intracellular pathogens and class II genes with extracellular pathogens, in terms of
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antigen presentation (Piertney & Oliver 2006; Spurgin & Richardson 2010), although
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the reverse may also occur less frequently (Neefjes et al. 2011).
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The functional importance of MHC in the immune system underlies its
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characteristic high levels of genetic diversity within natural populations, with a large
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repertoire of extant alleles (Piertney & Oliver 2006). Levels of nucleotide diversity in
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human MHC are approximately two orders of magnitude higher than the genomic
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average (Gaudieri et al. 2000). This high diversity is maintained by balancing
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selection, which is thought to be mediated through host-pathogen interactions and/or
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mate choice (e.g. Kundu & Faulkes 2004; Cutrera & Lacey 2006; Piertney & Oliver
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2006; Oliver et al. 2009; Spurgin & Richardson 2010). Almost without exception, MHC
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studies have observed the classical signatures of balancing selection from extant
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MHC variation. These include the occurrence of phylogenetic patterns such as trans-
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species polymorphism where allelic lineages are shared among species and persist
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over long evolutionary time (Klein et al. 1980), and the occurrence of an excess of
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nonsynonymous (dN) over synonymous (dS) substitutions, especially in the Peptide
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Binding Region (PBR) sites (Hughes & Nei 1989).
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One taxonomic group that can display relatively low levels of MHC diversity are
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marine mammals. For example, Murray et al. (1995) resolved only five DQB alleles
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among 233 beluga whales, Delphinapterus leucas (Pallas, 1776), while Munguía-
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Vega et al. (2008) reported one DQB and two DRB alleles in 25 and 29 vaquitas,
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Phocoena sinus Norris & McFarland, 1958. This reduced MHC variability has been
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attributed to weaker balancing selection operating on marine mammal populations,
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possibly due to lower pathogenic selection pressures (e.g. Slade et al. 1992;
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Villanueva-Noriega et al. 2013). However, this theory is still under debate (McCallum
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et al. 2004; Xu et al. 2010), since high levels of MHC diversity have also been
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described for several cetacean species (Baker et al. 2006; Xu et al. 2007, 2012).
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Moreover, some studies have resolved high spatial variation of extant adaptive
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diversity in odontocete populations across different geographical regions, which
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suggests that single point sampling of MHC variation may provide a biased view of
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diversity and selection operating over larger geographic scales (e.g. Tursiops
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truncatus (Montagu, 1821) and Orcinus orca (Linnaeus, 1758), Vassilakos et al.
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2009).
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Information on macro-scale host and parasite diversity, abundance and
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consequent interactions in marine ecosystems is still scarce relative to terrestrial
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systems, and mostly based on teleost fishes (Rohde et al. 1984; Luque & Poulin
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2008). Parasite diversity has already been associated with variables related to fish
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habitat, trophic level, temperature and oceanic distribution. As an example, several
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studies have reported a higher diversity of hosts and parasites in the Indo-Pacific
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compared to the Atlantic (Rohde 1978,1984; Luque & Poulin 2008; reviewed in
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Rohde 2010). Additionally, sea temperature also seems to influence the diversity of
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parasites, with marine ecto- and endoparasites showing a positive and negative
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correlation with temperature, respectively (Rohde 1984; Rohde & Heap 1998; Luque
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& Poulin 2008). Given the reported occurrence of latitudinal and longitudinal gradients
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in host and parasite diversity in different oceanic basins (e.g. Lambshead et al. 2002;
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reviewed in Rohde 2010), geographical variation in parasite burden may be expected,
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especially for host species with a large geographical range (Morand 2000) which in
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turn may affect standing genetic diversity in different areas, especially for genes such
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as the MHC.
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The long-finned pilot whale Globicephala melas (Traill, 1809) is a widely
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distributed, medium-sized odontocete (Reid et al. 2003). There have been few studies
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examining the patterns of genetic diversity across populations of this species (Fullard
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et al. 2000; Oremus et al. 2009; Monteiro et al. 2015a). Neutral markers, such as
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microsatellites and mitochondrial DNA, have shown some genetic differentiation
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between populations located within the same oceanic basin (e.g. Atlantic Ocean,
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Fullard et al. 2000; Monteiro et al. 2015a) and between oceanic basins (e.g. Atlantic
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and Pacific Ocean, Oremus et al. 2009), but such studies have not been extended to
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examine patterns of adaptive genetic diversity. The only data available are from a
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mass stranding of 225 long-finned pilot whales from New Zealand, where eight alleles
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were resolved both at DQA and DQB loci (Heimeier 2009).
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In the present study, the levels of MHC DRA and DQB diversity across long-
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finned pilot whales stranded in different locations of the North Atlantic will be analysed
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and contrasted with previous studies from New Zealand (South Pacific) (Heimeier
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2009) to assess the extent to which standing genetic variation at the MHC varies
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within and across ocean basins. It will be tested whether the patterns of diversity
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identified are consistent with the long-term effects of balancing selection, through the
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examination of trans-species polymorphism and the analysis of the ratio between
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nonsynonymous and synonymous substitutions within the PBR of the alleles
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resolved. The present study will comprise the first description of the allelic diversity at
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MHC loci in long-finned pilot whales from the North Atlantic.
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Materials and Methods
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Sample collection
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A total of 119 long-finned pilot whale samples was collected from animals
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stranded in several locations of the North Atlantic (Northwestern Iberian Peninsula
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(NI), United Kingdom (UK) and United States of America (USA), Fig. 1). In addition,
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this study used samples collected from animals taken in drive fisheries in 2010 and
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archived at the tissue bank of the Museum of Natural History of the Faroe Islands (FI,
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Fig. 1). Strandings occurred between 2001- 2009, 1992-2011 and 2002-2009 in NI,
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UK and USA, respectively. All tissue samples were either frozen or preserved in 70%
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ethanol.
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MHC data from the North Atlantic was compared to DQB locus data analysed
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from 224 pilot whales from five mass strandings in New Zealand (South Pacific, 1992
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– 2004, Heimeier 2009).
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DNA extraction, amplification and genotyping
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Skin samples were digested in cetyl trimethylammonium bromide (CTAB)
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extraction buffer and DNA was purified by a standard phenol–chloroform-isoamyl
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alcohol procedure (modified from Sambrook et al. 1989).
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The exon 2 region of the MHC DRA locus (188bp) was amplified in a total of 111
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samples from the North Atlantic (Fig. 1 and Table I), using the primers DRAf (5’- AAT
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CAT GTG ATC ATC CAA GCT GAG TTC-3’) and DRAr (5’- TGT TTG GGG TGT TGT
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TGG AGC G -3’) (Xu et al. 2007). For the MHC DQB locus, a 171bp fragment of the
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exon 2 region was amplified in a total of 96 samples from the North Atlantic (Fig. 1
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and Table I), using the primers DQB1 (5′-CTGGTAGTTGTGTCTGCACAC-3') and
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DQB2 5′-CATGTGCTACTTCACCAACGG-3′ (Murray et al. 1995). To allow analysis
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using Denaturing Gradient Gel Electrophoresis (DGGE), an additional 40bp GC-rich
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sequence (GC clamp) was added to the 5’ end of the primers DRAf and DQB1. The
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optimal GC clamp sequence was ascertained using WinMelt software (Bio-Rad). For
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both MHC loci, PCR reactions were carried out in a 10µl final volume reaction
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containing 1x PCR Buffer, 1.5 mM MgCl2, 0.2 mM DNTPs, 0.5 units of BIOTAQ DNA
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Polymerase (Bioline) and 0.4 µM of each primer. Cycling conditions for the DRA locus
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were: 2 min at 94º C, 19 cycles of 30s at 91ºC, 30s at 60-50ºC (decreasing 0.5ºC per
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cycle), 19 cycles of 30s at 91ºC, 30s at 50ºC followed by a final extension at 72ºC for
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1 min, while for the DQB locus conditions were: 3 min at 94º C, 30 cycles of 30s at
5
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94ºC, 30s at 58ºC and 30s at 72ºC followed by a final extension at 72ºC for 5 min.
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PCR products were first visualized on a 1.5% agarose gel.
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DGGE was performed in a Bio-Rad DCode System. One microliter of each PCR
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product was applied directly onto 1 mm thick 10% polyacrylamide gels
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(acrylamide:bis-acrylamide at 37:5:1) in 1x TAE (40 mM Tris-acetate and 1 mM
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EDTA, pH 8.3), mixed with varying concentrations of denaturing agents (according to
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the desired denaturing gradient) and polymerized by the addition of 0.1% TEMED and
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0.1% ammonium persulphate. Denaturing gradients consisted of increasing
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concentrations of urea and formamide in the polyacrylamide solutions (0.07 M urea
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and 0.4% of formamide per % denaturant) and were formed using the Gradient
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Delivery System (Bio-Rad). Electrophoresis conditions were optimized for maximum
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band separation and resolution to 14 h at a constant voltage (50 V) and temperature
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(60ºC), in a linear 40% to 50% denaturing agent gradient for the DRA locus and 50%
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to 70% for the DQB locus. After electrophoresis, the gels were silver stained,
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consisting of a 30 min immersion of the gel in a fixative solution (10 % absolute
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ethanol/0.5 % acetic acid), followed by a 20 min immersion in 0.1 % silver nitrate
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solution and a final immersion in a developing solution (3% sodium hydroxide and
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1.5% of 37% formaldehyde solution), until total development of the bands. DGGE
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bands were sequenced after excision from the gel and re-amplification. Re-
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amplification was performed with the original primer set of each locus, as described
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above, except that for DQB locus the PCR comprised 28 cycles and the annealing
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temperature was increased to 60ºC. PCR products were purified using QIAquick PCR
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purification columns (Qiagen), according to the manufacturer’s protocol. DNA
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sequencing was undertaken using the each forward primer on an ABI3700 automated
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DNA sequencer (Applied Biosystems, CA, USA), according to the manufacturer’s
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instructions. Ambiguous sequences were re-sequenced using the reverse primer.
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A sequence variant was identified as a new allele only when it was in accordance
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with criteria laid out in Kennedy et al. (2002), namely that when using DNA cloning
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and sequencing there have to be at least three identical clones, identified in either two
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independent PCR reactions from the same individual, or from PCRs from at least two
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different individuals. Therefore, to validate each allelic sequence, at least 12
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replicates of each putative allelic band (when possible), taken from different
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individuals across random gels, were sequenced using the forward primer and every
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allele was re-run with the reverse primer at least three times. To ensure consistency
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in scoring between runs, alleles found in previously cloned pilot whales, according to
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the standard pGEM (Promega Ltd protocol), were used as a standard and added to
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each gel.
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Both DRA and DQB exon 2 alleles of long-finned pilot whale were designated
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according with the nomenclature described by Klein et al. (1990) for MHC in non-
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human species. The alleles resulting from the analyses of DRA and DQB loci were
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phylogenetically compared with previously published sequences of the DRA and DQB
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exon 2 regions of different cetacean. While for DRA locus, all the cetacean
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sequences available at Genbank were included (31 sequences), for DQB locus a
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random selection of 21 sequences was used in order to include several odontocete
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species in the analysis (see Fig. 2 for Genbank accession numbers).
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Statistical analysis
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Genetic diversity and differentiation
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Sequence visualisation, alignment and translation into amino acid sequences
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were performed using Clustal W (Thompson et al. 1997) and MEGA 6.0 (Tamura et
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al. 2013). Sequences were confirmed as MHC DRA and DQB sequences by the
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National Center for Biotechnology Information (NCBI) BLAST comparison. Allelic
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richness, nucleotide diversity, observed and expected heterozygosity (Nei & Tajima
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1981), and deviation from Hardy-Weinberg equilibrium (20,000 bootstrap replicates to
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test the null hypothesis that loci are in Hardy-Weinberg equilibrium) were calculated
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using FSTAT 2.9 (Goudet 1995) and ARLEQUIN 3.5.1.2 (Excoffier & Lischer 2010).
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To test for differences in allele frequencies per locus, within the North Atlantic
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and between the North Atlantic and New Zealand (South Pacific), pairwise analyses
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were performed in GENEPOP, using Fisher´s exact test (Raymond & Rousset 1995).
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Differentiation analysis between North Atlantic and South Pacific was based only on
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DQB locus alleles. Statistical significance was estimated using a Markov chain
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algorithm with 10,000 dememorization steps, 100 batches and 5,000 iterations. A
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Bonferroni correction was applied as an adjustment of critical p-values for
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comparisons across the North Atlantic.
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Indentifying historical signatures of selection
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The long-term effects of selection on MHC loci were assessed by testing
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whether dN:dS > 1 in the DQB and DRA loci (including analysis for all sites and
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separately for PBR and non PBR sites), and confirming retention of allelic lineages
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across speciation events (trans-species polymorphism).
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The relative rate of nonsynonymous (dN) to synonymous (dS) mutation, applying
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the method of Nei & Gojobori (1986), with Jukes–Cantor correction for multiple
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mutations at single sites, were calculated using MEGA 6.0 (Tamura et al. 2013). The
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probability of rejecting the null hypothesis of neutrality (dN
=
dS) in favour of the
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alternative hypothesis of positive selection (dN
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(Nei & Kumar 2000). Standard errors for these estimates were estimated through
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100,000 bootstrap replicates. Nucleotides within the Peptide Binding Region (PBR)
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were determined as predicted by Brown et al. (1993).
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dS) was determined using a Z-test
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Phylogenetic relationships were assessed using Maximum Likelihood as
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implemented in PAUP 4.0 (Swofford 2003) and Bayesian analysis as implemented in
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MrBayes 3.2 (Ronquist et al. 2012). Likelihood analysis was performed using 1000
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bootstrap replicates with tree-bisection-reconnection branch swapping. For Bayesian
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phylogeny estimation, two independent runs of four Metropolis-coupled MCMC chains
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(temperature= 0.2) were run for 1000,000 generations (every 1000th tree was
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sampled). The first 25% of trees were discarded as burn-in, resulting in 750 trees
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from which parameter values and trees were then summarized and a consensus tree
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was drawn using the program TREEVIEW 1.6 (Page 1996). The model of sequence
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evolution recommended by JModeltest 2.1 (Darriba et al. 2012) was the Kimura 2
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Parameter model (Kimura 1980), with gamma-distributed rate variation across sites
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for the DRA locus (gamma distribution = 0.295) and HKY + G (A = 0.2234, C =
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0.2542, G = 0.3835, T = 0.1389 and gamma distribution = 0.1540) for the DQB locus.
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Bos taurus (U77794) was used as outgroup for the DQB locus.
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Results
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Genetic diversity and differentiation
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No more than two sequences were resolved for any individual at either the DRA
or DQB locus, suggesting that one single locus was amplified in both cases.
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The DRA exon 2 region (186bp) was sequenced from 111 long-finned pilot
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whales, across different geographic locations in the North Atlantic. A total of 13
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variable sites (6.9%) in the nucleotide sequence defined three unique DRA allelic
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sequences: Glme-DRA*01, Glme-DRA*02, which were previously described by Xu et
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al. (2009) in other cetacean species, including Grampus griseus (G. Cuvier, 1812)
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(GenBank accession: EF375601 and EF375602, respectively), and the newly
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described Glme-DRA*03 (Table I.Ia and Fig. 2a). The three nucleotide alleles
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translated to two different amino acid sequences (of 62 amino acids in length), with
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eight variable sites (Table I.Ib). DRA exon 2 diversity is given for each Atlantic region
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in Table II. Overall, there was an average nucleotide diversity of 0.49 ± 0.38%,
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ranging between 0.22 ± 0.23% (FI) and 0.86 ± 0.58 (UK) (Table II). Allelic richness
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was highest in NI and UK and lowest in the FI and USA (Table II). Observed
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heterozygosity ranged between 0.14 (UK) and 0.70 (USA), having an overall value of
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0.40. Overall, long-finned pilot whales from the North Atlantic were in Hardy-Weinberg
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equilibrium. When considering each region analysed, the UK and USA presented
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significant deviations from Hardy-Weinberg expectations, with a significant deficit and
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excess of heterozygotes, respectively.
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The DQB exon 2 region (171bp) was sequenced from 96 long-finned pilot
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whales, from different geographic locations in the North Atlantic. A total of 17 variable
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sites (9.9%) in the nucleotide sequence defined three unique DQB allelic sequences:
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Glme-DQB*01 (previously described in long-finned pilot whales in the Pacific;
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Heimeier 2009); Glme-DQB*02 (previously described in Tursiops truncatus (Genbank
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accession: AB302053); and Glme-DQB*03 (previously described in long-finned pilot
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whales in the Pacific; Heimeier 2009) (Table I.IIa). Each of the three nucleotide alleles
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translated to a different amino acid sequence (57 amino acids), with 10 variable sites
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(Table I.IIb). For the 14 amino acids corresponding to the PBR, variability was
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detected at seven sites (50%), while for the remaining 43 amino acids variability was
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detected at three sites (7.0%) (Table I.IIb). Table II summarizes the data on indicators
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of DQB exon 2 diversity in each sampling group. Overall, there was an average
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nucleotide diversity of 4.60 ± 2.39%, ranging between 4.21 ± 2.24% (FI) and 4.54 ±
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2.44% (NI) (Table II). Allelic richness was highest in NI and lowest in the USA (Table
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II). Observed heterozygosity ranged between 0.22 (USA) and 0.34 (UK), having an
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overall value of 0.35. Overall, long-finned pilot whales from the North Atlantic were
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not in Hardy-Weinberg equilibrium. However, although in all regions analysed the
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expected heterozygosity exceeded the observed values, none of them presented
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significant deviations from Hardy-Weinberg expectations.
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There was some geographic variation in MHC allele frequencies across the North
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Atlantic, most notably Glme-DQB*02 which shows a higher frequency in NI in
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comparison to remaining regions, to Glme-DRA*01 in NI and USA which seem to
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show lower frequencies when compared to UK and FI, and to Glme-DRA*03 which is
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shared only by pilot whales from the NI and the UK (Fig. 1). The levels of genetic
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differentiation found within the North Atlantic (Table III) revealed significant
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differences of allelic frequencies between some of the analysed regions. In particular,
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for DRA locus there were only significant differences in allele frequencies between
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the UK and USA, while for DQB locus there were significant differences in allele
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frequencies between NI and all the remaining analysed locations (Table III).
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The comparison between North Atlantic and New Zealand (South Pacific)
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identified the occurrence of significant differences between DQB allele frequencies of
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pilot whales from both oceanic basins (Table III).
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Analysis of historical signatures of selection
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For the DRA exon 2 region, the proportion of nonsynonymous substitutions (dN =
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4.70%) was not significantly different from synonymous substitutions (d S = 5.50%) (Z-
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test P > 0.05). None of the amino acid substitutions was located in a site that has
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been considered to be involved in peptide binding, as described by Brown et al.
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(1993) (Table I.Ib).
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For the DQB exon 2 region, a significantly higher value for the proportion of
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nonsynonymous substitutions (dN = 9.70 ± 3.30%) was shown when compared to the
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proportion of synonymous substitutions (dS = 0.80 ± 0.80%) over all sites (Z-test P <
340
0.01) (Table IV). For codons within both the PBR and non-PBR, the rate of
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nonsynonymous substitutions (dN = 34.30 ± 15.40% and 3.50 ± 2.40%, respectively)
342
exceeded that of synonymous substitutions (dS = 3.60 ± 3.90%, and 0.0 ± 0.0,
343
respectively), although this difference was only significant in PBR (Z-test P < 0.05)
344
(Table IV).
345
Phylogenetic analysis of DRA exon 2 was based on sequences described in this
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study for long-finned pilot whale and sequences from other cetacean species
347
described in previous studies (Xu et al. 2007, 2008, 2009; Ballingall 2010) (Fig. 2a).
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In general, no species-specific or even suborder-specific clades were observed, since
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the three alleles described in this study for long-finned pilot whale were more closely
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related with alleles from other species or suborders than with alleles belonging to the
351
same genus, revealing a trans-specific sharing of alleles (Fig. 2a). The strong
352
evidence of trans-species polymorphism can be observed in Glme-DRA*01 allele
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which grouped with Risso’s dolphin (Grampus griseus), striped dolphin Stenella
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coeruleoalba (Meyen, 1833), pantropical spotted dolphin Stenella attenuata (Gray,
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1846) (identical alleles) and Indo-Pacific bottlenose dolphin Tursiops aduncus
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(Ehrenberg, 1832) (> 50% bootstrap values). Likewise, it was also evident between
357
Glme-DRA*03 allele and Omura’s whale (Balaenoptera omurai Wada, Oishi &
358
Yamada, 2003) (> 50% bootstrap values and > 0.95 posterior probability values). As
359
in DRA exon 2, no species-specific clade was observed in the DQB long-finned pilot
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whale sequences analysed in the present study. Instead, this species DQB
361
sequences grouped with sequences from different species described in previous
362
studies (Fig. 2b), evidencing the occurrence of trans-specific sharing of alleles,
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specifically
364
AB302053, identical alleles) (> 50% bootstrap values and > 0.95 posterior probability
365
values).
between
Glme-DQB*02
and
Tutr-DQB*10
(Genbank
accession:
366
367
Discussion
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The present study described the levels of diversity of the MHC DQB and DRA
369
loci in long-finned pilot whales from different regions of the North Atlantic, for
370
comparison with previous results from the South Pacific (Heimeier 2009). For both
371
DRA and DQB locus, the variation resolved in the North Atlantic is comparable with
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the low levels of MHC allelic diversity already reported in some cetacean species (e.g.
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Murray et al. 1995; Munguia-Vega et al. 2007; Xu et al. 2007, 2008, 2009). Clearly,
374
any comparison of levels of genetic diversity must be done with recourse to the
375
potential sources of bias inherent to the samples taken. In this case, three bias
376
sources were considered: limited sample size, failure to resolve all variation using
377
DGGE, and non-random sampling of individuals with a bias caused by the sampling
378
of family groups. In this case, several individuals displaying the same DGGE pattern
379
were sequenced, and no cryptic DNA diversity was resolved. Similarly, the sample
380
sizes used in the present study are directly comparable to those used previously, so
381
the estimates of allelic richness are directly comparable even if all of the extant
382
diversity has not been resolved across the range. The largest concern is whether
383
these samples are random, given that the sampled stranded animals may have a high
384
degree of social structure. However, a recent study, based on some of the samples
385
analysed by Heimeier (2009), revealed that there is evidence that mass strandings of
386
pilot whales involved multiple matrilines and unrelated individuals (Oremus et al.
387
2013), suggesting that the potential for downward bias of genetic diversity is minimal.
388
It may be the case that the observed MHC diversity is related to the frequently
389
suggested weaker pathogenic pressure in marine environments, leading to weaker
390
balancing selection acting on the MHC in marine mammals compared to terrestrial
391
mammals (Slade et al. 1992; Villanueva-Noriega et al. 2013). However, this
392
hypothesis is inconsistent with some data from other species. High levels of adaptive
393
diversity have already been reported in some cetacean species (e.g. Baker et al.
394
2006; Xu et al. 2012; Arbanasic et al. 2014). Recent studies have described high
395
levels of DQB nucleotide and allelic diversities of finless porpoise, Neophocaena
396
phocaenoides (Cuvier, 1829), baiji, Lipotes vexillifer (Miller, 1918) (e.g. Yang et al.
397
2005; Xu et al. 2007, 2009) and baleen whales (Baker et al. 2006).
398
Moreover, some studies have resolved high spatial variation of extant adaptive
399
diversity in dolphin populations across different geographical regions, suggesting that
400
single point sampling of MHC variation may provide a biased view of diversity and
401
selection operating over larger geographic scales (e.g. Vassilakos et al. 2009; Xu et
402
al. 2010). In the present study, within the North Atlantic, significant differences in
403
allelic frequencies were detected between the NI and all the remaining regions
404
analysed across this oceanic basin for DQB locus, and between the UK and the USA,
11
405
for the DRA locus. Additionally, significant differences were also observed in allele
406
frequencies between the North Atlantic and New Zealand. One of the putative
407
ecological factors underpinning the occurrence of geographic variation in adaptive
408
diversity is pathogen-mediated selection (e.g. Piertney & Oliver 2006; Vassilakos et
409
al. 2009; Spurgin & Richardson 2010; Xu et al. 2010), as exemplified by Vassilakos et
410
al. (2009). These authors suggested that dolphin populations with a worldwide
411
distribution across different geographic locations would be under differential pathogen
412
selective pressures. Hence, differential parasite pressures across the distribution
413
range of long-finned pilot whales (e.g. Raga & Balbuena 1993; Abollo et al. 1998)
414
could be an explanation for the significant differences detected in allele frequencies,
415
in the North Atlantic and between the North Atlantic and New Zealand (South Pacific).
416
Indeed, a study based on the analyses of parasites in long-finned pilot whales from
417
the Faroe Islands, the Coast of France and the Mediterranean found some variation
418
in terms of parasite diversity between locations (Raga & Balbuena 1993).
419
The geographic differences in adaptive diversity may be particularly relevant
420
considering the latitudinal (mainly related to water temperature) and longitudinal
421
differences in host and parasites diversity already reported, with warmer waters and
422
the Indo-Pacific being richer than colder waters and the Atlantic (reviewed in Rohde
423
2010). These differences could help explain both the significant differences between
424
the allele frequencies of pilot whales in southern regions of the North Atlantic (warmer
425
waters) and from the North Atlantic and New Zealand (South Pacific), although higher
426
DQB nucleotide and allele diversity would be expected in New Zealand compared to
427
the North Atlantic, based on this theory. However, it is important to highlight that a
428
higher diversity of parasites in warmer waters and in the Pacific does not necessarily
429
predict levels of host-parasite interaction.
430
Associated with the existent latitudinal and longitudinal differences in parasite
431
diversity, the dietary differences exhibited by pilot whales in their distribution range
432
may also alter the exposure levels of this predator to different parasites. Previous
433
dietary studies of pilot whales, based on stomach contents and stable isotope
434
analyses, have shown the occurrence of dietary differences within Atlantic waters and
435
between Atlantic and Pacific basins (e.g. Desportes & Mouritsen 1993; Gannon et al.
436
1997; Beatson et al. 2007; Santos et al. 2014; Monteiro et al. 2015b). However, more
437
information is needed about the dietary habits and parasite burdens of pilot whales
438
and respective prey, in a broad geographic range, to be able to explore this theory.
439
In the present study, the analyses of MHC loci revealed convincing evidence that
440
balancing selection has been acting to maintain functional polymorphism of the DQB
441
locus in the North Atlantic, over long-term scale, due to the occurrence of trans12
442
species polymorphism (Klein 1980), together with higher levels of nonsynonymous
443
(versus synonymous) amino acid substitutions (dN:dS) both across the molecule and
444
also in the peptide binding region. Such phylogenetic patterns and inequality in dN:dS
445
(dN > dS, for PBR), characteristic of balancing selection, have already been reported in
446
several MHC studies in cetaceans (beluga, Murray et al. 1995; minke whale, Hayashi
447
et al. 2003; Hector’s dolphin, Heimeier et al. 2009), including long-finned pilot whales
448
from New Zealand (South Pacific) (Heimeier 2009). However, the occurrence of long-
449
term balancing selection may not reflect the influence of selection in contemporary
450
populations. Therefore, although geographical variation in parasites diversity
451
worldwide may be leading to geographical adaptive structure in pilot whales from the
452
North Atlantic and between the North Atlantic and New Zealand (Pacific), other
453
microevolutionary forces, such as migration or genetic drift, may have an important
454
role in shaping contemporary population genetic diversity or may be masking
455
contemporary signatures of selection at both oceanic basins as already described in
456
several studies (reviewed in Radwan et al. 2010). To disentangle which
457
microevolutionary forces could be contributing to the adaptive diversity on North
458
Atlantic and South Pacific long-finned pilot whales, it would be necessary to compare
459
adaptive and neutral variation (Radwan et al. 2010).
460
For the DRA locus, analysis of the long-term influence of balancing selection at
461
DRA locus showed no bias of dN to dS, suggesting a lack of effect of balancing
462
selection. Similar results were obtained by Xu et al. (2009), where the analysis of
463
several cetacean species revealed purifying historical selection on this locus. This
464
absence of balancing selection together with the low levels of diversity observed in
465
this locus may be due to loss of variation via random drift or it may represent a
466
nonclassical MHC locus, where low variation is due to a functional constraint (Babik et
467
al. 2008).
468
In conclusion, the levels of MHC allelic diversity found in long-finned pilot
469
whales from the North Atlantic were relatively low, and comparable with those
470
previously described in New Zealand (South Pacific) (Heimeier 2009). Inferences
471
drawn from dN:dS and the occurrence of trans-species polymorphism suggest that
472
balancing selection had an important role at maintaining DQB locus diversity in the
473
North Atlantic, historically. If the effect of balancing selection was acting in
474
contemporary populations of long-finned pilot whales in the North Atlantic, the
475
geographic variation found in adaptive allele frequencies across regions could reflect
476
the occurrence of differential pathogen selective forces. Likewise, the reported higher
477
levels of host and parasite diversity in the Pacific compared to the Atlantic Ocean,
478
may have resulted in the significant differences in allele frequencies between the New
13
479
Zealand (Pacific) and North Atlantic, although higher levels of adaptive diversity
480
would be expected in the former. Therefore, it may be that other microevolutionary
481
processes, such as migration and/or genetic drift are operating to shape the
482
distribution of adaptive genetic diversity in the North Atlantic and South Pacific. Future
483
studies should compare genetic variation at neutral and MHC loci in order to discern if
484
adaptive diversity of long-finned pilot whales in the Atlantic and Pacific Oceans is
485
driven by stochastic or deterministic microevolutionary processes.
486
487
Acknowledgments
488
Samples were collected under the auspices of strandings monitoring programs run by
489
Sociedade Portuguesa de Vida Selvagem, Coordinadora para o Estudio dos
490
Mamíferos Mariños (supported by the regional government Xunta de Galicia), the
491
Scottish Agriculture College Veterinary Science Division, the UK Cetacean Strandings
492
Investigation Programme (jointly funded by Defra and the Devolved Governments of
493
Scotland and Wales), the Museum of Natural History of the Faroe Islands and the
494
Marine Mammal Rescue & Research Program of the International Fund for Animal
495
Welfare. We thank all the members of those institutions for their assistance with data
496
and sample collection. Published in a collaboration between Universidade do Minho
497
and University of Aberdeen
498
499
Funding
500
Silvia S. Monteiro and Marisa Ferreira were supported by a Ph.D. grant from
501
Fundação
502
SFRH/BD/30240/2006, respectively). Alfredo López was supported by a postdoctoral
503
grant from Fundação para a Ciência e Tecnologia (ref SFRH/BPD/82407/2011).
504
Catarina Eira is supported by CESAM (UID/AMB/50017), from FCT/MEC through
505
national funds and FEDER (PT2020, Compete 2020). The work related with
506
strandings and tissue collection in Portugal was partially supported by the SafeSea
507
Project EEAGrants PT 0039 (supported by Iceland, Liechtenstein and Norway
508
through
509
NAT/PT/000038 (funded by the European Union–Program Life+) and by the project
510
CetSenti
511
(Funded by the Program COMPETE and Fundação para a Ciência e Tecnologia).
para
the
a
EEA
FCT
Ciência
Financial
e
Tecnologia
Mechanism),
RECI/AAG-GLO/0470/2012;
(ref
by
SFRH/BD/38735/2007
the
Project
and
MarPro–Life09
FCOMP-01-0124-FEDER-027472
512
513
514
14
515
516
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Figure legends
717
718 Fig.1 Location of the strandings of long-finned pilot whale analysed in this study (n =
719 119). Sample size is indicated for both MHC loci above each pie chart (right: DQB
720 locus; left: DRA locus). Pies charts show allele relative frequencies for the MHC class II
721 DQB and DRA. White: DQB allele 1; Black: DQB allele 2; Green: DQB allele 3; Red:
722 DRA allele 1; Orange: DRA allele 2; Yellow; DRA allele 3. NI: Northwest Iberia; UK:
723 United Kingdom; FI: Faroe Islands; USA: United States of America
724
725 Fig.2 Phylogenetic relationships between MHC alleles of long-finned pilot whales of the
726 North Atlantic, based on Maximum Likelihood and Bayesian analysis. a) MHC DRA
727 exon 2; b) MHC DQB exon 2. * indicate posterior probability support values superior to
728 0.95 and/or likelihood bootstrap values superior to 50%. For the DQB locus, Bos taurus
729 was used as outgroup. Genbank accession numbers are shown within brackets. Dede 730 Delphinus delphis; Stco - Stenella coeruleoalba; Tutr - Tursiops truncatus; Tuad 731 Tursiops aduncus; Glma - Globicephala macrorhynchus; Glme - Globicephala melas;
732 Grgr - Grampus griseus; Phma - Physeter macrocephalus; Laob - Lagenorhynchus
733 obliquidens; Meno - Megaptera novaeangliae; Dele - Delphinapterus leucas; Cehe 734 Cephalorhynchus hectori; Deca - Delphinus capensis; Phph - Phocoena phocoena;
735 Stat - Stenella attenuata; Pobl - Pontoporia blainvillei; Plga - Platanista gangetica;
736 Phda - Phocoenoides dalli; Neph - Neophocaena phocaenoides; Live - Lipotes
737 vexillifer; Baom - Balaenoptera omurai.
738
739
740
741
742
743
744
745
746
747
748
20
749
750
751
752
753
21
754 Tables
755 Table I. Nucleotide (a) and predicted amino acid sequences (b) for: I.I) MHC class II DRA exon 2 alleles and I.II) MHC class II DQB exon 2
756
alleles in long-finned pilot whale. A dot indicates identity with the top sequence. Asterisks and numbers in bold identify sites in the putative
757 peptide-binding region as predicted by Brown et al. (1993).
758
759
Table I.I a)
760
Glme*DRA*01
T C T C T G T C C C C T T A T C A A T C A A A C G A G T T T A T G T T T G A C T T T G A T G G T G A T G A G A T T T T C C A
Glme*DRA*02
.
.
.
.
.
.
.
.
.
.
Glme*DRA*03
.
.
.
.
.
. G T .
.
.
. G . C .
Glme*DRA*01
C G T G G A T A T G G A A A A G A A G G A G A C A G T G T G G C G G C T T A A A G A A T T T G G A A A T T T T G C C A G T T
Glme*DRA*02
.
.
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.
C .
Glme*DRA*03
.
.
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.
T .
.
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.
G .
.
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C .
Glme*DRA*01
T T C A G G C T C A G G G T G C A T T G G C C A A T A T G G C T G T G G G A A G A G C C A A C C T G G A C A T C T T G A T A
Glme*DRA*02
.
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Glme*DRA*03
.
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T .
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A .
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A .
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.
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. .
761
762
763
764
765
22
766
Table I.I b)
767
Position PBR
2
2
2
4
2
6
3 3
1 2
4
3
5
1
5 5 5
3 4 5
5
8
6
2
6
5
6 6
8 9
7
2
*
*
*
* *
*
*
* * *
*
*
*
* *
*
Glme*DRA*01
S L S P Y Q S N E F M F D F D G D E I
F H V D M E K K E T V WR L K E F G N F A S F Q A Q G A L A N M A V G R A N L D I
L I
Glme*DRA*02
.
.
Glme*DRA*03
.
. V . D .
.
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R .
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I
.
.
.
K .
.
.
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.
M .
768
769
770
771
Table I.II a)
772
Glme*DQB*01 A C G G A G C G G G T G C G G G T C A T G A A C A G A T A C A T C T A T A A C C G G G A G G A G T T C G T G C G C T T C G A
Glme*DQB*02
.
.
.
.
.
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.
.
.
.
. C A . G .
.
Glme*DQB*03
.
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. T C . G .
. G .
. G .
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. T G .
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. .
.
.
.
Glme*DQB*01 C A G C G A C G T G G G C G A G T A C C G G G C G G T G A C C G A G C T G G G C C G G C C G G A C G C C G A G T A C T G G A
Glme*DQB*02
.
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. T C .
Glme*DQB*03
.
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. T .
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. .
Glme*DQB*01 A C A G C C A G G A G G A C A T C C T G G A G G A G G A A C G G G C C G C G C T G G A C A C G
Glme*DQB*02
.
.
.
.
.
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.
. A .
.
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.
. C G .
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C A C G .
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.
Glme*DQB*03
.
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23
773
774
Table I.II b)
Position PBR
2
8
3
0
3
2
3 3
7 8
4
7
5
6
6 6
0 1
6
5
6
8
7 7
0 1
7
4
*
*
*
* *
*
*
* *
*
*
* *
*
Glme*DQB*01 T E R V R V M N R Y I
Y N R E E F V R F D S D V G E Y R A V T E L G R P D A E Y WN S Q E D I
L E E E R A A L D T
Glme*DQB*02 .
.
.
.
.
H V S .
L .
.
.
.
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.
.
.
.
.
.
F .
.
.
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.
F .
.
.
K .
.
.
.
R .
.
.
H V .
.
Glme*DQB*03 .
.
.
.
.
S V D .
.
.
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.
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F .
.
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.
24
775
25
776
Table II. Summary of genetic diversity statistics for the MHC DRA and DQB loci of
777
long-finned pilot whale analysed in the present study. Mean values ± Standard
778
Deviation are shown. n: sample size; π: nucleotide diversity. Ho: observed
779
heterozigoty; He: expected heterozigoty; P: probability of rejecting the null hypothesis
780
that loci are in Hardy-Weinberg equilibrium (ns: non-significant P > 0.0125; sig:
781
significant P ≤ 0.0125, considering bonferroni correction). Abbreviations are described
782
in figure 1.
alleles
Allelic richness
NI
3
UK
3
FI
2
USA
2
2.99
2.99
2.00
2.00
Overall
3
2.72
π (%)
DRA
0.86 ± 0.58
0.22 ± 0.23
0.27 ± 0.26
0.49 ± 0.38
0.15
0.36
0.70
0.40
0.33
0.30
0.48
0.42
sig
ns
sig
ns
3
3
3
3
3
3.00
2.98
2.89
2.82
2.82
4.54 ± 2.44
4.50 ± 2.37
4.21 ± 2.24
4.36 ± 2.34
4.60 ± 2.39
Ho
0.39
0.41
0.33
0.27
0.35
He
0.45
0.51
0.44
0.46
0.51
P
ns
ns
ns
ns
sig
Ho
He
P
alleles
Allelic richness
DQB
0.61 ± 0.45
π (%)
0.46
0.50
ns
783
784
785
786
787
788
789
790
791
792
793
794
795
26
796
Table III. Results from Fisher´s exact test for genetic differentiation within North
797
Atlantic locations and between North Atlantic (NA) and Pacific (PAC). Above the
798
diagonal: DRA locus; Below diagonal: DQB locus. ns: non-significant; sig: significant P
799
≤ 0.0083 within NA comparisons (considering Bonferroni correction) and P ≤ 0.05 for
800
NA vs. PAC comparison. Abbreviations are described in figure 1.
801
802
803
804
805
NI
UK
FI
USA
NI
sig
sig
sig
UK
ns
ns
ns
FI
ns
ns
ns
USA
ns
sig
ns
-
NA
PAC
NA
sig
PAC
-
806
807
808
809
810 Table IV. The relative rate of dN and dS substitutions among alleles for codons in the
811 PBR and non-PBR of DQB exon 2 and test of positive selection for North Atlantic long812 finned pilot whale. dN: nonsynonymous substitutions; dS: synonymous substitutions;
813 PBR: peptide binding region; P: probability of rejecting the null hypothesis of neutrality
814 (dN = dS) in favour of the alternative hypothesis of positive selection (dN > dS) (Nei & Kumar
815 2000); N: Number of codons used for the test. Mean value ± standard error based on
816 100,000 replicates.
817
818
N
dN (%)
dS (%)
P
819
Overall
57
9.7 ± 3.3
0.8 ± 0.8
0.002
820
PBR
14
34.3 ± 15.4
3.6 ± 3.9
0.01
Non-PBR
43
3.5 ± 2.4
0.0 ± 0.0
0.07
27
821
822
28