Immune recognition of transmissible cancers in Tasmanian devils with MHC-I deletion

Immune recognition of transmissible cancers in Tasmanian devils with MHC-I deletion | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Immune recognition of transmissible cancers in Tasmanian devils with MHC-I deletion View ORCID Profile Kimberley C. Batley , View ORCID Profile Ruth J. Pye , View ORCID Profile Katherine A. Farquharson , Yuanyuan Cheng , View ORCID Profile Andrew S. Flies , View ORCID Profile Carolyn J. Hogg , View ORCID Profile Katherine Belov doi: https://doi.org/10.1101/2025.03.20.644438 Kimberley C. Batley 1 School of Life and Environmental Sciences, The University of Sydney , Sydney, NSW, 2006, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kimberley C. Batley Ruth J. Pye 2 Menzies Institute for Medical Research, University of Tasmania , Hobart, TAS, 7000, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ruth J. Pye Katherine A. Farquharson 1 School of Life and Environmental Sciences, The University of Sydney , Sydney, NSW, 2006, Australia 3 ARC Centre of Excellence for Innovations in Peptide and Protein Science, The University of Sydney , Sydney, NSW, 2006, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katherine A. Farquharson Yuanyuan Cheng 1 School of Life and Environmental Sciences, The University of Sydney , Sydney, NSW, 2006, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew S. Flies 2 Menzies Institute for Medical Research, University of Tasmania , Hobart, TAS, 7000, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrew S. Flies Carolyn J. Hogg 1 School of Life and Environmental Sciences, The University of Sydney , Sydney, NSW, 2006, Australia 3 ARC Centre of Excellence for Innovations in Peptide and Protein Science, The University of Sydney , Sydney, NSW, 2006, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carolyn J. Hogg For correspondence: carolyn.hogg{at}sydney.edu.au Katherine Belov 1 School of Life and Environmental Sciences, The University of Sydney , Sydney, NSW, 2006, Australia 3 ARC Centre of Excellence for Innovations in Peptide and Protein Science, The University of Sydney , Sydney, NSW, 2006, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katherine Belov Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Devil facial tumour disease (DFTD), caused by transmissible cancers, has decimated the wild Tasmanian devil ( Sarcophilus harrisii ) population. Devil facial tumour 1 (DFT1) cancer cells have spread due to low major histocompatibility complex class I (MHC-I) diversity in the species, as well as epigenetic regulation of MHC-I proteins on DFT1 cells to evade allograft responses. Tumour regression, recovery from disease, and immune recognition of DFT1 cells have been documented in a small number of cases. Here we tested the hypothesis that antibody response to DFT1 was associated with dissimilarity of host and tumour MHC-I types. We found that most individuals with antibodies against DFT1 cells do not share any alleles with DFT1 at the MHC-I UA locus. In addition to allelic mismatches, deletion of the UA locus increases the likelihood of immune response against DFT1 cells. Strikingly, we show that loss of the UA locus is being selected for at long-term disease sites. We conclude that deletion of an entire MHC locus provides some protection against DFT1. However, not all individuals that generate antibody responses are protected from DFT1, and loss of UA is not sufficient to ensure survival. Our study provides the first evidence of a complete gene loss in a species in response to a disease threat. Further evolutionary loss of MHC-I diversity will increase the species’ risk of future disease epidemics and further jeopardise the long-term viability of the species. Significance Statement Tasmanian devil populations have been decimated by devil facial tumour 1, an infectious cancer that spreads due to low major histocompatibility complex class I (MHC-I) diversity. We show that a deletion of the UA locus increases the likelihood of immune response against DFT1 cells and conclude that the loss of an entire MHC locus provides some protection against DFT1 and show that it is increasing in frequency in long-term diseased sites. This is the first evidence of a complete gene loss in a species’ response to a disease threat, but we caution that loss of MHC-I diversity in devils will increase their risk of extinction due to further loss of genetic resilience. Introduction Tasmanian devil ( Sarcophilus harrisii ) numbers have undergone a rapid and widespread decline following the emergence of devil facial tumour disease (DFTD) ( 1 , 2 ). DFTD is caused by two genetically distinct clonal transmissible cancers, DFT1 and DFT2, that are passed between individual devils through biting ( 3 , 4 ). DFT1 was first observed in 1996 and is now present across most of Tasmania ( 1 , 5 ). In 2014, the first case of DFT2 was identified and this cancer is currently detected only in southeast Tasmania ( 4 , 6 ). Transmission of vertebrate cells from one individual to another is usually prevented by the immune system. Major histocompatibility complex class I (MHC-I) molecules should be recognised as foreign in devils that are infected with DFT1 or DFT2 cells and result in allograft-mediated immune rejection. In DFT1 this does not occur for two primary reasons. Firstly, devil populations have experienced a series of severe population crashes that has reduced their genetic diversity, including MHC diversity ( 7 - 10 ). Such low levels of MHC diversity in populations means that most individuals share MHC-I alleles with the tumour, and therefore immunogenic recognition of the tumour does not occur. Secondly, DFT1 cells epigenetically down-regulate cell surface MHC-I, providing an additional effective mechanism of immune escape ( 11 ). MHC-I is amongst the most polymorphic regions of the genome in vertebrate species. The significance of this diversity lies in the role of MHC-I recognising self- and non-self-antigens and facilitating immune responses against foreign pathogens ( 12 ). MHC-I diversity is associated with the immune fitness of a species and its ability to adapt and survive in a changing environment. In humans, having the HLA-B * 15:01 allele was associated with asymptomatic SARS-COV-2 infections, demonstrating that single MHC-I alleles can play a major role in disease resistance ( 13 ). Despite the key role that MHC-I has in allorecognition, it is notoriously difficult to find evidence of pathogen-mediated selection of individual MHC-I alleles in wild animal populations. Devils have three classical MHC-I genes: Saha-UA, -UB and -UC, (UA, UB, and UC for short) that generally exhibit low allelic diversity, and relatively high levels of homozygosity ( 14 ). To date, 61 MHC-I alleles have been characterised across the three genes, and alleles with similar amino acids at antigen-binding sites have been grouped into 13 distinct supertypes ( 10 ). Alleles within each supertype are expected to bind similar antigens, and these are distinct from the antigens recognised by alleles in different supertypes. The number of functional MHC-I alleles and supertypes in an individual devil range from one to six ( 10 , 15 ) due to structural variations (deletion of exons and introns) that result in non-functional alleles ( 14 ). Although previous work has shown there to be a deletion of the UA locus in some individuals from West Pencil Pine, a population with known reduced disease effects ( 16 ), the association between this deletion and DFT1 susceptibility remains to be determined ( 17 ). Epigenetic downregulation of MHC-I allows DFT1 cells to evade allograft responses against MHC-I ( 11 ), but this downregulation is reversible. Exposure of DFT1 cells to the inflammatory cytokine interferon gamma (IFN-γ) and/ or the transcriptional coactivators NLRC5 and CIITA, upregulates MHC-I expression in vitro , providing a mechanism for allorecognition and immune mediated tumour regression ( 11 , 18 ). There is some evidence for this occurring in vivo . Variable levels of MHC-I protein expression have been reported in tumour biopsies ( 19 ), and a small number of wild devils observed to have undergone tumour regression had serum antibodies against cultured DFT1 cells that expressed surface MHC-I ( 20 ). Ong et al ( 18 ) knocked out the MHC-I accessory gene beta-2 microglobulin to demonstrate the target for these antibodies was the MHC-I molecule. Furthermore, anti-MHC-I antibodies were present in captive devils that had DFT1 regressions following immunotherapy ( 21 ). Given that MHC-I can be transiently upregulated on DFT1 cells, we hypothesized that devils that are most genetically dissimilar to the tumour at MHC-I alleles will be the most likely to produce anti-MHC-I antibodies. Furthermore, we postulated that the high mortality rate of the disease would select for survival of devils with the fewest number of MHC-I alleles matched to the DFT1 cells. To test this hypothesis, we aimed to ( 1 ) determine whether host MHC-I alleles are associated with the presence or absence of anti-MHC-I antibodies, and ( 2 ) investigate MHC-I allele frequency changes over time in wild devil populations affected by DFT1. To address these aims, we genotyped the MHC-I (UA, UB, and UC) loci in two DFT1 cell lines and in 61 wild devils with DFT1 and/or serum antibody responses against DFT1. We then MHC-I typed a further 537 devils from nine field sites across Tasmania that were born between 2012 and 2021 to understand whether DFT1 is driving selection of MHC-I diversity in wild devils. Results Wild devils with anti-DFT1 immune responses Blood and ear biopsy samples were collected from 116 Tasmanian devils aged two years and older that were examined during routine field monitoring between 2015 and 2023 across five sites. Serum samples were analysed for antibodies against whole DFT1 cells not expressing surface MHC-I (MHC-I-DFT1 cells), and whole DFT1 cells treated with interferon gamma (IFN-γ) to induce surface expression of MHC-I proteins (MHC-I+ DFT1 cells). For downstream analysis, only devils with clinical signs of DFT1 and/ or with anti-MHC-I antibodies were selected to ensure that devils had been exposed to DFT1. This selection process resulted in 61 devils, 25 of which had anti-MHC-I antibodies. Four of these devils also had serum antibodies against MHC-I -DFT1 cells (Table S1). Thirteen (52%) of the devils with anti-MHC-I serum antibodies had no clinical signs of DFT1, and the remaining 12 (48%) devils were diseased (Table S1). Since the arrival of DFT1 it is unusual for devils in diseased populations to live beyond three years ( 2 ). In our dataset, only two (3.6%) of the 55 devils without antibodies or DFT1, and seven (11.5%) of the 61 devils with antibodies or DFT1 were aged four years or older. Six (85.7%) of the seven older devils with antibodies or DFT1, including two that were six years old and one seven-year-old, had serum antibodies against MHC-I and were disease free at their last sampling date. Notably, one of these devils had undergone tumour regression ( Fig. 1 , TDKi; Table S1 TDKi), and three others were trapped over two or more years, all remaining free of DFT1 and positive for serum anti-MHC-I antibodies ( Fig. 1 , TDSy; Table S1 TDSy, TDTy, TDGa). Download figure Open in new tab Fig. 1. Photographs and serum antibody responses against untreated (MHC-I -) and interferon gamma treated (MHC-I +) DFT1 cells of five individual devils. The devil identification and year of sample collection are indicated along the first row of each column. Photographs of the devils’ confirmed DFT1 tumours or other lesions are shown in the second row. The third row indicates the health status of the devil: D = DFT1 present (confirmed by histopathology), H (R) = healthy following DFT1 regression; H * = presence of a lesion suggestive of DFT1 (blue arrow) but samples not collected for laboratory diagnostic confirmation; H = healthy (no DFT1). Flow cytometry histograms showing serum antibody responses against DFT1 cells not expressing surface MHC-I (MHC-I -) are along the fourth row, and against DFT1 cells expressing surface MHC-I (MHC-I+) are along the final row. The individual devils’ antibody responses are shown in red and compared to a naive devil (negative control) shown in black. The fluorescence intensity of devil IgG bound to tumour cells as detected by fluorochrome AF647 is shown in log scale on the x-axis, the cell count is on the y-axis. A positive antibody response of the individual devil is indicated by a shift to the right of the red histogram when compared to the black negative control. Sex, age and location of these devils is found in Table S1. Anti-DFT1 immune responses correlate with MHC-I mismatches Long-read amplicon sequencing of the devil MHC-I genes (Saha -UA, -UB, -UC ) resulted in the sequencing of 41 different alleles (Table S1) that were grouped into one of the 13 supertypes referred to in ( 10 ). Non-functional MHC-I alleles were present in 72% (44/61) of devils, with the number of functional alleles per individual in this dataset ranging between three and six. The two DFT1 cell lines (C5065 and 1426) share identical MHC genotypes to each other, with both having six functional MHC-I alleles (Table S1) and five supertypes. All the DFT1 MHC-I alleles were detected in the devil samples included in this study (Table S1). Generalized linear models (GLM) revealed that age and the number of functional MHC-I alleles a devil had were strong predictors of the anti-DFT1 antibody response, with older devils and those with less alleles more likely to mount serum antibodies (Table S2). Genetic similarity between host and DFT1 across all MHC-I loci (calculated using Wetton’s formula: D AB = 2F AB /(F A + F B ) (as per ( 15 )) was not a strong predictor. As the frequency of non-functional MHC-I alleles was greatest within the UA loci, we tested whether the effect of the number of MHC-I alleles was driven by genetic similarity at individual loci. Genetic similarity at UA was negatively correlated with anti-DFT1 antibody responses, while UB and UC had no association with antibody responses (Table S2). Of the 25 devils with an anti-DFT1 antibody response, 23 (92%) had zero matches to DFT1 at the UA loci ( Fig. 2 ). In comparison, of the 36 devils with DFT1 and no detectable anti-DFT1 response, 28 (78%) had at least one match with DFT1 at UA loci. Download figure Open in new tab Fig. 2. Hierarchical clustering of Tasmanian devils and DFT1 cells based on their UA genotype. Labels above branches depict the UA genotype of each cluster (del = deletion; ST = supertype). Labels below branches identify individual devils, except for the black sample labels (C5065 and C1426) which identify the DFT1 cell lines. Devils marked with squares or circles had an antibody response against DFT1. Circles represent individuals with antibodies against MHC-I positive and MHC-I negative DFT1 cells; squares represent individuals with antibodies against MHC-I positive DFT1 cells only. Filled symbols represent devils that showed clinical signs of DFT1, while unfilled symbols represent devils that showed no clinical signs of DFT1. A complete deletion of the UA locus had a strong positive effect on the antibody response (Table S3), with 15 (60%) of the 25 devils with an antibody response having no functional UA loci ( Fig. 2 ). Eight (32%) of the 25 devils with an antibody response had at least one functional UA supertype not shared with DFT1 at UA (Supertypes 1 and 7) ( Fig. 2 ). The frequency of these supertypes was low in our dataset ( Fig. 2 ) and were either not included in the statistical analysis, or were not strong predictors of the antibody response, likely due to small sample sizes (e.g., low frequency). The base model (antibody response ~ age) was a stronger predictor of the antibody response than the UB and UC genotypes, suggesting that the UA genotype has the strongest effect on the antibody response (Table S3). Frequency of UA deletion in wild populations After finding a correlation between MHC-I type at UA and an anti-DFT1 immune response, we next investigated a) whether the number of functional alleles at UA and/or the frequency of UA supertypes has changed in devil populations over time, and b) whether the frequency of the non-functional UA allele was higher in older age classes. We genotyped another 540 devils (601 in total) born between 2012 and 2021 across nine field sites at MHC-I loci. Four (Buckland, Narawntapu, Stony Head, wukalina) of the nine sites have been genetically managed through supplementation of healthy devils (Figure S1; ( 22 , 23 )) however, no hybrid individuals were included in the dataset to ensure observed signals were in response to DFT1 and not supplementation. We observed contrasting trends between the two most common UA alleles; the non-functional alleles and supertype 2 ( Fig. 3 ). DFT1 has been present at all sites, except Granville Harbour and Narawntapu, for at least 15 years. There is a slight increasing trend in the frequency of the UA deletion at all long-term disease sites, except wukalina, where DFT1 has been present for over 28 years and a high frequency of devils with the non-functional UA allele was observed when the first samples were collected in 2014. The trend for increasing UA deletions was particularly evident at Fentonbury, where the frequency of the non-functional UA was initially low compared to other sites. This trend is not observed at either Granville Harbour, where disease has only been present for 9 years, or at wukalina. At Kempton, the frequency of supertype 2 shows a declining trend, with a sharp uptick in non-functional UA alleles only in the most recent sampling years. Interestingly, at this site the frequency of supertypes 1 and 7 (not shared with DFT1) show a general increasing trend (Figure S2). Download figure Open in new tab Fig. 3. Temporal changes in the frequency of the a) non-functional UA allele (not shared with DFT1) and b) supertype 2 (shared with DFT1). Vertical dashed lines indicate the year that DFT1 arrived at each sampling site, with the x-axis representing the number of years between disease arriving and sampling collection. As Woolnorth was free of DFT1 during this study, the x-axis indicates the year that samples were collected. In comparison to the DFT1-affected field sites, the frequency of the non-functional UA allele and supertype 2 remains steady at Woolnorth, which was disease-free at the conclusion of fieldwork for this study. Comparing the frequency of the non-functional UA alleles and supertype 2 in different age classes showed that at disease sites, the frequency of the non-functional UA allele was higher in devils that lived to at least four years of age, compared with one-year olds ( Table 1A &B). This is particularly evident when we calculated the frequency of the most common supertypes that are not shared with DFT1 (supertypes 1, 7 and the non-functional UA) within each age class. All animals from disease sites that were resampled at five years of age did not share an allele with DFT1, nor did a high proportion of three- and four-year-olds ( Table 1C ). In most cases, the contrast of this was observed for supertype 2. Like the temporal trends, the frequency of both alleles at Woolnorth was similar between all age classes. Altogether these data suggest that DFT1 is driving selection for the loss of UA, increasing the chance of allorecognition of MHC-I+ DFT1 cells. View this table: View inline View popup Download powerpoint Table 1: A) Frequency of the non-functional UA allele in different age classes; B) Frequency of the supertype 2 (shared with DFT1) in different age classes; C) Frequency of the most common UA alleles that are not shared with DFT1 (supertypes 1, 7, and the non-functional UA allele). Shaded boxes indicate age classes with no samples Discussion Devil facial tumour disease has spread through regional devil populations rapidly over the last three decades resulting in massive population crashes but no local extinctions ( 1 ). Here we provide strong evidence that the MHC-I of devils influences their ability to mount an immune response against DFT1. Serum antibodies against the MHC-I proteins of DFT1 are found in hosts that have MHC-I types that are distinct from DFT1. These MHC-I types have increased in frequency in diseased populations suggesting that the disease is driving evolutionary selection for hosts that are able to mount an immune response and live to reproduce. Specifically, devils that have lost at least one functional UA allele are most likely to have serum antibodies against DFT1 due to allorecognition of tumour MHC-I, while devils who share MHC supertype 2 with the tumour rarely have antibody responses. The frequency of the UA deletion is slowly increasing over time in wild diseased populations while the frequency of supertype 2 is decreasing. Our initial speculation in 2012 that the higher frequency of the UA deletion in western individuals may provide animals with some level of resilience to DFT1, resulting in slower disease progression, appears to be correct ( 14 ). Our results suggest that anti-MHC-I antibodies are protective in some cases. Seven devils in our dataset of 61 individuals were aged four years or older, and six of these were free of DFT1 and had anti-MHC-I antibodies. The average life span of a devil before the arrival of DFT1 was four to five years, with some living to six years old ( 24 ). The immune capacity of devils declines with age and this, along with the increasing risk of DFT1 exposure over time likely explain the high DFT1 prevalence amongst adults and the loss of older age classes (three years and over) observed in DFT1 affected populations ( 2 , 25 ). It is reasonable to expect that the six older DFT1 free devils with anti-DFT1 immune responses have been exposed to DFT1 on multiple occasions during their lifetime. Additionally, they may be comparable to the individual devil in Pye et al ( 20 ) that had undergone observed tumour regression and remained antibody positive and DFT1 free for three years and lived to the age of six years after which time it was not re-trapped. However, serum antibodies against DFT1 do not always prevent disease progression. Twelve of the 25 devils that seroconverted had DFT1, with tumours varying from early to advanced stages. This is not unexpected given the role anti-MHC-I antibodies play in human graft rejection. Matching MHC-I alleles, particularly HLA-A and HLA-B types between human donor and recipient is critical for avoiding graft rejection by the recipient ( 23 ). But where anti-HLA antibodies occur, the spectrum of antibody-mediated graft injury is wide, ranging from acute rejection to accommodation with no apparent damage ( 25 ). The complement binding capacity of anti-HLA antibodies influences this antibody mediated graft rejection ( 26 , 27 ). Future work that determines whether anti-MHC-I antibodies differ in isotype and their complement binding capacity might help predict which anti-DFT1 immune responses protect against the tumours. Ascertaining any differences between anti-MHC-I antibodies and their protective capacity will help inform current DFT1 vaccine development. Our work shows that DFTD is driving overall loss of MHC-I diversity in devils, with selection for supertypes that are missing the UA locus. This was particularly evident at two sites where there was a clear increase in the frequency of a non-functional UA allele (Fentonbury; Table 1 ; Fig. 3 ) or supertypes 1 and 7 which are not shared with DFT1 (Kempton; Table1; Figure S2). Population level density at Fentonbury is increasing ( 2 ), as are the proportion of older devils at both Fentonbury and Kempton despite the high prevalence of DFT1 (Save the Tasmanian Devil Program Annual Report 2021-2022). While there appears to be some fluctuation in allele frequencies, the observation of no change in the frequency of the non-functional allele and supertype 2 at the only DFT1-free site at the time of sampling (Woolnorth), supports the idea that DFT1 is driving selection ( 26 ) for a different UA genotype to DFT1. Management actions of the species in the wild should not change because of these findings. The overall goal is to maintain enduring Tasmanian devil populations in the wild ( 27 ).Current efforts focused on the use of trial supplementations to support overall genetic diversity in wild populations work ( 22 , 28 ). The release of genetically differentiated individuals to resident populations introduces new genetic variation that reduces relatedness and inbreeding ( 28 ), provides resilience to internal and external parasites, and introduces new MHC alleles that remain for at least three generations post-supplementation ( 22 ). Greater diversity at MHC specifically will provide protection against new emerging diseases. However, we show here that natural selection processes will continue to further restrict MHC diversity in diseased populations. Given the importance of immune fitness for devils’ long-term survival, we recommend the supplementation of wild populations with genetically differentiated individuals to those populations that are known to be under genetic pressure ( 29 ) and have limited gene flow ( 30 ). Support for this comes from the trial Wild Devil Recovery project ( 23 ) that has recently shown supplementation efforts across multiple sites successfully increased overall genetic diversity as well as MHC diversity ( 22 , 28 ). These activities did not result in a change in DFT1 prevalence at these sites ( 22 ). In addition to the supplementation of wild populations both demographically and genetically, another arm of our conservation action is the development of a vaccine to reduce DFT1 prevalence and protect the species ( 31 ). In a previous experimental DFT1 vaccine trial, three vaccinated devils that had developed DFT1 tumours following challenge underwent complete tumour regressions in response to immunotherapy with live MHC-I+ DFT1 cells ( 21 ). Florid T cell infiltration was present in serial biopsies collected from the tumours as they regressed, indicative of cell mediated immune rejection. Serum anti-MHC-I antibodies were also present in these devils during tumour regression. These results suggest a vaccine can help trigger immune mediated rejection of DFT1, however, consistent with the results presented here, these three devils possessed deletions and/ or supertype 7 alleles at UA (Table S4). Development of a DFT1 vaccine that protects devils regardless of MHC type should reduce selective pressure by DFT1. This, alongside supplementation of genetically diverse devils would help to preserve a genetically diverse wild devil population. All evidence is showing that without these interventions the long-term viability of the species hangs in the balance. In conclusion, we show that selection for loss of MHC-I diversity in the Tasmanian devil is occurring in response to DFT1. While this loss of the UA locus may facilitate immune recognition and, in some cases, immune rejection of DFT1, it may also increase the susceptibility of devils to a wide range of current infectious and non-infectious diseases (reviewed in Fox and Seddon ( 23 )), or to diseases that may emerge in the future. High levels of diversity in MHC-I are directly linked to immunological fitness. While it is notoriously difficult to definitively demonstrate pathogen mediated selection for MHC-I ( 32 ), we believe our study is the first to show clear evidence of a pathogen, in this case a transmissible cancer, driving restriction (gene loss) of the MHC-I repertoire in a wildlife species. Materials and Methods Sample collection and non-genetic determinants of an antibody response Serum samples and ear biopsies were collected from Tasmanian devils during routine health monitoring trips by the Save the Tasmanian Devil Program according to previously described field methods ( 2 ). Samples from the 116 wild devils included in this study were collected between 2015 and 2024 from six sites across Tasmania (Fentonbury, Kempton, Stony Head, wukalina, Granville Harbor and Narawntapu National Park). Individual devils were examined and the sex, weight, head width, reproductive status (females only), tooth eruption and wear, and the presence of DFT1 were recorded. A size-adjusted measure of body weight to head width ratio was used (as a measure of body condition; see Farquharson, et al . ( 33 ) for details). Devils were aged using tooth eruption and wear ( 34 ). Only devils two years and older were included in this study as DFT1 prevalence is low in younger devils. For DFT1 status, individuals were categorised as ‘diseased’ when typical tumours were visible and/ or when DFT1 was confirmed by laboratory analysis (e.g. histopathology, fine needle aspirate). All other devils were categorised as ‘healthy’. Forty-eight of the 116 devils in this sample set had DFT1. The five non-genetic parameters of location, sex (male = 0, female = 1), body condition, age and DFT1 status (healthy = 0, diseased = 1) were tested for their correlation with the antibody response (Table S1 for metadata). As four of the sites (Buckland, Narawntapu, Stony Head, wukalina) had been genetically managed through supplementation, pedigree reconstruction was used to ensure the individuals in the dataset were not hybrid (see McLennan, et al . ( 22 ) for details). Detection of serum anti-DFT1 antibodies Serum samples were analysed for antibodies against whole DFT1 cells not expressing surface MHC-I, and whole DFT1 cells treated with interferon gamma (IFN-γ) to upregulate surface expression of MHC-I proteins. The method relied on indirect immunofluorescence and flow cytometry and is described in detail in ( 20 ). In brief, cultured DFT1 cells (cell line C5065) were treated with recombinant devil IFN-γ (5□ng/mL) to induce cell-surface expression of MHC-I, confirmed by flow cytometry with positive staining for the beta 2 microglobulin (B2M) subunit of MHC-I. Serum samples diluted to 1:50 with a flow-cytometry buffer (phosphate-buffered saline with 1% bovine serum albumin and 0.1% sodium azide) were incubated with treated□DFT1 cells, and separately with untreated□DFT1 cells. After washing, cells were incubated with a monoclonal mouse anti-devil IgG antibody (A4-D1-2). Cells were washed, then incubated with a fluorochrome-labelled goat anti-mouse IgG antibody (Alexa Fluor® 647). After a final wash, cells were resuspended in flow-cytometry buffer containing cell-viability dye (4’,6-diamidino-2-phenylindole, dilactate) to allow gating of dead cells, and analysed by flow cytometry (BD Canto II) and FCS Express v7 (DeNovo software). The median fluorescence intensity ratio (MFIR) was used to classify the antibody responses. The MFIR is defined as the median fluorescence intensity (MFI) of DFT1 cells labelled with immune serum divided by the MFI of DFT1 cells labelled with the negative control serum. The negative control serum samples came from captive devils that had never been exposed to DFT1. This ratio accounts for any background serum IgG present and standardises the responses between individual devils. Antibody responses were classified as positive when the MFIR > 1.5 x MFI of the negative control sample ( 20 ). See Fig. 1 for examples of positive and negative antibody responses. MHC-I typing DNA was extracted from devil ear biopsies and DFT1 and DFT2 cell lines using the MagAttract HMW DNA kit (Qiagen, Germany). The three functional domains (exons 2, 3 and 4) of the devil MHC-I genes (Saha -UA, -UB, -UC ) were amplified following a long-read MHC typing method previously described ( 10 ). In brief, the MHC-I genes were amplified using formerly designed forward (/5AmMC6/gcagtcgaacatgtagctgactcaggtcacGTGTCCCCCCCTCCGTCTCAG) and reverse (/5AmMC6/tggatcacttgtgcaagcatcacatcgtagCCTAACTCCCCCTGCTCCTTCTG) primers and the Platinum SuperFi II PCR Master Mix (Invitrogen). Amplicons were indexed using the Barcoded Universal F/R Primers Plate-96v2 (Pacific Biosciences) and the Phusion Hot Start II High Fidelity PCR Master Mix (Thermo Scientific) (see ( 10 ) for PCR conditions). Samples were pooled and purified using AMPure PB magnetic beads (Pacific Biosciences), to keep only the target weight molecules. Libraries were prepared using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences) and sequenced on a PacBio Sequel II platform at the Australian Genome Research Facility. Data processing and MHC genotyping The raw subread data was processed using the PacBio Secondary Analysis Tools on BioConda ( 35 ) (available at https://github.com/PacificBiosciences/pbbioconda ), following the methods described in ( 10 ). In short, circular consensus sequencing (CCS) reads were generated with --by-strand using ccs v6.4.0 and were filtered to keep reads with a minimum of 5 read passes and read quality ≥ 0.995. Reads were demultiplexed using lima v2.7.1, with concatemer and partial reads removed using isoseq refine v3.8.2. The remaining reads were aligned to the devil MHC-I reference sequence (Saha-UA) using pbalign v0.4.1, with a minimum alignment length of 1300 bp, and mapping quality of 254. As the call rate and accuracy of genotyping has been shown to degrade below 250 reads ( 36 ), individuals with fewer than 250 CCS reads were removed. Alleles were called for each individual using Bellerophon ( https://github.com/yuanyuan929/bellerophon ) and assigned allele names from a database of known devil MHC-I alleles ( 10 ). Alleles were further grouped into known supertypes based on their biochemical properties (see ( 10 )). A dataset containing genetic variables was generated to test whether an antibody response is influenced by a devil’s MHC repertoire. As majority of the MHC variation in devils is driven by CNV, the number of MHC alleles per individual (N a ) was included. As heterozygosity may impact an individual’s ability to mount an immune response and is vital to recognising a wide range of pathogens, we included standardised heterozygosity (H s ) as a genetic variable. This was calculated by dividing the individual observed heterozygosity by the mean observed heterozygosity across all samples, using the ‘genhet’ package ( 37 ) in R v4.3.1 ( 38 ). To understand whether a particular gene drives variation in the antibody response, heterozygosity at each gene (coded 1/0 for heterozygote/homozygote), genotypic data (coded as a 0/1 for absence/presence), and supertype data (coded as a 0/1 for absence/presence) were included in the genetic dataset. Only genotypes that were present within at least five individuals were included. We estimated the overall genetic similarity (D AB ) between self and non-self at the allele and supertype level. All individuals included in this study were captured at sites where only DFT1 is currently present. We therefore calculated genetic similarity between the host and DFT1 only, as per ( 15 ). The overall D AB (similarity at the three MHC genes) between an individual and DFT1 was calculated using Wetton’s formula: D AB = 2F AB /(F A + F B ) ( 39 ). Here, F AB is the total number of shared alleles or supertypes between DFT1 and an individual, and F A and F B are the total number of alleles or supertypes of DFT1 (A) and an individual (B). We also tested whether the D AB at each gene had a greater influence on the antibody response than the overall MHC similarity. Modelling the effects of the MHC-I repertoire on antibody responses Generalised Linear Models (GLM) were generated using the ‘glm’ function in R to predict which variable had the strongest effect on the logistic response variable (antibody response). We ran multiple models to understand whether 1. non-genetic variables influenced the antibody response (antibody response ~ age + sex + body condition + disease status + site), 2. MHC-I influenced the antibody response and 3. A particular genotype influenced the antibody response (Supp for all models). We used an information theoretic approach to model selection using AICc (corrected Akaike information criterion) and averaging of the top models following ( 40 ). Models were ranked based on their AICc values, with models <2 AICc from the top model considered to influence the antibody response. Where multiple models best described the data (non-genetic variables), only the top models (Δ AICc < 2) were selected, and the relative importance (RI) of predictors was calculated using the ‘MuMIn 1.47.5’ R package ( 41 ). Only predictors with a RI = 1 (equivalent to the predictor appearing in all top models) were considered well supported. For all genetic variables, only one top model best described the data. Therefore, the model effect sizes, their standard errors and P-values (α=0.05), were used to infer the importance of predictors. Model predictors were standardised by subtracting the mean and diving by one standard deviation. The relationship between -UA genotypes and antibody responses were visualised by clustering devils and DFT1 (based on their -UA genotype) using the ‘Factoextra’ R package ( 42 ). Temporal analysis Changes in supertype frequencies at disease and disease-free sites over time were visualised using R v4.1.1. ( 38 ). The 601 samples were collected between 2012 and 2021. All individuals included in the temporal analysis were non-hybrids. Funding Australian Research Council grants LP180100244 and LP210301148 (K.C.B, Y.C, C.J.H, K.B) Australian Research Council grants LP210301148 (A.S.F, R.J.P) and FT240100092 (A.S.F) Save the Tasmanian Devil Program San Diego Zoo Wildlife Alliance, Wildcare Tasmania Nature Conservation Fund (R.J.P) Federal Group through funds from Saffire Freycinet, University of Tasmania Advancement Office through funds raised by the Save the Tasmanian Devil Appeal (A.S.F, R.J.P), including support from the AAT Kings - Treadright Foundation, Smitten, and Pure Foods Eggs Charitable organisation from the Principality of Liechtenstein (A.S.F, R.J.P) Select Foundation Research Fellowship (A.S.F) Tall Foundation (A.S.F) Australian Research Council grant CE200100012 (K.A.F) Diversity, equity, ethics, and inclusion The authors of this paper include early, mid and late career researchers, in addition to conservation practitioners. All procedures were approved by Animal Ethics Committees from The University of Sydney (research authority project numbers 2019/1562 and 2022/2243) and The University of Tasmania (permit numbers A0014599 and 26159). Data and materials availability All data will be deposited onto Dryad upon acceptance of the manuscript. Any original code is available from the lead contact upon request. Acknowledgements We acknowledge the traditional custodians of the land on which these devil populations live, and pay respects to their elders past and present. Due to the size and complexity of this project there are many people to acknowledge and thank. Thank you to the field teams of the Save the Tasmanian Devil Program who collected samples over the years, particularly Samantha Fox, Billie Lazenby, Bill Brown, Stewart Huxtable, Clare Lawrence, Phil Wise, Sarah Michael, Jodie Elmer and David Pemberton. We also thank Greg Woods and Alexandra Sharland for comments on MHC immunology. We thank Jocelyn Darby and Chrissie Ong of the Wild Immunology Group for assistance with immunology assays and laboratory management.□ We also thank the members of the AWGG lab team, Kim Heasman, Konstanze Gebauer, Elspeth McLennan, and Andrea Schraven who made the map for Figure S1. Footnotes Competing Interest Statement: No competing interests to declare. References 1. ↵ C. X. Cunningham et al. , Quantifying 25 years of disease-caused declines in Tasmanian devil populations: host density drives spatial pathogen spread . Ecol Lett 24 , 958 – 969 ( 2021 ). OpenUrl CrossRef PubMed 2. ↵ B. T. Lazenby et al. , Density trends and demographic signals uncover the long-term impact of transmissible cancer in Tasmanian devils . J Appl Ecol 55 , 1368 – 1379 ( 2018 ). OpenUrl CrossRef PubMed 3. ↵ M. Pearse , K. Swift , Allograft theory: transmission of devil facial-tumour disease . Nature 439 , 549 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 4. ↵ R. J. 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