Genome-wide signatures of complex introgression and adaptive evolution in the big cats.

The great cats of the genus Panthera comprise a recent radiation whose evolutionary history is poorly understood. Their rapid diversification poses challenges to resolving their phylogeny while offering opportunities to investigate the historical dynamics of adaptive divergence. We report the sequence, de novo assembly, and annotation of the jaguar (Panthera onca) genome, a novel genome sequence for the leopard (Panthera pardus), and comparative analyses encompassing all living Panthera species. Demographic reconstructions indicated that all of these species have experienced variable episodes of population decline during the Pleistocene, ultimately leading to small effective sizes in present-day genomes. We observed pervasive genealogical discordance across Panthera genomes, caused by both incomplete lineage sorting and complex patterns of historical interspecific hybridization. We identified multiple signatures of species-specific positive selection, affecting genes involved in craniofacial and limb development, protein metabolism, hypoxia, reproduction, pigmentation, and sensory perception. There was remarkable concordance in pathways enriched in genomic segments implicated in interspecies introgression and in positive selection, suggesting that these processes were connected. We tested this hypothesis by developing exome capture probes targeting ~19,000 Panthera genes and applying them to 30 wild-caught jaguars. We found at least two genes (DOCK3 and COL4A5, both related to optic nerve development) bearing significant signatures of interspecies introgression and within-species positive selection. These findings indicate that post-speciation admixture has contributed genetic material that facilitated the adaptive evolution of big cat lineages

Deliberating stratospheric aerosols for climate geoengineering and the SPICE project

Increasing concerns about the narrowing window for averting dangerous climate change have prompted calls for research into geoengineering, alongside dialogue with the public regarding this as a possible response. We report results of the first public engagement study to explore the ethics and acceptability of stratospheric aerosol technology and a proposed field trial (the Stratospheric Particle Injection for Climate Engineering (SPICE) ‘pipe and balloon’ test bed) of components for an aerosol deployment mechanism. Although almost all of our participants were willing to allow the field trial to proceed, very few were comfortable with using stratospheric aerosols. This Perspective also discusses how these findings were used in a responsible innovation process for the SPICE project initiated by the UK’s research councils

High incidence of metastatic disease in primary high grade and large extremity soft tissue sarcomas treated without chemotherapy

BACKGROUND: The risk of metastasis and the survival in patients with primary extremity soft tissue sarcomas is worse when tumour size is large and the grade of malignancy is high. Such tumours may receive chemotherapy and/or radiation therapy (RTX) for optimising local control. Irradiation can either be applied preoperatively or after tumour resection. The question arises if the kind of RTX in the absence of chemotherapy influences the outcome concerning local control, metastatic disease, survival and complications. METHODS: We retrospectively reviewed the clinical outcome of 233 patients with a primary extremity soft tissue sarcoma treated between 1990 – 2000 with a mean follow-up of 35.8 (4–120) months in our institute. 41 patients had high grade, deep and large tumours (>8 cm), an AJCC stage III (no evidence of metastasis prior to treatment) and were treated with limb salvage surgery and irradiation but stayed without additional chemotherapy. Two groups of patients were compared: the first group received postoperative RTX after tumour resection (n = 33); the second group was treated with preoperative RTX (n = 8). Both groups did not differ concerning clinical parameters. We analysed primary and secondary outcomes. RESULTS: 56% (23/41) of the population developed metastatic disease, 24% (10/41) local recurrence. The risk of metastasis was higher in the group with preoperative irradiation (p = 0.046). The overall (p = 0.0248) and relapse free survival (p = 0.104) were worse in this group. The delay to tumour resection amounted 8 weeks on average in the preoperative group. Local control was not different (p = 0.38) in both study groups. Wound infections and other combined therapy related complications were equally distributed (p = 0.22). CONCLUSION: Without chemotherapy there remains a high risk of metastasis in AJCC grade 3 patients. In high risk patients treated without chemotherapy the elapsed time to tumour resection after preoperative radiation might contribute to the development of metastasis. This outcome may support the thesis that a combination of RTX and offensive multimodal treatment protocols is advantageous in such a subset of patient

Breed-Specific Hematological Phenotypes in the Dog: A Natural Resource for the Genetic Dissection of Hematological Parameters in a Mammalian Species

Remarkably little has been published on hematological phenotypes of the domestic dog, the most polymorphic species on the planet. Information on the signalment and complete blood cell count of all dogs with normal red and white blood cell parameters judged by existing reference intervals was extracted from a veterinary database. Normal hematological profiles were available for 6046 dogs, 5447 of which also had machine platelet concentrations within the reference interval. Seventy-five pure breeds plus a mixed breed control group were represented by 10 or more dogs. All measured parameters except mean corpuscular hemoglobin concentration (MCHC) varied with age. Concentrations of white blood cells (WBCs), neutrophils, monocytes, lymphocytes, eosinophils and platelets, but not red blood cell parameters, all varied with sex. Neutering status had an impact on hemoglobin concentration, mean corpuscular hemoglobin (MCH), MCHC, and concentrations of WBCs, neutrophils, monocytes, lymphocytes and platelets. Principal component analysis of hematological data revealed 37 pure breeds with distinctive phenotypes. Furthermore, all hematological parameters except MCHC showed significant differences between specific individual breeds and the mixed breed group. Twenty-nine breeds had distinctive phenotypes when assessed in this way, of which 19 had already been identified by principal component analysis. Tentative breed-specific reference intervals were generated for breeds with a distinctive phenotype identified by comparative analysis. This study represents the first large-scale analysis of hematological phenotypes in the dog and underlines the important potential of this species in the elucidation of genetic determinants of hematological traits, triangulating phenotype, breed and genetic predisposition

Strategies for implementing genomic selection in family-based aquaculture breeding schemes: double haploid sib test populations

<p>Abstract</p> <p>Background</p> <p>Simulation studies have shown that accuracy and genetic gain are increased in genomic selection schemes compared to traditional aquaculture sib-based schemes. In genomic selection, accuracy of selection can be maximized by increasing the precision of the estimation of SNP effects and by maximizing the relationships between test sibs and candidate sibs. Another means of increasing the accuracy of the estimation of SNP effects is to create individuals in the test population with extreme genotypes. The latter approach was studied here with creation of double haploids and use of non-random mating designs.</p> <p>Methods</p> <p>Six alternative breeding schemes were simulated in which the design of the test population was varied: test sibs inherited maternal (<it>Mat</it>), paternal (<it>Pat</it>) or a mixture of maternal and paternal (<it>MatPat</it>) double haploid genomes or test sibs were obtained by maximum coancestry mating (<it>MaxC</it>), minimum coancestry mating (<it>MinC</it>), or random (<it>RAND</it>) mating. Three thousand test sibs and 3000 candidate sibs were genotyped. The test sibs were recorded for a trait that could not be measured on the candidates and were used to estimate SNP effects. Selection was done by truncation on genome-wide estimated breeding values and 100 individuals were selected as parents each generation, equally divided between both sexes.</p> <p>Results</p> <p>Results showed a 7 to 19% increase in selection accuracy and a 6 to 22% increase in genetic gain in the <it>MatPat</it> scheme compared to the <it>RAND</it> scheme. These increases were greater with lower heritabilities. Among all other scenarios, i.e. <it>Mat, Pat, MaxC</it>, and <it>MinC</it>, no substantial differences in selection accuracy and genetic gain were observed.</p> <p>Conclusions</p> <p>In conclusion, a test population designed with a mixture of paternal and maternal double haploids, i.e. the <it>MatPat</it> scheme, increases substantially the accuracy of selection and genetic gain. This will be particularly interesting for traits that cannot be recorded on the selection candidates and require the use of sib tests, such as disease resistance and meat quality.</p

Effect of non-random mating on genomic and BLUP selection schemes

<p>Abstract</p> <p>Background</p> <p>The risk of long-term unequal contribution of mating pairs to the gene pool is that deleterious recessive genes can be expressed. Such consequences could be alleviated by appropriately designing and optimizing breeding schemes i.e. by improving selection and mating procedures.</p> <p>Methods</p> <p>We studied the effect of mating designs, random, minimum coancestry and minimum covariance of ancestral contributions on rate of inbreeding and genetic gain for schemes with different information sources, i.e. sib test or own performance records, different genetic evaluation methods, i.e. BLUP or genomic selection, and different family structures, i.e. factorial or pair-wise.</p> <p>Results</p> <p>Results showed that substantial differences in rates of inbreeding due to mating design were present under schemes with a pair-wise family structure, for which minimum coancestry turned out to be more effective to generate lower rates of inbreeding. Specifically, substantial reductions in rates of inbreeding were observed in schemes using sib test records and BLUP evaluation. However, with a factorial family structure, differences in rates of inbreeding due mating designs were minor. Moreover, non-random mating had only a small effect in breeding schemes that used genomic evaluation, regardless of the information source.</p> <p>Conclusions</p> <p>It was concluded that minimum coancestry remains an efficient mating design when BLUP is used for genetic evaluation or when the size of the population is small, whereas the effect of non-random mating is smaller in schemes using genomic evaluation.</p

Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules

Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results

Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632–0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register