Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida, United States of America.
Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, Florida, United States of America.
PLoS One. 2018 Aug 2;13(8):e0201299. doi: 10.1371/journal.pone.0201299. eCollection 2018.
The Major Histocompatibility Complex (MHC) is a critical element in mounting an effective immune response in vertebrates against invading pathogens. Studies of MHC in wildlife populations have typically focused on assessing diversity within the peptide binding regions (PBR) of the MHC class II (MHC II) family, especially the DQ receptor genes. Such metrics of diversity, however, are of limited use to health risk assessment since functional analyses (where changes in the PBR are correlated to recognition/pathologies of known pathogen proteins), are difficult to conduct in wildlife species. Here we describe a means to predict the binding preferences of MHC proteins: We have developed a model positional scanning library analysis (MPSLA) by harnessing the power of mixture based combinatorial libraries to probe the peptide landscapes of distinct MHC II DQ proteins. The algorithm provided by NNAlign was employed to predict the binding affinities of sets of peptides generated for DQ proteins. These binding affinities were then used to retroactively construct a model Positional Scanning Library screen. To test the utility of the approach, a model screen was compared to physical combinatorial screens for human MHC II DP. Model library screens were generated for DQ proteins derived from sequence data from bottlenose dolphins from the Indian River Lagoon (IRL) and the Atlantic coast of Florida, and compared to screens of DQ proteins from Genbank for dolphin and three other cetaceans. To explore the peptide binding landscape for DQ proteins from the IRL, combinations of the amino acids identified as active were compiled into peptide sequence lists that were used to mine databases for representation in known proteins. The frequency of which peptide sequences predicted to bind the MHC protein are found in proteins from pathogens associated with marine mammals was found to be significant (p values <0.0001). Through this analysis, genetic variation in MHC (classes I and II) can now be associated with the binding repertoires of the expressed MHC proteins and subsequently used to identify target pathogens. This approach may be eventually applied to evaluate individual population and species risk for outbreaks of emerging diseases.
主要组织相容性复合体（MHC）是脊椎动物针对入侵病原体产生有效免疫反应的关键因素。对野生动物种群中 MHC 的研究通常集中于评估 MHC 类 II（MHC II）家族中肽结合区（PBR）的多样性，尤其是 DQ 受体基因。然而，这种多样性的衡量标准对于健康风险评估的作用有限，因为在野生动物物种中进行功能分析（即 PBR 中的变化与已知病原体蛋白的识别/病理学相关）非常困难。在这里，我们描述了一种预测 MHC 蛋白结合偏好的方法：我们利用基于混合物的组合文库的力量来探测不同 MHC II DQ 蛋白的肽景观，从而开发了一种模型位置扫描文库分析（MPSLA）。使用 NNAlign 提供的算法来预测为 DQ 蛋白生成的肽集合的结合亲和力。然后，这些结合亲和力用于反向构建模型位置扫描文库筛选。为了测试该方法的实用性，将模型筛选与人类 MHC II DP 的物理组合筛选进行了比较。为来自佛罗里达大西洋沿岸和印度河泻湖的宽吻海豚的 DQ 蛋白生成了模型文库筛选，并与 Genbank 中来自海豚和其他三种鲸目动物的 DQ 蛋白的筛选进行了比较。为了探索来自 IRL 的 DQ 蛋白的肽结合景观，将确定为活性的氨基酸组合成肽序列列表，这些列表用于挖掘数据库中已知蛋白质中的代表。预测与 MHC 蛋白结合的肽序列在与海洋哺乳动物相关的病原体相关蛋白质中出现的频率是显著的（p 值<0.0001）。通过这种分析，现在可以将 MHC（I 类和 II 类）的遗传变异与表达 MHC 蛋白的结合库联系起来，并随后用于识别靶病原体。这种方法最终可能被应用于评估个体种群和物种爆发新兴疾病的风险。
