矿山酸性废水（AMD）是一个全球性的环境问题，同时也是极端环境生物学研究的重要模式系统。最近十余年，基于16S rRNA基因高通量测序和组学方法的研究极大地拓展了对AMD中原核生物多样性、群落功能和进化的了解。然而，作为群落重要组分并执行重要生态系统功能的真核微生物获得的关注极其有限。本项目将利用高通量测序技术，大规模解析我国不同地理区域AMD中真核微生物群落的18S rDNA序列，结合地球化学参数和地理距离数据，深入揭示这些真核生命的多样性、生态分布格局及其主要驱动因子；在此基础上，通过代表性样品真核群落的宏转录组测序，生成AMD浮游真核微生物的参考基因集，探求其表达特征以及对生物地理和环境变化的响应；最后，基于以上物种和功能数据集，利用人工神经网络方法，构建AMD环境中真核微生物群落组成和功能结构的预测模型。本研究将深入揭示极端AMD环境真核微生物的多样性、群落功能及其适应性机制。

Acid mine drainage (AMD) is a worldwide environmental problem and an important model system for the study of life in extreme environments. The last decade has witnessed a great progress in the study of the diversity, community functions and evolution of prokaryotic microbes in AMD systems. However, the eukaryotes have not been extensively investigated while they play an important role in the composition and functions of AMD community. In this study, we will perform a comprehensive analysis of the 18S rDNA sequences and geochemical parameters from diverse AMD sites across China to gain insights into the diversity, ecological patterns and associated driving factors of these microorganisms. Comparative metatranscriptomic analyses will then be conducted on representative communities to build a reference gene set of AMD planktonic eukaryotic microorganisms. Community gene expression profiles and their responses to the variations of biogeography and environmental variables will be explored. With these species and functional datasets, a modeling approach based on artificial neural networks (ANN) will be applied to predict the microbial community composition and functional structure in response to environmental changes in the AMD systems. These efforts will provide novel insights into the diversity, community functions and adaptive mechanisms of eukaryotes in extreme AMD environments.