Kuang Jialiang, Huang Linan, He Zhili, Chen Linxing, Hua Zhengshuang, Jia Pu, Li Shengjin, Liu Jun, Li Jintian, Zhou Jizhong, Shu Wensheng
State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen University, Guangzhou, People's Republic of China.
Institute for Environmental Genomics and Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA.
ISME J. 2016 Jun;10(6):1527-39. doi: 10.1038/ismej.2015.201. Epub 2016 Mar 4.
Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities.
预测群落组成和功能属性对环境变化的响应动态是群落生态学的一个重要目标，但仍然是一个重大挑战，尤其是在微生物生态学领域。在此，我们以一个物种丰富度较低的模型系统为研究对象，通过16S核糖体RNA焦磷酸测序和综合微阵列（GeoChip），探究了中国东南部40个酸性矿山排水（AMD）微生物群落的分类和功能结构的空间分布。尽管存在大规模的地理隔离，但仍观察到了类似的优势微生物谱系和关键功能基因的环境依赖模式。功能和系统发育β多样性显著相关，而功能代谢潜能受环境条件和群落分类结构的强烈影响。我们使用基于人工神经网络的先进建模方法，成功预测了分类和功能动态，与相对微生物丰度（相似度66.8）相比，代谢潜能的预测准确率显著更高（平均Bray-Curtis相似度87.8），这意味着在功能基因水平而非分类水平上可能能更好地预测天然AMD微生物群落。此外，推断出在酸性更强和金属含量更高的条件下，许多关键生态功能（如氮和磷利用、金属抗性和应激反应）相关基因的相对代谢潜能会增加，这表明这些特殊群落中存在关键的应激适应策略。总体而言，我们的研究结果表明，自然选择而非地理距离在塑造AMD微生物群落的分类和功能模式中起着更关键的作用，这些模式可以通过建模方法轻松预测，并且基于模型的方法对于更好地理解天然嗜酸微生物群落至关重要。
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