已经出现了将细菌16SrRNA基因扩增子数据与基于原核参考基因组的功能基因联系起来的计算方法。本研究旨在验证或反驳功能基因预测工具在评估和比较实验治疗之间的群落功能的适用性。诱导根际微生物群落组成和功能的快速或慢速响应。小麦和大麦的根际样本是连续两年在活跃和成熟生长阶段从有机和常规农田中收集的,并采用全球变化实验设施的环境或未来气候处理。通过16SrRNA基因扩增子测序确定细菌群落组成,以及与碳有关的五种胞外酶的活性(β-葡萄糖苷酶,纤维二糖水解酶,和木糖苷酶),氮(N-乙酰氨基葡萄糖苷酶),和磷(酸性磷酸酶)循环测定。结构群落数据用于使用Tax4Fun和PanFP预测根际群落的功能模式。随后,将预测与测量的活动进行比较。尽管不同的处理主要驱动群落组成(植物生长阶段)或测量的酶活性(农业系统),这些预测以定性而非定量的方式反映了治疗中的模式。测量值和预测值之间的大多数差异来自植物生长阶段(快速群落响应),其次是农业管理和气候(社区反应较慢)。因此,我们的结果表明,预测工具适用于动态较小的环境系统中土壤群落功能的比较调查。重要性将土壤微生物群落结构与其功能联系起来,这对维持生态系统的健康和服务很重要,仍然具有挑战性。除了结构群落分析的巨大进步,功能等价物,例如宏基因组学和超转录组学,仍然是时间和成本密集型的。最近的计算方法(Tax4Fun和PanFP)旨在根据参考基因组从结构社区数据预测功能。尽管这些工具的可用性已经得到宏基因组数据的证实,到目前为止,缺少预测函数和测量函数之间的比较。因此,这项研究包括对这些工具在不同环境条件下的性能的广泛现实测试,包括相关的全球变化因素(土地利用和气候)。这项工作为预测工具在不同已建立的土壤生态系统中比较土壤群落功能的适用性提供了有价值的验证,并提出了它们在解开给定群落结构所提供的广泛功能方面的可用性。
Computational approaches that link bacterial 16S rRNA gene amplicon data to functional genes based on prokaryotic reference genomes have emerged. This study aims to validate or refute the applicability of the functional gene prediction tools for assessment and comparison of community functionality among experimental treatments, inducing either fast or slow responses in rhizosphere microbial community composition and function. Rhizosphere samples of wheat and barley were collected in two consecutive years at active and mature growth phases from organic and conventional farming plots with ambient or future-climate treatments of the Global Change Experimental Facility. Bacterial community composition was determined by 16S rRNA gene amplicon sequencing, and the activities of five extracellular enzymes involved in carbon (β-glucosidases, cellobiohydrolase, and xylosidase), nitrogen (N-acetylglucosaminidase), and phosphorus (acid phosphatase) cycles were determined. Structural community data were used to predict functional patterns of the rhizosphere communities using Tax4Fun and
PanFP. Subsequently, the predictions were compared with the measured activities. Despite the fact that different treatments mainly drove either community composition (plant growth phase) or measured enzyme activities (farming system), the predictions mirrored patterns in the treatments in a qualitative but not quantitative way. Most of the discrepancies between measured and predicted values resulted from plant growth stages (fast community response), followed by farming management and climate (slower community response). Thus, our results suggest the applicability of the prediction tools for comparative investigations of soil community functionality in less-dynamic environmental systems. IMPORTANCE Linking soil microbial community structure to its functionality, which is important for maintaining health and services of an ecosystem, is still challenging. Besides great advances in structural community analysis, functional equivalents, such as metagenomics and metatranscriptomics, are still time and cost intensive. Recent computational approaches (Tax4Fun and
PanFP) aim to predict functions from structural community data based on reference genomes. Although the usability of these tools has been confirmed with metagenomic data, a comparison between predicted and measured functions is so far missing. Thus, this study comprises an expansive reality test on the performance of these tools under different environmental conditions, including relevant global change factors (land use and climate). The work provides a valuable validation of the applicability of the prediction tools for comparison of soil community functions across different sufficiently established soil ecosystems and suggest their usability to unravel the broad spectrum of functions provided by a given community structure.