The KEGG pathways of all enzymes with high gains.

Soil health relies on the actions and interactions of an abundant and diverse biological community. Current soil health assessments rely heavily on a suite of soil biological, chemical, and physical indicators, often excluding molecular information. Soil health is critical for sustainable agricultural production, and a comprehensive understanding of how microbial communities provide ecosystem services can help guide management practices. To explore the role of microbial function in soil health, 536 soil samples were collected from 26 U.S. states, representing 52 different crops and grazing lands, and analyzed for various soil health indicators. The bacterial functional profile was characterized using 16S ribosomal RNA gene sequencing paired with PICRUSt2 to predict metagenome functions. Functional data were used as predictors in eXtreme Gradient Boosting (XGBoost), a powerful machine learning algorithm, and enzymes important to soil health indicators were compiled into a Molecular Index of Soil Health (MISH). The overall MISH score significantly correlated with non-molecular measures of soil health and management practice adoption. Additionally, several new enzymes were identified as potential targets to better understand microbial mediation of soil health. This low-cost, DNA-based approach to measuring soil health is robust and generalizable across climates.

土壤健康的维持依赖于丰富多样的生物群落的活动与相互作用。当前土壤健康评估高度依赖一系列土壤生物、化学与物理指标，且往往未纳入分子层面的相关信息。土壤健康对于可持续农业生产至关重要，全面解析微生物群落提供生态系统服务的机制，可为农业管理实践提供指导。为探究微生物功能在土壤健康中的作用，研究团队从美国26个州采集了536份土壤样本，涵盖52种不同作物与牧地，并对多项土壤健康指标进行了检测分析。研究采用16S核糖体RNA基因测序（16S ribosomal RNA gene sequencing）结合PICRUSt2的方法，对细菌功能谱进行表征，以预测宏基因组功能。将功能数据作为预测变量输入高性能机器学习算法极限梯度提升树（eXtreme Gradient Boosting, XGBoost），并将与土壤健康指标密切相关的酶类整合为土壤健康分子指数（Molecular Index of Soil Health, MISH）。整体MISH评分与非分子层面的土壤健康检测结果及管理措施采用情况显著相关。此外，研究还鉴定出多种可用于深入解析土壤健康微生物介导机制的潜在靶标酶。该低成本、基于DNA的土壤健康检测方法稳定性强，且可在不同气候条件下推广应用。