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<p>Understanding dissolved organic matter (DOM) transformation is crucial for comprehending soil biogeochemical cycling. However, the extent that soil microbes mediate DOM transformation at the molecular level, and whether this is regulated by fertilization remain largely unknown. Here we investigated soil DOM transformations under long-term fertilization using Fourier-transform ion cyclotron resonance mass spectrometry, high-throughput sequencing, and machine learning. Fertilization greatly promoted transformation potential of DOM molecules. Organic fertilization increased the mean transformation number of DOM molecules by 260% compared to no-fertilization, while chemical fertilization increased it by 193%. Machine learning indicated that intrinsic DOM molecular characteristics could predict transformation potential, especially for medium- or low-transformation-potential molecules. However, high-transformation-potential DOM molecules were more influenced by soil microorganisms. Our study provides a parameter to characterize potential transformation capacity of DOM molecules, the effects of different fertilization treatments on this potential, and highlights microbial contributions to molecular transformation processes, identifying the key microbial groups.</p><p></p>

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Long-term fertilization promotes the microbial-mediated transformation of soil dissolved organic matter

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月16日

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  • Mingming Xia,
  • Pengfa Li,
  • Jia Liu,
  • Wenjing Qin,
  • Qingjun Dai,
  • Meng Wu,
  • Zhongpei Li,
  • Daming Li,
  • Ming Liu

摘要

Understanding dissolved organic matter (DOM) transformation is crucial for comprehending soil biogeochemical cycling. However, the extent that soil microbes mediate DOM transformation at the molecular level, and whether this is regulated by fertilization remain largely unknown. Here we investigated soil DOM transformations under long-term fertilization using Fourier-transform ion cyclotron resonance mass spectrometry, high-throughput sequencing, and machine learning. Fertilization greatly promoted transformation potential of DOM molecules. Organic fertilization increased the mean transformation number of DOM molecules by 260% compared to no-fertilization, while chemical fertilization increased it by 193%. Machine learning indicated that intrinsic DOM molecular characteristics could predict transformation potential, especially for medium- or low-transformation-potential molecules. However, high-transformation-potential DOM molecules were more influenced by soil microorganisms. Our study provides a parameter to characterize potential transformation capacity of DOM molecules, the effects of different fertilization treatments on this potential, and highlights microbial contributions to molecular transformation processes, identifying the key microbial groups.