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<p>Untargeted metabolomics can comprehensively map the chemical space of a biome, but is limited by low annotation rates (&lt; 10%). We used chemical characteristics vectors, consisting of molecular fingerprints or chemical compound classes, predicted from mass spectrometry data, to characterize compounds and samples. These chemical characteristics vectors (CCVs) estimate the fraction of compounds with specific chemical properties in a sample. Unlike the aligned MS1 data with intensity information, CCVs incorporate the chemical properties of compounds, allowing chemical annotation to be used for sample comparison. Thus, we identified compound classes differentiating biomes, such as ethers which are enriched in environmental biomes, while steroids enriched in animal host-related biomes. In biomes with greater variability, CCVs revealed key clustering compound classes, such as organonitrogen compounds in animal distal gut and lipids in animal secretions. CCVs thus enhance the interpretation of untargeted metabolomic data, providing a quantifiable and generalizable understanding of the chemical space of natural biomes.</p> Graphical Abstract <p></p>

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Chemical characteristics vectors map the chemical space of natural biomes from untargeted mass spectrometry data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月26日

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  • Pilleriin Peets,
  • Aristeidis Litos,
  • Kai Dührkop,
  • Daniel R. Garza,
  • Justin J. J. van der Hooft,
  • Sebastian Böcker,
  • Bas E. Dutilh

摘要

Untargeted metabolomics can comprehensively map the chemical space of a biome, but is limited by low annotation rates (< 10%). We used chemical characteristics vectors, consisting of molecular fingerprints or chemical compound classes, predicted from mass spectrometry data, to characterize compounds and samples. These chemical characteristics vectors (CCVs) estimate the fraction of compounds with specific chemical properties in a sample. Unlike the aligned MS1 data with intensity information, CCVs incorporate the chemical properties of compounds, allowing chemical annotation to be used for sample comparison. Thus, we identified compound classes differentiating biomes, such as ethers which are enriched in environmental biomes, while steroids enriched in animal host-related biomes. In biomes with greater variability, CCVs revealed key clustering compound classes, such as organonitrogen compounds in animal distal gut and lipids in animal secretions. CCVs thus enhance the interpretation of untargeted metabolomic data, providing a quantifiable and generalizable understanding of the chemical space of natural biomes.

Graphical Abstract