Li Tao, Li Yuhua, Salas Erika, Tian Ye, Liu Xiaofei, Wanek Wolfgang
Division of Terrestrial Ecosystem Research, Department of Microbiology and Ecosystem Science, Centre for Microbiology and Environmental Systems Science, University of Vienna, Djerassiplatz 1, A-1030 Vienna, Austria.
Doctoral School in Microbiology and Environmental Science, University of Vienna, Djerassiplatz 1, A-1030 Vienna, Austria.
Anal Chem. 2025 Jun 24;97(24):12679-12689. doi: 10.1021/acs.analchem.5c01358. Epub 2025 Jun 12.
Bound amino compounds (amino acid and amino sugar polymers) comprise a significant proportion (∼40%) of soil organic nitrogen and therefore represent an essential source of nitrogen for plant and microbial nutrition. The analysis of their content and isotope enrichment still represents a significant challenge due to the low isotope enrichment levels reached under near-native soil conditions and the lack of isotopically labeled standards for some key amino compounds. In this study, we used both a C-labeled and an unlabeled amino acid mixture to establish isotope calibration curves for 16 amino compounds, using the 6-aminoquinolyl--hydroxysccinimidyl carbamate (AQC) derivatization method and ultrahigh-performance liquid chromatography with high-resolution Orbitrap mass spectrometry (UPLC-Orbitrap MS). Molecular ions of AQC-derivatives for all standard amino compounds were identified at the expected / values of the respective isotopologues. The isotope calibration curves exhibited excellent linear fits across the whole C enrichment range and polynomial fits in the low C enrichment range ( > 0.990). However, the polynomial fitting terms differed between individual amino acids. Subsequently, we developed equations to relate the calibrated regression terms to the physicochemical properties of the respective amino acids, here mainly the ratio of amino compound-C atoms to total C atoms in AQC-amino compound derivatives. Based on these regressions, we could ultimately predict isotope calibration curves for those amino compounds unavailable as C labeled standards, for example, muramic acid, hydroxyproline, and diaminopimelic acid. To test the model accuracy, we compared the outcomes of measured calibrations with predicted calibrations for amino acids where we had isotopically enriched standards. The results of linear regression between measured and predicted data were excellent, where was >0.97, and mean absolute (percentage) deviations, MAD and MAPD, were 0.334 and 15.8%. Finally, we applied both standard and predicted calibration curves to low C amended soil samples and unlabeled controls to test the applicability of our model. The limit of detection (LOD) as the minimum detectable atom % C incorporation of amino compounds ranged from 0.0003 to 0.14 atom % among different amino compounds. This general predictive model can be used to comprehensively quantify isotope enrichments across the entire soil amino compound profile, including amino sugars and proteinogenic and nonproteinogenic amino acids, providing valuable insights for a better understanding of the overall fate of different amino compounds in soils and other complex environmental systems.
结合态氨基化合物(氨基酸和氨基糖聚合物)占土壤有机氮的很大比例(约40%),因此是植物和微生物营养的重要氮源。由于在接近天然土壤条件下达到的同位素富集水平较低,以及一些关键氨基化合物缺乏同位素标记标准品,对其含量和同位素富集的分析仍然是一项重大挑战。在本研究中,我们使用一种碳标记和一种未标记的氨基酸混合物,采用6-氨基喹啉-N-羟基琥珀酰亚胺基氨基甲酸酯(AQC)衍生化方法和超高效液相色谱与高分辨率轨道阱质谱联用(UPLC-Orbitrap MS),为16种氨基化合物建立同位素校准曲线。在各自同位素异构体的预期质荷比处鉴定出所有标准氨基化合物的AQC衍生物的分子离子。同位素校准曲线在整个碳富集范围内呈现出极好的线性拟合,在低碳富集范围内呈现多项式拟合(R²>0.990)。然而,各个氨基酸的多项式拟合项有所不同。随后,我们建立了方程,将校准回归项与相应氨基酸的物理化学性质相关联,这里主要是AQC-氨基化合物衍生物中氨基化合物-碳原子与总碳原子的比例。基于这些回归,我们最终可以预测那些没有碳标记标准品的氨基化合物的同位素校准曲线,例如胞壁酸、羟脯氨酸和二氨基庚二酸。为了测试模型的准确性,我们将测量校准结果与有同位素富集标准品的氨基酸的预测校准结果进行了比较。测量数据与预测数据之间的线性回归结果非常好,R²>0.97,平均绝对(百分比)偏差,即MAD和MAPD,分别为0.334和15.8%。最后我们将标准校准曲线和预测校准曲线应用于低碳改良土壤样品和未标记对照,以测试我们模型的适用性。不同氨基化合物的检测限(LOD),即氨基化合物最低可检测的原子%C掺入量,范围为0.0003至0.14原子%。这个通用的预测模型可用于全面量化整个土壤氨基化合物谱中的同位素富集,包括氨基糖、蛋白质氨基酸和非蛋白质氨基酸,为更好地理解不同氨基化合物在土壤和其他复杂环境系统中的整体归宿提供有价值的见解。