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尿液排泄蛋白的 1H NMR 信号是概率商归一化的偏倚来源。

H NMR Signals from Urine Excreted Protein Are a Source of Bias in Probabilistic Quotient Normalization.

机构信息

Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.

National Phenome Centre, Imperial College London, Hammersmith Campus, IRDB Building, London W12 0NN, United Kingdom.

出版信息

Anal Chem. 2022 May 17;94(19):6919-6923. doi: 10.1021/acs.analchem.2c00466. Epub 2022 May 3.

Abstract

Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels ( = 0.423, < 2.2 × 10). This correlation was significantly reduced ( = 0.163, < 2.2 × 10) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.

摘要

为了解释尿液代谢谱,必须将尿液稀释的变化归一化。概率商归一化(PQN)在代谢组学中经常使用,但对光谱谱图中很大一部分(>50%)共享的系统变化敏感。在使用氢核磁共振(NMR)光谱时,尿液蛋白的存在会抬高光谱基线,并对最终的谱图产生实质性影响。利用住院 COVID-19 患者的 ISARIC 4C 研究中采集的点尿样的 H NMR 谱测量,我们确定 PQN 系数与观察到的蛋白水平显著相关(= 0.423,< 2.2 × 10)。在用一种称为小分子增强光谱(SMolESY)的计算方法去除蛋白性基线后,这种相关性显著降低(= 0.163,< 2.2 × 10),该方法用于抑制大分子信号。这些结果突出了蛋白尿是 H NMR 代谢谱研究中常见但被忽视的偏倚来源,可以通过在估计归一化系数之前使用 SMolESY 或其他大分子信号抑制方法来有效缓解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/edc6/9118196/cdda8f204da0/ac2c00466_0001.jpg

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