Tryptophan-NAD+ pathway metabolites as putative biomarkers and predictors of peroxisome proliferation
Tryptophan-NAD+ pathway metabolites as putative biomarkers and predictors of peroxisome proliferation
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DOI：
10.1007/s00204-004-0625-5
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发表时间：
2005-04-01
影响因子：
6.1
通讯作者：
Waterfield, CJ
中科院分区：
文献类型：
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作者：
Delaney, J;Hodson, MP;Waterfield, CJ
本研究旨在通过向大鼠给予已知的过氧化物酶体增殖物激活受体α（PPR α），提供关于尿中N-甲基烟酰胺（NMN）和/或其代谢产物N-甲基-4-吡啶酮-3-甲酰胺（4PY）和N-甲基-2-吡啶酮-3-甲酰胺（2 PY）水平升高与过氧化物酶体增殖相关性的进一步信息。（PPARalpha）配体[非诺贝特、邻苯二甲酸二乙酯（DEHP）和长链脂肪酸（LCFA）]和其他被认为通过PPARalpha非依赖性机制调节脂质代谢的化合物（辛伐他汀、肼和氯丙嗪）。尿NMN与过氧化物酶体增殖的标准标志物和血脂参数相关，目的是确定尿NMN是否可用作大鼠过氧化物酶体增殖的生物标志物。本研究的数据也用于验证先前构建的大鼠过氧化物酶体增殖（PP）的多变量统计模型。预测模型基于尿的1H核磁共振（NMR）光谱，使用NMN、4PY和其他内源性代谢物的光谱模式来预测肝细胞过氧化物酶体计数。每种治疗均诱导其类别的药理学（血脂）效应特征，但仅非诺贝特、DEHP和辛伐他汀增加过氧化物酶体数量并升高尿NMN、2 PY和4PY，辛伐他汀对后者仅具有一过性效应。这些化合物还降低了氨基羧基粘康酸-半醛脱羧酶（ACMSDase，EC 4.1.1.45）的mRNA表达，该酶被认为参与调节色氨酸通过该途径的通量，效力顺序为非诺贝特（-10.39倍）> DEHP（-3.09倍）>辛伐他汀（-1.84倍）。在其他处理中，仅LCFA影响ACMSDase（3.62倍降低）和喹啉酸磷酸核糖基转移酶（QAPRTase，EC 2.4.2.19）（-2.42倍）的mRNA表达，而尿NMN排泄没有任何变化。虽然尿NMN浓度与血脂指标无相关性，但与过氧化物酶体计数（r（2）= 0.63）和酰基辅酶A氧化酶活性（r（2）= 0.61）相关。与其他治疗相比，这些相关性因对非诺贝特的大反应而偏倚;然而，数据确实表明了Rhehan-NAD（+）途径和PPAR α依赖性途径之间的关系，使该代谢物成为检测PP的潜在有用生物标志物。为了加强观察到的与Dahan-NAD（+）途径相关的代谢物之间的联系，并更准确地预测PP，将其他尿液代谢物纳入预测统计模型。发现该统计模型使用2倍平均对照过氧化物酶体计数的预定阈值预测了26/27例观察到的PP。该模型还预测了非诺贝特组过氧化物酶体计数的时间依赖性增加，这在考虑使用这种建模来预测尸检样本中观察到的PP发生和进展时很重要。
The present study was designed to provide further information about the relevance of raised urinary levels of N-methylnicotinamide (NMN), and/ or its metabolites N-methyl-4-pyridone-3-carboxamide (4PY) and N-methyl-2-pyridone-3-carboxamide (2PY), to peroxisome proliferation by dosing rats with known peroxisome proliferator-activated receptor alpha ( PPAR alpha) ligands [fenofilbrate, diethylhexylphthalate (DEHP) and long- chain fatty acids (LCFA)] and other compounds believed to modulate lipid metabolism via PPAR alpha-independent mechanisms ( simvastatin, hydrazine and chlorpromazine). Urinary NMN was correlated with standard markers of peroxisome proliferation and serum lipid parameters with the aim of establishing whether urinary NMN could be used as a biomarker for peroxisome proliferation in the rat. Data from this study were also used to validate a previously constructed multivariate statistical model of peroxisome proliferation ( PP) in the rat. The predictive model, based on 1 H nuclear magnetic resonance (NMR) spectroscopy of urine, uses spectral patterns of NMN, 4PY and other endogenous metabolites to predict hepatocellular peroxisome count. Each treatment induced pharmacological ( serum lipid) effects characteristic of their class, but only fenofilbrate, DEHP and simvastatin increased peroxisome number and raised urinary NMN, 2PY and 4PY, with simvastatin having only a transient effect on the latter. These compounds also reduced mRNA expression for aminocarboxymuconate-semialdehyde decarboxylase (ACMSDase, EC 4.1.1.45), the enzyme believed to be involved in modulating the flux of tryptophan through this pathway, with decreasing order of potency, fenofibrate ( -10.39-fold) > DEHP (-3.09-fold) > simvastatin (-1.84- fold). Of the other treatments, only LCFA influenced mRNA expression of ACMSDase (3.62- fold reduction) and quinolinate phosphoribosyltransferase (QAPRTase, EC 2.4.2.19) (-2.42- fold) without any change in urinary NMN excretion. Although there were no correlations between urinary NMN concentration and serum lipid parameters, NMN did correlate with peroxisome count (r(2)= 0.63) and acyl-CoA oxidase activity (r(2)= 0.61). These correlations were biased by the large response to fenofibrate compared to the other treatments; nevertheless the data do indicate a relationship between the tryptophan-NAD(+) pathway and PPAR alpha-dependent pathways, making this metabolite a potentially useful biomarker to detect PP. In order to strengthen the observed link between the metabolites associated with the tryptophan-NAD(+) pathway and more accurately predict PP, other urinary metabolites were included in a predictive statistical model. This statistical model was found to predict the observed PP in 26/27 instances using a pre-determined threshold of 2-fold mean control peroxisome count. The model also predicted a time-dependent increase in peroxisome count for the fenofibrate group, which is important when considering the use of such modelling to predict the onset and progression of PP prior to its observation in samples taken at autopsy.