Mikaliunaite Lina, Synovec Robert E
Department of Chemistry, Box 351700, University of Washington, Seattle, WA, 98195, USA.
Department of Chemistry, Box 351700, University of Washington, Seattle, WA, 98195, USA.
Talanta. 2022 Jul 1;244:123396. doi: 10.1016/j.talanta.2022.123396. Epub 2022 Mar 24.
A computational method for the untargeted determination of cycling yeast metabolites using a comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) dataset is presented. The yeast metabolomic cycle for the diploid yeast strain CEN.PK with a 5 h cycle period relative to the O concentration level is comprehensively examined to determine the metabolites that exhibit cycling. Samples were collected over only two cycles (10 h with a total of 24 time-point sampling intervals at 25 min each) as an experimental constraint. Due to the limited number of cycles expressed in the dataset, a computational method was devised to determine with statistical significance whether or not a given metabolite exhibited a temporal signal pattern that constituted cycling in the context of the 5 h cycle period. The computational method we report compares the experimentally obtained 24 time-point metabolite signal sequences to randomly generated signal sequences coupled with statistically based confidence level LOF metrics to determine whether or not a given metabolite expresses cycling, and if so, what is the phase of the cycling. Initially the GC×GC-TOFMS dataset was analyzed using tile-based Fisher ratio (F-ratio) analysis. Since there were 24 time-point intervals, this constituted 24 sample classes in the F-ratio calculation which produced 672 metabolite hits. Next, application of the computational method determined that there were 210 of the 672 metabolites exhibiting cycling: 55 identified metabolites and 155 unknown metabolites. Furthermore, the 210 cycling metabolites were categorized into four groups, and where applicable, a phase determined: 1 cycle/5 h period (106 metabolites), 2 cycles/5 h period (13 metabolites), spiky pattern (12 metabolites), or multimodal pattern (79 metabolites).
本文介绍了一种计算方法，用于使用全二维气相色谱-飞行时间质谱（GC×GC-TOFMS）数据集非靶向测定循环酵母代谢物。我们全面研究了二倍体酵母菌株CEN.PK相对于氧气浓度水平具有5小时循环周期的酵母代谢组循环，以确定表现出循环的代谢物。作为实验限制，样本仅在两个循环（10小时，共24个时间点采样间隔，每个间隔25分钟）内收集。由于数据集中表达的循环次数有限，设计了一种计算方法，以确定给定代谢物是否在5小时循环周期的背景下表现出构成循环的时间信号模式，且具有统计学意义。我们报告的计算方法将实验获得的24个时间点代谢物信号序列与随机生成的信号序列以及基于统计的置信水平LOF指标进行比较，以确定给定代谢物是否表现出循环，如果是，则循环的相位是什么。最初，使用基于图块的费舍尔比率（F比率）分析对GC×GC-TOFMS数据集进行分析。由于有24个时间点间隔，这在F比率计算中构成了24个样本类别，产生了672个代谢物命中结果。接下来，应用该计算方法确定672种代谢物中有210种表现出循环：55种已鉴定代谢物和155种未知代谢物。此外，将这210种循环代谢物分为四组，并在适用的情况下确定相位：1个循环/5小时周期（106种代谢物）、2个循环/5小时周期（13种代谢物）、尖峰模式（12种代谢物）或多峰模式（79种代谢物）。
