posted on 2019-09-05, 20:03authored byShuang Zhao, Hao Li, Wei Han, Wan Chan, Liang Li
Chemical
isotope labeling (CIL) liquid chromatography mass spectrometry
(LC-MS) is a powerful technique for in-depth metabolome analysis with
high quantification accuracy. Unlike conventional LC-MS, it analyzes
chemical-group-based submetabolomes and uses the combined results
to represent the whole metabolome. Due to analysis time and cost constraint,
not all submetabolomes can be profiled and thus knowledge of chemical
group classification is important in guiding submetabolome selection.
Herein we report a study of determining the distribution of functional
groups of compounds in a database and then examine how well we can
experimentally analyze the major chemical groups in two representative
samples (i.e., human plasma and yeast). We developed a computer algorithm
to classify chemical structures according to their functional groups.
After removing lipids which are targeted molecules in lipidomic analysis,
inorganic species and other molecules that are unique to drug, food,
plant, and environmental origins, five groups (i.e., amine, phenol,
hydroxyl, carboxyl, and carbonyl) are found to be the dominant classes.
In the databases of MCID (2683 filtered metabolites), HMDB (5506),
KEGG (11598), YMDB (1107), and ECMDB (1462), 94.7%, 85.7%, 86.4%,
85.7%, and 95.8% of the filtered metabolites belong to one or more
of the five groups, respectively. These groups can be analyzed in
four-channel CIL LC-MS where hydroxyls (H), amines and phenols (A),
carboxyls (C), and carbonyls or ketones/aldehydes (K) are separately
profiled as individual channels using dansyl and DmPA labeling reagents.
A total of 7431 peak pairs were detected with 6109 unique-mass pairs
from plasma, while 5629 pairs with 4955 unique-mass pairs were detected
in yeast. Compared to group distributions of database compounds, hydroxyl-containing
metabolites were severely underdetected, which might indicate that
the current method is less than optimal for analyzing this group of
metabolites. As a result, the overall experimental coverage is likely
significantly lower than the database-derived coverage. In short,
this study has shown that high metabolome coverage is theoretically
attainable by analyzing only the H, A, C, and K submetabolomes and
the group classification information should be helpful in guiding
future analytical method development and choices of submetabolomes
to be analyzed.