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Combining small-volume metabolomic and transcriptomic approaches for assessing brain chemistry.
Knolhoff AM, Nautiyal KM, Nemes P, Kalachikov S, Morozova I, Silver R, Sweedler JV.
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The integration of disparate data types provides a more complete picture of complex biological systems. Here we combine small-volume metabolomic and transcriptomic platforms to determine subtle chemical changes and to link metabolites and genes to biochemical pathways. Capillary electrophoresis-mass spectrometry (CE-MS) and whole-genome gene expression arrays, aided by integrative pathway analysis, were utilized to survey metabolomic/transcriptomic hippocampal neurochemistry. We measured changes in individual hippocampi from the mast cell mutant mouse strain, C57BL/6 Kit(W-sh/W-sh). These mice have a naturally occurring mutation in the white spotting locus that causes reduced c-Kit receptor expression and an inability of mast cells to differentiate from their hematopoietic progenitors. Compared with their littermates, the mast cell-deficient mice have profound deficits in spatial learning, memory, and neurogenesis. A total of 18 distinct metabolites were identified in the hippocampus that discriminated between the C57BL/6 Kit(W-sh/W-sh) and control mice. The combined analysis of metabolite and gene expression changes revealed a number of altered pathways. Importantly, results from both platforms indicated that multiple pathways are impacted, including amino acid metabolism, increasing the confidence in each approach. Because the CE-MS and expression profiling are both amenable to small-volume analysis, this integrated analysis is applicable to a range of volume-limited biological systems.
Figure 1. Schematic for
experimental workflow: metabolomic and transcriptomic
analyses were combined to enable the characterization of related chemical
changes between genotypes.
Figure 2. Metabolites
with significant differences in relative abundance
in pairwise comparisons of Wsh/Wsh, Wsh/+, and +/+ mice. Bars show the mean of normalized abundances measured
in the biological replicates for each genotype; error bars represent
standard error; p-values from the corresponding Student’s
t-tests are tabulated to the left. Statistically significant differences
in the levels of these analytes (p ≤ 0.05)
are also supported by gene expression data. Metabolites associated
with differentially expressed metabolic pathways identified by gene
expression analysis are marked with black bold in the table. An asterisk
indicates that the intensity is reflective of the second peak in the
isotopic series due to its high concentration. Key: WT (W), Wsh/+ (H), Wsh/Wsh (S).
Figure 3. Hierarchical clustering
of affected metabolites based on their
participation in pathways containing differentially expressed genes.
Pathways (rows) were clustered against metabolites (columns) monitored
by CE–ESI-MS using the UPGMA clustering algorithm with the
Euclidean distance as a similarity measure. A yellow block in the
diagram indicates that a given metabolite is involved in the corresponding
pathway containing mast cell-associated genes found through the expression
analysis.
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