Level 5: Quantitative Models
INTEGRATE 43
INTEGRATE is a computational pipeline that integrates metabolomics and
transcriptomics data to characterize multi-level metabolic regulation.
The pipeline first computes differential reaction expression from
transcriptomic data and uses constraint-based modeling to predict if the
differential expression of metabolic enzymes directly originates
differences in metabolic fluxes. In parallel, the pipeline uses
metabolomics to predict how differences in substrate availability
translate into differences in metabolic fluxes. It is an
upscaling/inference algorithm.
This algorithm uses level 4 stoichiometries as constraints for flux
balance analysis, RNA levels as enzyme abundance proxies to predict
metabolomic fluxes then compare it with the observed data. The prior
information is a further curated subset of RECON3D metabolomic
construction called ERGO2. Once the metabolomic and transcriptomic data
is mapped to the network intermediary scores are calculated for Feasible
Flux Distributions (based on static analysis), Reaction Activity Scores
(based on RNA levels) and Reaction Propensity Score (based on substrate
levels). Agreement between these metrics, calculated by Variation
Concordance Analysis is the final output and can be used for both
explanation/extraction and upscaling.
The pipeline was applied to a set of immortalized normal and cancer
breast cell lines. The results showed that the pipeline was able to
identify metabolic reactions that are regulated at both the metabolic
and gene expression levels. The pipeline was also able to identify
metabolic reactions that are differentially regulated in cancer cells
compared to normal cells.
SUMMER 44
SUMMER (Shiny Utility for Metabolomics and Multiomics Exploratory
Research) uses reaction rate potentials to perform pathway enrichment
analysis on metabolomic data. SUMMER uses level 4 metabolomic networks
from the KEGG database7. This is a network upscaling
method from level 4 to level 5 as a first step of quantitative modeling.
SUMMER uses reaction rate potentials to model the feedback effects
between an enzyme, its substrate(s), and its product(s). It also infers
the catalytic activity of each enzyme using integrated transcriptomics
or proteomics data. The method then uses this integrated model to
understand the change in reaction rate potentials between a perturbed
condition and a reference condition. The resulting ratio of the
resulting reaction rate potentials between a perturbed condition and a
reference condition is then bootstrapped to calculate a ranking score
between each reaction. Using the rank scores, SUMMER identifies the
“hotspot” reactions in the network.
The authors applied SUMMER to re-analyze a metabolomic and
transcriptomic dataset generated from a mouse model of accelerated aging
and dementia. They wanted to understand the pathways that were altered
by a neuroprotective compound. They found that treatment with this
compound was associated with an increase in acetyl-CoA activity and an
enrichment of TCA cycle activity.