Understanding the liver under heat stress with statistical learning: an integrated metabolomics and transcriptomics computational approach.

BACKGROUND: We present results from a computational analysis developed to integrate transcriptome and metabolomic data in order to explore the heat stress response in the liver of the modern broiler chicken. Heat stress is a significant cause of productivity loss in the poultry industry, both in terms of increased livestock morbidity and its negative influence on average feed efficiency. This study focuses on the liver because it is an important regulator of metabolism, controlling many of the physiological processes impacted by prolonged heat stress. Using statistical learning methods, we identify genes and metabolites that may regulate the heat stress response in the liver and adaptations required to acclimate to prolonged heat stress. RESULTS: We describe how disparate systems such as sugar, lipid and amino acid metabolism, are coordinated during the heat stress response. CONCLUSIONS: Our findings provide more detailed context for genomic studies and generates hypotheses about dietary interventions that can mitigate the negative influence of heat stress on the poultry industry.

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism.

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.

On the Inverse Correlation of Protein and Oil: Examining the Effects of Altered Central Carbon Metabolism on Seed Composition Using Soybean Fast Neutron Mutants.

Protein and oil levels measured at maturity are inversely correlated across soybean lines; however, carbon is in limited supply during maturation resulting in tradeoffs for the production of other reserves including oligosaccharides. During the late stages of seed development, the allocation of carbon for storage reserves changes. Lipid and protein levels decline while concentrations of indigestible raffinose family oligosaccharides (RFOs) increase, leading to a decreased crop value. Since the maternal source of carbon is diminished during seed maturation stages of development, carbon supplied to RFO synthesis likely comes from an internal, turned-over source and may contribute to the reduction in protein and lipid content in mature seeds. In this study, fast neutron (FN) mutagenized soybean populations with deletions in central carbon metabolic genes were examined for trends in oil, protein, sugar, and RFO accumulation leading to an altered final composition. Two lines with concurrent increases in oil and protein, by combined 10%, were identified. A delayed switch in carbon allocation towards RFO biosynthesis resulted in extended lipid accumulation and without compromising protein. Strategies for future soybean improvement using FN resources are described.

Identifying mechanisms of regulation to model carbon flux during heat stress and generate testable hypotheses.

Understanding biological response to stimuli requires identifying mechanisms that coordinate changes across pathways. One of the promises of multi-omics studies is achieving this level of insight by simultaneously identifying different levels of regulation. However, computational approaches to integrate multiple types of data are lacking. An effective systems biology approach would be one that uses statistical methods to detect signatures of relevant network motifs and then builds metabolic circuits from these components to model shifting regulatory dynamics. For example, transcriptome and metabolome data complement one another in terms of their ability to describe shifts in physiology. Here, we extend a previously described linear-modeling based method used to identify single nucleotide polymorphisms (SNPs) associated with metabolic changes. We apply this strategy to link changes in sulfur, amino acid and lipid production under heat stress by relating ratios of compounds to potential precursors and regulators. This approach provides integration of multi-omics data to link previously described, discrete units of regulation into functional pathways and identifies novel biology relevant to the heat stress response, in addition to generating hypotheses.