Flexibility in energy metabolism supports hypoxia tolerance in Drosophila flight muscle: metabolomic and computational systems analysis Jacob Feala Laurence Coquin, PhD Andrew McCulloch, PhD Giovanni Paternostro, PhD Cardiac Mechanics Research Group, UCSD Bioengineering Degenerative Diseases, Burnham Institute for Medical Research

Systems analysis of hypoxia response Hypoxia is a cause of cell death in many diseases All cells have intrinsic defenses Hypoxia tolerant organisms have highly orchestrated regulation Complex balances –ATP charge –Redox potential –Metabolic intermediates –pH Systems biology to understand and model the complex control systems Hochachka, 2003 Hochachka, 1996

Drosophila as a model for hypoxia research Flies are hypoxia tolerant Simple system, genetic tools and libraries A previous screen found genes required for tolerance [Ref] One gene for hypoxia tolerance was successfully transferred to mammals [Ref] human fly Adams, et. al., 2000

General hypothesis: flexible metabolic regulation major source of hypoxia tolerance –Immediate (minutes) –Global (ATP production, biosynthesis, protein translation) Systems approach (ATP supply) –Metabolomics to find all anaerobic pathways –Flux-balance analysis to simulate pathways under restricted oxygen –Generate specific hypotheses for hypoxia tolerance

1 H NMR spectroscopy of hypoxic fly muscle 0.5% O minutes supervised by Laurence Coquin MAMMALIAN TISSUE:

Concentrations measured by targeted profiling (Chenomx): peak identification, alignment, subtraction Lower confidence group due to spectra overlap Global metabolic profile

1 H NMR spectroscopy of flight muscle at t=0,1,10,60,240 minutes Significant metabolites

Reconstructing the Drosophila metabolic network Database integration –KEGG: metabolic genes, enzymes, reactions, EC numbers, pathways –Flybase: complete genome, proteins, function, compartment, mutant stocks, references Filtered gene index Pathways109 EC numbers437 Genes1322 Genes (mitochondrial)125 Genes (stocks available)507

Network model of central metabolism –162 genes, 143 proteins and 158 reactions –Includes glycolysis, TCA cycle, oxidative phosphorylation, β –oxidation, amino acids –Elementally- and charge- balanced Reconstructing the network Gene-protein-reaction associations Literature and Databases Annotated Genome Stoichiometric matrix Metabolic network reconstruction Drosophila central metabolism

Alanine Acetyl-CoA α -Oxoglutarate Glutamate Cytosol Mitochondria Acetate Acyl-carnitine shuttle Glucose PyruvateLactate Main energetic pathways in model ATP Oxaloacetate NADH Acetyl-CoA Citrate ATP Pyruvate ATP NADH NADH/FADH 2 NADH NADH/FADH 2 O2O2 H2OH2O TCA cycle Oxidative phosphorylation Glycolysis NADH FADH α -GPDH shuttle Products seen in NMR Hypothesized pathways Known Drosophila pathways ATP CO 2 NH 4

Flux-balance analysis Steady state assumption Optimize for objective function Mass and charge balance inherent –ATP supply and demand –Redox potential –pH Particular solution (optimal) Null Space of S Solution space Metabolic network reconstruction S matrix

Simulation conditions - Glucose (and equivalents) only carbon substrate - Lactate, alanine, acetate constrained to NMR fluxes - Varied O2 uptake constraint - Objective: maximize ATP production Flux-balance analysis of hypoxia lac ala ac glc

Hypoxia simulation: 3 pyruvate pathways vs 1 Abbreviations: atp: ATP production co2: CO2 production glc: glucose uptake h: proton production ac: acetate accumulation lac: lactate accumulation ala: alanine accumulation Drosophila (Pseudo-) Mammalian Reduced glucose uptake Stable pH Equivalent ATP

Conclusions ‘Exotic’ anaerobic pyruvate pathways in fly may contribute to hypoxia tolerance New hypotheses to test: alanine and acetate production essential under hypoxia Systems modeling revealed emergent behavior

Candidate genes Genetic perturbation Model Experiment NMR metabonomics Validate Perturbation Analysis of Energy Metabolism in Hypoxic Myocardium Refine

Questions Acknowledgements –Polly Huang –Palsson lab

Future work: Metabolic reconstruction Expand reconstruction to whole-cell myocyte (explore automated tools) Integrate fluxes from isotopomer study Further refine for cardiomyocyte –Cardiac phenotypes of enzyme mutations –Existing heart models (human, mouse) –No biochemical data! In-vitro study? Aim 2

Research Plan: Iterative model building Hypoxic cardiac phenotype of unmeasured genes from modules Metabolomic analysis of control point mutations Detailed follow-up for novel genes of high interest Overexpression with UAS-GAL4 system, cardiospecific promoters Gene deletion with assay to confirm loss of function Transfection to mammals for cardioprotective effects Use the refined model to study cardiac aging –Metabolomics of aging flies –Test hypotheses with the model Loss of metabolic flexibility (flux variability analysis) Loss of regulation at control points Degradation of highly connected enzymes Aim 3

Constraint-based modeling Reaction 3 Flux (v 3 ) Reaction 1 Flux (v 1 ) Reaction 2 Flux (v 2 ) Particular solution (optimal) Null Space of S Solution space Metabolic network reconstruction S matrix Flux balance analysis: Sv = dx/dt S = stoichiometric matrix v = reaction flux vector x = metabolite concentration vector Steady state assumption: Sv = 0

Flux variability