Genome-Scale Model Reveals Metabolic Basis of Biomass Partitioning in a Model Diatom

Abstract

Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.

Fig 1. Metabolic network reconstruction workflow.

In step one we obtained a draft reconstruction based on P. tricornutum’s genome annotation and reference reconstructions. This draft reconstruction was manually curated using several resources such as an improved genome annotation, subcellular localization predictions and external databases. All reactions were elementally and charge balanced, QC/QA was performed and a biomass objective function was defined before transforming the reconstruction into a computational model. In an iterative process, the in silico predictions are compared with experimental observations to validate and improve the metabolic model.

Fig 2. Reconstruction characteristics iLB1027_lipid.

(A) Reactions per subsystem. Most reactions are involved in lipid metabolism. Our FTIR measurements underline the fact that the lipids make up the highest fraction of biomass. Due to the presence of multiple compartments and the fact that many pathways are split among compartments, many reactions are attributed to intracellular transport. The modeling subsystem contains ATP maintenance, biomass, demand, sink, and exchange reactions. (B) Percent reactions and metabolites per compartment. Most reactions and metabolites are present in the cytosol, followed by chloroplast and mitochondria in the case of reactions and mitochondria and chloroplast for metabolites. Peroxisome, extracellular space, and thylakoid contain less than 5% and 8% of all reactions and metabolites in the reconstruction, respectively.

Fig 3. Subcellular localization prediction pipeline.

Schematic representation of the implemented subcellular localization prediction pipeline for Phaeodactylum tricornutum adapted from previous work [57]. Subcellular compartments are given in ellipses and bioinformatics programs are displayed in rectangles. Our added steps are highlighted in gray. The ER retention signal is (K/D)-(D/E)-E-L in the protein C-terminal region. A protein is categorized as peroxisomal if the signal (S/A/C)-(K/R/H)-(L/M) or S-S-L is found in the C-terminal region.

Fig 4. FTIR spectrum and culture data.

A typical FTIR spectrum for Phaeodactylum tricornutum is shown in (A). Peaks corresponding to lipids, proteins and carbohydrates are highlighted (see Table A in S1 File for specific wavelengths). Panel (B) shows the growth curve and photosynthetic efficiency of the culture used for model calibrations and the biomass objective function. The decline in F_v/F_m indicates the onset of nitrogen starvation (n = 1). Percent dry weight of the cells in terms of carbohydrates, lipids, and proteins according to FTIR spectra and the calibrated linear model (n = 5, error bars represent five independent FTIR scans) is displayed in (C).

Fig 5. Genes in reconstruction over predicted genes in genome against genome size for selected eukaryotic metabolic reconstructions.

The three reconstructions with the highest ratio of genes in reconstruction per genes in genome are highlighted. bna572+ has a comparable ratio as iLB1025 and iLB1027_lipid, iTO977 has a higher ratio. Compared to iTO977 and bna572+, iLB1025 and iLB1027_lipid contain more reactions. The number of reactions in the respective reconstructions is used to scale the circle diameters. Note the discontinuous x-axis. Abbreviations: AraGEM: Arabidopsis thaliana [73]; bna572+: Brassica napus [74]; AlgaGEM: Chlamydomonas reinhardtii [75]; iRC1080: Chlamydomonas reinhardtii [39]; iRS1563: Zea mays [76]; iLB1025 and iLB1027_lipid: Phaeodactylum tricornutum, this study; iTO977: Saccharomyces cerevisiae Sc288 [77]; Recon2: Homo sapiens [78]; iMM1415: Mus musculus [79].

Fig 6. Light-dependent carbon partitioning.

(A) Simulations indicated as photon uptake exceeds carbon uptake, excess redox potential is stored in triacylglycerol. The saturation of carbon uptake is shown in black. (B) Percent of carbon fixed in TAG against percent of metabolite flow through NADHOR (vNADHOR; EC 1.6.5.3,1.6.99.3) over metabolite flow through PSI (vPSI; EC 1.97.1.2) at a super-saturating photon uptake of 22 mM. According to our simulations TAG accumulation is inversely proportional to energetic coupling. TAG accumulation is prohibited when at least 35% of photosynthetically fixed electrons are redirected to the mitochondria.

Fig 7. Chloroplastic ornithine cycle as revealed by the model.

Metabolic network usage of a chloroplastic ornithine cycle is shown under a saturating photon constraint of 16 mM allowing maximum carbon uptake. Minor reactants and products are omitted for visual clarity (i.e., water, protons and phosphate). Metabolite and reaction abbreviation suffixes indicate cellular compartment; c, cytosol; h, chloroplast; m, mitochondria. Reversible reactions are indicated by arrowheads at both ends. The filled arrowhead indicates the direction in which the reaction is running, i.e. from substrate (open arrowhead) to product (filled arrowhead). Abbreviations used: ACOAT, acetylornithine transaminase; AGK, acetylglutamate kinase; AGPR, N-acetyl-δ-glutamyl-phosphate reductase; GACT, glutamate N-acetyltransferase; GLNA, glutamine synthase; GLTS, glutamate synthase (ferredoxin dependent); GLUDH2, glutamine dehydrogenase (NAD dependent); GLUSA, glutamate semialdehyde degradation (spontaneous); OAT, ornithine aminotransferase; P5CDH, 1-pyrroline-5-carboxylate dehydrogenase; acorn, N-acetylornithine; acglu, N-acetyl-L-glutamate; acg5p, N-acetyl-L-glutamate 5-phosphate; acg5sa, N-Acetyl-L-glutamate 5-semialdehyde; adp, ADP; akg, α-ketoglutarate; atp, ATP; fdxox, ferredoxin (oxidized); fdxrd, ferredoxin (reduced); gln__L, L-glutamine; glu__L, L-glutamate; glu5sa, L-glutamate 5-semialdehyde; nad, NAD⁺; nadh, NADH; nadp, NADP⁺; nadph, NADPH; nh4, ammonium ion; orn, ornithine; 1pyr5c, (S)-1-Pyrroline-5-carboxylate.