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Transcriptional Orchestration of the Global Cellular Response of a Model Pennate Diatom to Diel Light Cycling under Iron Limitation - PubMed

  • ️Fri Jan 01 2016

Transcriptional Orchestration of the Global Cellular Response of a Model Pennate Diatom to Diel Light Cycling under Iron Limitation

Sarah R Smith et al. PLoS Genet. 2016.

Erratum in

Abstract

Environmental fluctuations affect distribution, growth and abundance of diatoms in nature, with iron (Fe) availability playing a central role. Studies on the response of diatoms to low Fe have either utilized continuous (24 hr) illumination or sampled a single time of day, missing any temporal dynamics. We profiled the physiology, metabolite composition, and global transcripts of the pennate diatom Phaeodactylum tricornutum during steady-state growth at low, intermediate, and high levels of dissolved Fe over light:dark cycles, to better understand fundamental aspects of genetic control of physiological acclimation to growth under Fe-limitation. We greatly expand the catalog of genes involved in the low Fe response, highlighting the importance of intracellular trafficking in Fe-limited diatoms. P. tricornutum exhibited transcriptomic hallmarks of slowed growth leading to prolonged periods of cell division/silica deposition, which could impact biogeochemical carbon sequestration in Fe-limited regions. Light harvesting and ribosome biogenesis transcripts were generally reduced under low Fe while transcript levels for genes putatively involved in the acquisition and recycling of Fe were increased. We also noted shifts in expression towards increased synthesis and catabolism of branched chain amino acids in P. tricornutum grown at low Fe whereas expression of genes involved in central core metabolism were relatively unaffected, indicating that essential cellular function is protected. Beyond the response of P. tricornutum to low Fe, we observed major coordinated shifts in transcript control of primary and intermediate metabolism over light:dark cycles which contribute to a new view of the significance of distinctive diatom pathways, such as mitochondrial glycolysis and the ornithine-urea cycle. This study provides new insight into transcriptional modulation of diatom physiology and metabolism across light:dark cycles in response to Fe availability, providing mechanistic understanding for the ability of diatoms to remain metabolically poised to respond quickly to Fe input and revealing strategies underlying their ecological success.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Physiological response of P. tricornutum to low Fe across diel cycles.

Diel patterns in cell division and fluctuations of Fv/Fm in P. tricornutum grown at 20 pM, 40 pM, and 400 pM Fe′ over 12:12 L:D cycles, lights on = 9am, lights off = 9pm. (A) Bar plot of the time point averaged percentage of dividing cells shown with standard deviation across biological replicates. (B) Time point averaged Fv/Fm. Legend is shared for both plots and shaded background indicates dark time points.

Fig 2
Fig 2. Genome wide transcriptome dynamics and transcript level expression of Fe status biomarker genes.

The transcriptome of semi-continuous cultures of P. tricornutum grown over diel cycles (12:12) at Fe limited (20 and 40 pM Fe′) and Fe replete (400 pM Fe′) conditions is shown. (A) Heat map shows standardized transcript abundance (standardized RPKM across Fe conditions) for each gene. Genes organized into co-expression modules (n = 8418 genes, labeled with module color names) and ordered by hierarchical clustering results. (B) Percentage of genes from each expression module belonging to response type groups (light, dark, low Fe, or high Fe determined with Skillings-Mack) indicated. Black bars indicate genes previously identified as Fe-responsive in [19]. Identities of genes and module and response type assignments can be found in S1 Dataset. (C) Time point averaged transcript abundance (RPKM) is shown, plotted on a log scale, error bars omitted for clarity (D) Standardized transcript abundance (calculated independently for each Fe condition) is also plotted to illustrate dark phase expression maxima. Legend includes gene names and Phatr3 PIDs. Background shading indicates dark time points. *Binned low Fe by Skillings-Mack, †Binned low Fe by EdgeR

Fig 3
Fig 3. Expression of cell cycle progression-related genes, ribosome biogenesis, and cyclins and cyclin-dependent kinases (CDKs).

(A) Data show average standardized transcript abundance (across Fe conditions) in WGCNA co-expression modules enriched for gene functions associated with cell cycle progression (tan–selected genes see S1 Dataset, darkgreen) and ribosome biogenesis (darkgrey) genes at 20, 40, and 400 pM Fe′ with shaded area indicating one standard deviation (S1 Dataset for gene IDs). (B) Cyclin and CDK transcript abundance. Waves of co-expressed cyclins and CDKs were identified with hierarchical clustering (S10 Fig). Standardized cluster averages (within each Fe condition, positive values only) are shown. (C) Transcript abundance (RPKM) of wave 1 cyclins and dsCYC2-regulating transcription factors (bZIP10 and Aureo1a) are shown. Dashed lines indicate data are plotted on the secondary y-axis.

Fig 4
Fig 4. Light harvesting antenna protein expression.

(A) Heat map shows min/max transcript abundance (RPKM across Fe conditions, plotted on a log scale). Dendogram shows hierarchical clustering (Pearson correlation) of RPKM values with corresponding module assignment from the WGCNA clustering analysis. Expression clades are labeled to indicate Fe-response types (high Fe, low Fe). Dominant protein family composition of expression clades (Lhcf/Lhcr and Lhcx/Lhcz) is also indicated. Gene identifiers (Phatr3 protein IDs) can be found in S3 Fig. (B) Selected expression patterns of Fe-sensitive light harvesting complex antenna proteins from the Lhcx, ac2, and Lhcz families. Lhcz (Phatr3_J16481) is from the red module and is indicated on the heat map with a red arrow.

Fig 5
Fig 5. Coordinated expression of central carbon metabolic pathways.

(A) Biological replicate averaged transcript abundance (for each time point) shown for genes from the Calvin Benson cycle, mitochondrial glycolysis pathway, cytosolic glycolysis pathway, and TCA cycle for each Fe condition (error bars omitted for clarity). Series labeled with gene names (see S4 Dataset for abbreviations) and Phatr3 PID. (B) Schematic representation of reactions catalyzed by GAPDH and PGK across the cytosol, mitochondria, and chloroplast (shown with 4 membranes). NADPH and ATP produced from light harvesting depicted in green, and NAD(P)H and ATP regenerated in the cytosol or mitochondria depicted in magenta. Yellow/blue highlighted arrows indicate day/night upregulation respectively. Solid arrows indicate likely transport processes, dashed arrow indicates hypothesized carbon skeleton transport. GAP: glyceraldehyde 3-phosphate, 3PG: 3-phosphoglycerate, FAB: fatty acid biosynthesis. (C) Transcript abundance for chloroplast, mitochondrial, and cytosolic targeted isoforms of glyceraldehyde phosphate dehydrogenase (GAPDH) and phosphoglycerate kinase (PGK) are plotted (TPI-GAPDH: triosephosphate isomerase-GAPDH fusion protein).

Fig 6
Fig 6. Fluctuations in metabolite composition of P. tricornutum.

Abundant and/or condition-modulated metabolites are shown. Heat map colors scale to metabolite relative minimum and maximum across Fe conditions. Data are clustered hierarchically (Pearson correlation). Abundant metabolites (upper 50th percentile of average relative abundance) are indicated with black bars. Metabolites significantly more abundant at high Fe/low Fe are indicated (High Fe: 400pM Fe′, Low Fe: 20 & 40 pM Fe′ combined). Metabolites significantly more abundant during the light/dark phases are labeled (Light or Dark).

Fig 7
Fig 7. Dark phase expression of catabolism and pyruvate hub enzymes.

(A) Biological replicate-averaged transcript abundance (for each time point) shown for genes from the β-oxidation (mitochondrial and peroxisomal combined), glyoxylate cycle, branched chain amino acid (BCAA) degradation pathways and mitochondrial pyruvate hub (error bars omitted for clarity). Series labeled with gene names (see S4 Dataset for abbreviations) and Phatr3 PID. (B) Pathway diagram showing reactions and co-reactants (H+ omitted) of the pyruvate hub. Each arrow represents a unique isozyme. PEP = phosphoenolpyruvate, OAA = oxaloacetate, PK = pyruvate kinase, ME = malic enzyme, MDH = malate dehydrogenase, PEPCK = phosphoenolpyruvate carboxykinase, PEPC = phosphoenolpyruvate carboxylase.

Fig 8
Fig 8. Diel expression of the N-assimilation pathway.

Diagram shows the N-assimilation pathway and enzyme subcellular localizations with the general pattern of expression of genes indicated. Compartments are labeled, and the chloroplast is shown with four membranes. NRT = nitrate transporter, NR = nitrate reductase, NOD = nitric oxide dioxygenase, FNT = formate-nitrite transporter, Fd-NIR = ferredoxin-dependent nitrite reductase, NIR = NADH-dependent nitrite reductase, GS(II/III) = glutamine synthetase, GOGAT = glutamine oxoglutarate aminotransferase, NMO = nitronate monooxygenase. See S6 Fig for PIDs and normalized transcript abundance.

Fig 9
Fig 9. Urea cycle and chloroplast ornithine cycle expression and metabolites.

(A) Pathway diagram shows the localization of reactions in the urea cycle, distributed between the mitochondria and the cytosol, and the chloroplast ornithine cycle [116,119]. Compartments are labeled, and the chloroplast is shown with four membranes. Dashed arrow indicates hypothesized transport. unCPS = carbamoyl-phosphate synthase, OTC = ornithine transcarbamylase, ASuS = arginosuccinate synthase, ASL = arginosuccinate lyase, FUM = fumarase, ARG = arginase, ACOAT = acetylornithine aminotransferase, NAGS = N-acetyl glutamate synthase, GACT = glutamate acetyltranferase, AGK = acetylglutamate kinase, AGPR = acetyl-glutamyl phosphate reductase. (B) Metabolite abundance (replicate averaged) over diel cycles in iron-replete conditions, shown normalized to maximum value within each metabolite. Average transcript abundance (across Fe conditions) for arginase is also plotted (with standard deviation).

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This work was supported by National Science Foundation (www.nsf.gov) grants MCB-1024913 (AEA and CLD) and OCE-0727997 and OCE-0727733 (AEA and ABK), United States Department of Energy Genomics Science program (http://genomicscience.energy.gov) grants (DE-SC00006719 and DE-SC0008593; AEA and CLD) and Gordon and Betty Moore Foundation (https://www.moore.org) grant GBMF3828 (AEA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.