ANALYSIS OF CARBON AND ENERGY FLUXES DURING NITROGEN DEPRIVATION IN CHLAMYDOMONAS  By  Matthew Thomas Juergens               A DISSERTATION  Submitted to  Michigan State University in partial fulfillment of the requirements  for the degree of  Plant BiologyÑDoctor of Philosophy  2016ABSTRACT  ANALYSIS OF CARBON AND ENERGY FLUXES DURING NITROGEN DEPRIVATION IN CHLAMYDOMONAS  By  Matthew Thomas Juergens  Algae have been considered as sources for renewable bioenergy due their ability to accumulate high cellular percentages of biomass as energy dense molecules, namely starch and triacylglycerols. The accumulation of these compounds is most readily induced through exposing cells to nutrient deprivation, especially from the macronutrient nitrogen. Multiple large scale studies have been carried out to study the metabolism behind starch and oil accumulation in an effort to increase production; however, no sufficient strategy for over accumulation has been determined. Additionally, little is known about the actual motivations for algae to accumulate these compounds in the first place. Current hypothesized roles for these molecules range from aiding recovery from nutrient deprivation to providing sinks for overflowing photosynthetic energy as metabolism slows. The exact relationship between carbon accumulation and photosynthesis remains unresolved. To study the interaction between photosynthesis (energy and carbon input) and carbon accumulation (stored carbon and energy), Chlamydomonas reinhardtii was chosen as a model. Chlamydomonas is the premier algal model system and can accumulate significant amounts of starch and TAG. Second, it can grow auto-, mixo-, and heterotrophically enabling experimentation under a range of light and media conditions.   Further, extensive information available on the genome, transcriptome, proteome, and metabolic environment of Chlamydomonas allow for deeper interpretation of systems biology approaches.  !!!!!!"!In this dissertation I explore the relationship between photosynthesis and carbon accumulation during nitrogen deprivation from the perspective of carbon and energy fluxes. Through systems biology methods, physiological measurements, and biomass analysis across multiple stages of nutrient deprivation I show that carbon accumulation is not dependent upon photosynthesis under mixotrophic conditions, pointing to a storage role for starch and TAG, most likely to aid nutrient recovery. Further, through flux modeling efforts using measured constraints, I report on the complexity of starch and TAG accumulations and how they change over the course of nutrient deprivation. This work suggests deeper regulatory interactions between starch and TAG accumulation based upon cellular programming and not simply energy overflows. It is my sincere hope that this work will help guide future engineering efforts  concerning biofuel production in algae.                        !!!!!!"#!TABLE OF CONTENTS    LIST OF TABLESÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.......É...vii  LIST OF FIGURESÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉ...viii  KEY TO ABBREVIATIONSÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..xi  CHAPTER- 1 Algae and Their Use in Biofuel Production: A Literature ReviewÉÉÉÉ...É1  ForewordÉÉÉÉ.ÉÉ..ÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..É.....2    Evolution and Phylogeny of Algae and their relationship to Higher Plants and Cyanobacteria...ÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉ...ÉÉ... 3 Chlamydomonas reinhardtiiÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉÉÉ......7 Algal aquaculture, biofuels and high value productsÉÉÉÉÉÉÉÉÉÉÉÉ....É9             Nutrient stress and the induction of storage metabolism in algaeÉÉÉÉÉÉÉ..É.15 Starch biosynthesis in Chlamydomonas and how it changes during N deprivationÉÉ18 Triacylglycerol metabolism during nutrient deprivationÉÉÉÉÉÉÉÉÉÉ...É..20 Why do algae make TAG under N limitation?............................................................... 25 Recovery from nutrient deprivationÉ....É.ÉÉÉÉ..É.ÉÉÉÉÉÉÉÉÉÉ.... 26 REFERENCES ÉÉ.. ÉÉÉÉ......ÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉ.ÉÉÉ..É.29  CHAPTER- 2 The Regulation of Photosynthetic Structure and Function During  Nitrogen Deprivation in Chlamydomonas reinhardtii ÉÉÉ.ÉÉÉÉÉÉÉÉÉ..48  ABSTRACTÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉ...49  INTRODUCTIONÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ 50 RESULTSÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...É.. 54 Changes in the levels of transcripts and proteins of photosynthesis...ÉÉ....É.54 Pigment levels and their regulation..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ... 61 Thylakoid lipid levels and composition...ÉÉÉÉÉÉÉÉÉÉÉÉÉ...É. 65       Energy capture and conversion is down regulated during N- DeprivationÉÉ....ÉÉÉÉ.ÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉ..É. 69 Oxygen evolutionÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉ. 71 Total PSI turnover rate falls during N deprivationÉ..ÉÉ.ÉÉÉ.ÉÉÉÉ.. 71        Electrochromic shift measurements to probe thylakoid proton motive  force and fluxes..ÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉ..É 73 Photosynthetic carbon fixationÉ.ÉÉ.ÉÉÉ....ÉÉÉÉÉÉÉÉÉÉ..... 76 Starch SynthesisÉ..ÉÉÉÉ...ÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉ.... 76 DISCUSSIONÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉÉ..ÉÉ 78 Regulation of Photosynthetic ActivityÉÉÉÉÉÉÉÉÉÉÉ..ÉÉ..ÉÉ 78 Differential Regulation of PSI and PSIIÉÉÉÉÉÉÉ...ÉÉÉÉÉÉÉ....79                        Photosynthesis is Down Regulated in a Controlled and Orderly MannerÉÉ... 80            METHODSÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.....83                   CultureÉ...ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉ...É.83       TranscriptomicsÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉ...ÉÉ. 83 !!!!!!#!ProteomicsÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.É.84 Annotation and Expression AnalysisÉÉÉÉ..ÉÉ..ÉÉÉÉÉÉÉÉ.É. 84 Chlorophyll ConcentrationÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉ.85 Phytol and Isotope LabelingÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉ.É 85 Carotenoid QuantitationÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉ.... 86 Lipid AnalysesÉÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉ.. 87 Electron MicroscopyÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉ..87 Oxygen Evolution and ConsumptionÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉ.....88 In Vivo SpectroscopyÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉ.ÉÉ....88 77K Chl Fluorescence Emission SpectraÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉ...90 13C Bicarbonate LabelingÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉ...... 90 Starch AnalysisÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉ..91  ACKNOWLDEGEMENTSÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.. 92  REFERENCESÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.É 93  CHAPTER- 3 The Relationship of Triacylglycerol and Starch Accumulation to Carbon and  Energy Flows During Nutrient Deprivation in ChlamydomonasÉÉÉÉÉÉÉÉÉ.ÉÉ 105 ABSTRACTÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉ..106 INTRODUCTIONÉÉÉÉÉ...ÉÉÉ...ÉÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉ.107 RESULTSÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ118 Growth Rates in N Replete MediaÉÉÉÉÉ..ÉÉÉÉÉÉÉÉ...ÉÉÉ112   BiomassÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉ.É.114   The Accumulation of TAG and StarchÉÉÉ.ÉÉÉÉÉÉÉÉÉÉ..É..116   13C labeling ResultsÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉ..ÉÉ119  Light Absorption, Utilization Efficiency and DissipationÉÉÉÉ..É.ÉÉ..125 Photosynthetic FluxesÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉ..É127 Carbon Assimilation and ReleaseÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉ....129 ANALYSIS AND DISCUSSIONÉÉÉÉ.ÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉ...131 CONCLUSIONSÉÉÉÉÉÉÉÉÉÉÉ.É.ÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.142 METHODSÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉÉÉÉÉ.ÉÉ...É.ÉÉÉÉÉÉ144 CulturingÉÉ.ÉÉ..ÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉ.É144  Chlorophyll ConcentrationÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉ144  Ash Free Dry WeightsÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.145  Carbon and Nitrogen ContentsÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉ..É..145  Lipid AnalysesÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...É..145  Oxygen Production and Consumption RatesÉÉÉ.ÉÉÉÉÉÉ...ÉÉÉ146  In Vivo Fluorescence SpectroscopyÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉ.147  CO2 Production and Consumption RatesÉÉÉ.ÉÉÉ.ÉÉÉÉÉÉÉÉ148  Starch AnalysisÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉ148 Acetate AnalysisÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..148   Calculation of Carbon and Energy BalancesÉÉÉÉ..ÉÉÉÉÉÉ..ÉÉ.149 13C labelingÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..149 ACKNOWLDEGEMENTSÉÉÉ.É.ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ150  REFERENCESÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉ.151   !!!!!!#"!CHAPTER- 4  Flux Balance Analysis of Chlamydomonas during Nitrogen DeprivationÉ..159 ABSTRACTÉÉÉ..É...ÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉ160 INTRODUCTIONÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉÉÉÉÉ161  METHODSÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.165   Model CurationÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉÉÉÉÉÉ.165   Flux Balance AnalysisÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉ.169 Cell Cultivation, Growth, and Constraint MeasurementsÉÉÉ..ÉÉ.....É..170 RESULTSÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ....171   Nitrogen Replete ConditionsÉÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉÉÉ..É..181   Photosynthesis During N DeprivationÉ.ÉÉÉ.ÉÉÉÉÉÉÉÉÉÉÉ.184   Nitrogen Deprivation MetabolismÉÉÉ.É.ÉÉÉÉÉÉÉ...ÉÉÉÉÉ188 DISCUSSIONÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...É.189 MODEL CHALLENGESÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉ....191 FUTURE DIRECTIONSÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉ.193 ACKNOWLEDGEMENTSÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉ...ÉÉÉÉÉ..196 REFERENCESÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉ..ÉÉÉÉÉÉ..197  CHAPTER-5 Conclusions and Future DirectionsÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...É.204 CARBON ACCUMULATIONS DURING NUTRIENT LIMITATIONS É.............205 PHOTOSYNTHESIS AND NUTRIENT DEPRIVATIONÉÉÉÉÉÉÉÉ.....É.206 OVERFLOW HYPOTHESIS CONCLUSIONSÉÉÉÉÉÉÉÉÉÉÉ.É..É.. 207 MODELING OF NUTRIENT DEPRIVED METABOLISMÉÉÉÉÉ.ÉÉÉÉ.209 FUTURE EXPERIMENTS AND DIRECTIONÉÉÉÉÉÉÉÉÉ..ÉÉÉÉ.....210 FINALÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉÉ.ÉÉÉ.212 APPENDIXÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉ..214  REFERENCESÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..É..221 !!!!!!#""!LIST OF TABLES   Table 3.1. Overflow Hypothesis predictions and verification ÉÉÉÉÉÉÉÉÉ...ÉÉ..133  Table 4.1. Changes made to model related to photosynthesisÉÉÉÉÉÉÉÉÉ.ÉÉ.É166  Table 4.2 Photosynthetic FluxesÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉ..187 !!!!!!#"""!LIST OF FIGURES   Figure 1.1. Algal PhylogenyÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉ..ÉÉÉÉÉÉÉÉ4  Figure 1.2. Starch Synthesis in ChlamydomonasÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉ.É 19  Figure 1. 3. Glycerolipid biosynthesis in ChlamydomonasÉÉÉÉÉÉÉ.ÉÉÉÉÉÉ.. 23  Figure 2.1.  Photosynthetic transcripts and protein expressionÉÉ.ÉÉÉ.ÉÉÉÉÉÉ... 55  Figure 2.2. The levels of transcripts (A) and proteins (B) of photosynthetic pigment  metabolism and starch metabolismÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉ.ÉÉ.. 57  Figure 2.3. Statistical analysis of photosynthetic transcriptsÉÉÉÉÉÉÉÉ.ÉÉÉÉÉ 58  Figure 2.4. Chlorophyll concentrations and synthesisÉÉ...ÉÉÉÉÉÉÉÉ..ÉÉÉ..É 62  Figure 2.5. Carotenoid levelsÉÉÉÉÉÉÉÉÉÉÉ...ÉÉÉÉÉÉÉÉÉ..ÉÉÉ.... 64  Figure 2.6. Relative changes in thylakoid lipid levelsÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉ.. 66  Figure 2.7. Chloroplast Imaging with electron microscopy...ÉÉÉÉÉÉÉÉÉÉ..ÉÉ. 67  Figure 2.8. Photosynthetic Fluorescence Functional MeasurementsÉÉÉÉÉÉ....ÉÉ.... 68  Figure 2.9. qLÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...ÉÉ.ÉÉÉ.70  Figure 2.10. Oxygen evolution, ECS, and P700 spectroscopyÉÉÉÉÉÉÉÉÉ..ÉÉ.É72  Figure 2.11. PSII inhibited P700/ !H+ during N deprivationÉÉÉÉÉÉÉÉÉÉÉÉ.....74  Figure 2.12. 13C IncorporationÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉ.. 75  Figure 2.13. Starch AnalysisÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.É.É 77  Figure 3.1. The main Carbon and Energy inputs and outputs of Chlamydomonas cellsÉÉ.110  Figure 3.2. Specific growth rates during exponential growth in nitrogen replete mediaÉÉ.113  Figure 3.3. Cellular Biomass and % Carbon and Nitrogen before and during nitrogen  deprivationÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..É...115  Figure 3.4. Carbon to Nitrogen (C:N) ratios during N deprivationÉÉÉÉ...............ÉÉ...116 !!!!!!"$! Figure 3.5. Starch, FAME and TAG levels before and during nitrogen deprivation..É....É.118  Figure 3.6. 13C labeling of fatty acids and starch during N deprivation in cells cultured in TAP media at 160 µmol photons m-2 s-1ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉÉ..121  Figure 3.7. 13C labeling of C16:3 fatty acids during N deprivation in cells cultured in TAP media at 160 µmol m-1 s-1 ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..É...ÉÉ123  Figure 3.8. 13C labeling of C18 fatty acids during N deprivation in cells cultured in TAP media at 160 µmol m-1 s-1ÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉÉ124  Figure 3.9. Chlorophyll, Photosynthetic efficiency and Non Photochemical  QuenchingÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉ126  Figure 3.10. Oxygen evolution and electrochromic shift measurementsÉÉÉ.ÉÉÉÉ....128  Figure 3.11. Carbon assimilation ratesÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉ..É..129  Figure 3.12. Cellular carbon accumulation rates before and during the first 24hrs after nitrogen deprivationÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...........................ÉÉ..135  Figure 3.13.Accumulated Carbon V.S Potential absorbed lightÉÉÉÉÉ..ÉÉÉÉÉ.É138  Figure 3.14. µg Starch and FAME per uptaken acetateÉÉÉÉÉÉÉÉÉÉ..É..ÉÉÉ140  Figure 3.15 Light energy intake and energy stored in accumulated biomass (as heat of  combustion) in TAP (panels A and C) and HS (panels B and D)ÉÉÉÉÉÉÉÉÉÉ.....141  Figure 4.1 Photosynthetic fluxes allowed in modelÉÉÉÉÉÉÉÉÉ...ÉÉÉÉÉ.É.168  Figure 4.2. A. Nitrogen Replete Mixotrophic GrowthÉÉÉÉÉÉÉÉÉÉÉÉÉ....É.172  Figure 4.2. B. Nitrogen Deplete Mixotrophic Growth, 24 hrs post N DeprivationÉÉ...É...173  Figure 4.2. C. Nitrogen Deplete Mixotrophic Growth, 96 Hrs Post DeprivationÉÉÉ.É...174  Figure 4.2 D. Nitrogen Replete Autotrophic GrowthÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ...175  Figure 4.2 E. Nitrogen Deplete Autotrophic Growth, 24 hrs post N DeprivationÉÉÉ..É.176   Figure 4.2 F. Nitrogen Deplete Autotrophic Growth, 96 Hrs Post DeprivationÉÉÉ.ÉÉ.177  Figure 4.2 G. Nitrogen Replete Heterotrophic GrowthÉÉÉÉÉÉÉÉ..ÉÉÉÉÉÉ..178  Figure 4.2 H. Nitrogen Deplete Heterotrophic Growth, 24 hrs post N DeprivationÉÉÉ....179 !!!!!!$! Figure 4.2. I. Nitrogen Deplete Heterotrophic Growth, 96 Hrs Post Deprivation...É...ÉÉ.180  Figure 4.3. Photon demand v.s  growth rate at 5, 40, 15, and 160 µmol photon/m2/sÉÉÉ..181  Figure 4.4. Photon demand v.s  relative absorbed light at 5, 40, 15, and 160 µmol  photon/m2/sÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉÉ.ÉÉ..É.183  Figure 4.5. Light Demand predictions during N deprivationÉÉÉÉÉÉÉ.É..ÉÉ..É..184  Figure 4.6. FBA Predicted Photon Use v.s. Calculated photons availableÉÉÉÉÉ...ÉÉ186  Figure 5.1. Changes in cell counts, biomass and Chl after nitrogen readditionÉÉ..ÉÉ.....216 . Figure 5.2. TAG and Starch Degradation during N recoveryÉÉÉÉÉÉÉÉÉ.ÉÉÉ.217  Figure 5.3.  Photosynthetic measures during N recoveryÉÉÉÉÉÉ.É..ÉÉÉ....ÉÉ..218  Figure 5.4. Acetate uptake during nutrient recoveryÉÉÉÉÉÉÉÉÉÉÉ.É.ÉÉÉ..219   !!!!!!$"!KEY TO ABBREVIATIONS   (CCM) Carbon Concentrating Mechanism  (CDW) Cell Dry Weight  (Chl) Chlorophyll  (CEF) Cyclic electron flow  (DIRK) Dark Interval Relaxation Kinetics  (DGDG) Digalactosyldiacylglycerol  (ECS) Electrochromic Shift  (G.E.O) Gross Evolved Oxygen rates  (LED) Light Emitting Diodes  (LHC) Light Harvesting Centers  (LEF) Linear electron flow  (MGDG) Monogalactosyldiacylglycerol  (N) Nitrogen  (OH) Overflow Hypothesis (P700+)  Oxidized primary donor of photosystem I  (PG) Phosphatidylglycerol  (PS) Photosystem  (PMF) Proton motive force  (P700) PSI Primary Electron Donor  (QA) PSII Primary Quinone A (qL) Percent of open QA based upon the lake model !!!!!!$""!(qP ) Percent of open QA based upon the puddle model (!II) Quantum Efficiency of PS II photochemistry in the light  (ROS) Reactive Oxygen Species  (SQGD) Sulfoquinovosyldiacylglycerol  (SCX) Strong Cation Exchange  (vH+) Total Proton Efflux  (TAG) Triacyl glycerol  (TAP) Tris-Acetate-Phosphate   !!!%!!   CHAPTER- 1   Algae and Their Use in Biofuel Production: A Literature Review !!!&!!Foreword  Algae are a diverse group of photosynthetic eukaryotes that play deeply important roles in the natural world. Ranging in size from microscopic single cells to multicellular bodies over 50m in length, these primarily aquatic organisms provide one of the largest primary food sources for the planet. In addition, through their photosynthetic activities (accounting for 25% of global CO2 fixation (Jardillier et al. 2010)), these organisms have affected the global carbon balances, oxygen levels, and global ecosystems. During the last two hundred years, algae have also been employed in research on cell physiology, photosynthesis, and biofuel production (Beijerinck 1904, Ehrenberg 1838, Merrill 1916, Van Iterson et al. 1921, Williams 2012). The work described in this thesis concerns the flows of carbon and energy in the model alga Chlamydomonas reinhardtii during nitrogen deprivation. Studies focusing on photosynthetic structure and function, storage compound accumulation and system wide metabolic fluxes are described in chapters 2-4 in the form of publication manuscripts. An analysis of photosynthesis, biomass, and storage compound utilization during nitrogen recovery is described in chapter 5. Conclusions and synthesis are presented in the final chapter. While each chapter introduces the specific literature and methods used, this introduction chapter aims to provide a broader context. First, there is a brief discussion of algal evolution and its relation to both higher plants and cyanobacteria. Next there is a discussion of algal culturing for industrial purposes as well as products and services that are derived from them. Following, there is an introduction to nutrient deprivation responses in algae, carbon compounds accumulated (starch and triacylglycerols) and the metabolism that supports those accumulations. The final section reviews what is known about algae during nutrient recovery and the roles that accumulated carbon compounds may play in the process. !!!'!!Evolution and phylogeny of Algae and their relationship to Higher Plants and Cyanobacteria Algae are a diverse group of eukaryotic organisms originating from one or more endosymbiotic events between heterotrophic eukaryotic cells and photosynthetic cyanobacteria (Bhattacharya et al. 1998, Bjorkman et al. 1987). Algae arising from initial (primary) endosymbiotic events include the green algae (Chlorophyta) which are thought to have given rise to higher plants (Leliaert et al. 2012), red algae (Rhodophyta), and the less studied Glaucophyta (Figure 1.1). Secondary endosymbiotic events with heterotrophic eukaryotes engulfing and forming an endosymbiosis with green algae gave rise to Euglenoids and Chlorarachniophytes (Keeling 2010). Similarly, endosymbiotic events with red algae serving as the endosymbiont gave rise to other important algal phyla including the Heterokonts (golden algae, diatoms, and brown algae), Haptophyta (coccolithophores), Cryptomonads, and the Dinoflagellates (Keeling 2009). Algae from single endosymbiotic events have two membranes around their chloroplast (inner and outer membranes) while algae arising from further endosymbiosis will have 3 or 4 (peri- and epi-plastidic membranes) depending on the number of events and how the event occurred or if one of the membranes was subsequently lost (Keeling 2009).  !!!(!!  Figure 1.1. Algal Phylogeny.  All algae are derived from the endosymbiosis of a cyanobacteria and a heterotrophic eukaryote. Primary endosymbiosis gave rise to the Glaucophytes, the Chlorophytes (Green algae), and Rhodophytes (Red algae). The green algae underwent secondary endosymbiosis giving rise to the Euglinids and the Chlorarachinophytes. Red algae also underwent secondary endosymbiosis, generating the Cryptomonades, Haptophytes, Heterokonts, and the Alveolates which comprises the ciliates, Apicomplexa and the Dinoflagellates. The figure was adapted from Archibald and Keeling, 2002, Trends in Genetics. !)*+,-.+/012"+!+,3!41012-02-56"/!789+2*-01!:2";+2*!7,3-<*;."-<"<!=6-3-56*0+!>1/-,3+2*!7,3-<*;."-<"<!)2*50-;-,+31!4+50-56*01!41012-9-,0!?@#1-@+01<!)"@"+01<!?5"/-;5@1$+!A",-B@+C1@@+01<!D@+8/-56*0+!)6@-2-56*0+!>1/-,3+2*!7,3-<*;."-<"<!78C@1,"3<!)6@-2+2+/6",-E!56*01<!!!!F!!Algal lineages are characterized by different carbon storage and photosynthetic strategies. First, while most algae store their carbon as starch, the location differs, with Chlorophyte lineages storing it in the chloroplast while Rhodophyte and Glaucophyte lineages store it in the cytosol (Ball et al. 2011, Plancke et al. 2008). Starch is replaced as a major storage compound by triacylglycerol (TAG) lipid and chrysolaminaran (polysaccharide similar to starch except with "(1:3) and "(1:6) glycosidic bonds) in algae such as the diatoms (i.e. Phaeodactylum triconum (Kroth et al. 2008)) and other members of the heterokonts including Nannochloropsis species (Radakovits et al. 2012). In comparing the photosynthetic light harvesting apparatus of algae, the Rhodophyte and Glaucophyte lineages contain chlorophyll a and use the phycobilisome with phycobilin pigments phycocyanin and phycoerythrin as accessory light pigments for light capture (Larkum 2003, Larkum et al. 2007). Chlorophytes on the other hand have lost their cyanobacteria-derived phycobilisome structures and use chlorophyll a and b incorporated into light harvesting complexes, similar to the arrangement in higher plants (Larkum 2003). Chlorophyte lineages also have stacked thylakoid structures while Rhodophyte and Glaucophyte lineages do not (Douglas 1998). Glaucophyta are thought to be the oldest algal lineage and distinguish themselves from both Rhodophyta and Chlorophyta by having chloroplasts that maintain the endosymbiontÕs peptidoglycan wall (Douglas 1998, Jackson et al. 2015). Algae have many structural and biochemical differences from cyanobacteria, especially in relation to photosynthesis. First, cyanobacteria contain no traditional organelles and one genome while algae contain several organelles and three genomes, one each in the chloroplast, mitochondria, and nucleus. This makes the genetic modification of cyanobacteria easier than algae. Next, cyanobacteria primarily store carbon as glycogen while algae store carbon as starch and/or triacylglycerols (Ball, et al. 2011). Cyanobacteria and some algae possess carbon-!!!G!!concentrating systems; however, these differ in structure in the two groups. Cyanobacteria contain a specialized protein encapsulated bacterial microcompartment, the carboxysome, which houses the CO2/bicarbonate interconverting carbonic anhydrase enzyme and the carbon fixing enzyme RuBisCO in a crystalline matrix (Cameron et al. 2013, Rae et al. 2013). Many algae on the other hand contain the pyrenoid, a starch encapsulated (Ramazanov et al. 1994) compartment containing several carbonic anhydrases and close interaction with the thylakoid membrane to increase CO2 concentrations around RuBisCO (Morita et al. 1997). Further distinguishing the physiology of algae and cyanobacteria, some cyanobacteria can fix atmospheric nitrogen in special heterocyst cells (Anabaena) or in the dark (Trichodesmium) while algae cannot (Stal 2001, Wolk 1996). Finally, although both cyanobacteria and algae can be motile, they use different methods. Cyanobacteria depend upon a poorly understood gliding method for motility dependent upon manipulation of their cell surface while algae predominantly use flagellae, a feature that is believed to have been inherited from the symbiotic, eukaryotic heterotroph (Hoiczyk 2000). Although a common ancestor is shared with higher land plants (including bryophytes, mosses, and hornworts and above), members of the algal lineage are distinguished by a number of important differences (Bhattacharya, et al. 1998, Leliaert, et al. 2012). First, even though some algae such as seaweeds are multicellular, they do not form a vascular system, leaf structure or a true root structure found in higher plants. In addition, higher plants rely on stomata for gas exchange while algal cells are directly exposed to the environmental gases (usually dissolved) (Farquhar et al. 1982, Spalding 2009). Next, while algae have pyrenoid CO2 concentrating structures, most higher plants do not (the exception being the nonvascular hornworts (Villarreal et al. 2012)). Some plants however have evolved C4 and CAM photosynthetic mechanisms that !!!H!!concentrate CO2 at RuBisCO and which require specialized metabolic and anatomical developments (Ramazanov, et al. 1994, Sage et al. 2012). Many algae contain the betaine membrane lipid diacylglyceryl-O-4#-(N,N,N,-trimethyl) homoserine (DGTS) while higher land plants are generally devoid of it (Klug et al. 2001). Transient (day/night cycle) and long term (nutrient deprivation, hibernation) carbon storage compounds such as starch and TAG are handled differently in algae and higher land plants. Algae store both transient and long term carbon molecules in individual cells while higher land plants store transient carbon as starch in the leaves and long term storage carbon in specialized cell types in their stems, roots and tubers. Finally, the vast majority of higher plants are true autotrophs (with the exception of a small number of parasitic and insectivorous species or those like orchids that are heterotrophic at the earliest developmental stages) while many algae may be capable of mixotrophic or heterotrophic growth.  Chlamydomonas reinhardtii Several algal species have served as important research tools in the investigation of photosynthetic organisms, none more prominently than the green alga Chlamydomonas reinhardtii (Goodenough 2015, Harris 2001, Merchant et al. 2012b). This organism is a haploid unicellular flagellate, has a rapid doubling time (<10hrs), can grow on reduced carbon substrates such as acetate, and can reproduce both sexually and asexually (Harris 2009a, Levine et al. 1960). The Chlamydomonas genus was originally described in the 1830s by G.C. Ehrenberg (Ehrenberg 1838) and it took another 50 years before Chlamydomonas reinhardtii was described (Proschold et al. 2001). Work by early geneticists and physiologists used Chlamydomonas to gain valuable insight into cell reproduction, growth, light responses, motility, and photosynthesis !!!I!!(Desroche 1912, Levine 1960, Pasher 1918). Foundational research on the cell cycle, genetic recombination, and gene transfer were done with Chlamydomonas (Ebersold 1967, Ebersold et al. 1959, Ebersold et al. 1962, Levine et al. 1958a, Levine et al. 1958b, Levine, et al. 1960, Rosen et al. 1972, Smyth et al. 1975). Chlamydomonas has also provided in depth information about sexual reproduction and mating types through early work on nutrient deprivation (Armbrust et al. 1995, Martin et al. 1975a). Researchers also found this organism valuable in elucidating the components, structures, and formations of flagella (Harris et al. 2009, Lewin 1953, Silflow et al. 2001, Witman et al. 1972). Further, this organism has contributed to our understanding of day night cycles, lipid metabolism, and the mechanisms behind carbon concentrating mechanisms (Cross et al. 2015, Giroud et al. 1988, Liu et al. 2013, Mittag et al. 2003, Moroney et al. 1987). Finally, as it is able to grow heterotrophically on acetate, photosynthetic mutations that would have otherwise prevented the growth of autotrophs can be cultured and studied, which has provided a wealth of insights into the components and mechanisms of photosynthesis (Dent et al. 2001, Rochaix 2002, Spreitzer et al. 1981). In recent years, a wealth of information has been assembled on this Chlamydomonas species including an annotated 121 megabase genome (Blaby et al. 2014, Grossman et al. 2003, Merchant et al. 2007), several proteomic and metabolomic libraries (Lee do et al. 2008, May et al. 2008, Wang et al. 2014), in addition to multiple large scale omic studies of this organismÕs response to different environmental stresses (Blaby et al. 2013, Bolling et al. 2005, Jamers et al. 2009, Juergens et al. 2015, Lee do, et al. 2008, Miller et al. 2010, Park et al. 2014, Schmollinger et al. 2014). Building on these large systems level data sets, system-wide metabolic modeling studies using metabolic flux analysis (MFA) and flux balance analysis (FBA) have been used to computationally study ChlamydomonasÕs metabolic networks (Boyle et al. 2009, Chang et al. !!!J!!2011a, Chapman et al. 2015, Dal'Molin et al. 2011, Imam et al. 2015, Kliphuis et al. 2012, Lopez Garcia de Lomana et al. 2015, Wienkoop et al. 2010).  Here physiological and systems data provide constraints on the possible solution space of Chlamydomonas metabolism, allowing metabolism to be predicted and studied via computational perturbation (Chapman, et al. 2015). Further expanding the use of Chlamydomonas as a model organism, extensive mutant libraries exist for Chlamydomonas, which increases the ease with which gene-phenotype relationships can be studied.  One such library has 1935 mapped mutants with ~75% confidence and has helped identify genes related to TAG production (Li et al. 2016).  Algal aquaculture, biofuels and high value products Humans have been culturing algae since ancient times as they provide many valuable products and functions (Buchholz et al. 2012). Civilizations with the longest known history of algal utilization include those from Asia (Japan) (Tamura 1966) and South America (Chile) (Dillehay et al. 2008). The oldest method of algal cultivation is open water algal farms in coastal areas where macroalgae are attached to a scaffold such as a rope and grown in shallow waters. Scientists in the 1800s attempted to culture microalgae but Beijerinck was the first to obtain true axenic cultures (Beijerinck 1904). Both macroalgae (seaweeds) and microalgae are cultivated for food products, fuel sources, high value specialty chemicals, and for purposes such as feed for aquatic animal farming and wastewater remediation (Mathiesen 2012, Muller-Feuga 2000, Schmidt et al. 2003, Selvaratnam et al. 2014). These diverse organisms play a great role in the natural world. It is estimated that marine algae contribute 44% of oceanic CO2 fixation (Jardillier, et al. 2010). Algae also play central roles in global aquatic nutrient cycles including those of nitrogen and phosphate which are !!!%K!!frequently limiting in environments such as the ocean. In aquatic environments of high nitrogen and phosphate concentrations, such as where fertilizer and sewage runoff have collected, algae can bloom causing eutrophication of the local environment along with hypoxia and/or toxin secretion (Anderson et al. 2008, Le et al. 2010). As such, algae are important primary producers and members of nutrient cycles in food webs and environments. Algae produce a range of valuable products and services spanning from food products, to fuels. Macroalgae have traditionally been cultivated for use in foods, one of which we commonly see as nori (the wrapping on sushi (Garcia-Jimenez et al. 2015)). Algae are additionally cultivated for producing bioactive carotenoids (astaxanthin), and antioxidants, as well as proteins and biomass for animal feeds (Gimpel et al. 2015, Lemoine et al. 2010). Additionally, important long chain polyunsaturated fatty acids (PUFAS) necessary for many higher life forms including eicosapentanoic acid (EPA 20:5n-3) and docosahexanoic acid (DHA, 22:6n-3) are produced in large quantities by algae (Abedi et al. 2014, Gladyshev et al. 2013). Humans have limited capacity to produce these compounds and diets supplemented with DHA and EPA have been shown to benefit brain development in children (Innis 2007). Markets are rapidly growing for algal products such as raw biomass food supplements worth $80 million annually, PUFAS worth $300 million annually (Enxing et al. 2014), and the carotenoid market worth up to $1.2 billion (Sun et al. 2014). Researchers are also developing algae for the biomanufacturing of bioactive metabolites and proteins that are hard to produce in other systems (Borowitzka 2013, Mayfield et al. 2007, Rasala et al. 2015). Algae provide a system for proper post translational modification of various proteins with uses ranging from food additives to anticancer agents (Rasala, et al. 2015). Other heavily used algal products include the silica shell of diatoms which are harvested and used as fine polish in toothpaste and agarose gelatin which is used in the biotech industry !!!%%!!(Lebeau et al. 2003, Renn 1984). As an example of the scale and value of algal products, more than 10,000 tons of agar are produced globally each year from cultivation of  red algae from the genera Gracilaria and Gelidium (most prominantly Gelidium amansii ) with a market value of over $175 million (Bixler et al. 2011). Algae are also cultivated as a nutrient source for the fish, shrimp, and mollusk farming industries (valued at $106 billion globally), enabling animal feed to be grown onsite (Mathiesen 2012, Muller-Feuga 2000). Algae are also being assessed for their ability to recover nutrients and provide treatment services in waste water treatment plants (Schmidt, et al. 2003). In addition to food and bioactive compounds, considerable research has been undertaken to make algae a competitive renewable energy resource (Durrett et al. 2008, Hu et al. 2008). Early researchers pioneered these efforts through the analysis of methane production from anaerobic digestion of the algal carbohydrate fraction (Meier 1955, Oswald et al. 1960). This work was revived during the energy crisis of the1970s and included research on hydrogen production (Benemann et al. 1977, Miyamoto et al. 1979, Uziel et al. 1975, Weissman et al. 1977). Further responding to the energy crisis, then US President Carter launched the Solar Energy Research Institute (SERI) in 1978 as part of the Department of Energy (DOE), currently known as the National Renewable Energy Laboratory (NREL). Within SERI, the Aquatic Species Program (ASP), whose work is summarized in a final report (Sheehan et al. 1998) was founded to research renewable energy from aquatic plants. Over two decades this program discovered over 3000 strains of algae, isolated and characterized the first plant-related acetyl-CoA carboxylase enzyme, which initiates fatty acid synthesis, developed protocols for pond system culturing of algae, and performed cost and resource analyses for cultivation. However, after much progress and effort, no clear method or organism was determined as an economically !!!%&!!competitive source. This along with decreased oil costs in the late 1980Õs and early nineties led to the termination of that program. As oil prices rose, concern about rising atmospheric CO2 levels increased, and the future of energy security became subjects of public policy planning, intensive efforts to study algae as a renewable energy source were renewed (Pienkos et al. 2009). To further develop algal bioenergy, several multifaceted programs were funded. One of the most recent was the National Alliance for Advanced Biofuels and Bioproducts (NAABB), funded by the DOE from 2010 to 2013. Research in NAABB was focused on 7 areas: Algal Biology, Cultivation, Harvesting Processes, Extraction Processes, Direct Conversion processes, Lipid Conversion Processes, and lipid extracted algae conversion and application (Olivares 2013). Major breakthrough came from the development of new algal strains, improved algal cultivation with the ARID pond system, decreases in harvesting cost through electrocoagulation and most importantly, improvements to hydrothermal liquefaction processing of biomass (see below). This expansive effort helped bring the price of algae biocrude from $240/gal to $7.50/ gal (Olivares 2013). Other National and Institutional programs, including both the Navy and Department of Defense, have contributed to research aimed at developing algal bioenergy applications. Several of these have involved researchers at Michigan State University, including, the DOE funded Plant Research Laboratory, the Center for Advanced Biofuels systems (CABS), the California Center for Algae Biotechnology (Cal-CAB), The Algal Biotechnology Consortium (ABC)(Cambridge), the International Center for Advanced Renewable Energy and Sustainability (I-CARES) and the Arizona Center for Algae Technology and Innovation (AzCATI). A large focus of many of these programs in on the advancement of algal culturing and growth regimes.  !!!%'!!Algae are cultivated today in a variety of ways (Brennan et al. 2010, Chen et al. 2011a, Chisti 2007, Costa et al. 2014, Jones et al. 2012). Recent developments have led to algae being grown in large ponds or under artificial, controlled environment bioreactors (Chaumont , Grima et al. 1999, Pulz 2001). In the pond method, algae are typically grown in circular ponds or elongated raceways with pumps, stirrers, or large paddles being used to keep them suspended and mixed, with or without aeration. Cultures in these open-air ponds rely on sunlight for photosynthesis, which are kept shallow to prevent excessive shading. Additional challenges with ponds include exposure to contaminants including dust, competing natural algae, algal predators such as rotifers, and viruses that can devastate cultures (Carney et al. 2014). Finally, as ponds are exposed to the environments, they can only be productively utilized at those times and places when temperature and light intensity are compatible with moderate-to-high algal growth rates. To address these challenges, some algae being considered or used for open air ponds are extremophiles, being able to grow in saline water, high or low pHs, variable temperatures, or being able to produce allopathic toxins (Barnard et al. 2010). Examples of these include Sulphuraria galdieria, Nannochloropsis species, and Cyanidium caldarium (Luca et al. 1981, Schmidt et al. 2005, Sukenik et al. 2009). Photobioreactors on the other hand are closed system growth chambers that use natural or artificial light and environmental control to allow for continuous regulated growth of algae (Brennan, et al. 2010, Chaumont , Chen, et al. 2011a, Grima, et al. 1999, Pulz 2001, Yen et al. 2014). Here, contamination is minimized and growth conditions can be optimized and tailored to an organismÕs needs or to the synthesis of desired products. These reactors however require many times more energy than ponds to operate. In general, low cost commodity products from algae such as fuels or total biomass are produced via raceway ponds while high value products !!!%(!!such as target pharmaceuticals and other health products with high standards are best suited to controlled bioreactors. In culturing algae for bioenergy purposes, they are grown under either optimal or stressed conditions (Chisti 2007). Under optimal conditions, algae undergo rapid growth and biomass accumulation. While it is advantageous to have high lipid accumulations, frequently no major biomass component (protein, lipid, carbohydrate, etc.) is selected for during optimal growth as each contributes to biocrude formation using hydrothermal liquefaction (HTL, see below). Alternatively, growers may induce stress on the organism through nutrient deprivation (nitrogen deprivation is the most prominent, see below) to increase energy-enriched carbohydrate (starch) and/or triacylglycerol (TAG) contents. Through strain selection or transgenic manipulation, TAG compositions can be tailored to specific needs and be directly converted to biodiesel while the remaining biomass and carbohydrates can be used for anaerobic digestion (hydrogen and methane production), HTL or animal feed. Nutrient stress (particularly sulphur deprivation) can be used to produce hydrogen gas from green algae under anaerobic, illuminated conditions (Melis et al. 2000). Here, PSII and catabolic processes provide electrons for ferredoxin production of hydrogen (Antal et al. 2003, Posewitz et al. 2004, Tsygankov et al. 2006). In addition to considering the energy yields of the biomass component during culturing, energy expenditures in downstream harvesting and processing need to be considered. One of the largest challenges associated with this is the separation of algae from culture media which is energy intensive. Residual water can reduce the downstream quality of generated products. During logarithmic growth, Wet Algal biomass is over 99% water by weight and needs to be ÒdewateredÓ to 50% water or better depending on the intended use of the biomass. Current dewatering strategies include concentrating cells through flocculation with different pH or !!!%F!!chemical shocks and then further concentration through centrifugation and filtration (Danquah et al. 2009, Uduman et al. 2010). The dewatering process is one of the most costly aspects of algal cultivation. After collection, biomass can be treated in a number of ways. If TAG accumulation was the focus of the culturing method, biodiesel can be generated through chemical extraction of TAGs and transmethylation after extensive dewatering (Meher et al. 2006). With the biomass/ growth strategy, cells are processed by hydrothermal liquefaction (HTL) (Barreiro et al. 2013, Biller et al. 2011, Sudasinghe et al. 2014), to generate biocrude. HTL generates biocrude fuel through the controlled medium temperature/ high pressure depolymerization of still-wet biomass. Different temperature/ pressure schemes can generate different pyrolysis component/ qualities depending on the downstream usage of the product (Elliott et al. 2015, Sudasinghe, et al. 2014). While HTL has the advantage of require less dewatering, it does require special equipment and careful monitoring (Li et al. 2014). Nutrient deprivation strategies increase both the energy density of individual cells as well as reduce processing energy costs by having a near drop in fuel product.  Nutrient stress and the induction of storage metabolism in algae Like other organisms, algae require mineral micro- and macronutrients such as nitrogen, phosphate, sulfur, and iron, for growth and reproduction. In almost all natural environments the levels of one or more of these nutrients limit growth at different times. Algae have evolved sophisticated mechanisms to survive nutrient deprivation and to respond quickly once a resource becomes available. Many algae reside in aquatic environments such as the open ocean where mineral nutrients are usually limiting, with some ecosystems relying on coastal runoff or periodic upwelling from lower waters. In response to nutrient limitation algae accumulate carbon stores !!!%G!!and upregulate nutrient fixation pathways to facilitate uptake and incorporation of different forms of the limited resource (Miller, et al. 2010, Msanne et al. 2012, Park, et al. 2014). Intense competition for resources puts selective pressure favoring organisms that can respond rapidly. As algae are poised to uptake nutrients rapidly, giant algal blooms can occur when pollutants such as fertilizers, sewage, or other waste accumulate in water sources (Anderson et al. 2002, Heisler et al. 2008). These blooms can deprive local environments of oxygen (eutrophication) and in some cases secrete toxins that are harmful to other life forms such as fish and invertebrates(Anderson, et al. 2002). The study of the relation between algae and their nutrient environment began in the mid to late 1880s when scientists began to culture them from pond and river sources (Beijerinck 1904, Van Iterson, et al. 1921). Martinus Beijerinck was a notable pioneer in algal and cyanobacterial isolation and physiology who conducted experiments on both nutrient excess and deprivation. He seems to have been the first to describe carbon accumulations during nitrogen deprivation as well as nitrogen fixation in cyanobacteria (Beijerinck 1904, Van Iterson, et al. 1921). Further work sought to understand how the nutrient environment affect the composition of biocomponents (protein, carbohydrate, lipid) (Spoehr et al. 1949). Algal TAG accumulation has been studied during deprivation or limitation of several nutrients including iron, sulfur, and phosphate with the most frequently investigated being nitrogen  (Blaby, et al. 2013, Bolling, et al. 2005, Cakmak et al. 2012, Grossman 2000, Grossman et al. 2010a, Hsieh et al. 2013, Moseley et al. 2002, Urzica et al. 2013, Wykoff et al. 1998). Nutrient deprivation induces a complex array of changes in algae at multiple regulatory and physiological levels. The most rapid system-wide changes occur early during the first 48hrs of nutrient deprivation with a secession of cell growth, increases in cell size and mass, and !!!%H!!falling chlorophyll levels. Functional measures of photosynthesis and respiration have been found to progressively decrease during the first days of nutrient deprivation suggesting a slowing of metabolism (Berges et al. 1996, Juergens, et al. 2015, Plumley et al. 1989b, Schmollinger, et al. 2014, Simionato et al. 2013, Young et al. 2003). To aid in the long term survival of nutrient deprivation some algae such as Chlamydomonas undergo gametogenesis, where cells produce vegetative zygotes better able to handle dormancy (Martin et al. 1975b, Sager et al. 1954). Under various nutrient deprivation stresses, cells have also been shown to decrease the expression of genes related growth, photosynthesis and respiration metabolism as well as upregulating genes responsible for nutrient assimilation as well as starch and fatty acid metabolism at both transcript and protein levels (Blaby, et al. 2013, Collier et al. 1992, Dong et al. 2013, Juergens, et al. 2015, Miller, et al. 2010, Msanne, et al. 2012, Nguyen et al. 2011, Park, et al. 2014, Tsai et al. 2014, Yang et al. 2013a). Expression of nutrient uptake transporters and associated proteins poises cells to rapidly uptake and utilize a nutrient once it returns. Several studies have reported increases in carotenoid concentrations as well (Simionato, et al. 2013, Solovchenko et al. 2014). In the case of nitrogen deprivation, nitrogen is scavenged from any non-essential protein, free amino acids, and other nitrogen containing compounds to support stress response protein production. Carbon to nitrogen ratios increase as carbon accumulates and nitrogen levels per cell stay the same (Schmollinger, et al. 2014). Nutrient deprivation time courses in Chlamydomonas reinhardtii and Scenedesmus obliquus show that carbon accumulation is sequential with starch accumulating first and then TAG (Breuer et al. 2014, Fan et al. 2012, Siaut et al. 2011). TEM images show multiple large starch and lipid droplets after several days of nitrogen deprivation (Goodenough et al. 2014, Juergens, et al. 2015, Moellering et al. 2010, Siaut, et al. 2011, !!!%I!!Warakanont et al. 2015) with membrane lipid levels falling as TAG levels rise. Starch and lipid metabolism, especially in response to nitrogen deprivation are reviewed in the following sections of this chapter, as they are at the heart of carbon fluxes related to energy handling in both natural and cultivation systems.  Starch biosynthesis in Chlamydomonas and how it changes during N deprivation In Chlamydomonas, starch synthesis is upregulated rapidly after the onset of nutrient deprivation resulting in large early increases in starch accumulation. Electron microscopy images show multiple large starch granules appear in the chloroplast of Chlamydomonas within the first 6 hrs of N deprivation (Fan, et al. 2012, Juergens, et al. 2015, Siaut, et al. 2011).  During nutrient replete growth starch levels rise and fall during day/night cycles, however these levels dramatically increase upon nutrient deprivation (Ball et al. 1990, Hicks et al. 2001). Starch is a semi-crystalline polymer of glucose held together by $(1-4)  and $(1-6) glucosidic linkages. The $(1-4) linkages produce long glucose chains which are branched at $(1-6) linkages. Unbranched chains of glucose of at least 1000 glucose residues are referred to as amylose while large chains with branches are referred to as amylopectin (Ball et al. 2009) To make these starch structures, glucose is first phosphorylated by ADP-Glucose Phosphorylase. The ADP-glucose that is formed is then bound to a glycan chain by starch synthase using the $(1-4) bond. Branching enzymes are used to make $(1-6) glucosidic linkages to form amylopectin (Figure 1. 2). Starch accumulation precedes TAG biosynthesis in Chlamydomonas and accumulates to a greater extent. In other algal types such as the diatoms and brown algae, starch plays a much less important role in both diurnal carbon storage and in response to stress. !!!%J!!These organisms accumulate chrysolaminarin and TAG predominantly (Beattie et al. 1961, Percival et al. 1951).                      Figure 1.2. Starch Synthesis in Chlamydomonas. Starch Is synthesized in the plastid starting Glucose-6-phosphate being converted to Glucose-1-phosphate(G-1-P) via Phosphoglucomutase. Next, ADP-Glucose Pyrophosphorylase converts G-1-P into Adenosine diphosphate(ADP) Glucose via reaction with an Adenosine triphosphate. Starch Synthase then forms a $-1,4- glycosidic bond between the activated ADP-Glucose and a glucan chain. The branching enzyme will also add glucose monomers to glycan chains via $-1,6- glycosidic bonds.  !Glucose 1-phosphate Glucose 6 phosphate ADP-Glucose Starch Phosphogluco-mutase ADP-Glucose Pyrophosphorylase Starch Synthase, Branching enzyme ATP PP ADP !!!&K!! During nitrogen deprivation, our work in Chapter 2 found an increase in expression of starch synthesis genes such as starch synthase I and branching enzyme within four hrs. Additionally, we have found starch degradation enzymes upregulated which we interpret as enzymes accumulating poised to degrade starch when Nitrogen returns. We additionally found starch to accumulate linearly immediately after the onset of N stress. These levels appeared to top off after 72hrs of stress under multiple light conditions and are presented in Chapter 3. This information demonstrates an immediate importance of starch metabolism in nutrient deprivation in Chlamydomonas as well as regulation of starch metabolism after several days of nutrient stress that is not currently understood.  Triacylglycerol metabolism during nutrient deprivation Our current understanding of lipid metabolism in Chlamydomonas and literature on which it is based has been described in recent reviews by the labs of Benning (Liu, et al. 2013), Merchant (Merchant, et al. 2012b), and Fan (Fan et al. 2011). Because of its relevance to the work of this thesis, it is summarized here (Figure 1.3). During nutrient deprivation genes related to fatty acid biosynthesis, lipid remodeling, and TAG synthesis are upregulated, changes that are associated with large accumulations of TAG. TAGs consist of a glycerol backbone with three fatty acid chains attached by ester bonds. Fatty acids are produced in the chloroplast from 2 carbon acetyl-CoA precursors. Initially an acetyl CoA is carboxylated to produce malonyl CoA by the Acetyl-CoA Carboxylase enzyme. Another acetyl-CoA is condensed with malonyl CoA by "-Ketoacyl-ACP Synthase III, producing "-Ketoacyl-Acyl Carrier Protein (ACP) and releasing carbon dioxide. Ketoacyl-ACP is then reduced using NAPH Ðby Ketoacyl-ACP Reductase. The "-Hydroxyacyl-ACP product is then dehydrated with "-Hydroxyacyl-ACP !!!&%!!dehydratase to form trans-%2-enoyl-ACP. This product is further reduced with NADPH in the Enoyl-ACP reductase-catalyzed reaction to form a 4 carbon Acyl-ACP. This Acyl-ACP will then be further extended, starting with "-Ketoacyl-ACP Synthase III and another AcetylCoA. This cycle is terminated when the chain lengths are typically either 16 or 18 carbons in length. At this point fatty acids can be used for lipid synthesis or further modified by desaturation enzymes which introduce double bonds at specific carbon:carbon bond positions (Ratledge 2004, Ratledge et al. 2002). Animals and fungi have the fatty acid synthesis enzymes joined in one multi-functional polypeptide operating in the cytosol (eukaryotic pathway) while chloroplasts and bacteria have separate enzymes for different reactions (prokaryotic fatty acid synthase complex (Wakil 1989)). Generated fatty acids are used for the synthesis of membrane lipids, di- and triacylglycerols, for the acylation of proteins and other biomolecules, and in some tissues for the production of the hydrocarbon components of waxes and other surface lipids.  To make membrane glycerolipids and triacylglycerols, fatty acids must be first attached to a glycerol backbone. In the Kennedy pathway, Acyl-ACP-glycerol 3-phosphate acyl-transferase carries out the first transfer of a fatty acid to glycerol 3-phosphate forming lysophosphatidic acid. A second fatty acid is added to form phosphatidic acid. The phosphate is then removed via a phosphatase reaction to form diacylglycerol (DAG). DAGs can be further processed to form the glycolipids monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG), the phospholipids phosphatidylenthanolamine (PtdEtn), phosphatidylglycerol (PtdGro), phosphatidylinositol (PtdIns), the sulpholipids such as sulfoquinovosyldiacylglycerol (SQDG), the betain lipid diacylglyceryl-N,N,N-trimethylhomoserine (DGTS) or can be directly acylated to produce TAG. In TAG synthesis !!!&&!!fatty acids can also be transferred via various membrane lipid pools, with different desaturation and/or elongation modifications of fatty acids being catalyzed while attached to different glycerophospholipid moieties. While TAG biosynthesis was thought to be produced in the endoplasmic reticulum, recent studies have provided evidence that Chlamydomonas produces TAG also at the chloroplast (Fan, et al. 2011). In most eukaryotes, one of the most important membrane lipids for fatty acids trafficking and modification is phosphatidylcholine (Kennedy 1956, Kennedy et al. 1956, Li et al. 2013). This membrane lipid is absent from the model alga Chlamydomonas reinhardtii (Giroud, et al. 1988) and thought to be functionally replaced by the betaine lipid DGTS (Liu, et al. 2013, Moore et al. 2001, Sato et al. 1991, Schlapfer et al. 1983). Several genes related to fatty acid trafficking and oil filling have been implicated or experimentally verified (Boyle et al. 2012, Deng et al. 2012, La Russa et al. 2012, Li et al. 2012a, Li et al. 2012b). !!!&'!!    Figure 1.3. Glycerolipid biosynthesis in Chlamydomonas. Plastid Cytoplasm/ ER Acetyl-CoA Malonl-CoA Malonyl-ACP Acyl-ACP Acyl-ACP Free-FA Acyl-ACP G-3-P Lyso-PtdOH PtdOH Acyl-CoA DAG MGDG DGDG SQDG ASQD CDP-DAG PtdGro Free-FA G-3-P Lyso-PtdOH PtdOH Acyl-CoA DAG CDP-DAG PtdGro PtdIns PtdEtn DAG DGTS Acyl-CoA TAG ACP 1 3 2 6 5 8 9 4 7 11 10 12 13 14 14 14 15 16 17 18 19 20 21 22 17 24 23 ML L-ML TAG 23, 24 !!!&(!!  Several mutant algal lines defective in either starch or TAG accumulation have been studied in relation to their effect on total biofuel production. Starch accumulation was prevented in the starch synthase mutant Sta6 (BAFJ5) and found to induce higher accumulations of TAG during nutrient deprivation (Blaby, et al. 2013, Goodenough, et al. 2014, Li et al. 2010, Work et al. 2010). It was postulated that energy that would have gone into starch was redirected into FA metabolism, resulting in higher TAG accumulations. Surprisingly, the Sta6 mutant was also found to contain plastidic TAG bodies, a feature not seen in other algae (Goodson et al. 2011). Work from Siaut et al. provides data contradicting the idea of TAG accumulating the carbon not Figure 1. 3. (contÕd).This figure shows the metabolic pathways and distribution of the major glycerolipids in the plastid and cytoplasm/endoplasmic reticulum(ER) in Chlamydomonas. Numbers indicate enzyme reactions listed below, Bold indicates base molecules that are added to or modified. Enzymes. 1, Acetyl-CoA carboxylase; 2, Malonyl-CoA: ACP transacylase; 3, Fatty acid synthase complex; 4, Glycerol-3-P: Acyl-ACP acyltransferase; 5, Lyso-phosphatidate: Acyl-ACP acyltransferase; 6, CDP-diacylglycerol synthetase; 7, Phosphatidylglycerolphosphate synthase, Phosphatidylglycerolphosphate phosphatase; 8, Phosphatidate phosphatase; 9, Sulfolipid synthase; 10, Sulfolipid 2 - O-acyltransferase; 11, Monogalactosyldiacylglycerol synthase; 12, Digalactosyldiacylglycerol synthase; 13, Acyl-ACP thioesterase,; 14, Long-chain acyl-CoA synthetase; 15, Glycerol-3-P: Acyl-CoA acyltransferase; 16, Lyso-phosphatidate: Acyl-CoA acyltransferase; 17, Phosphatidate phosphatase; 18, Betaine lipid synthase; 19, CDP-ethanolamine: diacylglycerol ethanolamine phosphotransferase; 20, CDP-diacylglycerol synthetase;  21, Phosphatidylglycerolphosphate synthase,Phosphatidylglycerolphosphate phosphatase; 22, CDP-DAG: Inositol phosphotransferase; 23, and acyl-CoA: diacylglycerol acyltransferase; 24, phospholipid: diacylglycerol acyltransferase . Abbreviations: ACP, acyl carrier protein;  ASQD, 2 - O-acyl-sulfoquinovosyldiacylglycerol; CDP, cytidine-5 -diphosphate; CoA, coenzyme A; DAG, diacylglycerol; DGDG, digalactosyldiacylglycerol; DGTS, diacylglyceryl-N,N,N-trimethylhomoserine; FA, fatty acid; G-3-P, glycerol-3-phosphate; MGDG, monogalactosyldiacylglycerol;  ML, Membrane glycerolipidl;, Lyso-membrane glycerolipid; PtdEtn, phosphatidylethanolamine; PtdGro, phosphatidylglycerol; PtdGroP, phosphatidylglycerolphosphate; PtdIns, phosphatidylinositol; PtdOH, phosphatidic acid; SQ, sulfoquinovose; SQDG, sulfoquinovosyldiacylglycerol.; TAG, Triacylglycerol Figure modified from Riekof and Benning, 2009, and from Liu and Benning, 2013. !!!!&F!!put into starch in starchless mutants. They find no noticeable increases in TAG during N deprivation and attribute other labs increased TAG claims to the comparison of different cell lines as opposed to mutants and their complementation lines(Siaut, et al. 2011).  Mutant screens for Chlamydomonas deficient in TAG production led to the discovery of an MGDG lipase mutant (PGD1) implicated in the trafficking of de novo FA into TAG during nutrient deprivation. This mutant showed phenotypes of increased rates of chlorosis and photosynthetic degradation and decreases in cell survival and Total FA accumulation (Li, et al. 2012b).   Why do algae make TAG under N limitation? Several explanations have been proposed for the induction of TAG accumulation in algae under stress, ranging from storing reduced carbon as an energy source for survival and/or future recovery, to lipid reorganization during photosynthetic down-regulation and/or subsequent up-regulation, to photo-protection (Akita et al. 2015, Grossman, et al. 2010a, Khozin-Goldberg et al. 2005, Klok et al. 2014, Kohlwein 2010, Murphy 2001). Roessler (Roessler 1990) appears to have been the first to postulate that algae accumulate TAG as a sink for excess photosynthetic energy and reductant to prevent photochemical damage. In this explanation, photosynthetic carbon and energy assimilation that can no longer be directed to growth when population increase is inhibited by nutrient deficiency or other stresses results in overflow products, particularly TAG. The concept of overflow metabolism has traditionally been associated with the export of metabolites by heterotrophic microbes (Neijssel et al. 1975) and has more recently also been applied to photosynthetic metabolism in cyanobacteria (Courchesne et al. 2009, Grundel et al. 2012, Hays et al. 2015) and higher plants (Weise et al. 2011). In microalgal work, the idea of photosynthetic overflow (excess photosynthetic energy and carbon) as the driver of oil !!!&G!!accumulation has become a widely accepted explanation (see for example (Hu, et al. 2008, Klok, et al. 2014, Li, et al. 2012b, Li, et al. 2013, Solovchenko 2012). This Overflow Hypothesis strongly influences research efforts and the interpretation of results in biological and engineering studies of microalgal metabolism and TAG accumulation and also has important implications for the ecophysiology of photosynthetic microbes. While frequently invoked in interpreting the results of nutrient deprivation studies, the Overflow Hypothesis protective role vs. storage role question has not been systematically assessed. Others have suggested a storage role for TAG accumulation. This idea posits that starch and TAG accumulate as storage compounds to be used for nutrient recovery, providing either energy for biosynthesis or carbon for a backbone for nitrogen reuptake and amino acid synthesis. Fan et al. has demonstrated a link between total carbon availability and TAG accumulation, an idea that supports a storage role vs. a protective role (Fan, et al. 2012). Once a nutrient returns, being able to quickly recover normal activities would give an algal an advantage over local competitors. Having evolved over millions of years to survive the common situation of nutrient deprivation this should come as no surprise.  Recovery from nutrient deprivation The roles of starch and TAG in the nutrient stress condition can be further explored during recovery after the return of nutrient supply. Despite much effort to study carbon accumulation during nutrient deprivation, very little has been spent on the fates of starch and TAG once nitrogen returns. In Chlamydomonas reinhardtii basic analysis of starch and TAG levels after nitrogen is returned has shown starch and TAG levels decrease, starting with starch and then TAG a few hrs later (Siaut, et al. 2011, Tsai, et al. 2014). During this initial degradation !!!&H!!cells appear to recover themselves with minimal increases in cell number and then divide after roughly 24 hrs (Plumley, et al. 1989b). The transcriptome, proteome, and metabolome of nitrogen deprived and recovered cells shows cells rapidly responding to nitrogen return with upregulation of starch and TAG degradatory metabolism along with that of protein and nucleic acid synthesis (Plumley, et al. 1989b, Tsai, et al. 2014, Valledor et al. 2014).  Although this work demonstrates starch and TAG are rapidly degraded after N recovery it fails to show actual roles for these compounds. The Chlamydomonas CHT7 mutant, which is unable to recover from the nitrogen deprived ÒquiescentÓ state stops accumulating TAG but remains chlorotic and degrades TAG much more slowly than the wild-type (WT) (Tsai, et al. 2014). Further, a mating regulatory mutant (mat3) with decreased cell size was found to have a lag time before recovery(Armbrust, et al. 1995).  Although unmeasured, it is imaginable that decreased starch and TAG accumulations in the smaller cells may partly cause the delay in recovery. Outside of Chlamydomonas there have been a number of studies analyzing cells during nutrient recovery. While these studies are dominated by the study of nitrogen metabolism (Syrett et al. 1988) or cell physiology (Brussaard et al. 1998, Cagnon et al. 2013, Zhao et al. 2015), some give insight into the carbon metabolism. C14 radiolabeling studies in Parietochloris incisa has demonstrated that the fatty acid arachidonic acid accumulated in TAG is partially reutilized to form chloroplastic membrane lipids, demonstrating a carbon storage role for TAG (Khozin-Goldberg, et al. 2005). The focus of this study was on only one fatty acid and it is possible that not all fatty acids are treated in the same way during nutrient recovery. This group has additionally shown that the algae recover their nitrogen replete pigment characteristics rapidly after N repletion (Merzlyak et al. 2007). Work in Dunaleilla Tertiolecta further demonstrated a !!!&I!!rapid return of photosynthetic function after nitrogen deprivation and re-supply (Young, et al. 2003). Even after long periods of nitrogen deprivation (1 month), Nannochloropsis oceanica has been demonstrated to recover full photosynthetic efficiency after 3 just days (Dong, et al. 2013). Finally, a study in the carotenogenic alga Chromochloris zofingiensis presented detailed timecourses of starch, TAG, and carotenoid degradation during nitrogen recovery (Mulders et al. 2015). While they do not follow the carbon stores in the organism they do suggest cells could be recovered in the dark and feed of the TAG and starch.  !!!&J!!           REFERENCES !!!'K!!REFERENCES   Abedi, E. and Sahari, M.A. (2014) Long-chain polyunsaturated fatty acid sources and evaluation of their nutritional and functional properties. Food Science & Nutrition, 2, 443-463. Akita, T. and Kamo, M. (2015) Theoretical lessons for increasing algal biofuel: Evolution of oil accumulation to avert carbon starvation in microalgae. Journal of theoretical biology, 380, 183-191. Anderson, D.M., Burkholder, J.M., Cochlan, W.P., Glibert, P.M., Gobler, C.J., Heil, C.A., Kudela, R.M., Parsons, M.L., Rensel, J.E.J., Townsend, D.W., Trainer, V.L. and Vargo, G.A. 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Journal of Phycology, 51, 726-738. !!!!(I!       Chapter-2   The Regulation of Photosynthetic Structure and Function During Nitrogen Deprivation in Chlamydomonas reinhardtii            _____________________________ This research was published in:  Matthew T. Juergens, Rahul R. Deshpande, Ben F. Lucker, Jeong-Jin Park, Hongxia Wang, Mahmoud Gargouri, F. Omar Holguin, Bradley Disbrow, Tanner Schaub, Jeremy N. Skepper, David M. Kramer, David R. Gang, Leslie M. Hicks, and Yair Shachar-Hill(2015)The Regulation of Photosynthetic Structure and Function During Nitrogen Deprivation in Chlamydomonas reinhardtii. Plant Physiology In this work, Lesli Hicks, JJ Park, Hongxia Wang, Mahmoud Gargouri and David Gang generated proteomic and transcript data. I assisted in the annotation of the transcripts and proteins and generated the heat maps based upon that data. I also generated the hierarchical clustering and k means clustering analysis with the assistance of Omar Holguin. Further, I carried out all of the physiological photosynthetic measurements and figures generated in the work. Additionally, I generated cell samples for Jeremy Skepper to analyze via electron microscopy. Bradley Disbrow assisted me in the starch quantification and gene annotation and analysis. The manuscript was drafted by myself with the assistance of Yair Shachar-hill and Bradley Disbrow. Ben Lucker and David Kramer provided training and assistance in the interpretation of photosynthetic experiments.!!!!(J!ABSTRACT  The accumulation of carbon storage compounds by many unicellular algae after nutrient deprivation occurs despite declines in their photosynthetic apparatus. To aid in understanding the regulation and roles of photosynthesis during this potentially bioenergetically valuable process, we analyzed photosynthetic structure and function after nitrogen deprivation in the model alga Chlamydomonas reinhardtii. Transcriptomic, proteomic, metabolite and lipid profiling, and microscopic time course data were combined with multiple measures of photosynthetic function. Levels of transcripts and proteins of photosystem I and II and most antenna genes fell with differing trajectories; thylakoid membrane lipid levels decreased while their proportions remained similar and thylakoid membrane organization appeared to be preserved. Cellular chlorophyll content decreased more than two-fold within 24 hrs and we conclude from transcript, protein, and 13C labelling rates that chlorophyll synthesis was down regulated both pre- and post-translationally and that chlorophyll levels fall because of a rapid cessation in synthesis and dilution by cellular growth rather than because of degradation. Photosynthetically driven oxygen production and the efficiency of photosystem II as well as P700+ reduction and electrochromic shift kinetics all decreased over the time course without evidence of substantial energy overflow. The results also indicate that linear electron flow fell ~15% more than cyclic flow over the first 24 hrs. Comparing Calvin-Benson cycle transcript and enzyme levels with changes in photosynthetic 13CO2 incorporation rates also point to a coordinated multi-level down regulation of photosynthetic fluxes during starch synthesis before the induction of high triacylglycerol accumulation rates.    !!!!FK!INTRODUCTION  Our current dependence on fossil fuels is unsustainable, motivating the development of bioenergy resources. Among these, microalgal oil and biomass production has shown promise (Atabani et al. 2012, Chisti 2007, Ohlrogge et al. 2009, Williams et al. 2010) because of the photoautotrophic growth of algae and their ability to reach high cell densities, accumulate high dry weight percentages of triacylglycerol (TAG), and their potential for much higher productivity per hectare than terrestrial biofuel crops (Hu, et al. 2008, Wijffels et al. 2010). The unicellular green alga Chlamydomonas reinhardtii is among the most widely studied models of photosynthesis (Rochaix 2002) and other cellular processes including lipid accumulation by algae under stress (Merchant, et al. 2012b). Its advantages for studying photosynthetic mechanisms and regulation include the ability to grow both photo- and heterotrophically so that photosynthetic down regulation and photosynthetically impaired mutants can be isolated (Dent, et al. 2001), a fully sequenced genome, collections of mutants, and the ease with which its growth, physiology, and photosynthetic rates can be measured.   In a range of microalgae, nitrogen (N) deprivation induces high accumulation of starch and TAG (Granum et al. 2002, Hu, et al. 2008, Liu, et al. 2013, Martin, et al. 1975a, Shifrin et al. 1981). In an effort to understand algal TAG production during N deprivation and to guide engineering of higher oil yields, omics-based approaches have been employed (Jamers, et al. 2009). Using the annotated Chlamydomonas genome (Merchant, et al. 2007), the Chlamydomonas transcriptome was analyzed before and after N deprivation, which revealed multiple changes in gene expression that affect diverse parts of metabolism (Blaby, et al. 2013, Goodenough, et al. 2014, Miller, et al. 2010, Schmollinger, et al. 2014). In addition, proteomics have been used to profile the changes in protein expression during N deprivation in !!!!F%!Phaeodactylum tricornutum (Yang, et al. 2013a), Chlamydomonas reinhardtii (Longworth et al. 2012, Nguyen, et al. 2011, Schmollinger, et al. 2014), and Nannochloropsis oceanica (Dong, et al. 2013) and metabolomics have been used to assess changes in metabolite pools sizes (Bılling et al. 2005, Lee et al. 2012). These and other studies point to wide ranging changes in the structure and operation of the metabolic and other cellular networks. Several studies have considered the relationships among metabolic processes including how carbon and energy fixation by photosynthesis affects oil accumulation during N deprivation (Johnson et al. 2013, Miller, et al. 2010, Msanne, et al. 2012). However an integrated systems analysis of both the machinery and the physiological functioning of the photosynthetic apparatus during nutrient deprivation is still lacking, and this limits our understanding of the multilevel regulation of energy and carbon fluxes.  During N deprivation in algae, energy and carbon for de novo synthesis of TAG can come directly from photosynthesis (Msanne, et al. 2012) or from external carbon substrates (Johnson, et al. 2013, Wang et al. 2009). Photosynthetic yields decrease during N deprivation, even under phototrophic conditions where cells are entirely dependent upon photosynthesis (Philipps et al. 2012, Simionato, et al. 2013). Chlorophyll (Chl) fluorescence, which is sensitive to environmental changes and stress conditions that induce alterations in photosynthetic components (Iwai et al. 2008), can monitor the efficiency of linear electron flow (Baker et al. 2007) and has indicated that in Nannochloropsis (Simionato et al. 2013) and other algae, including Chlamydomonas, (Berges, et al. 1996, Blaby, et al. 2013, Li, et al. 2010) photosynthetic efficiency falls after N deprivation. Photosynthetically driven metabolic fluxes can also be probed by supplying 13C labeled bicarbonate/CO2 and quantifying 13C incorporation into metabolite pools (Feng et al. 2010, Shastri et al. 2007, Young et al. 2011).   !!!!F&! Thylakoid membrane lipids are central to photosynthetic function, including the stabilization of photosynthetic complexes and oxygen evolution (Boudi‘re et al. 2013, Jarvis et al. 2000, Jones 2007, Leng et al. 2008, Mizusawa et al. 2012) . The most abundant classes are the neutral lipids monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), and the anionic lipids sulfoquinovosyldiacylglycerol (SQGD) and phosphatidylglycerol (PG) (Frentzen 2004, Mizusawa, et al. 2012, Shimojima et al. 2010). Stress, nutrient deprivation, or changing environmental conditions cause changes in lipid composition, leading to effects on photosynthetic yield and efficiency (Li, et al. 2012b, Philipps, et al. 2012). Cellular Chl and carotenoid concentrations provide additional measures of photosynthetic potential. Carotenoids play a role in protecting cells from excess energy harvesting and act as structural components in the photocenters (Takaichi 2011). In Chlamydomonas, decreasing Chl content is associated with down regulation of synthesis genes and potentially also degradation and follows a decrease in photosynthetic yield during N deprivation (Li, et al. 2010, Philipps, et al. 2012).   This paper examines photosynthetic changes at the level of transcript, protein, lipid, and functional fluxes during the first 48 hrs after N deprivation. Until recently studies of nutrient limitation in microalgae have focused on the effects seen one to several days after deprivation (Moseley, et al. 2002, Urzica, et al. 2013, Wykoff, et al. 1998) when oil accumulation rates are high (Li, et al. 2010, Miller, et al. 2010, Philipps, et al. 2012, Plumley, et al. 1989b, Simionato, et al. 2013, Yang et al. 2013b) and photosynthetic functions are already substantially diminished. Here we focus on the induction phase to investigate the transition into a nutrient deprived, oil accumulating state and the regulation of photosynthesis during that transition. The results demonstrate coordinated changes in photosynthetic gene expression as well as the levels of the photosynthetic machinery and their activities. Photosynthetic transcripts and to a lesser extent !!!!F'!protein levels for different parts of the photosynthetic infrastructure were down-regulated as were the levels of thylakoid lipids and Chl. Photosynthetic function as measured by dynamic optical parameters indicated modest decreases in the efficiency of light utilization during the first 24 hrs and a relative decrease in linear compared to cyclic electron flow. Calvin cycle protein and transcript levels decreased modestly after 6 hrs though 13C labeling incorporation from CO2 was significantly reduced by that time. The results provide a multilevel description of the controlled down-regulation of photosynthetic components and photosynthetic fluxes, highlight the value of combined multiomic and functional measurements, and have significant implications for current hypotheses about the relationship between photosynthesis and TAG accumulation in algae.  !!!!F(!RESULTS Changes in the levels of transcripts and proteins of photosynthesis   C. reinhardtii samples were harvested at 0, 0.5, 1, 2, 4, 6, 12, and 24 hrs after N deprivation and transcripts were analyzed by high throughput sequencing as described in Park et al. (Park, et al. 2014). Nitrogen replete cells (time 0) were used as the reference for all subsequent time points. Quantitative proteomic analysis was performed using iTRAQ labeling combined with strong cation exchange (SCX) fractionation and nanoLC-ESI-MS/MS at all the time points except for 0.5 hrs.  192 photosynthetically related genes with putative or confirmed annotations were collected using genomic annotation, the literature and additional databases (see Methods). Genes were classed as belonging to the following functional groups: photosynthetic reaction center complexes, light harvesting complexes, electron transport, photosynthetic antennae, carbon concentrating mechanisms, the Calvin-Benson cycle, oxidative stress responses, and pigment production and degradation. The transcript and protein level changes for these are presented in Figures 2.1 and 2.2. As previously reported, (Merchant, et al. 2007, Schmollinger, et al. 2014), expression of most photosynthetic genes is down regulated during N deprivation with a wide range of trajectories.   !!!!FF! Figure 2.1. Photosynthetic transcripts and protein expression  !!!!!FG!Figure 2.1. (contÕd). Expression levels represented for the first 24 hrs of N deprivation for transcripts (left hand panel) and the corresponding proteins (right hand panel). Details of quantification and validation of transcript and proteomic levels are presented in (Park et al 2014). Expression values are normalized to N replete expression levels with yellow indicating down-regulation and blue indicating up-regulation; the range shown for transcripts is +/- log2 and for proteins is two-fold up or down. Some expression levels are outside this range and are shown as saturated. Spaces left blank indicate that transcript/ protein detection did not meet the quality criteria for inclusion.     !!!!FH!  Figure 2.2. The levels of transcripts (A) and proteins (B) of photosynthetic pigment metabolism and starch metabolism. Details of quantification and validation of transcript and proteomic levels are presented in (Park, et al. 2014). Expression values are normalized to N replete expression levels. The range shown for transcripts is +/- log2 and for proteins is two-fold up or down. Spaces left blank indicate that transcript/ protein detection did not meet the quality criteria for inclusion.   In order to analyze gene expression for different functional groups within photosynthesis in a statistically objective manner, Kmeans clustering was performed, which divided the transcripts into 7 different expression patterns or classes (average 2.1 within class variance over 50 iterations) (Fig 2.3A). Agglomerative hierarchical clustering of the expression classes demonstrated 4 overarching expression trends with Classes 5, 3, 2, and 7, clustering together and class 6; class 1; class 4 by themselves. The distribution of the genes for each functional group across the expression pattern classes (K-means classes) are shown in Fig2.3B and the !!!!FI!contribution of genes from each functional group to the different K-means classes is shown in Fig 2.3C.                  Figure 2.3. Statistical analysis of photosynthetic transcripts. A) K means clustering class centroid expression (log fold change (+/- log2)) of classes derived from statistical analysis. Class1:blue diamond, 2: red square, 3: green triangle, 4: purple X, 5: teal star, 6: orange circle, 7: black cross. Panels B) and C) present heatmaps of class fractional enrichment by functional group member totals (Rows total normalized to add to 1) and class member totals (Columns total normalized to equal 1). 0 member enrichment is indicated by black while enrichment of 0.7 or more is indicated by white. Gene functional categories are listed between panels B and C. Dendograms above B and C heatmaps represent statistical difference between each expression class in Panel A while numbers indicate class number being presented.  !!!!FJ!  The transcript levels for many genes involved in the photosynthetic light reactions decreased within 30 min after N deprivation and continuing to 24 hrs. There are several notable exceptions, especially those found in expression class 1, including genes encoding the Photosystem (PS) II 22kDa protein and cytochrome c6, whose transcripts increased in abundance. Additionally, PSII and PSI genes in general appear in two different expression classes with PSII dominantly in expression class 1 while PSI appears in expression class 6; this difference in photosystem regulation has been found in other organisms (Berges, et al. 1996). The decrease in transcript abundance was greatest for three genes corresponding to oxygen evolution enhancers 1-3 in PSII and for most genes in PSI, with decreases of the order of 10 to 100 fold. Photosynthetic antenna proteins showed 10 to 20 fold decreases in transcript abundance for both light harvesting centers 1 (LHC1) and 2 during the first 24 hrs of N deprivation. LHC stress related proteins LHCSR1, LHCSR2, LHCSR3 (Class1), also involved in non-photochemical quenching (Maruyama et al. 2014, Peers et al. 2009, Tokutsu et al. 2013), increased in protein abundance by 2.5 fold. Transcript abundance for most genes of the Calvin-Benson cycle are found in expression class 1, increasing in the first four h and then dropping to the original values or lower by 24 hrs. Calvin-Benson cycle protein abundances are maintained through 12 hrs and then decrease slightly by 24 hrs.  While PSI and PSII genes were down regulated, genes implicated in both cyclic electron flow (CEF) pathways were upregulated. In the PGR5 mediated pathway, Proton Gradient Regulation 5 (PGR5) and PGR-Like-1B (PGRL-1B), both found in green algae and higher plants, aid transfer of electrons from reduced ferredoxin to plastoquinol (Hertle et al. 2013, Johnson et al. 2014, Munekage et al. 2002, Petroutsos et al. 2009).  These genes both doubled in !!!!GK!transcript abundance within 1 hr with levels falling to N replete levels by 24 hrs, similar to values reported in (Schmollinger, et al. 2014); protein levels were not reliably quantified. PGRL1 was also found to respond to iron deficiency (Petroutsos, et al. 2009). A type 2 NADPH Dehydrogenase, NDA2, is thought to be responsible for the second CEF pathway in Chlamydomonas (Jans et al. 2008, Johnson, et al. 2014). NDA2 Transcript abundances increased slightly by 24 hrs while protein level increased 1.5 fold by 24 hrs suggesting that CEF through this route may increase during N deprivation.  Carbon concentrating mechanism (CCM) related genes (reviewed in Wang et al. (2011)) such as the carbonic anhydrases CAH1 (Van et al. 1999) and CAH3 and carbon limiting associated genes Carbon Concentrating protein 1(CCP1), CCP2, Low Carbon Induced E(LCIE), and LCI9 showed increased transcript abundance in the first 4 hrs which then decreased, represented by expression Class 1 while protein abundance increased by 24 hrs. This was likewise found in the marine alga Dunaliella tertiolecta Butcher (Young et al. 2005) and Chlorella (Beardall et al. 1982, Beardall et al. 1991), where both Fe and N limitation led to an increased affinity for inorganic carbon.  Others such as CAH2 showed a decrease in transcript expression over time (Class 7). CAH1 and similar proteins have been found in the periplasm and growth media under ambient air CO2 concentrations (Coleman et al. 1984).   Genes involved with removing radical oxygen species such as  Iron superoxide dismutase 1 (FSD1) (Myouga et al. 2008) and Catalase 1 (CAT1) (Yoshida et al. 2003) showed  declines in transcript and protein levels. An exception is GPX5 (a glutathione peroxidase), whose transcript levels increased by ~30% by 24 hrs of deprivation and whose protein abundance increased ~20%. GPX5 is known to metabolize peroxide, and like other oxidative stress response genes is !!!!G%!known to be up regulated under photosynthetic stress conditions like high light (Fischer et al. 2009). Protein levels followed transcript changes 6 to 12 hrs after N deprivation.   Questions have been raised about the importance and mechanisms of alternative electron acceptor pathways, such as chlororespiration, in regulating photosynthetic energy fluxes during N deprivation (Peltier et al. 1991). The alternative oxidases, plastid plastoquinol terminal oxidases 1 and 2 (PTOX1, PTOX2), are thought to be involved in the chlororespiration in Chlamydomonas (Bailleul et al. 2010, Houille-Vernes et al. 2011, Rumeau et al. 2007) and changes in protein and transcript abundance have been found to be associated with nutrient deprivation in algae, particularly iron deprivation (Cardol et al. 2008) as well as light stress (Houille-Vernes, et al. 2011). PTOX1&2 transcripts decreased in abundance 1.5 fold within the first half hour but then increased through 24 hrs to slightly less or greater than N replete conditions. PTOX2 protein values increased 2.4 fold by 24 hrs suggesting that during N deprivation, in addition to linear and cyclic electron transport being down regulated, additional electrons are returned to oxygen, which may help to regulate the ATP/NADPH ratio and photosynthetic output (Peltier, et al. 1991). This may help explain why we observed only minor decreases in the oxidation level of PSII primary quinone A (QA) (Figure 2.8D, below).    Pigment levels and their regulation  Chlorophyll content decreased during N deprivation with levels dropping from ~33 µg&mg-1 cell dry weight (CDW) to less than 10 µg&mg-1 CDW after 24 hrs of N deprivation (Fig.2.4A), with decreases being most rapid soon after deprivation. Chl content per million cells (Fig 2.4B) fell from 0.9 to 0.63 µg Chl/million cells within the first 12 hrs and then declined more slowly to 0.43 µg Chl/million cells by 48hrs. The ratio of Chl a to Chl b was not !!!!G&!significantly altered. However Figure 2.4 B shows that Chl levels per culture volume were initially unchanged after N deprivation, and decreased rather slowly after 6 hrs.                 Figure 2.4. Chlorophyll concentrations and synthesis. (A) Chl concentration per cell dry weight (CDW) in N replete and N depleted cultures (solid and open rectangles respectively). The ratio of Chl a to Chl b for N replete and N depleted (solid and open circles respectively). (B) µgChl per culture volume of N replete (solid square) and N deplete (hollow square). (C) 13C incorporation into phytol chains (as percent of molecules containing 13C label) in N replete and N depleted cultures (solid and open rectangles respectively). Error bars indicate standard deviation, N=3  !!!!G'!Chl synthesis genes for both the porphyrin ring and phytol chain were rapidly down-regulated at both the transcript and protein levels with transcripts falling by one half and proteins by ~30% of initial values by 2 hrs, while transcript abundances for Chl catabolic genes, such as chlorophyllase, were significantly elevated after 1hrs. 13C labeling and analysis of phytol 13C contents was therefore used to follow Chl synthesis rates during N deprivation (Fig. 2.4C). N depleted cells supplied with 13C acetate incorporated 13C into Chl or phytol only within the first 3 hrs, indicating that de novo Chl synthesis ceases soon after N deprivation. This, together with the continued growth (cell division continued for 12 hrs after N deprivation substantial rates of biomass increase continued beyond that), is sufficient to account for the large decreases in Chl content over the first 24 hrs without significant Chl degradation. It has been suggested that Chl decreases following N deprivation in cyanobacteria are due to dilution from cell growth (Collier, et al. 1992) which we demonstrate here with 13C labeling. Chl degradation has been previously inferred to occur both to remobilize nitrogen (from both Chl and associated proteins) and to reduce light stress (Allen et al. 1969, Geider et al. 1998).   Carotenoids are involved with light absorption and energy dissipation, reducing radical oxygen species production (Lohr 2009).  Within the first 4 hrs of N deprivation, the expression of some carotenoid synthesis and modification genes were strongly upregulated while most stayed at the same level or decreased. After 4 hrs, most of the related transcript abundances decreased, although they were still elevated compared with N replete conditions. Transcript levels of the degradation enzymes carotenoid cleavage dioxygenases 1 and 2 were elevated during N deprivation.  Carotenoid levels, determined by HPLC at 450nm (Figure 2.5 A-F) showed that levels of the xanthophyll cycle carotenoid violaxanthin (Jahns et al. 2012) decreased continuously, being undetectable by 48 hrs, while zeaxanthin levels doubled by 24 hrs relative to !!!!G(!time 0 hrs levels then returned to initial levels at 48 hrs after N deprivation. Levels of other xanthin carotenoids, neoxanthin and loraxanthin, and lutein also decreased after N deprivation with neoxanthin falling to undetectable levels after 48 hrs of deprivation. "-carotene levels showed a trend similar to free zeaxanthin with an increase to 24 hrs then a decrease back to initial values at 48 hrs.             Figure 2.5. Carotenoid levels. Carotenoid levels at successive times following N deprivation determined by extraction, HPLC separation and measurement of absorbance at 450nm compared to authentic standards.  A) Violaxanthin, B) Neoxanthin, C) Loroxanthin, D) "-carotene, E) Zeaxanthin, F) Lutein. * Indicates that the pigment was not detected at 48 hrs. Error bars indicate standard error, N=3. !!!!GF!Thylakoid lipid levels and composition  Over the course of N deprivation, we observed a decrease in the levels of thylakoid membranes lipids. This includes the glycolipids monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), and the anionic lipids sulfoquinovosyldiacylglycerol (SQGD), and phosphatidylglycerol (PG) (Benning 2010, Shimojima, et al. 2010). There was a sharp decrease in their levels in the first six hrs and then a more gradual decrease until 24 hrs (Figure 2.6). Proportions of polar (PG, SQGD) and nonpolar (MGDG, DGDG) thylakoid lipid levels did not change, which suggests that the thylakoid membrane composition and charge density are maintained. Thylakoid structure was examined by electron microscopy during the first 48 hrs of N deprivation (Fig. 2.7), revealing that the chloroplast decreases in size, consistent with the decreases observed in plastid membrane lipid levels. Thylakoid stack structure appears normal until at least 72 hrs, which contrasts with the deterioration of cytosolic structure and suggests that photosynthetic capacity is maintained. !!!!GG! Figure 2.6. Relative changes in thylakoid lipid levels. Thylakoid Galactolipids (MGDG, DGDG) and Anionic Lipids (SQDG, PG) were quantified by high mass resolution mass spectrometry as described in the accompanying paper. Values represent the sum of ion counts for individual members of each lipid class relative to internal standards and then normalized to the values at time 0 hrs (N replete). Filled symbols represent the galactolipids, unfilled ones represent the anionic lipids. Error bars indicate standard deviation, N=3. -1 -0.5 0 0.5 1 1.5 0 10 20 30 40 50 Relative Concentration Time (Hours) !!!!GH!                         Figure 2.7. Chloroplast Imaging with electron microscopy. Images from left to right: (A) N replete cell, (B) N replete chloroplast, (C) 6 hr N- chloroplast, (D) 12 hr N- chloroplast, (E) 24 hr N- chloroplast, (F) 48 hr N- chloroplast. Bars indicate 100nm except in panels A and E where they indicate 500nm. C) Chloroplast; M) Mitochondria; N) Nucleus; OB) Oil Bodies; P) Pyrenoid; S) Starch; T) Thylakoids; V) Vacuole. !!!!GI!                       Figure 2.8. Photosynthetic fluorescence functional measurements. (A) Fv/Fm, (B) 'II, (C) NPQ, (D) qP, and (E) 77K fluorescence. Contribution to efficiency decreases were measured through Chl fluorescence  experiments over 48 hr of N deprivation using PAM fluorimetry. For A, B, C, & D: solid shapes indicate N plus while empty shapes indicate N minus. (E) 77K fluorescence spectra during N deprivation. Control (solid black line), 6 hr N- (black hashed line), and 24 hr N- (gray hashed line) are shown. PSI associated fluorescence (maximum at 715nm) decreased markedly from 95% to 65% (±1%) of the fluorescence intensity at PSII (maximum at 685nm) by 6 hrs and then decreased slightly (58 ±7%) by 24 hrs. Not shown are 48 hrs and 72 hrs curves which had fluorescence signals at 715nm with 64% and 66% (±3%) of the signal at 685nm respectively. Error bars indicate standard deviation, N=3. 77K data presented as average of three measurements. !!!!!GJ!Energy capture and conversion is down regulated during N-deprivation  We used chlorophyll fluorescence spectroscopy to assess photosynthetic function during N deprivation. Over the 48-hour N deprivation time-course a slow, approximately linear, decrease in the theoretical maximum quantum efficiency of photosystem II (Fv/Fm, (Baker 2008)) occurred.  After 24 hrs Fv/Fm was ~85% that of N replete values and at 48 hrs it was ~75%, similar to the levels reported by (Schmollinger, et al. 2014) and (Li, et al. 2010). The operating quantum efficiency of photosystem II photochemistry in the light (!II) (Baker 2008, Genty et al. 1989) decreased continually after 6 hrs reaching close to half the initial value by 48 hrs (Fig. 2.8B). In N replete cells !II was 15% lower than the Fv/Fm theoretical maximum while N deprived !II was almost 55% lower. Non-photochemical quenching (NPQ) during N deprivation was also measured (Fig. 2.8C), showing that in N deprived cells, NPQ decreased from ~0.3 to ~0.07 by 48 hrs.   In PSII, the efficiency of electron transfer is partly dependent on the number of open/ oxidized quinone A (QA). qP and qL are two parameters used to describe the percent of open PSII reaction centers (oxidized QA pool) (Kramer et al. 2004). During N deprivation, qP fell from 0.93 to 0.82 in 6 hrs and further to 0.75 by 48 hrs indicating a decrease in the abundance of open reaction centers while in N replete control cultures its levels remained constant throughout the timecourse (Fig. 2.8D). qL is similar to qP, except that it takes in to account the lake model of shared light harvesting centers, in these experiments its values were ~1-2% lower than qP. Both qP and qL demonstrated that the QA pool was increasingly reduced over time (Figure 2.9) !!!!HK! Figure 2.9. qL.  Relative amount of oxidized QA based upon the lake model. Error bars indicates standard deviation, N=3   To probe potential changes in the allocation of light energy between photosystems, 77K fluorescence spectra, which are a measure of the amount of light harvesting complex chlorophyll associated with different photocenters, were acquired (Fig. 2.8E). A substantial fall in the fluorescence peak at 715nm relative to the PSII peak at 685nm was observed at 6h and beyond (consistent with previous reports in nitrogen limited Chlamydomonas (Plumley et al. 1989a)). STT7 (Class1), a protein kinase responsible for inducing state transitions and adaptation to different light conditions (Depege et al. 2003), increased threefold in transcript abundance within 2 hrs then returned to initial levels by 24 hrs while its protein levels decrease from 6hrs to 24 hrs. !!!!H%!77K spectra and protein data suggest a decreasing role for state transitions during N deprivation. Alternatively, the 77K spectra could represent rapid remodeling of antennae and photocenter arrangements.  Oxygen evolution  Photosynthetic oxygen evolution in the light and its consumption immediately upon transfer to the dark were measured over the N- time course (Fig. 2.10A). Under the growth conditions of this study (160 microEinsteins (uE m(2 s(1 ), light driven gross oxygen evolution and consumption rates per cell decreased by half in the first 12 hrs with minor decreases through 48 hrs. Similar trends in oxygen exchange rates were recently reported by (Schmollinger, et al. 2014) using higher light levels for measurement than for culturing. Thus the electron flux through PSII was down regulated over the first 24 hrs after nitrogen deprivation, and this can be accounted for by the measured decreases in the efficiency of PSII and Chl levels.  Total PSI turnover rate falls during N deprivation  The absorbance changes at 705nm under PSII inhibited conditions are proportional to the oxidation/reduction of PSI (Alric 2010, Johnson et al. 2012). These P700 absorbance changes shown in Fig 2.10 are attributed to redox turnover at PSI due to cyclic electron flow (CEF, Fig. 2.10B). Thus CEF rates fall by ~50% over 48hrs when expressed on a per cell basis when expressed on a per Chl basis the decreases are more modest. This indicates that cellular rates of CEF fall more because of decreases in the numbers of photocenters per cell (as reflected in chl levels) than because of the turnover rates of each photocenter.   !!!!H&! Figure 2.10. Oxygen evolution, ECS, and P700 spectroscopy. For A, B, and C, hollow squares indicate per million cells and hollow diamonds indicates per µg Chl. (A) Gross Oxygen evolution and consumption (open circle) (nmol/min), (B) P700 (%A705 /s) X 1000, (C) vH+ (%A520 /s) X 1000 (Hollow triangle, dashed line indicates samples normalized to PSII inhibited P700/ECS ratios), (D) vH+ / Gross evolved Oxygen (G.E.O.) X 1000. vH+/ G.E.O: hollow square, solid line, dashed line; vH+ / G.E.O. normalized to PSII inhibited P700/ vH+  ratios:  hollow triangle.. Error bars indicate standard deviation, N=3 !!!!H'!Electrochromic shift measurements to probe thylakoid proton motive force and fluxes The electrochemical potential across thylakoid membranes was assessed using electrochromic shift (ECS) induced absorbance changes at 520nm (Avenson et al. 2005, Bailleul, et al. 2010, Lucker et al. 2013). When pigments are immobilized within an electric potential, a shift in the dipole moment can be induced and changing the spectral properties of the pigment absorbance changes during brief periods of dark are due to the dissipation of the thylakoid proton motive force (pmf). This is believed to occur primarily through proton translocation through the ATP synthase. The initial slope of ECS induced absorbance change during light to dark transitions give estimates of total proton efflux (vH+) during steady state photosynthesis. Figure 2.10 C shows that vH+ decreased by ~50% in the first 12hrs after N deprivation, then another 20% of by 48hrs.   The amplitude of the ECS signal is proportional to the concentration of the relevant pigments and the amplitude of the electrochemical potential. Therefore, to account for any changes in pigment concentrations on the vH+ calculation during the N deprived timecourse, we normalized the ECS signal using the P700 absorbance changes in the presence of DCMU, this was rationalized as follows. Under PSII inhibited conditions, changes in absorbance at 705nm during light to dark transitions are directly proportional to PSI turnovers under PSII inhibited conditions, and are linearly proportional to the ECS520 signal (Lucker, et al. 2013). We can therefore calibrate changes in the ECS signal due to pigment changes with the PSI signal. The PSII inhibited )705nm to )520nm ratio (Figure 2.11) was found to increase only approximately 10% by 48 hrs, demonstrating that pigment changes had minimal impact on measured ECS absorbance changes over the time course and allowing greater confidence in the estimates of proton flux. Normalized !H+ is presented in Figure 2.10C.  !!!!H(!                  Figure 2.11 PSII inhibited P700/ !H+ during N deprivation. Error bars indicates standard deviations, N=3   To determine the relative contributions of linear and cyclic electron flows to total photosynthetic electron, we compared vH+ to gross evolved oxygen rates (G.E.O) during nutrient deprivation in. Figure 2.10D shows that during N deprivation, vH+ / G.E.O increases ~15% by 24 hrs indicating modest increases in the contribution of CEF relative to LEF to the chloroplastic pmf. From 24 to 48 hrs, vH+/ G.E.O decreased to N replete levels. Calibrated vH+ / G.E.O shows a small increase in favor of CEF vs. LEF over the timecourse, most significant at 24 hrs.  0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 10 20 30 40 50 P700/ !H+ (with DCMU) Time (Hrs) !!!!HF!                     Figure 2.12. 13C Incorporation. Time course of 13C incorporated into glucose-6P (A), glycine (B), and serine (C) after addition of 13C bicarbonate to Chlamydomonas cultures in TAP medium deprived of N for 6 hr. The proportion of molecules containing 13C label was determined by GC mass spectrometry after rapid quenching of cells in -70oC methanol and subsequent extraction. Error bars indicate standard deviation, N=3 0 0.05 0.1 0.15 0.2 0 100 200 300 % 13C incorporated  Time (s) 0 0.1 0.2 0.3 0 100 200 300 % 13C incorporated Time (s) B C 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0 2 4 6 8 %13C incorporation Time (min) A !!!!HG!Photosynthetic carbon fixation   Steady state 13C labeling experiments showed that under nutrient replete conditions, photosynthetic carbon fixation contributed approximately a third of carbon used for growth. To assess changes in flux from photosynthetic carbon fixation into soluble metabolite pools, cells were pulse-labeled with 13C bicarbonate before or 6 hrs after N deprivation (Figure 2.12) The rate of labeling of glucose-6-phosphate and particularly of glycine and serine, was reduced by between 15 and 50% within 6 hrs of N deprivation, indicating that fluxes through the photosynthetic dark reactions of the Calvin cycle are substantially reduced before any significant changes in the relevant enzyme levels.   Starch Synthesis  In Chlamydomonas, N deprivation induces the accumulation of starch sooner and to a greater extent than TAG, (Fan, et al. 2012, Siaut, et al. 2011). Starch levels (Figure 2.13), show a linear increase starting within 6 hrs and increasing from 5µg/million cells to ~25µg/million cells by 48 hrs. Transcripts for genes related to starch synthesis (2.13A) especially soluble starch synthase I, are up regulated within 30 minutes of N deprivation while most genes related to starch degradation were down regulated early on. The levels of most of the corresponding protein closely followed changes in transcripts. Interestingly, the expression of some starch degradation genes, including many starch binding and debranching enzymes, are upregulated after 4 hrs. Such findings suggest that the starch pool may be turned over during N- which would point to starch metabolism having more than a simple storage function. Starch turnover has been demonstrated during normal growth (Klein 1987) and has been suggested to occur during N deprivation based on observations on starch breakdown mutants (Tuncay et al. 2013). Starch !!!!HH!accumulation occurs during a time when cells are downregulating photosynthesis, decreasing carbon fixation, and reducing ATP and NADPH production. While oxygen production rates before N deprivation are sufficient to sustain the initial starch accumulation rates, within ~12 hrs this is no longer the case and we therefore suggest that starch accumulation is not primarily driven by photosynthetic carbon or energy overflow under these conditions.      Figure 2.13. Starch Analysis. Transcript (A) and protein (B) abundance values over a 24 hrs N- time course for genes related to starch metabolism. Transcript values expressed as log fold change, and protein values expressed as ratios of value at time point to original value at time = 0 hrs; both follow the color scales denoted below their respective panels, with blue representing an increase in expression and yellow representing a decrease. Panel C shows N replete (solid diamonds) and starved (hollow diamonds) starch levels in µg per106 cells. Error bars indicate standard deviation, N=3. ABC0 5 10 15 20 25 30 0 10 20 30 40 50 Starch (!g/ 106 cells) Time (Hrs) 0 0.5 1 2 4 6 12 24 0 1 2 4 6 12 24 Starch Synthesis Starch Binding Starch Degradation -2 0 2 0.7 1 1 -2 0 2 -2 1 0.7 1.7 !!!!HI!DISCUSSION Regulation of Photosynthetic Activity  When microalgae are deprived of nitrogen or other inorganic nutrients, growth slows, photosynthesis is down regulated, and starch and oil accumulate. Pronounced and rapid decreases in transcripts for most genes encoding the photosynthetic complexes, electron transport, light harvesting centers, photosynthetic pigments, and the Calvin-Benson cycle were observed here and in other studies (Blaby, et al. 2013, Miller, et al. 2010, Schmollinger, et al. 2014) pointing to a central role for transcriptional regulation in reducing photosynthetic rates. Protein levels for many of these genes also decreased, albeit to a substantially lesser extent and with delays generally in the 6 to 12 hr range. However, analyses of photosynthetic energy fluxes and of 13C labeling rates show that regulation at the post translational level is at least as important as expression changes in the short term response to nutrient deprivation. Thus photosynthetic CO2 fixation and chlorophyll synthesis rates are both markedly inhibited before the corresponding protein levels are significantly decreased. Likewise, the timing of changes in the photosynthetic apparatus and in electron and proton fluxes does not correlate with the expression levels of the relevant genes. For example as a whole, expression of almost all PSI genes are decreased relative to that of PSII genes, whereas CEF fluxes at PSI increase relative to LEF, which are initiated at PSII. Another significant posttranslational change is the relative partitioning of light harvesting complexes between the photosystems, which occurs within 6 hrs, before the levels of the antennae proteins have changed significantly. Post-translational regulation of photosynthesis is known to be important in a range of stress responses as well as in diurnal changes (de Vitry et al. 1989, Geiger et al. 1994, Pfalz et al. 2012, Turkina et al. 2006). Such changes include structural modifications of individual proteins and photosynthetic complexes as well as allosteric regulation of enzyme activities. !!!!HJ! Differential Regulation of PSI and PSII  It has been reported that PSI and PSII respond quite differently during N deprivation with PSII protein and activity decreasing more than that of PSI (Berges, et al. 1996, Plumley, et al. 1989a, Wykoff, et al. 1998). Plumley et al. 1989 (in Chlamydomonas) and Berges et al 1996 (in Thalassiosira weissflogii, Dunaliella tertiolecta, and Synechococcus), concluded that Òthe major effects of nitrogen deprivation are in PSIIÓ and that Òthere is a relatively large capacity for cyclic electron flowÓ under these conditions. Similarly, Wykoff et al. 1998 studying Chlamydomonas under S and P deprivation found that Òelectron flow was inhibited at PSII, whereas PSI activity was essentially unchanged in starved cellsÓ. By using multiple measurement methods of to assess photosynthetic fluxes we conclude that under the culture conditions used, fluxes through the two photosystems are both reduced, and that while linear electron fluxes fall more than cyclic ones in the first day of nutrient deprivation, the difference is much less dramatic than previously concluded.  At the transcript level PSII gene expression is increased within 2 hrs of N deprivation (Class1) while PSI transcript levels are fall (Class 6). This suggests that PSII protein levels should stay higher longer than PSI, however this is not the case. Second, from 77K fluorescence data we find a higher proportion of chlorophyll fluorescence associated with PSII than PSI over time, which is the reverse of findings in Nannochloropsis (Simionato, et al. 2013). In contrast, the gross oxygen evolution rate, a measure of PSII activity, decreases somewhat more than the !H+ during the first 24 hrs, indicating a decrease in the contribution of LEF compared to CEF. From 24 to 48 hrs, there appears to be a rebalancing of photosynthetic electron fluxes with LEF increasing relative to CEF. Interestingly, the maximum quantum efficiency of PSII does not !!!!IK!decrease rapidly suggesting that PSII complexes remain functional and that LEF is being downregulated in another manner partly due to dilution of Chl per cell. As mentioned above, the PTOX protein abundance increases and may be involved in redirecting electrons from linear electron transport towards chlororespiration (Peltier, et al. 1991) while the increases in PGR5, PGRL1B, and NDA2 transcript and protein levels suggest increased CEF capacity (Johnson, et al. 2014). The increase over the first 24 hrs of the proportion of proton efflux apparently driven by CEF implies there is an increase in demand for photosynthetically generated ATP relative to NADPH. We postulate that such a shift reflects a decreasing demand for NADPH for de-novo biosynthesis as growth slows while ATP demands for maintenance and remodeling of the proteome remain closer to those during normal growth.  Photosynthesis is Down Regulated in a Controlled and Orderly Manner  During N deprivation, it has been suggested that photosynthesis induces significant stress via energetic overflows leading to potential oxidative damage, and driving starch and oil accumulation as energy sinks (Hu, et al. 2008, Li, et al. 2012b, Roessler 1990). Consistent with this, GPX5, a gene associated with reactive oxygen species (ROS), was upregulated. The PSBS gene, believed to be involved in NPQ (Bonente et al. 2008, Miller, et al. 2010), the LHCSR genes and the PSBA D1 protein are also upregulated and zeaxanthin levels doubled within 6 hrs.  While Zeaxanthin accumulation and LHCSR and PSBS upregulation are typically associated with light induced stress and thylakoid acidification (Gilmore et al. 1993, Horton et al. 1994, Peers, et al. 2009), our other results provide evidence that photosynthetic fluxes are down regulated sufficiently to prevent significant stress. NPQ, which is elevated markedly under photosynthetic stress conditions such as drought or high light exposure (Muller et al. 2001, !!!!I%!Niyogi et al. 1997), did not increase following N deprivation. It seems likely that the moderate light levels at which Chlamydomonas is cultured, and at which NPQ was measured here are too low to induce significant stress or quenching. Also we observed little or no increase in the levels of ROS related proteins, (with the exception of modest increases in GPX5), which is consistent with a recent study in which no increases in ROS levels were detected during N deprivation in wild type Chlamydomonas (Li, et al. 2012b).  Chl levels per cell decrease strongly, and this together with changes in photosynthetic light use efficiency can be estimated to reduce energy capture by approximately threefold within 24 hrs, consistent with the observation of strong reduction in the light-driven oxygen production rate. It is likely that during the first 24 hrs decreased light capture is a more important factor than changes in Calvin cycle enzyme levels or activities in the decreased carbon fixation rates. Also consistent with a picture of an orderly down regulation of photosynthesis that preserves structure and function while avoiding stress from energy overflow are the findings that Chl levels fall by dilution without the induction of degradation, and the same apparent pattern in the decreasing levels of thylakoid membranes, with their proportions and organization in the thylakoid stacks being preserved. The fact that starch and later TAG accumulation increase while photosynthetic energy capture is greatly decreased, suggests that photosynthetic energy overflow may not be the primary driver of storage compound accumulation. Thus for example, maximal starch production rates are achieved before growth rates have slowed significantly. A full energetic and carbon balance under autotrophic as well as mixotrophic conditions would be needed to quantitatively assess the contribution of photosynthesis to storage compound accumulation. As algae in nature commonly face nutrient deprivation (Grossman 2000, Grossman, et al. 2010a, Merchant et al. 2012a) it should perhaps !!!!I&!not be surprising that they are able to respond in an orderly manner, preventing substantial energetic overflows and oxidative damage. !!!!I'!METHODS   Culturing  Chlamydomonas reinhardtii strain cc400 cw-15 mt++ was obtained from the Chlamydomonas Research Center and grown at 23 ¡C in liquid Tris-Acetate-Phosphate (TAP) media (Gorman et al. 1965) in flasks shaken at 125 rpm under continuous illumination at 160 µmol photons m(2 s(1  and ambient CO2 concentrations. Cell growth was determined by optical density measurements at 750 nm using a DU 800 spectrophotometer (Beckman-Coulter). Cultures were grown to cell densities of between 0.15 and 0.3 O.D. to minimize self-shading which becomes significant in denser cultures. Cells were counted using and Z series Coulter Counter cell and particle counter (Beckman-Coulter).  For N deprivation, cells were centrifuged and resuspended in TAP media lacking Ammonium Chloride (nitrogen source). For labeling studies, acetate was replaced with 13C2 acetate.  Transcriptomics  Transcript analysis was conducted as described in (Park, et al. 2014), which includes the mapping, parameters for SNP analysis, statistical data tools, and functional annotations. Briefly, cells were harvested by centrifugation at 0oC and resuspended in RNAlater solution (Qiagen Sciences, Gaithersburg MD, USA) and stored at between -80 and -60oC; RNA was extracted from two biological replicates for each time point using TRIzol reagent (Invitrogen, Karlsruhe, Germany) according to the manufacturerÕs protocol. cDNA library preparation and RNA-seq analyses were performed as previously described by He. et al. (He et al. 2012).   !!!!I(!Proteomics    Cells were harvested by centrifugation and frozen in liquid N. Proteins were extracted and trypsin digested as described in (Wang et al. 2012). 100 *g of each digested sample was labeled using 4plex iTRAQ reagents according to the manufacturerÕs instructions (AB Sciex, Foster City, USA). Two 4plex iTRAQ experiments were performed for each biological replicate of the seven time-points as described in the accompanying paper. Labeled samples from each experiment with iTRAQ labels 114-117 were mixed and fractionated via SCX as in (Alvarez et al. 2011). Fractions from 5 to 24 min were analyzed by nano-LC-MS/MS using an LTQ-Orbitrap Velos mass spectrometer (ThermoFisher Scientific, Rockford, USA) coupled with a nanoLC Ultra (Eksigent, Dublin, USA). Mascot Distiller v2.4 was used to process data Mascot Daemon (Matrix Science, London, UK) was used to search data against the Chlamydomonas protein database from Phytozome 9 and the Chlamydomonas chloroplast and nuclearsome database from NCBInr (Phytozome 9, 7 Jan, 2013, 17,114 sequences, 12,173,409 residues). The false positives rate of protein identification was assessed with an automatic decoy database search and identified proteins were grouped based on 98% homology with Scaffold v3.1 (Proteome Software, Portland, USA).  Full details can be found in (Park et al., 2014).  Annotation and expression analysis   Transcripts and proteins identified during sequencing were filtered for relatedness to photosynthetic function based upon annotation in Phytozome 10.1 (translated from 9.1), Kyoto Encyclopedia of Genes and Genomes (KEGG), the Chlamydomonas sourcebook (Harris E et al. 2008). Genes with unknown function were further checked for similarity to arabidopsis protein !!!!IF!and DNA sequences provided on TAIR (The Arabidopsis Information Resource), as well as papers cited in this work.  Kmeans and agglomerative hierarchical clustering for transcript expression was carried out using XLSTAT (Addinsoft) in Microsoft Excel. Kmeans clustering randomly picks a designated number of centroids randomly in the clustering space and then determines the number of members in the cluster based upon their Euclidean distance from the centroid. A new centroid is then calculated based upon the center of each of the clusters and the distance between members and the centroid is calculated again. This is repeated until a true class center is determined. The number of expression classes was chosen based upon least number of classes with least amount of within class variance generated after 500 iterations. Agglomerative hierarchical clustering was then carried out on the expression classes based upon their Euclidean distances between eachother and presented in dendogram format.  Chlorophyll Concentration  Cells were collected by centrifugation of 1 ml culture and Chl was extracted in 1 ml of 80 % acetone for 20 minutes from pelleted samples after supernatant was removed. After extraction, samples were pelleted by centrifugation and the supernatant was used for analysis. Chl was quantified spectroscopically as described in (Ritchie 2006) using a DU 800 spectrophotometer (Beckman Coulter).  Phytol Isotopic Labeling   For determination of Chl synthesis rates, 13C incorporation into the phytol side chain of the Chl molecule was measured using GC-MS. Exponentially growing cells were centrifuged !!!!IG!and transferred to 13C2 acetate TAP media with and without N. 2 mL of cells (~0.15 mg CDW) were harvested at various time points. Lipids were extracted with chloroform-methanol (2:1) and dried under a stream of N. The dried lipid extract was saponified using methanolic potassium hydroxide at 100 ¼C for 2 h in a 2ml screw capped tube. The unsaponified components which contained the phytol chain was then extracted using hexane and dried under N. The extract was derivatized to its TMS derivative using 50 *L of N-Methyl-N-(trimethylsilyl)trifluoroacetamide with 1 % trimethylchlorosilane (Sigma Aldrich, USA) by incubating at 80 ¡C for 1 h. One *L of the derivative was injected into the gas chromatography-mass spectrometry (GC/MS) consisting of HP 6890 GC (Hewlett Packard, Palo Alto, CA, USA) equipped with DB-5MS column (5 % phenyl-methyl-siloxan-diphenylpolysiloxan; 30 m # 0.251 mm # 0.25 µm, Agilent, Waldbronn, Germany) and a quadrupole mass spectrometer (MS 5975, Agilent, Waldbronn, Germany). Electron ionization was carried out at 70 eV. The separation was carried out in GC (carrier gas 1.1 mL min-1 helium) with the following temperature program: initial column temperature of 100 ¼C, increase of 20 ¼C min-1 until 150 ¼C, 4 ¼C min-1 until 225 ¼C, 30 ¼C min-1 until 325 ¼C with a final holding time of 5 min. The inlet and quadrople both had temperature set to 320 ¼C and the quadrupole of 320 ¼C. The ions 143, 144, 145, 146 and 147 of the mass spectra were monitored, which represent a four carbon fragment of the phytol chain. The obtained mass spectrometric data were corrected for the natural abundance of the elements to give fractional 13C labeling.   Carotenoid Quantification  Folch extracts were analyzed for free carotenoid content via HPLC as described by (Rodriguez-Uribe et al. 2012).  Extracts were diluted in CHCl3:MeOH (2:1, v/v) and a 20 µL aliquot was separated on an 4.6 x 250 mm YMC carotenoid column.  Carotenoid peaks were !!!!IH!detected by a 996 PDA detector 400-600 nm (Waters, Milford, USA), peak detection and integration was performed using the extracted 450 nm absorbance chromatogram. Peak areas were integrated for relative quantification by peak area using the Empower software (Waters, Milford, USA).  Astaxanthin, loroxanthin and neoxanthin was identified by comparison of the UV absorbance spectra to their respective referenced UV spectra in the Carotenoids:Handbook (Britton et al. 2009).  Lipid analyses  Cells were centrifuged at 0oC, lyophilized and total lipids were extracted from ~10 mg of dry cell mass by the method of Folch (Folch et al. 1957). Briefly, the lyophilized tissue was weighed into 2 mL eppendorf vials and extracted with 200 µL of CHCl3:MeOH (2:1, vol/vol) shaking on a vortexer for 30 min.  The sample was then centrifuged and the supernatant was collected, dried under N and stored at -20 ¡C until analysis.  Thylakoid membrane lipids were quantified by direct infusion into an FT-ICR MS as previously described (Holguin et al. 2013).  Electron microscopy  During N deprivation at selected time points, 50ml cells at .2 O.D. 750nm were harvested, pelleted and resuspended in fixation solution of TAP media with + 0.5% glutaraldehyde + 10 *L H2O2 to 10 mL, for 1h in the dark at 4¡C on a rotary plate. After fixation, cells were pelleted (3500rpm) and then resuspend 0.5 ml TAP media. Cells were packaged at MSU and sent to Cambridge for analysis. Fixed cell samples were then treated with 1% osmium ferricyanide at 25¡C for 2 hrs then were rinsed in deionized water five times. Pelleted cells were then treated with 2% uranyl acetate in 0.05M maleate buffer at pH 5.5 for 2 hrs at 25¡C. They !!!!II!were again rinsed in dionized water and dehydrated in an ascending series of ethanol solutions from 70% to 100and then treated with 2 changes of dry acetonitrile and infiltration with Quetol epoxy resin. Sections were cut at 50 nm on a Leica Ultracut S. Samples were stained with uranyl acetate and lead citrate and viewed at 120kV in an FEI Tecnai G2. Images were captured with an AMT XR60B camera using Deben software.  Oxygen evolution and consumption  Changes in dissolved oxygen was measured with an NEOFOX analyzer FOXY-R probe with a FOXY-AF-MG coating (Ocean Optics). Probe was placed in 2 ml of culture in a capped 3ml cuvette with stir bar to keep samples mixed. Net oxygen evolution was measured for 5 minutes at 160 µmol photons m(2 s(1 and consumption was measured in the dark immediately after the light period for 1 minute. Gross oxygen evolution was calculated as net oxygen evolution minus consumption.  As cells consume acetate in the dark, they are net CO2 producers during the period of dark adaptation, increasing the concentration of CO2 and decreasing O2 during this period. It was found that cells were carbon limited under our growth regime (see chapter three) and adjusting the amount of time cells spent during the dark conditions had an affect on the illuminated O2 production. While O2 rates reported in this study were done under as consistent conditions as possible, these values may not represent true growth O2 production rates.   In vivo spectroscopy All spectroscopic measurements were performed with biological triplicates at each timepoint. Light-induced absorbance and chlorophyll fluorescence yield were measured using a !!!!IJ!kinetic spectrophotometer/fluorimeter (Hall C et al. 2011, Livingston et al. 2010, Sacksteder et al. 2001) modified for liquid samples by replacing the leaf holder with a temperature-controlled, stirring enabled cuvette holder (standard 1 cm pathlength). Cells were treated with a far red light LED (730nm) in otherwise darkness for 20 min to oxidize the plastoquinone pool for accurate F0 measurements. After dark/ far red adaptation, the first saturating pulse for Chl fluorescence measurements was taken with a pulsed measuring beam (505 nm peak emission LED) filtered through a BG18 (Edmund Optics) glass filter. The sample was then illuminated with 160 *mol photons m-2s-1 of photosynthetic photon flux density (PPFD), provided by a pair of light emitting diodes (LEDs) (Luxeon III LXHL-PD09, Philips) with maximal emission at 620 nm, directed towards opposite sides of the cuvette, perpendicular to the measuring beam. Fluorescence yields from saturating pulses were measured under actinic light and averaged over 6 measurements, separated by 120 s intervals. Both absorption and fluorescence measuring pulses were 20-35 *s in duration and attenuated to produce less than 0.1% increase in chlorophyll fluorescence yield in dark-adapted samples. The first dark interval relaxation kinetics (DIRK) trace measuring the electrochromic shift kinetics (one trace per biological replicate) was measured after three minutes of actinic illumination, followed by one minute of actinic light. Actinic LEDs were calibrated using a Licor LI190 PAR quantum sensor.   The kinetics of PSI primary donor (P700+) oxidation/reduction kinetics measurements were performed 5 minutes after the cells had been treated with 10 *M 3(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU, Sigma Aldrich) as described in (Lucker, et al. 2013). Briefly, absorbance changes attributed to P700 redox changes were monitored using a measuring LED (peak emission: 720 nm) filtered through a 5 nm band pass filter centered at 700 nm, giving an emission peak at ~705 nm. A Schott RG695 color glass filter was used to protect the detector !!!!JK!from actinic light. The kinetics of P700+ reduction were measured by the absorbance changes during light to dark transition after 10 seconds of actinic illumination with 160 *mol photons m-2s-1.  77K Chl ßuorescence emission spectra   Cell cultures were taken directly from the incubator at the specified times and poured into a 3ml styrene cuvette in triplicates at 3µg Chl/ml. The cuvettes were immediately placed in a liquid N filled dewar, freezing the cells, and then stored at -80¡C until further analysis. Immediate freezing of samples was important to prevent state transitions. For analysis, cuvettes were placed in a liquid N filled Dewar. A beam of blue excitation light from an LED (peak 440nm) was directed through an optic fiber (Murakami 1997). Fluorescence emission spectra from 187-1110nm were detected using a fiber optic spectrophotometer (USB2000+ UV-VIS, Ocean Optics, Florida, USA) (Hill et al. 2012).  To determine relative intensity values, data from a TAP media blank was first subtracted. Samples were then normalized to the 685nm (PSII associated peak). PSI associated fluorescence was maximal at 715 nm and was used from comparison between samples.    13C Bicarbonate Labeling   13C bicarbonate from a stock solution was added to a final concentration of 17mM to 400 ml cultures of cells growing with or without N for 6h. Cells were harvested at 0, 1, 2.5, 5, 7, 10 and 15 minutes after the addition of label by quenching 40 ml culture aliquots with 10ml methanol at -80oC. Samples were centrifuged at 0¡C, the supernatant discarded and intracellular metabolites were extracted with 100% methanol then extracts were dried under a stream of N2. !!!!J%!Methoxyamine-pyridine (50 *L of a 25 mg Methoxyamine-HCl/ mL solution in pyridine) was added to the dried extract and incubated for 45 min at 80 ¡C followed by TMS derivatization as described above for phytol analysis. One microliter of derivative was used for GC-MS. For gas chromatographic separation helium was the carrier gas at a flow rate of 1.1 mL min-1 with the following temperature program: initial column temperature of 100 ¼C, increasing at 10 ¼C min-1 to 325¼C with a final holding time of 5 min. The inlet and quadrupole had temperatures of 320¼C.  Ions corresponding to TMS derivatives of amino acids and sugar phosphates were monitored and corrected for the natural abundance of the elements to give fractional 13C labeling.   Starch Analysis  Total glucose contained in starch was measured after amyloglucosidase and amylase digestion with the Megazyme total starch anaylsis kit ( Megazyme, Ireland), similar to Work et al. (Work, et al. 2010) Briefly, starch pellets remaining after 2:1 methanol:chloroform lipid extraction was autoclaved for 1 hr in 0.1 M Acetate buffer pH 4.8 then was treated with $-amylase and amyloglucosidase for 1 hour at 55!C. Free glucose was quantitated with a colorimetric assay at 510nm as described in the Total Starch Assay kit (Megazyme, Ireland). !!!!J&!ACKNOWLEDGEMENTS  We thank Dr. Bruria Shachar-Hill for electron microscopy. !!!!J'!            REFERENCES !!!!J(!REFERENCES   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!!!!%KF!           CHAPTER-3  The Relationship of Triacylglycerol and Starch Accumulation to Carbon and Energy Flows During Nutrient Deprivation in Chlamydomonas.1                        ______________________ 1This Manuscript has been submitted for Publication to Plant Physiology under the same title. Authors: Matthew T. Juergens, Bradley Disbrow, and Yair Shachar-Hill. I generated and analyzed all data presented in this chapter as well as wrote this chapter. Bradley Disbrow assisted in sample processing and Yair Shachar-Hill assisted with writing and data interpretation.    !!!!%KG! ABSTRACT Because of the potential importance of algae for green biotechnology, considerable effort has been invested in understanding their responses to nitrogen deprivation. The most frequently invoked reasons proposed for the accumulation of high cellular levels of triacylglycerol (TAG) and starch are variants of what may be termed the Overflow Hypothesis. According to this, growth inhibition results in the rate of photosynthetic energy and/or carbon input exceeding cellular needs; the excess input is directed into the accumulation of TAG and/or starch to prevent damage. The present study was aimed at providing a quantitative dataset and analysis of the main energy and carbon flows before and during nitrogen deprivation in a model system to assess alternative explanations. Cellular growth, biomass, starch and lipid levels as well as several measures of photosynthetic function were recorded for cells of Chlamydomonas reinhardtii cultured under 9 different autotrophic, mixotrophic and heterotrophic conditions during nutrient-replete growth and for the first four days of nitrogen deprivation. The results of a 13C labeling time course indicated that in mixotrophic culture, starch is predominantly made from CO2 and fatty acid synthesis is largely supplied by exogenous acetate, with considerable turnover of membrane lipids, so that total lipid rather than TAG is the appropriate measure of product accumulation. Heterotrophic cultures accumulated TAG and starch during N-deprivation, showing that these are not dependent on photosynthesis. We conclude that the overflow hypothesis is insufficient and suggest that storage may be a more universally important reason for carbon compound accumulation during nutrient deprivation. !!!!%KH! INTRODUCTION  The current rate of utilization of fossil fuel products for energy and the chemical industry is unsustainable. Among the alternative renewable replacements, microalgal oil and biomass production have shown considerable promise (Atabani, et al. 2012, Chisti 2007, Ohlrogge, et al. 2009, Williams, et al. 2010) because many algae are capable of rapid photoautotrophic growth to high cell densities and can accumulate high dry weight percentages of triacylglycerol (TAG) (Hu, et al. 2008, Jones, et al. 2012, Wijffels, et al. 2010).  TAG accumulation is induced in many microalgal taxa by a number of stresses including salt and nutrient deprivation (Murphy 2001). Beginning over 100 years ago (Beijerinck 1904) many researchers have reported  that nitrogen (N) deprivation induces high levels of accumulation of TAG and starch in a range of microalgal species at the expense of decreased cell growth and slower metabolism (Granum, et al. 2002, Hu, et al. 2008, Liu, et al. 2013, Martin, et al. 1975a, Shifrin, et al. 1981, Spoehr, et al. 1949). This has led to interest in understanding the underlying physiological and adaptive functions of algal TAG production during N deprivation to help guide engineering of higher oil yields.   In recent years, studies of the effects of nutrient deprivation have revealed many changes in the structure and function of networks across metabolism and other cellular functions, (Blaby, et al. 2013, Goodenough, et al. 2014, Juergens, et al. 2015, Miller, et al. 2010, Schmollinger, et al. 2014). Such studies and mutant screens have led to the identification of enzymes involved in TAG synthesis (Li, et al. 2012b, Merchant, et al. 2012b) and pointed to putative regulatory networks (Gargouri et al. 2015). However the interpretation of system-wide molecular changes and the choice of which among them to target for further investigation and practical purposes is !!!!%KI!uncertain and is strongly influenced by the prevailing views of the function of TAG accumulation.  Several explanations have been proposed for the induction of TAG accumulation in algae under stress, ranging from storing reduced carbon as an energy source for survival and/or future recovery, to lipid reorganization during photosynthetic down-regulation and/or subsequent up-regulation, to photo-protection (Akita, et al. 2015, Grossman, et al. 2010a, Khozin-Goldberg, et al. 2005, Klok, et al. 2014, Kohlwein 2010, Murphy 2001). Roessler (Roessler 1990) appears to have been the first to postulate that algae accumulate TAG as a sink for excess photosynthetic energy and reductant to prevent photochemical damage. In this explanation, photosynthetic carbon and energy assimilation that can no longer be directed to growth when population increase is inhibited by nutrient deficiency or other stresses results in overflow products, particularly TAG. The concept of overflow metabolism has traditionally been associated with the export of metabolites by heterotrophic microbes (Neijssel, et al. 1975) and has more recently also been applied to photosynthetic metabolism in cyanobacteria (Courchesne, et al. 2009, Grundel, et al. 2012, Hays, et al. 2015) and higher plants (Weise, et al. 2011). In microalgal work, the idea of photosynthetic overflow (excess photosynthetic energy and carbon) as the driver of oil accumulation has become a widely accepted explanation (see for example (Hu, et al. 2008, Klok, et al. 2014, Li, et al. 2012b, Li, et al. 2013, Solovchenko 2012). This Overflow Hypothesis (OH) strongly influences research efforts and the interpretation of results in biological and engineering studies of microalgal metabolism and TAG accumulation and also has important implications for the ecophysiology of photosynthetic microbes. While frequently invoked in interpreting the results of nutrient deprivation studies, the OH has not been systematically assessed.  !!!!%KJ! Such systematic assessments should include a quantitative analysis of the major carbon and energy flows during stress-induced oil accumulation, and should be carried out over a range of eco-physiologically and practically meaningful conditions in relevant model organisms (Smith et al. 2010). Despite a burgeoning literature on microalgal TAG accumulation under N deprivation, such studies are lacking. The green alga Chlamydomonas reinhardtii is the most widely studied species in research on microalgal oil production. Although it produces less TAG than some species and has a low tolerance of extreme conditions, its robust growth, ease of culture, ability to grow heterotrophically and the wealth of existing data and available genetic tools (including mutant collections, transformation tools, and a well annotated genome sequence), make it attractive for biofuel research (Merchant, et al. 2012b, Merchant, et al. 2007, Rochaix 2002).  Carbon (C) and Energy (E) balances are established tools in ecology and metabolic engineering and are employed to assess growth and yields during commercial applications. Widely used for monitoring and improving bacterial and yeast fermentation processes, measuring cellular C and E influxes and outflows has also been used in several cyanobacterial and algal studies (Chapman, et al. 2015, Slade et al. 2013, Wagner et al. 2006, Watanabe et al. 1995). Figure 1 shows the C and E entering a Chlamydomonas cell (photosynthesis and/or acetate uptake) as well as the major C and E outputs (growth, maintenance, dissipation, and TAG and Starch production. Figure 1 also illustrates the overflow hypothesis, in which nutrient deprivation causes a redirection of output fluxes from reproduction to TAG and starch synthesis.  Quantifying the relationships between C and E fluxes before and during stress conditions can point to processes such as dissipative metabolic or photochemical processes and can be used to !!!!%%K!make quantitative tests of predictions implied by the OH and alternative explanations for TAG accumulation.    Figure 3.1. The main Carbon and Energy inputs and outputs of Chlamydomonas cells. CO2 provides C input, light provides energy input and acetate provides both. Incoming energy can be dissipated as heat, consumed for maintenance, or used in cell growth or biosynthesis of storage compounds (starch and oil). Incoming carbon is used in growth, stored in starch or oil, or respired as CO2. !!!!%%%!Here we present measurements of cellular growth, biomass, starch, TAG, and total fatty acid accumulation, changes in photosynthetic fluxes and acetate uptake rates across 5 light levels and 2 media compositions before and during the course of 96 h of N deprivation. Results from a 13C labeling time course experiment highlight the involvement of multiple lipid pools during N deprivation, so that (a) total lipid rather than TAG alone is the appropriate pool for C & E balances; and (b) lipid is preferentially formed from exogenous fixed carbon (when available) rather than from photosynthate, the latter contributing more to starch production. TAG and starch accumulated to significant levels in the dark after N depletion, demonstrating that TAG accumulation is not dependent upon photosynthetic activity. Using the photosynthetic, biomass and uptake rates, C and E influxes and outflows are reported and compared to predictions deduced from the overflow hypothesis.  Several aspects of the findings do not support the OH in its straightforward form. We conclude that the overflow hypothesis is insufficient to explain carbon accumulation during nutrient deprivation and suggest that storage of biosynthetic precursors and/or chemical energy as adaptive survival traits may be more universal reasons. !!!!%%&! RESULTS Growth rates in N-replete conditions   To characterize the effect of light and trophic conditions on Chlamydomonas reinhardtii growth rates, cells were grown in defined media either with acetate as a fixed carbon source (Tris Acetate Phosphate, TAP media) or without acetate (High Salt, HS media) at light levels of 0, 5, 15, 40, and 160 µmol photons m(2 s(1. Cell growth measured during exponential (steady state) growth is presented as specific growth rate in Figure 3.2. This range of light intensity spans heterotrophic growth in the dark in TAP media, light-limited autotrophic and mixotrophic conditions and saturating light levels. Illumination was kept below levels (at or above 200 µmol photons m(2 s(1) where cells showed chlorosis and reduced growth rates indicative of light stress and/or photo-inhibition (Bonente et al. 2012, Juergens, et al. 2015, Peers, et al. 2009). Increasing illumination from 40 to 160 µmol photons m(2 s(1 did not increase growth rate, suggesting that these cells are carbon limited (Ref on C limitation in Algae) (Goldman et al. 1974, Spalding 1989). This suggestion was supported by the observation that growth rates increased approximately twofold when humidified ambient air was circulated through the culture flasks to increase CO2 availability.   !!!!%%'!      Figure 3.2. Specific growth rates during exponential growth in nitrogen replete media. Growth with acetate (TAP media, squares) and or without (HS media, circles) under continuous illumination at light levels from 0 to 160 µ mol photons m-2 s-1. Specific growth rate (SG) is the rate constant for log growth and is inversely related to doubling time as SG = Ln (2)/ (doubling time). Error bars indicate +/- SD (n=3 biological replicates).  0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0 50 100 150 Specific growth/Hr Light Intensity (µmol photons m-2 s-1 ) !!!!%%(! Biomass Biomass measured as ash free dry weight (AFDW) is shown in Figure 3.3A and Figure 3.3B. Cellular biomass in N replete cells is very similar across light levels, with mixotrophic and heterotrophic cultures (TAP medium) having approximately 25% more AFDW than autotrophic cultures. Cells cultured in TAP medium increased ~2 fold in cell biomass over 96 hrs whereas HS cells increased less in biomass, especially in the first 48h of deprivation.  Biomass and C and N contents of cells were measured to quantify net carbon accumulation rates and cellular nitrogen contents as a proxy for nitrogenous biomass (protein plus nucleic acids). Cellular C and N contents measured by elemental analysis are shown in Figure 3.3C to Figure 3.3F and C:N ratio is shown in Figure 3.4. Carbon accounts for ~40-47% of cell dry weight during growth in N replete media (Figure 3.3C and Figure 3.3D), levels which changed little during N deprivation in TAP media - whereas C levels decreased in cells deprived of N in HS media. Nitrogen levels as a percentage of dry weight were higher in nitrogen replete cells growing autotrophically than in TAP media and fell significantly during N deprivation (except for heterotrophic cultures). The falling cellular %N during deprivation is due to the accumulation of biomass components lacking N, so that total N contents per cell were little changed. !!!!%%F!   Figure 3.3. Cellular Biomass and % Carbon and Nitrogen before and during nitrogen deprivation. Biomass is given as Ash Free Dry Weight (AFDW) per million cells (A and B), %C (C and D), and %N (E and F) is reported for cultures grown in TAP (A C, and E) and HS (B, D and F) media and deprived of N at time 0. Empty diamonds, cells cultured at 160 µmol photons m-2 s-1 (µE); empty squares, 40 µE; empty triangles, 15 µE; filled diamonds, 5 µE; filled squares, 0 µE.  Error bars indicate +/- SD (n=3). !!!!%%G!  Figure 3.4. Carbon to Nitrogen (C:N) ratios during N deprivation. Data for TAP(A) and HS(B) cultures were taken before and during N deprivation. In all cases, 160 µmol m-1 s-1 is indicated by hollow diamonds, 40 µmol m-1 s-1 by hollowed squares, 15 µmol m-1 s-1  indicated by hollowed triangles , 5 µmol m-1 s-1  by solid diamonds, and 0 µmol m-1 s-1 by solid squares.  Error bars indicate SD (n=3).  The Accumulation of TAG and Starch   The first compound known to accumulate to high levels during N deprivation is starch (Klein 1987) and the upregulation of starch synthesis gene expression has been reported during N deprivation (Ball, et al. 1990, Juergens, et al. 2015, Miller, et al. 2010). Increases in starch levels during N deprivation are shown in Figure 3.5; they account for the majority of the increases in cellular dry weight. Starch levels rose linearly for 48 hrs after N deprivation in both TAP and HS media, with higher rates at higher light levels and in TAP compared to HS media. After 48 hrs starch accumulation rates decreased, with cessation of starch accumulation by 96 hrs for all but the cells cultures at the highest light level in TAP media and those at the lowest light levels in HS media.  !!!!%%H!During nitrogen-replete growth cultures contained close to 5*g of total fatty acid per million cells (Figure 3.5B and Figure 3.5C) and no TAG was detected (Figure 3.5E and 3.5F). During N deprivation in mixotrophic cultures, TAG levels were detected at 24 hours while little or no TAG was detected in HS cultures until 48h. Levels of starch and TAG were previously reported by Fan et al. (Fan, et al. 2012) and by Siaut et al. (Siaut, et al. 2011) for several C. reinhardtii strains cultured at 50 and 150 µmol photons m(2 s(1 respectively, after 2 or 3 days of N deprivation. Our results for these time points in cells supplied with 40 and 160 µmol photons m(2 s(1 are similar to those previously reported (except for mutant strains lacking starch). The observation that TAG accumulates during nitrogen deprivation in Chlamydomonas cells cultured heterotrophically in the dark separates the induction of TAG accumulation from photosynthesis. High levels of TAG accumulation have been reported in heterotrophically grown Chlorella species (Liu et al. 2011, Miao et al. 2004) and TAG accumulation was also observed in heterotrophically cultured Chlamydomonas after carbon starvation (Singh et al. 2014) and during N deprivation in the presence of high levels (60mM) of acetate (Fan, et al. 2012). !!!!%%I!    Figure 3.5. Starch, FAME and TAG levels before and during nitrogen deprivation. Starch (A,B), FAME (C,D), and TAG (E,F)  levels are reported in ug per million cells for cultures in TAP (A, C, E) and HS (B, D, F) media. Empty diamonds, cells cultured at 160 µmol photons m-2 s-1 (µE); empty squares, 40 µE; empty triangles, 15 µE; filled diamonds, 5 µE; filled squares, 0 µE.  Error bars indicate +/- SD (n=3).!!!!!%%J!The increases in total cellular FAME levels we observed were lower and slower to start than increases in TAG. This is consistent with previous reports that membrane lipid levels fall during N deprivation and that some fatty acids used in TAG synthesis come from membrane lipids   (Juergens, et al. 2015, Moellering et al. 2011, Yoon et al. 2012). However the extent to which fatty acids newly synthesized during N deprivation enter bulk membrane lipid pools is not known. The source(s) of carbon entering TAG and the fate of carbon assimilated during nutrient deprivation are relevant to the consideration of cellular metabolic input and output fluxes during TAG synthesis and were therefore probed in a labeling experiment.   13C labeling Results  Chlamydomonas cells growing in nitrogen-replete TAP media were transferred to TAP media without N and containing 13C labeled acetate (100% 13C1,2). After 40 h cells were transferred to TAP media containing unlabeled acetate and lacking N for the remainder of the 96 h deprivation period. Cells cultured mixotrophically at 160 µmol photons m(2 s(1 were used as these conditions are similar to those used in many prior studies and showed the highest sustained starch and lipid accumulation rates. After 40 h of N deprivation in the presence of 13C-acetate, the proportion of starch-derived ions detected that were labeled with 13C was found to be low, with over 65% of the ions containing no 13C label (Figure 3.6A) and over 80% of the total carbon being 12C. Label levels increased modestly during the initial washout period between 40 and 72 h (~28% of starch carbon at 72 h originated from the 13C acetate taken up between 0 and 40 h). Label levels in starch did not significantly change between 72 and 96 h. Thus photosynthetic CO2 fixation and/or cellular biomass components made before deprivation is the dominant source of carbon for net starch synthesis during N deprivation under mixotrophic conditions. In !!!!%&K!such cultures the total starch levels per cell doubled between 40 and 96 h (Figure 3.5A), during which time no 13C label was present in the medium, so we had expected a dilution of the 13C label in starch. The lack of a decrease in fractional labeling level between 40 and 96 h shows that 13C assimilated during nitrogen deprivation (0-40 h) contributed significantly to starch synthesis later.  Labeling in fatty acids from the experiment described above is also shown in Figure 3.6. Figure 3.6B shows the distribution of mass isomers from a representative mass spectrum of palmitate (C16:0, as the methyl ester) from cells collected at 40 h. Labeling results for C16:0 from TAG and two membrane lipid fractions (galactolipids, which are major components of chloroplast membranes, and a polar lipid fraction containing lipids from both chloroplast and extra-plastidic membranes) are shown in Figure 3.6 C to Figure 3.6E. Analogous data for other abundant fatty acids are given in the Figures 3.7 and 3.8. After 40 h of labeling and nitrogen deprivation, approximately 70% of the fatty acid molecules from TAG and membrane lipids contained one or more 13C labeled carbons. In the majority of fatty acid molecules over half the carbon atoms were 13C and the most abundant labeled mass isomer was the fully labeled one (13C in all positions) and approximately half the total carbon in cellular fatty acids was 13C. The preponderance of fatty acid molecules in all pools were either unlabeled or highly labeled, and since the total fatty acid content of the cultures approximately doubled during the 40 h labeling period we infer that 75% or more of the carbon used to synthesize fatty acids during N deprivation is derived from acetate taken up during deprivation.  !!!!%&%! Figure 3.6. 13C labeling of fatty acids and starch during N deprivation in cells cultured in TAP media at 160 µmol photons m-2 s-1. Cells grown to log phase in N replete media were moved at time 0 to TAP media lacking N with 100% uniformly labelled 13C acetate. After 40hrs the media was replaced with TAP media lacking N and containing unlabeled acetate for the remainder of the time course. The proportions of different mass isomers are plotted as a function of time. Panel A shows mass isomers of the m/z = 319 fragment ion containing carbons 2-6 of the glucose monomers of starch. Filled squares represent M+0, filled diamonds M+1, empty triangles m+2, XÕs M+3, and empty circles M+4. Panel B is a histogram of the mass isomer distributions for a representative 16:0 FAME mass spectrum showing the abundance of highly labeled molecules together with unlabeled molecules (M+0 and naturally-occurring M+1 masses). Panel C, D, and E represent mass isomer distributions for ions of intact C16:0 FAME molecules from TAG, MGDG, and Polar Lipid fractions respectively. Filled squares represent unlabeled molecules (M+0); filled diamonds, M+1 (one 13C atom); empty triangles, M+2; empty circles, the sum of M+3 through M+16 (fully labeled), Error bars indicate range (n=2).    !!!!%&&!High fractional labeling in galactolipids and polar lipids (Figure 5C and Figure 5D) after 40 h of N deprivation shows that membrane lipids are synthesized at a significant rate during nitrogen deprivation. Since the cellular levels of membrane lipids fall by more than 50% during this time (Juergens, et al. 2015), it is clear that substantial rates of simultaneous synthesis and breakdown of membrane lipids are occurring, suggesting acyl chain trafficking through these lipid classes. Therefore we consider that total cellular lipid rather than TAG is the appropriate pool to be measured when evaluating net carbon and energy fluxes into accumulated intracellular compounds during nitrogen deprivation.  After replacement of labeled with unlabeled acetate the proportion of fatty acid molecules that were highly 13C-labeled fell while the proportion of unlabeled molecules rose; this is consistent with dilution of highly labeled with newly synthesized unlabeled fatty acids. The similarity in C16:0 labeling patterns in different lipid pools is consistent with the continued flux of newly synthesized fatty acids into membrane lipids as well as TAG between 40 and 9 6h. In addition, during this ÒchaseÓ period there is an increase in the proportion of fatty acid molecules with low levels of labeling (M+1 and M+2) which we attribute to flow of C from non-lipid compounds synthesized during the initial stages of N deprivation into lipid synthesis at later stages.  !!!!%&'!        Figure 3.7. 13C labeling of C16:3 fatty acids during N deprivation in cells cultured in TAP media at 160 µmol m-1 s-1 . Cells grown to log phase in N replete media were moved at time 0 to TAP media lacking N with 100% uniformly labelled 13C acetate. After 40hrs the media was replaced with TAP media lacking N and containing unlabeled acetate for the remainder of the time course. The proportions of different mass isomers are plotted as a function of time. Panel A, B, and C represent mass isomer distributions for ions of intact C16:3 FAME molecules from TAG, MGDG, and Polar Lipid fractions respectively. Filled squares represent unlabeled molecules (M+0); filled diamonds, M+1 (one 13C atom); empty triangles, M+2; empty circles, the sum of M+3 through M+16 (fully labelled), Error bars indicate range (n=2).    !!!!%&(!                     Figure 3.8. 13C labeling of C18 fatty acids during N deprivation in cells cultured in TAP media at 160 µmol m-1 s-1 . Cells grown to log phase in N replete media were moved at time 0 to TAP media lacking N with 100% uniformly labeled 13C acetate. After 40hrs the media was replaced with TAP media lacking N and containing unlabeled acetate for the remainder of the time course. The proportions of different mass isomers are plotted as a function of time. Panel A, B, and C represent mass isomer distributions for ions of intact C18:1 FAME molecules from TAG, MGDG, and Polar Lipid fractions respectively. Panel D, E, and F represent mass isomer distributions for ions of intact C18:2 FAME molecules from TAG, MGDG, and Polar Lipid fractions respectively. Panel G, H, and I represent mass isomer distributions for ions of intact C18:3 FAME molecules from TAG, MGDG, and Polar Lipid fractions respectively. Filled squares represent unlabeled molecules (M+0); filled diamonds, M+1 (one 13C atom); empty triangles, M+2; empty circles, the sum of M+3 through M+16 (fully labeled), Error bars indicate range (n=2).   !!!!%&F!Light Absorption, Utilization Efficiency and Dissipation   To gauge photosynthetic light capture rates, levels of chlorophyll (chl) and the efficiency of photosystem II were measured. Chl levels are shown in Figure 3.9A and Figure 3.9B for cells cultured in TAP and HS medium respectively. Nitrogen-replete cells growing under mixotrophic conditions contained more Chl than autotrophically growing cells, with the exception of mixotrophic cells at the lowest light levels. During nitrogen deprivation chl levels fell continuously in mixotrophically cultured cells, whereas under autotrophic conditions chl levels fell during the first two days and did not change significantly thereafter.  Both theoretical quantum yield at photosystem II (FV/FM) and light driven yields ('II) were measured by fluorescence spectroscopy as measures of the efficiency with which absorbed light drives linear electron flow. In TAP media FV/FM and 'II (Figure 3.9C and Figure 3.9E respectively) declined through the nitrogen deprivation period, with the exception of cells under 5 µmol photons m-2 s-1. In autotrophic cells FV/FM and 'II (Figure 3.9D and Figure 3.9F) did not significantly change during nitrogen deprivation.  !!!!%&G! Figure 3.9. Chlorophyll, Photosynthetic efficiency and Non Photochemical Quenching. !!!!%&H!Figure 3.9 (contÕd). Panel A and B represent Chlorophyll levels in Tap and HS conditions respectively. Panels C and D represent Maximum photosynthetic efficiency from dark adapted cells for TAP and HS conditions respectively. Panels E and F represent photosynthetic efficiency in the light for TAP and HS conditions respectively. Panels G and H represent photosynthetic efficiency in TAP and HS conditions respectively. In all cases, 160 µmol m-1 s-1 is indicated by hollow diamonds, 40 µmol m-1 s-1 by hollowed squares, 15 µmol m-1 s-1 indicated by hollowed triangles , 5 µmol m-1 s-1  by solid diamonds, and 0 µmol m-1 s-1 by solid squares.  Error bars indicate SD (n=3).   During photosynthetic stress conditions and at high light levels, cells dissipate excess absorbed light energy, by non- photochemical quenching (NPQ) (Muller, et al. 2001). We measured NPQ before and during N deprivation under mixotrophic (Figure 3.9G) and autotrophic (Figure 3.9H) conditions using the same light level for NPQ measurement as the level under which the cells had been cultured. NPQ levels recorded before and throughout nitrogen deprivation for all cultures were lower than values reported in previous studies in which much higher light levels were used for measurement than for growth (Niyogi, et al. 1997, Peers, et al. 2009, Terauchi et al. 2010). The absence of significant increases in NPQ after N-deprivation suggests that there is little or no light stress due to excess energy intake under these conditions.   Photosynthetic Fluxes  Photosynthetic fluxes were assessed by measuring oxygen evolution and electrochromic shift (ECS) absorbance spectroscopy as indicators of linear and cyclic electron flow respectively (Figure 3.10). Oxygen evolution rates at 160 µmol photons m(2 s(1 for both autotrophic and heterotrophic cultures decreased markedly during N deprivation (Figure 3.10A and Figure 3.10b). At 40 µmol photons m(2 s(1 and below, net oxygen fluxes were much less during nitrogen replete growth but remained largely unchanged during deprivation. Mixtrophically !!!!%&I!cultured cells at lower light levels and autotrophic cells were net oxygen consumers. ECS decay rates, which reflect the proton fluxes across thylakoid membranes that drive photosynthetic ATP synthesis, decreased several fold during nitrogen deprivation for cells under the highest light level for both mixotrophic and autotrophic cells (Figure 3.10C and Figure 3.10D). At lower light levels ECS values for N replete cells were significantly less than at 160 µmol photons m-2 s-1 and decreased moderately or were not significantly changed during nitrogen deprivation.  Figure 3.10. Oxygen evolution and electrochromic shift measurements. Net oxygen evolution (A and B) and electrochromic shift decay rates (indicating proton fluxes across the thylakoid membrane, C and D) for cells grown in TAP (A and C) and HS (B and D) media. Results for cells cultured at 160 µmol m-2 s-1 are indicated by empty diamonds, 40 µmol m-2 s-1 empty squares, 15 µmol m-2 s-1 empty triangles, 5 µmol m-2 s-1 filled diamonds, and 0 µmol m-2 s-1 filled squares.  Error bars indicate SD (n=3).   !!!!%&J! Carbon Assimilation and Release    Net CO2 uptake and efflux rates were measured before and during N deprivation (Figure 3.11A and Figure 3.11B). In TAP media, cultures under 160 and 40 µmol photons m-2 s-1 of illumination and in HS media at all light levels showed net CO2 assimilation when provided with nitrogen while cultures exposed to lower light levels or in the dark in TAP media were net CO2 producers. During nitrogen deprivation, net CO2 uptake and output rates decreased, with TAP-grown cells at 40 µmol photons m-2 s-1 becoming net CO2 producers after 2d of deprivation. Acetate uptake rates for mixotrophic and heterotrophic cultures (Figure 3.11C) were higher for cells at lower light intensities and in the dark and decreased during nitrogen deprivation. Thus both photosynthetic CO2 assimilation and consumption of external fixed carbon decreased strongly during N deprivation, with decreases being more marked at higher light levels. !!!!%'K! Figure 3.11. Carbon assimilation rates.  !!!!%'%! ANALYSIS AND DISCUSSION  The first observation of microalgal TAG accumulation under nitrogen deprivation was made over a century ago (Beijerinck 1904) and the potential to utilize this phenomenon for biofuels has been recognized for over 70 years (Harder et al. 1942a, Harder et al. 1942b). Among several explanations offered, the overflow hypothesis (OH), now at least 25 years old (Roessler 1990), currently holds sway over much of the interpretation of algal TAG accumulation data. The OH explains the synthesis of TAG and/or starch by algae during nutrient deprivation as a response to excess photosynthetic energy and/or carbon assimilation. The results of this study allow an assessment to be made of the OH in its straightforward form by making a series of inferences from it about the relationships between photosynthetic carbon and energy uptake rates and the rates of accumulation of TAG and starch and comparing these expectations to the data(Table 3.1).  First, if accumulation is driven by surplus energy or carbon from photosynthesis, no accumulation is expected to occur in the dark. Starch accumulation rates in the dark were equal to or greater than rates for autotrophic cultures at all light levels and were exceeded only by mixotrophic cultures at the higher two light levels. We conclude either that starch accumulation during nitrogen deprivation is primarily driven by factors unrelated to photosynthetic overflow or that there is a separate additional explanation for its accumulation under heterotrophic conditions. TAG accumulation in the dark was also significant, although net Figure 3.11. (contÕd). Net CO2 uptake in TAP (A) and HS (B) media. Measurements of cells cultured at 160 µmol m-2 s-1 are indicated by empty diamonds, 40µmol m-2 s-1  by empty squares, 15µmol m-2 s-1  indicated by empty triangles , 5µmol m-2 s-1  by filled diamonds, and 0 µmol m-1 s-1 by filled squares. Acetate uptake in TAP cultures are in panel C for Nitrogen replete cells (White), 0-24hrs of N-deprivation (grey), and 24-96hrs of deprivation (black). Error bars indicate SD (n=3).   !!!!%'&!fatty acid accumulation was modest compared to cultures in the light. Indeed heterotrophic cells from taxa as diverse as fungi bacteria and mammals have been shown to accumulate TAG under nutrient deprivation (Alvarez et al. 2002, Frenk et al. 1958, Morin et al. 2011, Murphy 2001, Packter et al. 1995). !!!!%''!       Table 3.1. Overflow Hypothesis predictions and verification. This table summarizes the predictions associated with the Overflow Hypothesis of TAG accumulation and whether they were supported or not by this study.              !!!!%'(!Second, the OH links growth before nutrient deprivation to TAG production afterwards; initially we examine carbon balances.  Figure 3.12 shows a comparison of carbon accumulation rates before and during the first 24 h after N deprivation. The first 24 h after deprivation were selected for comparison with N-replete growth since at this time photosynthetic function is most similar to pre-deprivation rates. In this work, as elsewhere (Fan, et al. 2012, Siaut, et al. 2011) starch was found to be the dominant carbon sink, with total FAME accounting for less than 15% of net carbon accumulation during the first 24 h. Total FAME accumulation rates only match and begin to exceed those for starch after 48 h, by which time photosynthetic fluxes are substantially lower.  For autotrophic cells across all light levels the total carbon accumulation rates after deprivation are close to 90% of those during N replete growth, which is consistent with the OH as applied to total C. For cells cultured under mixotrophic or heterotrophic conditions, total carbon accumulation rates after deprivation are not well explained by the rates during N replete growth. Although there is a trend with increasing light levels in TAP media towards greater total C accumulation rates before and after deprivation, the slope corresponds to only 11% and the trend does not extrapolate meaningfully to low light and lower growth rates. Concerning the origins of carbon from which TAG and starch are synthesized, 13C acetate labeled both these pools in mixotrophic cultures that were grown at the highest light level to maximize photosynthetic inputs. Photosynthesis is therefore not the dominant metabolic source of acetyl-CoA used in TAG synthesis in mixotrophy. Fan et al. (Fan, et al. 2012) have suggested that TAG accumulation is dependent upon total carbon precursor availability rather than only CO2 from photosynthesis. !!!!%'F!  Figure 3.12. Cellular carbon accumulation rates before and during the first 24hrs after nitrogen deprivation. Accumulation during nitrogen replete growth was derived from growth rates and total cellular carbon contents; carbon accumulation after deprivation is due to starch and lipid production. Filled symbols represent autotrophic conditions (HS media), while empty ones represent mixotrophic and heterotrophic conditions (TAP media). Squares indicate values for total Carbon accumulation (starch plus total FAME) while diamonds and circles represent starch and total FAME respectively. Within each series, increasing light levels correspond to symbols from left to right Error bars depict +/- SD (n=3). The dashed line corresponds to a slope of 1 and the solid line is the least-squares best fit to the autotrophic total carbon accumulation results. !!!!%'G! Third, regarding redox energy balance the OH posits that the function of TAG synthesis during nutrient deprivation is to consume excess photosynthetically produced reductant and thereby prevent photodamage due to over reduction of electron transport chain components. We would expect from this that cellular fatty acid levels should rise most rapidly when net oxygen production is highest (early on in nutrient deprivation), which is not the case (compare Figure 4C and 4D with Figure 7A and 7B). Since cells at the highest light level were CO2 limited during N-replete growth, these might be expected to show either high rates of NPQ or elevated fatty acid accumulation rates compared to starch early during deprivation, neither of which was observed. Related to the potential role of TAG synthesis in mitigating over reduction in the electron transport chain is a fourth prediction, one that has received apparent support from past studies. In this version of the OH, following N deprivation cells experience stress from excess light energy uptake leading to the activation of energy dissipative mechanisms such as non-photochemical quenching and/or the Mehler reaction in addition to TAG synthesis. Increases in NPQ under nutrient deprivation have been previously observed, reaching levels that account for a substantial proportion of light energy reaching the photosystems (Allen et al. 2008, Antal et al. 2006, White et al. 2011, Wilson et al. 2007). However, those measurements of NPQ were made under much higher light levels than those under which cultures were maintained.  Measurements of NPQ under light levels under which cells had been cultured (Figure 3.9) show stable or decreasing NPQ in TAP media while in HS media cells exhibit decreasing NPQ following N deprivation. Slow decreases in photosynthetic efficiency, Chl levels and gas exchange rates in parallel with low and decreasing levels of NPQ indicate that a coordinated downregulation of photosynthetic structure and function that is used to match energy supply and demand under a wide range of !!!!%'H!energy input rates, making the idea of overflow from a mismatch of supply and demand less appealing. An alternative perspective on energy balances is to assess the relationship between photosynthetic energy input and the accumulation of TAG and starch. Figure 3.13A and Figure 3.13B show the values of an estimate of the rates of cellular light energy intake: the product of illumination level, chlorophyll content and 'II. This parameter accounts for light absorbed and the quantum efficiency with which light drives photosynthetic electron flow (Genty, et al. 1989). While only part of the energy capture estimated by this parameter becomes available to cellular functions in the form of ATP and NADPH, the dominant losses involved, such as that associated with light of wavelength < 680nm and the stoichiometries and/or irreversibilities of electron transport and H+/ATPÕase fluxes are such that the proportion lost is likely to be similar across the conditions used here. The photosynthetic energy capture rates span a range of approximately 50 fold under nitrogen-replete conditions, a range which narrows during N deprivation since the capture rates fall more strongly in cells cultured with lower light levels. The extent to which light capture rate is reflected in lipid, starch and total C accumulation rate is shown in Figure 3.13C to Figure 3.13H as the ratio of carbon accumulated to the light capture parameter. This ratio varies widely among culture conditions at any given time, with cells exposed to higher light levels accumulating less starch or FAME as a proportion of apparent light energy captured. The OH would lead one to expect higher rather than lower proportions of light energy to be used for carbon accumulation at higher light levels. Alternatively or additionally a threshold of light capture rates might be expected to be needed to result in photosynthetic energy overflow that triggers C accumulation; a comparison of Figures 3.13A and 3.13B with Figure 3.5 shows that no such threshold is evident.  Starch and TAG rates vs. acetate is presented in Figure 3.14. !!!!%'I! Figure 3.13. Accumulated Carbon V.S Potential absorbed light  !!!!%'J!Figure 3.13 (ContÕd). A metric of potential usable light by photosystem II was calculated from 'II times Chl levels* light intensity per million cells. TAP cultures are in panel A and HS culture data is in panel B. Panel C and D show the rate of starch accumulation vs. absorbed light for TAP and HS respectively. Panel E and F show rates of FAME accumulation vs. absorbed light for TAP and HS respectively. G and H display rates of total carbon accumulation vs. absorbed light. For TAP and HS repectively In all cases, 160 µmol photons m-2 s-1 (µE) is indicated by hollow diamonds, 40 µE by hollowed squares, 15 µE indicated by hollowed triangles , 5 µE by solid diamonds.  Error bars indicate SD (n=3).  The OH can also be considered in terms of the relationship between captured light energy and the energy deposited in accumulated compounds; for the latter we used heats of combustion for starch and glycerolipid (and for nitrogen replete growth also of protein/nucleic acids). This comparison is made in Figure 3.15A and Figure 3.15B for mixotrophic and autotrophic cultures respectively. For autotrophic cells at lower light levels and during N deprivation but not before, there is a correlation between photosynthetic energy input (X axis of figure 3.15B) and the accumulation of chemical potential energy in starch plus TAG (Y axis). For N replete, mixotrophic cultures and higher light levels, this correlation is not evident. The ratio of light energy input to chemical energy accumulated is shown as a function of deprivation time in Figure 3.15C and 3.15D. This ratio ranges by 20 fold across light levels with cells having higher light input rates accumulating less of it in starch and TAG, which is not expected if C compound accumulation is a large contributor to coping with light energy input. !!!!%(K!                Figure 3.14. µg Starch and FAME per uptaken acetate. Panels A and B represent Starch and FAME accumulation rates respectively for TAP conditions. In all cases, 160 µmol m-1 s-1 is indicated by hollow diamonds, 40 µmol m-1 s-1  by hollowed squares, 15 µmol m-1 s-1  indicated by hollowed triangles , 5 µmol m-1 s-1  by solid diamonds, and 0 µmol m-1 s-1 by solid squares.  Error bars indicate SD (n=3).     !!!!%(%!    Figure 3.15 Light energy intake and energy stored in accumulated biomass (as heat of combustion) in TAP (panels A and C) and HS (panels B and D). The relationship between this energy output into biomolecules and the energy input from photosynthetic light capture is shown in panels A and B. The ratio of chemical potential energy accumulated in each 24 h period to the light energy intake rate is shown in panels C and D. For A and B, Time 0 hrs is indicated by empty diamonds, 24hrs N- is indicated by empty squares, 48 hrs N- is indicated by empty triangles, 72hrs N- is indicated by empty circles, and 96hrs is indicated by XÕs. For C and D, 160 µmol photons m-2s-1 (µE) is indicated by empty diamonds, 40 µE by empty squares, 15 µE indicated by empty triangles, 5 µE by filled diamonds.  Error bars indicate +/- SD (n=3).  !!!!%(&!CONCLUSION   This study was aimed at obtaining sufficient data to assess of the OH. Some of the results, such as the trend towards higher rates of TAG accumulation at higher light levels, and the correlation between total net carbon assimilation before and after nutrient deprivation in autotrophic cells are consistent with the OH, although that they are also compatible with alternative, storage based explanations. Other observations, including starch and TAG accumulation in heterotrophic cultures, the timing of TAG accumulation relative to maximal photosynthetic rates, and the apparent absence of correlation between light stress or excess reductant and TAG or starch accumulation show that the OH is insufficient as an explanation for the C accumulating response of algae during nutrient deprivation.  The results of a number of studies have been interpreted in terms of the OH hypothesis. One such study concerns the Chlamydomonas PGD1 galactolipase mutant.  This mutant accumulates half as much TAG as wild type cells and is found to undergo severe chlorosis and cell death during N deprivation unless it is given DCMU (an inhibitor of PSII electron transfer). If excess photosynthetic energy is normally used for fatty acid synthesis when growth is halted by nutrient deprivation, reduction of TAG synthesis in this mutant could cause cell damage due to energy overflow. In light of the fact that most carbon and energy during N deprivation enter starch rather than TAG it would be important to measure starch levels to determine C and E balances. Also the OH explanation for the PDG1 phenotype assumes that the mutation has no significant effect on the chloroplast membrane and thus the environmnent around the photocenters, which may induce stress on the organism during N deprivation that would be alleviated by inhibiting PSII. It would also be of interest to measure such dissipative mechanisms as NPQ and the Mehler reactions in the PDG1 mutant under N deprived culture conditions, to !!!!%('!verify whether there is evidence for increased light stress. Finally, while PGD1 cells accumulate ROS, this was only found to accumulate 7 days after N deprivation - well past the point where photosynthetic fluxes are substantial. This suggests (as the authors note) that ROS is more likely linked to autophagy than to energy overflow.   We suggest that alternative explanations for starch and TAG accumulation by microalgae under nutrient deprivation should also be assessed experimentally. A plausible alternative role for TAG accumulation during N is that TAG serves as a storage site for, and a subsequent source of, acyl chains to aid in membrane degradation and resynthesis. One study following 14C labeled arachidonic acid found that this fatty acid was mobilized from TAG to chloroplast membrane during recovery {Khozin-Goldberg, 2005 #921}. The correlation between nutrient deprivation and the accumulation of TAG in photosynthetic algae has been known for over a century, its significance for green biotechnology development as well as for the ecophysiology of nutrient cycles makes understanding the adaptive causal links involved important and should be subjected to further experimental assessment. Future experiments should include 14C labeling during N deprivation and following the label during N recovery in total biomass, starch, TAG, amino acids and membrane lipids. !!!!%((! METHODS   Culturing  Chlamydomonas reinhardtii strain cc400 cw-15 mt++ was obtained from the Chlamydomonas Research Center and grown at 23 ¡C in liquid Tris-Acetate-Phosphate (TAP) media (Gorman, et al. 1965) and Sueoka High Salt (HS) media (Sueoka 1960) in 1L flasks shaken at 125 rpm under continuous illumination at 160, 40, 15, 5 and 0 µmol photons m(2 s(1 and ambient CO2 concentrations. Cell growth was determined by optical density measurements at 750 nm using a DU 800 spectrophotometer (Beckman-Coulter). Cultures were grown to cell densities of between 0.15 and 0.3 O.D. to minimize self-shading which becomes significant in denser cultures. Cells were counted using a Z series Coulter Counter cell and particle counter (Beckman-Coulter).  For N deprivation, cells were centrifuged and resuspended in TAP media lacking ammonium chloride (nitrogen source).  Chlorophyll Concentration  Cells were collected by centrifugation of 1 ml culture and Chl was extracted in 1 ml of 80 % acetone for 20 minutes from pelleted samples after the supernatant culture medium was removed. After extraction, samples were pelleted by centrifugation and the supernatant was used for analysis. Chl was quantified spectroscopically as described in (Ritchie 2006) using a DU 800 spectrophotometer (Beckman Coulter).    !!!!%(F!Ash Free dry weight 50 ml culture volume was harvested at each time point and cells were filtered onto Millipore Glass Fibre Prefilters (Millipore) under reduced pressure. Filtered cells were dried in an oven at 100¡C for 4 h and weighed on a Sartorius CP225D analytical balance. Weighed samples were then heated 540¡C to incinerate the cell biomass and the filters were reweighed (Zhu et al. 1997). Ash free dry weight (AFDW) was taken as the difference between sample weights before and after incineration.  Carbon and Nitrogen Contents  Aliquots of 50 ml of culture were harvested by centrifugation at each time point. Cells were then pelleted by centrifugation and frozen at -78¡C then lyophilized for 12h. Dried samples were weighed on a Sartorius CP225D analytical balance in tin capsules, which were then submitted to Duke Environmental Stable Isotope Laboratory (Duke University) for elemental analysis.  Lipid analyses  Cells harvested by centrifugation at 0oC and total lipids were extracted from samples containing the equivalent of ~10 mg of dry cell mass by the method of Folch (Folch, et al. 1957). Briefly, cells were extracted with 1ml of CHCl3:MeOH (1:2, vol/vol), vortexing samples for 2 min.  The sample was then centrifuged and the supernatant was collected; the extraction was repeated two more times and the supernatant extracts were pooled. Extracts were dried under flowing N2 gas at room temperature and stored at -20 ¡C until analysis.   Total TAG was quantified using Thin Layer Chromatography (TLC) separation and densitometry. Briefly total lipid extracts were loaded onto Analtech uniplate silica gel HL plates !!!!%(G!(Analtech) and separated with hexane: diethyl ether: acetic acid (70:30:1 vol/vol). After iodine staining, samples were scanned with a Cannon image class mf4690 scanner. ImageJ software (National Institute of Health) was used to quantitate TAG levels from the images. Conversion of integrated density into weight of TAG was performed by comparison with a biological standard sample (96hN-deprived Chlamydomonas TAG extract quantified by Gas Chromatography Flame Ionization Detection (GCFID) of its Fatty Acid Methyl Esters (FAMEs).  FAMEs were quantified by GCFID after derivatization of lipid samples. C15 TAG in toluene was first added to total cell lipid extracts as internal standard. Total lipid extracts were treated with 150ul 2M methanolic KOH in 1ml hexane and vortexed for 5 min at room temperature completion. 6N HCl was added to neutralize the pH, samples were vortexed for 1 min and the hexane phase was transferred to another glass tube and dried under flowing N2 gas at room temperature and stored at -20¡C. Remaining free fatty acids were derivatized with silylation agent N-tert-butyldimethylsilyl- N-methyltrifluoroacetamide (MTBSTFA) (Sigma, Saint Louis, MO) in pyridine. Samples were dried, resuspended in 1 ml hexane, and quantified using an Agilent GCFID with a DB-23 column (Agilent) as previously described(Pollard et al. 2015).  Oxygen Production and Consumption Rates  Changes in dissolved oxygen in cell suspensions were measured with a NEOFOX analyzer FOXY-R probe with a FOXY-AF-MG coating (Ocean Optics) as previously described (Juergens, et al. 2015).The O2 sensor was immersed in 2 ml of culture in a capped 3ml cuvette with stirring. Net oxygen evolution was measured for 5 minutes at the same illumination level as !!!!%(H!the culture had been grown in and O2 consumption was measured for 1 minute in the dark immediately after the light period.    In vivo fluorescence spectroscopy All spectroscopic measurements were performed with biological triplicates at each time point as previously described (Juergens, et al. 2015). Light-induced absorbance and chlorophyll fluorescence yield were measured using a kinetic spectrophotometer/fluorometer (Hall C, et al. 2011, Livingston, et al. 2010, Sacksteder, et al. 2001) modified for liquid samples by replacing the leaf holder with a temperature-controlled, stirring enabled cuvette holder (standard 1 cm pathlength). Cells were maintained under far red light LED (730nm) 20 min to oxidize the plastoquinone pool for accurate F0 measurements. After dark/ far red adaptation, the first saturating pulse for Chl fluorescence measurements was applied with a pulsed measuring beam (505 nm peak emission LED) filtered through a BG18 (Edmund Optics) glass filter. The sample was then illuminated with the its respective photosynthetic photon flux density (PPFD) using a pair of light emitting diodes (LEDs) (Luxeon III LXHL-PD09, Philips) with maximal emission at 620 nm, directed towards opposite sides of the cuvette, perpendicular to the measuring beam. Fluorescence yields from saturating pulses were measured under actinic light and averaged over 6 measurements, separated by 120s intervals. Both absorption and fluorescence measuring pulses were 20-35 *s in duration and attenuated to produce less than 0.1% increase in chlorophyll fluorescence yield in dark-adapted samples. The first dark interval relaxation kinetics trace measuring the electrochromic shift (ECS) kinetics (one trace per biological replicate) was recorded after three minutes of actinic illumination, followed by one !!!!%(I!minute of dark. Actinic LEDs were calibrated using a Licor LI190 PAR quantum sensor.    CO2 Production and Consumption Rates CO2 exchange measurements were carried out with a LICOR XT 6400 (LICOR) infrared gas analyzer. Air was continuously circulated through 250ml flasks flask containing 50ml cultures, which were maintained under culture incubation conditions, and CO2 levels were recorded for the air entering and leaving the flask. Input CO2 levels were adjusted so that returning air contained 400 ppm.  Starch Analysis  Total glucose contained in starch was measured after amyloglucosidase and amylase digestion with the Megazyme total starch analysis kit (Megazyme, Ireland), similar to Work et al. (Work, et al. 2010). Briefly, pellets remaining after extraction of lipids from cells with 2:1 methanol:chloroform were autoclaved for 1 hour in 0.1 M Acetate buffer pH 4.8 then treated with $-amylase and amyloglucosidase for 1 hour at 55oC. Free glucose was quantitated with a colorimetric assay at 510nm using a starch assay kit (Megazyme, Ireland) according to the manufacturerÕs instructions.  Acetate Analysis  Total acetate in culture media was measured with an acetate analysis Kit (K-ACETRM, Megazyme, Ireland). TAP media samples were diluted fivefold to be within the linear response range and 1.5 ml sub-samples were used for NADH consumption measurement by absorbance changes at 340nm. Briefly, acetic acid and ATP are converted to acetyl phosphate and ADP by !!!!%(J!acetate kinase. The forward reaction is maintained by phosphotransacetlyase reaction of acetyl phosphate and coenzyme A to form acetyl-CoA and inorganic phosphate. Added phosphoenolpyruvate and ADP are converted to pyruvate and ATP by pyruvate kinase to maintain ATP levels and to produce pyruvate in proportion to the original levels of acetate. D-lactate dehydrogenase then converts the pyruvate and NADH into D-lactic acid and NAD+.   Calculation of Carbon and Energy Balances Acetate and oxygen uptake rates were converted into µg C/ 106 cells/ hr. Net oxygen fluxes were assumed to represent net CO2 fluxes at a 1:1 ratio with 12g C/mol O2. Acetate consumption was multiplied by 2 moles C/mole acetate. Carbon accumulation rates in biomass were obtained from the percent carbon of the biomass and the biomass accumulated. FAME and Starch mass accumulation rates were converted to µg C/ 106 cells/ h by multiplying the by the mass fraction of carbon for those molecules (72 g C per 162g polymerized starch and 204gC /267g FAME).  13C labeling Algal cells were fed 100% uniformly 13C labeled Acetate in TAP media without N for the first 40h after N deprivation. Cells were then centrifuged and resuspended in unlabeled TAP media without N for a further 56 h (96 h of deprivation total). Samples were collected at 40, 72, and 96 h following N deprivation. Starch and Lipids were extracted and treated as above, glucose from starch samples was derivatized with methoxyamine and TMS as described in (Roessner et al. 2001). Labeling in FAME samples was analyzed by GC-MS as described previously (Allen et al. 2007) while glucose labeling was analyzed on GC-MS using the same chromatography and MS parameters previously used for amino acids by (Chen et al. 2011b). !!!!%FK! 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CHAPTER-4  Flux Balance Analysis of Chlamydomonas during Nitrogen Deprivation !!!!%GK!ABSTRACT   Algae have been suggested as sources for biofuel production due to their ability to grow quickly and accumulate carbon in the form of starch and triacylglycerols. Despite much research on these accumulations, there is little consensus on the best approaches to increase production as well as the reasons behind the accumulations. In Chapter 3 we measured various carbon accumulations, photosynthetic properties, acetate uptake, and gas exchange rates during nitrogen deprivation in Chlamydomonas across 5 light levels and two media conditions. In this chapter we use that data as constraints for flux balance analysis using a genome scale model of Chlamydomonas, iCre1355. Flux results demonstrate that the model is robust across multiple growth conditions and representative of current knowledge about metabolism under each condition. Nitrogen deprivation experiments show a single pathway for starch accumulation across all light and media conditions but different metabolic routes for TAG accumulation depending upon the growth condition used. We additionally correct the iCre1355 model to have proper photosynthetic reaction stoichiometry as well as add several reactions left out in the original model. This work provides a foundation for a deeper metabolic understanding of carbon accumulation during nutrient deprivation. !!!!%G%!INTRODUCTION  The current utilization of petroleum resources as fuel and chemical substrate is unsustainable (Solomon 2010). Further, variable fluctuations in the prices of these foreign derived commodities has dramatic effects on our economy (Brown et al. 2013). Additional costs of petroleum usage are found in deleterious effects on our environment, global warming, and public health. Developing dependable, clean, and sustainable renewable carbon and energy sources to replace petroleum is at the forefront of current bioenergy research. Renewable resources being developed rely on wind, solar, and the burning of photosynthetically derived biomass for energy. Biomass sources can be burned directly for electricity or processed through natural or artificial means to produce liquid and gas fuels as well as syngas and target molecules for the chemical industry. Crop plants such as maize and sugar cane were initially studied as feedstocks for biofuel (bioethanol); however, the focus has significantly shifted to developing fuels from single celled photosynthetic microalgae (Chisti 2007, Martin 2010, Ohlrogge, et al. 2009, Solomon 2010). Many algae have been studied for their capacity as industrial biofuel strains including Nannochloropsis (Radakovits, et al. 2012), Chlorella (Liu, et al. 2011), and Galdiaria (Selvaratnam, et al. 2014). Much of the research on biofuel production in microalgae however has been done on the model green alga Chlamydomonas reinhardtii (Fan, et al. 2012, Liu, et al. 2013, Siaut, et al. 2011). This alga has been studied extensively over the last fifty years (Goodenough 2015, Harris 2009b, Merchant, et al. 2012b) and has a plentiful list of resources such as a fully sequenced genome (Blaby, et al. 2014, Merchant, et al. 2007), multiple transcriptomic and proteomic analyses (Wang, et al. 2014), as well as various metabolic libraries (Bolling, et al. 2005, Lee do, et al. 2008, May, et al. 2008). Further, an extensive library of !!!!%G&!characterized mutants have been generated for Chlamydomonas enabling more rapid and extensive studies on gene-phenotype relationships (Li, et al. 2016). In recent years, several large scale studies have been done on Chlamydomonas during biofuel accumulation conditions, especially various nutrient deprivation regimes.  These studies provide guidance for rational engineering and strain development for biofuel and engineering purposes. Aside from describing the mechanisms behind a given phenotype, one of the ultimate goals of systems biology is to be able to accurately direct genetic engineering efforts through predictive modeling. Omics data can be used to determine the set of enzymes, metabolic reactions and metabolites present in an organism and allow the formation of a metabolic model (Borodina et al. 2005). Metabolic reactions are then described in the form of linear reaction stoichiometries, accounting for all energy, reductant, carbon, and other components necessary for biology. Measured physiological data such as biomass, metabolite uptake rates, and known reaction rates can be used as constraints for Flux Balance Analysis (FBA), a systems biology techniqueused to study the generated metabolic model (Baroukh et al. 2015, Shachar-Hill 2013). FBA uses linear computational programming to balance all of the reaction stoichiometries in the model based upon the given constraints, with flux values for each reaction as output. Generated fluxes are used to describe the flow of carbon/ metabolites/ energy in an organism and, through perturbation, aid in the predictions of hypothesis driven genetic engineering efforts. FBA has been used in the prediction of several successful engineering efforts, including those in Corynebacterium glutamicum (Vallino et al. 1993), Sacchromyces cerevisiae (Famili et al. 2003), and Staphylococcus aureus (Heinemann et al. 2005). Despite the importance of understanding the relationship between phenotypes and metabolic flux, FBA has only recently been applied to photosynthetic organisms. Early phototrophic !!!!%G'!metabolic models of cyanobacteria Synechocystis and Spirulina were simplistic in nature, modeling only central metabolism and simplistic photosynthetic expressions (Cogne et al. 2003, Yang et al. 2000). Shastri and Morgan generated another model of Synechocystis, this time taking into consideration the reactions validated by the sequenced genome (Shastri et al. 2005). At the same time, efforts to characterize higher plant metabolism involved single network C13 metabolic flux analysis, a steady state analysis requiring expensive C13 label and intensive analytical chemistry analysis (Allen et al. 2009, Kruger et al. 2009, Libourel et al. 2008). This process only allowed for the description of a network, unable to predict the effects of gene knockouts or other genetic engineering perturbations. Boyle and Morgan built upon the Synechocystis model, generating one to describe the eukaryotic, green alga Chlamydomonas reinhardtii and metabolic compartmentation (Boyle, et al. 2009). This provided groundwork for the production of larger scale models in algae and higher plants.  FBA genome scale models have thus been generated for Arabidopsis thaliana (de Oliveira Dal'Molin et al. 2010, Poolman et al. 2009) and Zea maize (Saha et al. 2011) with smaller, literature based models generated for barley (Grafahrend-Belau et al. 2009) and Brassica napus seeds (Hay et al. 2011, Pilalis et al. 2011). These models have helped determined where gaps are in our genome annotations, what reactions are in the same network, as well as described some interactions between metabolites in different compartments such as the cytosol, plastid, and mitochondria.  Due to the vast amount of omic data generated and genome sequencing (Merchant, et al. 2007) in recent years, FBA has been applied to the green alga Chlamydomonas. Morgan and Boyle generated the first metabolic reconstruction of central metabolism in Chlamydomonas reinhardtii. This model included three compartments (plastid, cytosol, and mitochondria), 484 metabolic reactions, and led to enzyme annotation (Boyle, et al. 2009). This model was then !!!!%G(!followed by a transcript verified model (Manichaikul et al. 2009). Significant improvements were made in three larger, genome scale models (GEMs), AlgaGEM (derived from an Arabidopsis model (Dal'Molin, et al. 2011), iRC1080 (generated from a bottom up approach) (Chang et al. 2011b),  and iCre1355 (a recently updated iRC1080) (Imam, et al. 2015). These models have been used to study the effect of CO2 and acetate upon photosynthesis (Chapman, et al. 2015, Melo et al. 2014), transcript regulation of nitrogen deprivation (Imam, et al. 2015, Lopez Garcia de Lomana, et al. 2015), and network reduction (Rugen et al. 2012). While these models are highly informed from genome, transcript, and protein systems analysies, they lack rigorous constraints based upon functional physiological measurements. In this work we used flux balance analysis to study the metabolic carbon and energy fluxes during normal growth and nutrient deprivation in Chlamydomonas. The iCre1355 metabolic model was modified to correct photosynthetic reaction stoichiometries and physiological measurements taken in chapter 3 were used to constrain the feasible flux space solutions. Carbon flux solutions generated gave results in congruence with current dogma on heterotrophic, autotrophic, and mixotrophic growth demonstrating the robustness of the model across several conditions. Photosynthetic fluxes and cellular photon demand results also provided support to conclusions of chapter 3. Nutrient deprivation experiments demonstrated a single source for starch synthesis in the plastid across all conditions while triacylglycerol carbon can be derived from several metabolic pathways depending upon the growth conditions of the cell. This work provides a foundation to explore ways to increase biofuel production in algae as well as study how metabolism responds to different growth environments.      !!!!%GF!METHODS Model Curation In the work we chose to use the genome scale model iCre1355 updated by Imam et al. (Imam, et al. 2015) from the iCR1080 model by Chang (Chang, et al. 2011b).  This model includes updated gene annotation and gene protein relations as well as deletions of unsupported gene reactions. Further, while other models for Chlamydomonas such as AlgaGem were generated from other organisms including Arabidopsis (Dal'Molin, et al. 2011, de Oliveira Dal'Molin, et al. 2010), iCre1355/ iCR1080 were built from the ground up and separate network reactions such as photosynthesis were separated into multiple equations instead of single stoichiometries. One example of this is linear electron flow where in AlgaGEM there is a single reaction while in iCre1355/iCR1080 there are separate reactions for PSII, b6f complex, PSI, ferredoxin NADPH reductase, and so on, allowing the model to more realistically demonstrate biology. !!!!"##!   Table 4.1. Changes made to model related to photosynthesis. Reactions were corrected to match current stoichiometric understandings as well as remove free energy generation from sodium fluxes. !!!!"#$!Despite other works having used iCR1080 and iCre1355 and ÒupdatedÓ them, we found several cases of incorrect photosynthetic stoichiometries and free energy generation through sodium transport reactions (Chapman, et al. 2015, Cogne et al. 2011, Gomez et al. 2016, Lopez Garcia de Lomana, et al. 2015, Rugen, et al. 2012). Presented in Table 4.1, we show corrections in the stoichiometries of photosystem I and II, cyclic electron flow (CEF), and Ferredoxin NADPH reductase, and b6f complex. Chapman et al. 2015 claimed to correct CEF stoichiometries and send flux through plastoquinone instead of plastocyanin pools; however, these are not found in their published model. Additionally, four reactions were added for photosynthesis; the Mehler reaction which generates oxygen radicals at PSI(Mehler 1951a, Mehler 1951b, Mehler et al. 1952), the Plastid Terminal Oxidase (PTOX2, Cre03.g172500) which dissipates excess photosynthetic electrons by dumping them onto water (Houille-Vernes, et al. 2011), the second CEF pathway through NADPH dehydrogenase (NDH), using NADPH to reduce the quinone pool (Peng et al. 2009, Saroussi et al. 2016), and finally the Q cycle used by the b6f complex. The radical oxygen generated from the Mehler reaction Òo2RÓ is now reduced to water through the ascorbate peroxidase pathway, whereas previously this pathway was non- functional. Additional photosynthetic changes included removing the Òdummy O2Ó demand function and adding a useful photon count reaction to aid photon constraints. PSII generated oxygen now enters the thylakoid O2 pool instead of simply dissipating away. Finally the Q cycle is split into two reactions, the first takes one electron from plastoquinol and sends it to the plastocyanin while the second reaction takes the second electron and uses it to reduce another plastoquinone. Figure 4.1 shows the current photosynthetic pathways and areas of possible perturbation in the model. !!!!"#%! Other general changes include the deletion of sodium demand functions from organelles which generated free energy for the cell through sodium flows. The model was additionally altered to remove constraints on oxygen evolution, RuBisCO activity, CO2 and other components not strictly controlled in the media. Next, from our experiments, the ATP maintenance costs used in Imam et al. were too high (model was infeasible with our constraints) so we used the lower ATP value in Chang et al. Further, the ATP maintenance cost flux was moved from the cytosol Figure 4.1 Photosynthetic fluxes allowed in model. Fluxes at each of these sites can be constrained or knocked out depending on the needs of the modeler. ATPase: ATP synthase; B6F: B6F complex with quinone cycle; P680: 680nm photons; P673: 673nm photons; PQ: Plastoquinone; PGH2: Plastoquinol; PSI: Photosystem I; PSII: Photosystem II; FDXOX: oxidized ferrodoxin; FDXRD; reduced ferrodoxin; PTOX: Plastid Terminal Oxidase; NDH: NADPH hydrogenase; PGR5: Proton Gradient Regulation 5 !!!!!"#&!to the plastid to remove a H2O2 and proton gradient generating reaction that helped moved ATP from the plastid to the cytosol. The biomass equations presented in the model were adjusted placing half of the ATP demand in the cytosol and half in the plastid. Finally, a NADPH burning reaction was added to the plastid as a feature to allow the study of the effects of increased reductant upon the system.  Flux Balance Analysis FBA was performed using a curated form of the pre-existing model of C. reinhardtii metabolism, iCre1355, as mentioned above. Measured CO2, biomass, TAG, starch, and acetate uptake rates were constrained in the models. Minimizing light use for autotrophic and CO2 emission for mixo- and heterotrophic cultures was used as an objective function. Flux predictions were generated for the various light and nutrient environments measured in Chapter 3. These constraints included growth rates, acetate uptake, light intensity, Ash free dry weights, carbon uptake, starch and TAG accumulation rates, and photosynthetic efficiencies.  The Growth LED designed by Chang et al. (year) was chosen as the light source for the model to simplify the photon fluxes to either PSI or PSII. Flux Balance simulations were carried out using the COBRA Toolbox in the MATLAB programming environment (MathWorks, Inc. http://www.mathworks.com/ products/matlab). Gurobi Optimizer was used as the linear solver (www.Gurobi.com). Flux variability analysis was used to determine the max and min flux for each of the reactions during N+ and N- conditions (Mahadevan et al. 2003).    !!!!"$'!Cell Cultivation, growth, and constraint measurements See Chapter 3 for growth and uptake measurement details. These include measurements of biomass % carbon, starch and TAG levels, as well as acetate, O2, and CO2 fluxes at 0, 5, 15, 40, and 160 µmol photons/m2/s light intensities in Tris-acetate- phosphate (mixotrophic) and high salt (autotrophic) conditions. !!!!"$"! RESULTS    Measurements of biomass composition, oxygen evolution, carbon dioxide fluxes, and acetate uptake from chapter 3 were used as constraints for flux balance analysis in Chlamydomonas reinhardtii. Conditions tested included autotrophic, mixotrophic, and heterotrophic growth regimes during N replete growth, and 24 and 96 hrs after nitrogen depletion. N-depleted samples were constrained additionally with measured starch and triacylglycerol (TAG) accumulations with their respective demand functions. For cultures grown in the light (mixotrophic and autotrophic), the objective function was the minimization of light use (Pcount flux) as otherwise the model could use as much light as possible and lead to wasteful energy usage. Heterotrophic cultures objective function was set to minimize CO2 emissions, forcing a conservative use of carbon. Flux data generated from the balance analysis is presented in figure 4.2, simplified to primary carbon metabolism. Numbers presented indicate mmol Carbon/ gDW/Hr.    !!!!"#$!    !"#$%&'()*'+)',"-%.#&/'0&12&-&'3"4.-%.15"6'7%.8-5!!!!!"#%!                         !"#$%&'()*'9)',"-%.#&/':&12&-&'3"4.-%.15"6'7%.8-5)'*('5%;'1.;-','<&1%"=>-"./!!!!!!"#&!  !"#$%&'()*'?)',"-%.#&/':&12&-&'3"4.-%.15"6'7%.8-5)'@A'5%;'1.;-','<&1%"=>-"./'!!!!"#'!                              !"#$%&'()*':)',"-%.#&/'0&12&-&'+$-.-%.15"6'7%.8-5)''!!!!"#(!!"#$%&'()*'B)'',"-%.#&/':&12&-&'+$-.-%.15"6'7%.8-5)'*('5%;'1.;-','<&1%"=>-"./'!!!!"##!           !"#$%&'()*'!)',"-%.#&/':&12&-&'+$-.-%.15"6'7%.8-5)'@A'5%;'1.;-','<&1%"=>-"./)'!!!!"#)!          !"#$%&'()*'7)',"-%.#&/'0&12&-&'C&-&%.-%.15"6'7%.8-5)''!!!!"#*!            !"#$%&'()*'C)',"-%.#&/':&12&-&'C&-&%.-%.15"6'7%.8-5)'*('5%;'1.;-','<&1%"=>-"./'!!!!")+!          !"#$%&'()*'D)',"-%.#&/':&12&-&'C&-&%.-%.15"6'7%.8-5)'@A'5%;'1.;-','<&1%"=>-"./!!!!!"#"! Nitrogen Replete Conditions  The flux maps presented under each of the three conditions demonstrates the robustness of the model to study various media conditions. Known metabolic activities of cells under each condition are supported - i.e., autotrophic growth has dominant fluxes through the Calvin cycle, mixotrophic uses both acetate and Calvin Benson cycle for carbon and energy, and the heterotrophic condition uses acetate and has an inactive Calvin Benson cycle. Both light using autotrophic and mixotrophic conditions also show a linear relationship between light consumed and growth rates demonstrating the models ability to represent different light conditions (Figure 4.3). Also, mixotrophic conditions receive more biomass per photon as acetate is present to aid biosynthesis and growth.       y = 709.85x + 0.1457 R! = 1 y = 1331.6x - 53.317 R! = 0.99898 0 5 10 15 20 25 30 35 40 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 Photon demand (mmol/gDW/hr) specific growth rate(Hr-1) Figure 4.3. Photon demand vs. growth rate at 5, 40, 15, and 160 µmol photon/m2/s. Hollow circle represent autotrophic conditions and hollow squares represent mixotrophic conditions.  !!!!"#$!These models additionally support the idea that cells at even moderate light intensities are exposed to more light than than needed. Figure 4.4 shows the calculated photon demands at 5, 40, 15, and 160 µmol photon/m2/s. vs. the relative light absorbed by the cell that was calculated in chapter 3. Here we find that although cells have the capacity to absorb significant amounts of light with increasing light intensity, only a small amount is actually needed. From chapter 3 we also found that our cells are carbon limited which will account for decreased photon demands. Autotrophic data shows this the most with almost no increase in light demand from 40 to 160 µmol photon/m2/s even though the potential absorbed light increases 3 fold.  Aside from interesting photosynthetic outputs, these flux balances present insight into general metabolism. First, none of the maps showed flux through plastidic fructose bisphospate aldolase, instead using oxidative pentose phosphate pathway (OPP) reactions to generate fructose bisphosphate for starch production. This is interesting as this is normally a major gluconeogenic pathway. Second, we find 3-phosphoglycerate to be the dominant triose phosphate exported from the plastid from photosynthesis while glyceraldehyde-3-phosphate is generally depicted of as the main trios exported. Next, the maps generally depict little to no flux through traditional citric acid cycle, opting for flux through the glyoxolate cycle in the mitochondria(activity naturally found in the peroxisome). Finally, for all the maps there is a general flux from the plastid to the mitochondria in the form of aspartate and alpha ketoglutarate to oxaloacetate, moving ammonia and carbon.  !!!!"#%!             Figure 4.4. Photon demand vs.  relative absorbed light at 5, 40, 15, and 160 µmol photon/m2/s. Hollow circle represent autotrophic conditions and hollow squares represent mixotrophic conditions. Relative absorbed light calculated from !ii * µg Chlorophyll/million cells* light intensity from data in chapter 3 for each light intensity. 0 5 10 15 20 25 30 35 40 0 20 40 60 80 100 Photon Demand (mmol/gDW/ Hr) Relative absorbed light !!!!"#&!Photosynthesis During N Deprivation  In chapter three, I measured decreases in photosynthesis during nitrogen deprivation in both mixorophic and autotrophic conditions. Here it was found that mixotrophic cultures had higher photosynthetic activities during nitrogen replete conditions than autotrophic conditions and fell to values significantly lower than autotrophic during later timepoints of N deprivation. We linked this to mixotrophic cells being able to rely on fed acetate while autotrophic cells were reliant upon photosynthesis for energy and carbon even under N deprivation. We find the same trends in each growth condition with the flux modeling, presented in Figure 4.5.             Figure 4.5. Light Demand predictions during N deprivation. A represents mixotrophic cultures while B represents autotrophic cultures. Black squares are 160, black diamonds are 40, hollow triangles are 15, and hollow circles are 5 µmol photons/ m2/s light intensities. Mixotrophic cells demonstrate a higher flux during N replete conditions and a lower photosynthetic flux during nutrient depletion.   !!!!"#'!To assess how efficient cells are using light at different light intensities, we compared flux balance predictions to measured maximum available photons. Predicted photon demands were converted from mmol photon/gdw/hr to µmol photon/million cells/hr. Total available photons were calculated from measured light intensity, chlorophyll(capacity to abosorb light) and "II(efficiency of light use around PSII), making the assumption that light was hitting both photosynthetic complexes equally and that each complex uses light with the same efficiency. Absorbed light was calculated using the extinction coefficient of chlorophyll, chlorophyll abundance, and BeerÕs law, Absorbance = #(extinction coefficienct)*chlorophyll concentration*L (path length of light). FBA predicted photon use divided by the calculated maximum available light to the cell is presented in Figure 4.6. There are a number of conclusions one can derive from this figure.  First, as light intensity increases, there is a decreasing efficiency of use from 80% at low light intensities down to 13% for higher light intensities. Second, both autotrophic and mixotrophic have similar light use efficiencies at higher light intensities but less so at low light intensities. Finally, it shows light constraints are relieved with increasing light intensity. The decreases in photosynthesis found under mixotrophic conditions extends beyond just photon demand. Table 4.2 shows how each photosynthetic flux changes during N deprivation. !!!!"#(!     !!)!)*"!)*$!)*%!)*&!)*'!)*(!)*+!)*#!)*,!)!')!"))!"')!$))!!"#$%&'(')$*+,-.$%'++/01,$%&'(')+$2/3&($/)(,)+/(4$5*6'1$%&'(').67.+8$-./0!1230!Figure 4.6. FBA predicted light use vs. calculated light available for photosynthesis. Predicted photons from FBA were divided by the calculated photons available to photosynthesis under nutrient replete conditions across multiple light intensities. Filled squares indicate mixotrophic conditions(TAP media)  while hollow squares indicate autotrophic conditions(HS media). !!!!"#+!   Table 4.2 Photosynthetic Fluxes. Flux values generated through flux balance analysis at each light intensity and media condition at N+, 24 hrs after N- and 96 hrs after N-.  All values are in  units of  mmol C/gDW/Hr.  !!!!"##!Nitrogen Deprivation Metabolism Chlamydomonas was studied using Flux balance analysis at 24 and 96 hrs after deprivation. Under these conditions, nitrate and ammonium sources were constrained to zero, eliminating all biomass production. During these conditions the only drivers of synthesis are that of starch and TAG, removing all metabolism related to normal biomass production. During the first 24 hrs, we measured some cell growth as reported in chapter 3 but were unable to factor that in for this experiment. Under all growth conditions, starch precursors are produced from the fructose bisphosphate to glucose 1 phosphate and so on using the OPP pathway reactions. TAG carbon is derived from multiple sources depending upon the growth condition with primary chains coming from the plastid and elongation reactions derived from the mitochondria AcCoA pools. Mixotrophic TAG is derived predominantly from acetate (in congruence with chapter 3 conclusions) while autotrophic TAG is derived from plastidic pyruvate to amino acid to AcCoa metabolism. Meanwhile heterotrophic TAG is predicted to come from the mitochondrial AcCoA derived from acetate and transported to the plastid for fatty acid synthesis.  With much interest in biofuel production this data suggests that the route to increased biofuel production depends upon the growth conditions one uses.  !!!!"#,!DISCUSSION  Here, the stoichiometric model iCre1355 was used to explore Chlamydomonas metabolism during both nitrogen replete and deplete conditions and found to produce accurate representations of known metabolism under three different growth conditions. Further, imposing measured constraints such as biomass and substrate uptakes upon the system gave photosynthetic results similar to those suggested in chapter three. In congruence with that data, actual light needed for starch and TAG biosynthesis is many times lower than that available to the cell. This is in congruence with evidence against the overflow hypothesis provided in chapter three. Finally this work showed that while nitrogen deprivation starch production comes from the plastid through fructose bisphosphate, TAG accumulations can come from various sources and pathways dependent upon growth conditions.  The iCre1355 model (Imam, et al. 2015, Lopez Garcia de Lomana, et al. 2015), although newly updated with current gene lists, has been found to be missing correct reaction stoichiometries and known gene reactions for photosynthesis. This model did present itself as functioning and somewhat predictive but requires more intensive biochemistry annotation. Updates that I have added to the model allow for a number of activities. First, photosynthetic reactions have been corrected allowing for more realistic modeling results. Second, a counter of photons as well as an NADPH burning reaction have been added to control photon usage and system reduction respectively. Finally,   The next step is to push the model to its limits, determining how much starch and TAG the system can make with current and potential carbon and energy inputs and how those more active metabolisms work and move carbon and energy. Metabolic pathways !!!!",)!with heightened fluxes may give rise to insight for engineering efforts. Additionally, the actual movement of fatty acids through membranes can be more fully characterized to give insight into lipid trafficking pathways and possible metabolic reasons for using one or another for TAG filling during nutrient deprivation. One way to do this might include adding an endoplasmic reticulum compartment and link the plastid lipids to it instead of the mitochondria as in this model.  !!!!","!MODEL CHALLENGES Before the model demonstrated fluxes representing known activities, several problems had to become overcome. Initial modeling efforts in both autotrophic and mixotrophic conditions demonstrated large fluxes through small molecule, radical oxygen, and fermentative pathways. Small molecule (carbon monoxide and nitrous oxide), and hydrogen peroxide production pathways were constrained (set to .001 mmol C/gDW/Hr), forcing flux through the fermentative pathways to produce ethanol, acetate, formate, and lactate. Attempts to constrain (limit) these pathways led to protons being dumped from NADPH onto O2, forming water though the added photosynthetic PTOX reaction. Limiting PTOX caused PSII fluxes to remain unchanged. We believed that PSII activity was too high in relation to the needs of the cell, causing an overly reduced environment. Attempts to minimize PSII gave the same flux as before suggesting PSII or downstream NADPH production and dissipation was necessary for the modeling environment. Interestingly, if we constrained PSII to a low value while keeping minimization of light use as an objective function, cycle electron flow activity increased. There are a number of transporters requiring a proton motive force to move molecules across membranes including those for ATP. Moving the ATP maintenance cost from the cytosol to the plastid reduced the small molecule/ peroxide/ fermentative fluxes several fold demonstrating protons requirements in transport were partly responsible for the NADPH consumption. Attempts to further pin point the exact cause of the NADPH consumption and the hidden roles of proton generation in the model led to several dead ends and infeasible models. To address the problem, we installed a NADPH consumption reaction in the plastid that simply dissipates NADPH into NADP+ and H+. !!!!",$!Solving the model with this flux removed most reactions that appeared to be wastefully burning NADPH or producing excessive amounts of small molecules. All flux maps presented in this work were carried out with the NADPH burning reaction active. Intensive probing and revaluation of reaction stoichiometries of the system will be required to determine the true cause of the NADPH burn requirement. As mentioned before, another challenge laid in making sure the photosynthetic stoichiometries were correct. iCre1355 and its predecessor iCre1080 both demonstrated incorrect photosynthetic proton transport, incomplete cyclic electron flow reactions, and lack of Q cycle, Mehler reaction, PTOX, or NADPH Plastoquinone reductase. Previous studies demonstrated no flux through cyclic electron flow during autotrophic conditions however after correcting the reactions and constraining the system, we find significant flux though this reaction under nitrogen repletion. Additionally, the original model allowed for free energy generation through free sodium transport. Sodium was generated outside the cell and destroyed inside the cell generating a free flow of sodium that allowed free transmembrane transport of various metabolites. Further, the original model used a Òdummy O2Ó reaction that removed PSII generated oxygen instead of it entering the thylakoid lumen oxygen pool.       !!!!",%!FUTURE DIRECTIONS  The work presented in this chapter is ongoing. While the fluxes calculated through our modeling efforts are representative of actual trends measured in the lab, they may not be completely true in respect to the actual metabolism active in the organism. The genome scale model used in this study is extensive and has been generated from all gene reactions currently supported by transcript and proteomic analysis or required for basic metabolism activity however only roughly half of the Chlamydomonas genome has been functionally annotated at this time (Blaby, et al. 2014, Grossman et al. 2010b). This leaves large room for improvement to the total reactome (summation of all reactions) of the model, which may drastically alter the final fluxes. Further, the model used is static, stoichiometric one meaning without extensive user given constraints, it does not have the ability to reflect changes in substrate concentrations, negative or positive feedback regulation, changes in proton and other gradients, or other dynamic situations. Additionally, without extensive constraints on metabolisms known to be nonfunctional under tested conditions, they may be active. In our case we saw extensive nitric oxide, carbon monoxide, fermentation, and reactive oxygen species generation under normal N+ growth conditions before constraining to low fluxes. Groups such as Imam et al. (Imam, et al. 2015) used transcript data during N deprivation to constrain their flux space but again only half of the known genes have been annotated and may leave much room for improvement upon further annotation.  Similarly, we chose light or CO2 use as the objective functions in this study however this may not be the actual. During normal growth it is not known what the primary objective function of an algae is and simply saying it is to grow as much as !!!!",&!possible or use as little light as possible may be very misleading. Further, using these constraints during nutrient deprivation may be equally far off the actual function of the cell. Cells are already flush with more light than they need under these conditions, what is stopping them from using as much light as they want wastefully, would it not provide an advantage in nature to absorb as much light as possible and prevent competitors from growing? Using different objective functions can give wildly different results and should be explored in future studies. To more properly constrain flux maps in the future and have a more accurate representation of the metabolism in Chlamydomonas, 13C metabolic flux analysis should be used(Allen, et al. 2009, Chen, et al. 2011b, Kim et al. 2008, Libourel, et al. 2008, O'Grady et al. 2012, Shachar-Hill 2013, Young, et al. 2011, Zamboni et al. 2009). This methodology uses 13C labeled substrate such as acetate or bicarbonate to monitor metabolism. Chlamydomonas grown in labeled substrate containing media will reach steady state incorporation and percent 13C labeling and patterning which can be analyzed through analytical methods such as mass spectrometry or nuclear magnetic resonance. Only a certain range of possible flux solutions exist to give rise to the labeling pattern seen in the organism and that can be calculated computationally. As this is a measure of actual metabolism in the organism, it can be used to check the validity of and guide constraints in flux balance analysis. Nitrogen deprivation is a continuously changing condition and will require more intensive dynamic labeling experiments instead of the steady state ones used for nitrogen replete conditions. Models can be further validated by using 13C substrates labeled at different positions as different carbons on a molecule may be directed to different metabolic end points.  !!!!",'! In conclusion, while the model used in this study may be extensive it is by no means complete and further, more intensive work needs to be done to constrain the models so they are more representative of current metabolism. Once validated, the flux balance modeling technique can be used to predict the outcomes of gene knockouts, over expressions, and other genetic engineering efforts.   !!!!",(!ACKNOWLEDGEMENTS  We would like the thank Saheed Imam, Stephen Chapman, and Jean-Marc Schwartz for their help in understanding their models and flux balance assistance.  !!!!",+!           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In the field of biofuel technology, researchers seek to use these accumulated molecules as sources of energy as fuels or for carbon substrate for the chemical industry. Algae are photosynthetic organisms that can accumulate these compounds under nutrient stress and have been the target of extensive research over the last several decades, especially for the production of TAG. Despite this work, the reasons cells  accumulate one carbon compound or another as well as the role these stored carbon compounds play in the nutrient deplete/ replete life cycle of organisms remain undefined.    Several explanations have been proposed for the induction of TAG accumulation in algae under stress, ranging from storing reduced carbon as an energy source for survival and/or future recovery, to lipid reorganization during photosynthetic down-regulation and/or subsequent up-regulation, to photo-protection from excess photosynthetic energy and carbon (Akita, et al. 2015, Grossman, et al. 2010a, Hu, et al. 2008, Khozin-Goldberg, et al. 2005, Klok, et al. 2014, Kohlwein 2010, Murphy 2001, Roessler 1990). These roles can be divided into four classifications: (i) sink for excess fixed carbon, (ii) sink for excess photosynthetic energy, (iii) carbon storage to aid nutrient recovery, and (iv) energy storage to aid nutrient recovery. The work in this thesis has sought to deepen the understanding of the relationship between photosynthesis and !!!!$)(!TAG accumulation in microalgae and either support or counter the above motivations through the analysis of carbon and energy fluxes.   PHOTOSYNTHESIS AND NUTRIENT DEPRIVATION  In Chapter 2, I present the effects of nitrogen deprivation on photosynthesis in Chlamydomonas reinhardtii. This work demonstrates drastic decreases in the photosynthetic capacity and efficiency during the first 48 hrs of N deprivation. Using transcript and protein profiles along with functional photosynthetic measurements we came to the conclusion that cells decrease photosynthesis in a controlled and coordinated manner. Further when non-photochemical quenching (a measure of photosynthetic stress) was calculated from chlorophyll fluorescence measurements, it was found that this parameter decreased overtime suggesting the cells were not under photosynthetic stress and were able to dissipate any ÒexcessÓ energy. Evidence for posttranslational control of photosynthetic activity demonstrates cells are able to decrease photosynthetic parameters well before there are changes in protein abundances. Another manuscript from the same study found transcript and protein increases for genes related to nitrogen assimilation demonstrating that the cells focus on priming themselves for the return of nitrogen (Park, et al. 2014). This suggests algae have evolved an encoded systemic response to nutrient deprivation that provides them some advantage to survive the stress. !!!!$)+!OVERFLOW HYPOTHESIS CONCLUSIONS  Several works have suggested the motivation for starch and TAG accumulation during nutrient deprivation is related to photosynthetic carbon or energy overflow(Hu, et al. 2008, Li, et al. 2012b, Roessler 1990). The most prominent study in support of the overflow hypothesis involves analyses of the Chlamydomonas PGD1 mutant which accumulates only half as much TAG as its parent line. My arguments against the mutants support of the overflow hypothesis are given in chapter three in more detail.   My work in chapter 2 did not support this hypothesis. To expand upon this and determine if there was evidence for these overflows I studied the relationship between photosynthesis and accumulated starch and TAG molecules during nutrient deprivation across 2 media conditions and 5 light intensities (presented in chapter 3). I chose to work at light intensities representative of normal physiological growth where the organism would not be expected to experience photosynthetic stress, allowing N deprivation to be the sole inducer if any existed. Further using both autotrophic and mixotrophic conditions would allow me to see the affects increased total carbon available on the system would be.  If photosynthetic overflows are the main drivers of carbon accumulation I would expect a number of things: (i) No carbon would accumulate in the dark during N deprivation, (ii) there would be no carbon accumulation at low light intensities where light would be expected to be growth limiting, (iii) All carbon accumulations would come from photosynthetic ally fixed carbon, even under mixotrophic conditions, (iv) Cells would demonstrate significant photosynthetic stress in the form of NPQ, (v) there would be a proportionate increase in carbon accumulation per !!!!$)#!increases in light intensity, and finally (vi) carbon accumulations would be highest when photosynthetic rates were highest, decreasing over time.  In this work I found the contrary to these points. Algae were able to accumulate significant amounts of starch and TAG at low light intensities and in the dark demonstrating carbon accumulations do not depend upon photosynthetically fixed carbon or energy. Second, under our conditions, algae demonstrated a decrease in total NPQ over the timecourse suggesting they are able to safely control their energy intake and are not under significant photosynthetic stress. Third, there was not a proportionate increase in starch or TAG accumulations with increasing light intensity for both mixotrophic and autotrophic conditions pointing to other regulatory mechanisms controlling the accumulations. Under mixotrophic conditions, through 13C acetate labeling during n deprivation, we found almost all TAG carbon coming from acetate with a minority of it supporting starch accumulations suggesting TAG and starch accumulations are under separate regulatory and carbon networks. Additionally, if there indeed is a photosynthetic overflow sink, it would be starch due to accumulation of photosynthetic carbon and it being the first pool to accumulate. Finally, I found TAG accumulation rates to be at their highest during the later phases of nutrient deprivation, well after photosynthetic capacity and efficiency have decreased excessively, a result counter-intuitive to the overflow hypothesis.   While this work has not described what the fundamental motivations of algal oil accumulation are, this contributes a strong step forward in saying what are not the drivers. My data strongly suggests TAG accumulations are not induced by photosynthetic stresses. This points to a regulated storage role for these molecules that !!!!$),!aids recovery from nutrient deprivation through either/ both carbon and energy stores( iii and iv above). Further, chapter 3 data points to a relationship between initial N+ growth rates and carbon accumulation rates with faster growing cells consuming and utilizing environmental N at a faster rate than slower growing cells, causing them to enter the N deprived carbon accumulating stage faster. Future research efforts should then be driven to look at the regulatory network, especially that around growth, for hints on controls of carbon accumulation.   MODELING OF NUTRIENT DEPRIVED METABOLISM  In chapter 4, I used a stoichiometric model of Chlamydomonas to study N+ and N- carbon metabolism to determine the most important metabolisms for growth, starch, and TAG accumulations. Here, physiological measurements from chapter 3 were used as constraints for Flux Balance Analysis to probe the genome scale metabolic model. Nitrogen deprivation experiments showed that across all light and media conditions, starch was being produced from pentose phosphate pathway fructose generation while TAG carbon came from multiple different locations depending upon the media source and light condition. Reduced carbon such as acetate is demonstrated as a preferred substrate for oil accumulation over photosynthetically derived carbon. Further corrections made to the photosynthetic reaction stoichiometries allows for more realistic flux studies on photosynthesis and its interaction with metabolism.  !!!!$")!FUTURE EXPERIMENTS AND DIRECTION  The preliminary data presented in Appendix 1 demonstrates interesting relationships between starch and TAG breakdown and cell growth during recovery from nutrient deprivation. First, it is demonstrated that starch degradation precedes TAG degradation suggesting unique roles for each storage pool. Second, photosynthesis was found to increase in efficiency and capacity during the first 24 hrs of N recovery suggesting starch and TAG degradation may help in the recovery process.   This data leaves many questions remaining. First, what are the individual roles of starch and TAG in nutrient recovery? Does starch carbon aid nitrogen fixation or is it burned for energy? Does TAG provide acyl chains for membrane synthesis, especially in the thylakoid where large amounts of membrane are required for photosynthesis? Finally how does the photosynthetic environment change over the course of nutrient recovery and how does that change with or without acetate?  To approach the roles of starch and TAG during nutrient recovery, I propose a 14C labeling experiment where cells are labeled with 14C bicarbonate during nitrogen deprivation and monitored during recovery with no label. Total counts of 14C in biomass per culture volume during nutrient recovery would show whether a significant amount of labeled carbon is retained during recovery or removed. If 14C is retained in total biomass, it would demonstrate that starch and TAG play carbon storage roles, providing carbon for new biomass, and if there is a small amount retained it would suggest these compounds play energy storage roles as they would simply be burned after N recovery. Next counting the change in 14C label in TAG and other glycerolipid pools after TLC separation might show flows of 14C label from one pool to another. I believe that there will be a significant amount of label going from TAG to MGDG !!!!$""!and DGDG pools as TAG would help restore photosynthetic membranes. Expanding upon this, if cells were labeled with 14C during 12-40 hrs(starch labeling) and from 40 to 96 hrs (TAG labeling) (See Chapter 3) during N deprivation, one could get label dominantly into either starch or TAG pools and be able to follow specific labeling from each stored compound. Counting 14C in other biomass such as protein pools may point to the starch and TAG utility for the assimilation of nitrogen. With this data trends, one may be able to tease out of starch, TAG, or both pools are in line with photosynthetic measurement increases, contributing to its recovery. One earlier study used labeled arachidionic acid to suggest TAG is used to help MGDG production during nutrient flow however this was in a different alga strain and only one Fatty acid species (Khozin-Goldberg, et al. 2005). Understanding the roles of starch and TAG during nutrient recovery may shed light on ways to increase their production during nutrient deprivation for bioenergy purposes.  !!!!$"$!FINAL  Determining the final destination of starch and TAG carbon helps determine reasons for the accumulation of these compounds. Further, systems approaches identifying gene/ proteins responsible for storage compound degradation may provide direction for engineering efforts to have algae that accumulate large amounts of carbon during normal growth. Although an inverse relationship between cell growth and carbon storage has been continually demonstrated there may still be a way to unhinge these relations. This may lie in deregulating photosynthetic controls decreasing potential carbon and energy fixation, or in regulation controlling N+ TAG and starch pool sizes.   In this dissertation I have shown TAG accumulations are not caused by photosynthetic stress/ overflow during nutrient deprivation under mixotrophy at normal physiological conditions. Additionally, I have shown that starch increases before TAG during deprivation and is used before TAG during nutrient recovery(see Appendix). This points to a regulatory control switch between starch and TAG synthesis suggesting one look at the regulatory environment of the cell when it switches from starch to TAG synthesis. Tuning those signals may allow for increased TAG and decreased starch synthesis. Next as autotrophic cells do not decrease their photosynthetic components as much as mixotrophic cells during N dep, it may be possible to compare the two conditions and pull out what regulatory controls are at play to help engineer an organism that has heightened energy and carbon capture during N- to aid carbon accumulation.  !!!!$"%!        In homage to all the general biology courses I have ever taken: --THE MITOCHONDRIA IS THE POWERHOUSE OF THE CELL--       !!!!$"&!        APPENDIX  !!!!!$"'! To address the functional roles of starch and TAG accumulations, these compounds need to be studied during nutrient recovery. Very little literature exists on nutrient recovery in algae, especially on carbon metabolism and the ultimate destination of the starch and TAG carbon and energy. Here, I have gathered preliminary data on the cell physiology, photosynthetic parameters, and kinetics of nutrient recovery to aid future research endeavors in this area. ! After 96 hrs of N deprivation in TAP media, nitrogen is added back to the media and the Chlamydomonas cells were followed. Figure 5.1 (Panel A) shows cells do not begin to divide until after 24 hrs. During this 24 hr lag period, cells synthesized new chlorophyll to reach near N+ level (Panel B). Further, cells decreased their Ash Free Dry Weights (AFDW) suggesting starch and TAG and being degraded within this period.   Total FAMES were also found to decrease in parallel. Both TAG and starch levels were back to N+ amounts by 36 hrs (Figure 5.2). This data demonstrates starch mobilization hrs before TAG suggesting starch is a first energy or carbon source for the cell which would enable the rapid uptake of nitrogen into amino acids. As TAG levels fall before total FAME, this demonstrates fatty acid distributions in the cell is changing and may represent movement of TAG fatty acids into membrane lipids.        !!!!$"(!     Figure 5.1. Changes in cell counts, biomass and Chl after nitrogen readdition. (A) Cell counts, (B) Chlorophyll abundance, and (C) Ash Free Dry Weights(AFDW) were measured at each timepoint after N readdition. Error bars indicate standard deviation, N=3. !0 2 4 6 8 10 12 14 16 0 20 40 60 80 million cells/ml Time(HRS) 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 ug AFDW/ million cells Time (HRS) A C B 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 20 40 60 80 ugChl/milion cells Time(HRS) !!!!$"+!               To learn about the relationship between starch and TAG and cell recovery, we measured the disappearance of these molecules (Figure 5.2). In Panel A of 5.2, starch rates were found to decrease almost immediately after the return of nitrogen. TAG levels were found to decrease starting after 6 hrs of recovery and  Figure 5.2. TAG and Starch Degradation during N recovery. Starch levels (A) begin to fall before TAG (hollow square) and FAME (hollow triangle) Levels (B) during N recovery. Error bars indicate standard deviation, N=3. !!!!!$"#!              !-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0 10 20 30 40 nmol O2/min/ million cells Time (hrs) 0 0.0005 0.001 0.0015 0.002 0.0025 0.003 0.0035 0 10 20 30 40 !h+/s/million cells Time (Hrs) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 20 40 60 80 Fv/FM, !ii, NPQ Time (Hrs) 0 0.2 0.4 0.6 0.8 1 1.2 0 20 40 60 80 Qp Time (Hrs) A C B D Figure 5.3.  Photosynthetic measures during N recovery. Chlorophyll fluorescence measurements (A) show FV/FM(hollow square), $ii (hollow square), and NPQ (hollow diamond) values reach N replete levels within 24 Hrs. Qp(B) measures demonstrate PSII quinone pools are open to N+ levels within 12 hrs of recovery. Thylakoid proton efflux rates (C) increase to N+ levels between 24- 36hrs. Net oxygen evolution(D) (hollow squares) and consumption ( hollow triangles) reach N+ levels by 36 hours. Error bars indicate standard deviations, N=3. !!!!!$",!                The Recovery of Photosynthesis was studied through chlorophyll fluorescence, absorbance spectroscopy, and gas measurements (Figure 5.3). PSII photosynthetic efficiency was found to return to N+ levels in the first 24 hrs of recovery and photosynthetic stress (NPQ) was found to return within the same period. The PSII quinone pool returned to N+ levels within the first 12 hrs demonstrating quick repair of PSII components. Proton efflux rates, a measure of ATP synthase, showed continued increase in rates through 36hrs suggesting cells continue to restore photosynthetic components through this time point. Oxygen evolution values show a similar trend as the total proton efflux supporting continued restoration. These collective data suggest Figure 5.4. Acetate uptake during nutrient recovery. Acetate levels do not appear to decrease rapidly until after 48hrs(A). Acetate uptake levels do no increase until after 48 hrs of N recovery(B). Error bars equal ranges of measurements, N=2. !!!!!$$)!cells rapidly restore the intrinsic efficiency of their photosynthetic components but continue to increase their capacity for photosynthesis even after 36 hrs of recovery. It is important to note that starch and TAG decrease when photosynthetic capacity is most rapidly restored suggesting these molecules play a part in the restoration.   As a measure of metabolic demand during recovery, media acetate levels were measured. Starting off slow, acetate began to be uptake at a faster rate from 12 to 48 hs with dramatic increases in uptake from 48 to 72 hrs. Acetate uptake per cell values are presented in panel B of figure 5.4. This suggests that cells start with a slowed metabolism during n deprivation which increases to N+ levels by 48 hrs. This coincides with increases in photosynthetic activity and cell growth. !!!!$$"!          REFERENCES !!!!$$$!REFERENCES   Akita, T. and Kamo, M. 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