Marius Arend

Yizhong Yuan

M Águila Ruiz-Sola

Nooshin Omranian

Zoran Nikoloski
email: nikoloski@mpimp-golm.mpg.de

Dimitris Petroutsos
email: dimitris.petroutsos@cea.fr

Widening the landscape of transcriptional regulation of algal photoprotection Authors

Keywords: STA2 -0, 45 -0, 68 Cre03, g188250 STA6 -0, 15 1, 56 degradation Cre08, g385500

PGI1 -0.68 0.55 Cre02.g086500 PGM10 -0.22 -1.30 Cre06.g293150 PGM12 -0.05 2.74 Cre03.g165900 PGM17 -1.12 -1.05 Cre16.g665800 SS4 -0.17 -0.76 Cre03.g185250 SSS2 -3.33 -2.

Résumé

Les organismes photosynthétiques convertissent l'énergie lumineuse solaire en énergie chimique sous forme d'ATP et de NADPH pour alimenter la fixation du CO2 en carbone organique par photosynthèse. Une partie du carbone organique se trouve sous forme d'amidon, l'une des principales formes de réserve d'hydrates de carbone chez les plantes et les algues. Outre son rôle évident de source d'énergie, la lumière joue également un rôle de signal informationnel, qui est perçu par les protéines photoréceptrices régulant un large éventail de fonctions cellulaires. Il a été démontré que la qualité de la lumière a un impact sur la répartition du carbone, la composition de la biomasse et le taux de photosynthèse chez différentes espèces de plantes et d'algues. Plus précisément, les algues vertes ont une teneur en amidon plus faible dans des conditions de lumière bleue que dans des conditions de lumière rouge ou blanche.

Cependant, les mécanismes moléculaires de cette régulation restent mal connus.

Dans cette thèse, nous avons étudié le rôle du photorécepteur-phototropine (PHOT) de la lumière bleue dans le métabolisme de l'amidon en utilisant l'organisme modèle d'algues vertes Chlamydomonas reinhardtii. La délétion de PHOT conduit à une teneur en amidon plus élevée. Nous avons également découvert que l'isoforme GAP1 de la glycéraldéhyde 3-phosphate déshydrogénase (GAPDH) est un acteur essentiel de ce phénotype. Au total, nous avons trouvé trois différentes voies de signalisation en cascade reliant PHOT au métabolisme de l'amidon chez Chlamydomonas reinhardtii, résumées ici comme suit : Voie 1 : La lumière bleue, via PHOT, réprime le facteur de transcription (TF) GAPR4 contenant le domaine bHLH, un activateur de GAP1. Le mutant PHOT knock-out phot et les lignées surexprimant GAP1 accumulent de grandes quantités d'amidon.

Voie 2 : La lumière bleue, via PHOT, active le TF RWP5 (appartenant à la famille des TFs RWP-RK), un répresseur de l'accumulation d'amidon indépendant de GAP1. Notre travail a fourni une réponse complète à la façon dont la qualité de la lumière affecte la répartition du carbone chez l'algue verte modèle Chlamydomonas reinhardtii. Dans un contexte plus général, ce travail améliore la compréhension actuelle de la façon dont la signalisation lumineuse contrôle le métabolisme chez les microalgues. Il ajoute également un aspect de plus au rôle multiple de PHOT rapporté jusqu'à présent pour contrôler la gamétogenèse en milieu appauvri en azote, l'expression des gènes codant pour la biosynthèse de la chlorophylle et des caroténoïdes, la taille du tache oculaire et la photoprotection.

Abstract

Photosynthetic organisms convert solar light energy into chemical energy in the form of ATP and NADPH to fuel CO2 fixation into organic carbon through photosynthesis. Part of the organic carbon is in the form of starch, one of the major carbohydrate reserve forms in plants and algae. Besides its evident role as energy source, light serves also a role of informational signal, that is perceived by photoreceptor proteins regulating a wide range of cellular functions. Light quality has been shown to impact carbon partitioning, biomass composition and photosynthesis rate in different plant and algae species. More specifically, green algae have a lower starch content under blue light conditions than red or white light conditions. However, the molecular mechanisms of this regulation are still unclear.

In this thesis, I investigated the role of blue light photoreceptor-phototropin (PHOT) in starch metabolism by using the green algae model organism Chlamydomonas reinhardtii. The deletion of PHOT leads to higher starch content. We further found that the GAP1 isoform of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is an essential player in this phenotype. Overall, we found three different signaling cascade pathways linking the PHOT with starch metabolism in Chlamydomonas reinhardtii, summarized here as follows:

Pathway 1: Blue light, via PHOT, represses the bHLH domain-containing transcription factor (TF) GAPR4, an activator of GAP1. Both the PHOT knock-out mutant phot and GAP1 over-expressing lines accumulate high amounts of starch.

Pathway 2: Blue light, via PHOT, activates the RWP5 TF (belonging to the RWP-RK family of TFs), a repressor of starch accumulation in a GAP1-independent manner. Pathway 3: PHOT-dependent phosphorylation of a serine/threonine kinase we provisionally name Blue Starch Kinase 1 (BSK1; orthologue of the Arabidopsis HT1 kinase that controls stomatal movements in response to CO2) mediates GAP1 and starch accumulation levels in a GAPR4-independent manner.

Our work provided a comprehensive answer to how light quality affects the carbon partitioning in the model green alga Chlamydomonas reinhardtii. In a more general context, this work advances the current understanding of how light signaling controls metabolism in microalgae. It also adds one more aspect to the multifaceted role of PHOT so far reported to control gametogenesis at low nitrogen, expression of genes encoding chlorophyll and carotenoid biosynthesis, the size of the eyespot and photoprotection.

First, I would like to thank Dr. Christian Fankhauser and Dr. Jean Alric for accepting to review my thesis. And also many thanks to my other jury members: Dr. Angela Falciatore, Dr. Arthur Grossman, Dr. Emilie Demarsy, Dr. Johannes I also deeply appreciate all my CSI committee members: Dr. Olivier Vallon, Dr.

Stephane Ravanel and Dr. Yonghua Li-Beisson (again). Their advice and help at the annual CSI meeting not only enriched the content of my thesis but also helped me to build my confidence in scientific research.

I would also like to thank all the present and previous lab members, especially to Anne-Flore Deton-Cabanillas (Big thanks for the correction of Résumé), Gaelle Villain, Georgios Kepesidis, Marcello De Mia, Dr. Xuelei Lai, Dr. Xue Zhao. Also, big thanks to all the collaborators, especially to Dr. Zoran Nikoloski, Marius Arend, Fabrizio Iacono, and Dr. Petra Redekop. Million thanks to all my best best best friends in my daily life for their encouragement and accompany, especially after the loss of my family. It is a super tough period for me. Without their help and support, I would have already crashed.

Finally, I wish all the people I know stay in good health and live happily. Chlamydomonas reinhardtii is a single-celled organism belongs to the Chlamydomonas genus which was named by C.G. Ehrenberg 1 in 1833. It is a green microalga with a large cup-shaped chloroplast, flagella, and eyespot. Because it can grow photoautotrophically or heterotrophically on acetate, mutations affecting genes for photosynthesis are not lethal but conditional, requiring acetate for growth 2 . This provides an important and convenient opportunity in the analysis of photosynthesis.

World peace!

As Chlamydomonas reinhardtii is vegetatively haploid, the effects of mutations are immediate without further crossing. Chlamydomonas reinhardtii has three genomesnuclear 3 , mitochondrial 4 , and chloroplast genome 5 , which are sequenced and subject to specific transformation. Many genetic tools have been developed for Chlamydomonas reinhardtii [START_REF] Dinc | LHCSR1 induces a fast and reversible pH-dependent fluorescence quenching in LHCII in Chlamydomonas reinhardtii cells[END_REF][START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF][START_REF] Li | A pigment-binding protein essential for regulation of photosynthetic light harvesting[END_REF][START_REF] Correa-Galvis | Photosystem II Subunit PsbS Is Involved in the Induction of LHCSR Protein-dependent Energy Dissipation in Chlamydomonas reinhardtii[END_REF][START_REF] Tibiletti | Chlamydomonas reinhardtii PsbS Protein Is Functional and Accumulates Rapidly and Transiently under High Light[END_REF][START_REF] Allorent | UV-B photoreceptor-mediated protection of the photosynthetic machinery in Chlamydomonas reinhardtii[END_REF] , such as zinc finger nuclease (ZFN) [START_REF] Redekop | PsbS contributes to photoprotection in Chlamydomonas reinhardtii independently of energy dissipation[END_REF][START_REF] Hanawa | Induction of a high-CO2-inducible, periplasmic protein, H43, and its application as a high-CO2-responsive marker for study of the high-CO2-sensing mechanism in Chlamydomonas reinhardtii[END_REF] , transcription activator-like effector nuclease (TALEN) [START_REF] Moroney | Isolation and Characterization of a Mutant of Chlamydomonas reinhardtii Deficient in the CO2 Concentrating Mechanism[END_REF][START_REF] Fukuzawa | Ccm1, a regulatory gene controlling the induction of a carbonconcentrating mechanism in Chlamydomonas reinhardtii by sensing CO2 availability[END_REF] , and CRISPR/Cas systems [START_REF] Hanawa | Induction of a high-CO2-inducible, periplasmic protein, H43, and its application as a high-CO2-responsive marker for study of the high-CO2-sensing mechanism in Chlamydomonas reinhardtii[END_REF][START_REF] Xiang | The Cia5 gene controls formation of the carbon concentrating mechanism in Chlamydomonas reinhardtii[END_REF] .

Additionally, a genome-wide, indexed, and insertion mapped Chlamydomonas reinhardtii mutant library (CLiP) containing ca. 58,101 mutants affected in 14,695 genes is available in Chlamydomonas resource center [START_REF] Yoshioka | The novel Myb transcription factor LCR1 regulates the CO2responsive gene Cah1, encoding a periplasmic carbonic anhydrase in Chlamydomonas reinhardtii[END_REF][START_REF] Petroutsos | A blue-light photoreceptor mediates the feedback regulation of photosynthesis[END_REF] .

Combining all the advantages, Chlamydomonas reinhardtii has become a popular and prominent model organism for different cellular processes research, such as photosynthesis [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF][START_REF] Maruyama | Transcriptional regulation of the stressresponsive light harvesting complex genes in Chlamydomonas reinhardtii[END_REF][START_REF] Miura | Expression profiling-based identification of CO2-responsive genes regulated by CCM1 controlling a carbon-concentrating mechanism in Chlamydomonas reinhardtii[END_REF][START_REF] Fang | Transcriptome-wide changes in Chlamydomonas reinhardtii gene expression regulated by carbon dioxide and the CO2-concentrating mechanism regulator CIA5/CCM1[END_REF] , flagellar assembly [START_REF] Brueggeman | Activation of the carbon concentrating mechanism by CO2 deprivation coincides with massive transcriptional restructuring in Chlamydomonas reinhardtii[END_REF] , phototaxis [START_REF] Aihara | Algal photoprotection is regulated by the E3 ligase CUL4-DDB1DET1[END_REF][START_REF] Redekop | Transcriptional regulation of photoprotection in dark-to-light transition-more than just a matter of excess light energy[END_REF][START_REF] Gabilly | Regulation of photoprotection gene expression in Chlamydomonas by a putative E3 ubiquitin ligase complex and a homolog of CONSTANS[END_REF] , mitochondrial respiration [START_REF] Tokutsu | The CONSTANS flowering complex controls the protective response of photosynthesis in the green alga Chlamydomonas[END_REF] , chloroplast biogenesis [START_REF] Kamrani | ROC75 is an Attenuator for the Circadian Clock that Controls LHCSR3 Expression[END_REF][START_REF] Omranian | Gene regulatory network inference using fused LASSO on multiple data sets[END_REF] , metabolism [START_REF] Marbach | Wisdom of crowds for robust gene network inference[END_REF][START_REF] Fang | Decoding the gene regulatory networks in diverse human and mouse conditions[END_REF][32] , stress response [33][START_REF] Schäfer | A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics[END_REF][START_REF] Zou | Regularization and variable selection via the elastic net[END_REF][START_REF] Gargouri | Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii[END_REF] , cell cycle [START_REF] Lämmermann | Ubiquitin ligase component LRS1 and transcription factor CrHy5 act as a light switch for photoprotection in <em>Chlamydomonas</em&gt[END_REF][START_REF] Zones | High-Resolution Profiling of a Synchronized Diurnal Transcriptome from Chlamydomonas reinhardtii Reveals Continuous Cell and Metabolic Differentiation[END_REF][START_REF] Strenkert | Multiomics resolution of molecular events during a day in the life of Chlamydomonas[END_REF] , sexual life cycle [START_REF] Fett | Regulation of Periplasmic Carbonic Anhydrase Expression in Chlamydomonas reinhardtii by Acetate and pH[END_REF][START_REF] Pérez-Rodríguez | PlnTFDB: Updated content and new features of the plant transcription factor database[END_REF][START_REF] Jin | PlantTFDB 4.0: Toward a central hub for transcription factors and regulatory interactions in plants[END_REF] .

Photosynthesis

Most life on earth depends on photosynthesis. Photosynthesis is carried out by diverse plants, algae, and some bacteria [START_REF] Fischer | SINGLET OXYGEN RESISTANT 1 links reactive electrophile signaling to singlet oxygen acclimation in Chlamydomonas reinhardtii[END_REF] . It can convert light energy into chemical energy. Photosynthetic organisms can convert CO2 and water into O2 and carbohydrates, such that 6 CO2 + 6 H2O → C6H12O6 + 6 O2 It occurs in two integrated processes:1) the photochemical light reactions and 2 the CO2 fixation reactions.

Photochemical light reactions

The photochemical light reactions use solar energy to extract electrons from water to produce reducing equivalent (NADPH). It requires several large multi-protein complexes, including PSI and PSII (light-harvesting photosystems), the cytochrome b6f, and ATP synthase. Light reactions usually occur in thylakoid membranes, except for Gloeobacter violaceus -the only cyanobacterium without thylakoid membranes [START_REF] Margolin | ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context[END_REF] .

Photosynthesis in G. violaceus occurs in the cytoplasmic membrane [START_REF] Margolin | ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context[END_REF] .

When pigments in LHCs (light-harvesting complexes) absorb light, electrons become excited to a higher energy state than can be passed on to another molecule.

Electrons flow from water will go to the mobile electron carrier-plastoquinone (PQ) through PSII, and then go through cytochrome b6f, then to PSI by another mobile electron carrier-plastocyanin (PC), then via ferredoxin to reduce NADP + to NADPH.

The linear electron transfer (LET) also contributes to a proton gradient to produce phosphorylating power (ATP) via the chloroplastic ATP synthase. The chemical energy produced in the form of ATP and NADPH is subsequently used to drive CO2 assimilation.

Excess light damage avoidance

When chlorophylls absorb light, it will be excited from ground state (Chl) to singlet excited state ( 1 Chl*). In order to relax back to the ground state, it has three ways: 1) it can be emitted as fluorescence, 2) its excitation can be used to fuel photochemistry and 3) it can be dissipated by non-photochemical quenching (NPQ) [START_REF] Barzel | Network link prediction by global silencing of indirect correlations[END_REF] . The three ways can reduce the amount of fluorescence. The fate of captured light energy depends on temperature, nutrient availability, and many other factors. When captured light energy exceeds the photosynthetic capacity, it may lead to photoinhibition and ROS (Reactive oxygen species) production, which can cause damage to photosystem components and even lead to cell death.

However, photosynthetic organisms have evolved diverse ways to cope with the absorption of excess light [START_REF] Omranian | Gene regulatory network inference using fused LASSO on multiple data sets[END_REF][START_REF] De Borda | Mémoire sur les élections au scrutin[END_REF] . Plants can dynamically change the size of the antennae complex to decrease the light incidence. Higher plants can also move their chloroplasts called chloroplast positioning [START_REF] Margolin | ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context[END_REF] , while algae with flagella swim to move into optimal light conditions [START_REF] Li | A genome-wide algal mutant library and functional screen identifies genes required for eukaryotic photosynthesis[END_REF] a process called phototaxis. Moreover, at the same time, plants can produce antioxidants to detoxify ROS, such as carotenoids, lutein, zeaxanthin, and violaxanthin [START_REF]UniProt: the universal protein knowledgebase in 2021[END_REF][START_REF] Mi | PANTHER version 16: A revised family classification, tree-based classification tool, enhancer regions and extensive API[END_REF] . Another photoprotective response is NPQ which encompasses three components: qE (Energy-dependent quenching), qT (State transition quenching), and qI (slow photoinhibitory quenching) [START_REF] Thiriet-Rupert | Transcription factors in microalgae: genome-wide prediction and comparative analysis[END_REF] . The qE response is the dominant photoprotective response in Chlamydomonas and will be analyzed further in the next paragraph.

Energy-dependent quenching-qE

In the higher plants model organism Arabidopsis thaliana, qE activation needs deepoxidized xanthophylls, a high pH gradient across the thylakoid membrane, and LHCs.

The de-epoxidized xanthophylls are produced by the xanthophyll cycle. The npq1 mutant lacking the VDE (violaxanthin de-epoxidase) gene has reduced NPQ capacity than WT. And the pgr1 and pgr5 mutants, lacking PROTON GRADIENT REGULATOR-LIKE 1 and PROTON GRADIENT REGULATOR 5 respectively, which could not sustain a high pH gradient in the thylakoid lumen, have lower qE than WT. The high pH gradient also activates the xanthophyll cycle due to the thylakoid lumen located VDE enzyme. Moreover, a high pH gradient also protonates the LHC proteins. PSBS, one of the LHC proteins, plays a crucial role in qE in Arabidopsis thaliana. The npq4 mutant lacking the PSBS gene shows a complete abolishment of qE phenotype [START_REF] Bushnell | A Fast, Accurate, Splice-Aware Aligner[END_REF] resulting in less seed set when placed in the natural environment [START_REF] Law | Precision weights unlock linear model analysis tools for RNA-seq read counts[END_REF] .

In Chlamydomonas reinhardtii, qE components are similar to those in plants:

xanthophyll cycle, pH gradient, and specific proteins.

However, in Chlamydomonas, there is no VDE or ortholog of VDE in its genome [START_REF] Anders | HTSeq-A Python framework to work with highthroughput sequencing data[END_REF] .

The double mutant npq1 lor1, which lacks zeaxanthin and lutein, shows a severe lack of qE [START_REF] Robinson | A scaling normalization method for differential expression analysis of RNA-seq data[END_REF] . This indicated that lutein and zeaxanthin might play an essential role in Chlamydomonas quenching.

In Chlamydomonas, the critical photoprotective proteins of qE are LHCSR3 (product of the LHCSR3.1 and LHCSR3.2 genes) [START_REF] Robinson | A Bioconductor package for differential expression analysis of digital gene expression data[END_REF] , LHCSR1 [START_REF] Huynh-Thu | Inferring regulatory networks from expression data using tree-based methods[END_REF] (product of the LHCSR1 gene), and PSBS (product of the PSBS1 and PSBS2 genes) [START_REF] Meyer | A r/bioconductor package for inferring large transcriptional networks using mutual information[END_REF][START_REF] Olsen | On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information[END_REF] . Those three proteins will accumulate in response to high light [START_REF] Robinson | A scaling normalization method for differential expression analysis of RNA-seq data[END_REF][START_REF] Huynh-Thu | Inferring regulatory networks from expression data using tree-based methods[END_REF][START_REF] Olsen | On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information[END_REF][START_REF] Faith | Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles[END_REF][START_REF] Feizi | Network deconvolution as a general method to distinguish direct dependencies in networks[END_REF][START_REF] Ritchie | Limma powers differential expression analyses for RNAsequencing and microarray studies[END_REF] . LHCSR3 plays a dominant role in Chlamydomonas qE. The npq4 mutant lacking the LHCSR3. 1 and LHCSR3.2 gene showed a severe loss of qE and cannot survive under high light conditions [START_REF] Robinson | A Bioconductor package for differential expression analysis of digital gene expression data[END_REF] .

When LHCSR3 is absent from the cell, LHCSR1 could also play a role in inducing qE [START_REF] Huynh-Thu | Inferring regulatory networks from expression data using tree-based methods[END_REF][START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF][START_REF] North | A note on the calculation of empirical P values from Monte Carlo procedures [1[END_REF] . While PSBS plays a crucial role in Arabidopsis qE, it seems less critical in Chlamydomonas qE [START_REF] Olsen | On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information[END_REF] and its exact function needs further investigation [START_REF] Olsen | On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information[END_REF][START_REF] Ritchie | Limma powers differential expression analyses for RNAsequencing and microarray studies[END_REF] . In recent years, the expression of all photoprotective genes has been found to be controlled by photoreceptor proteins; the blue light receptor phototropin (PHOT) was initially found to control LHCSR3.1 and LHCSR3.2 [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF] , and it was shown later that PHOT also controls expression of LHCSR1 and PSBS1/2 [START_REF] Ritchie | Limma powers differential expression analyses for RNAsequencing and microarray studies[END_REF][START_REF] Gorman | Cytochrome f and plastocyanin: their sequence in the photosynthetic electron transport chain of Chlamydomonas reinhardii[END_REF] . UV-B radiation via the UVR8 mainly controls LHCSR1 and PSBS1/2 and to a lesser extent LHCSR3 [START_REF] Ritchie | Limma powers differential expression analyses for RNAsequencing and microarray studies[END_REF][START_REF] Sueoka | Mitotic replication of deoxyribonucleic acid in Chlamydomonas reinhardii[END_REF][START_REF] Greiner | Targeting of Photoreceptor Genes in Chlamydomonas reinhardtii via Zinc-Finger Nucleases and CRISPR/Cas9[END_REF] .

CO2 fixation

The energy and reducing power from ATP and NADPH generated in photochemical light reactions will be transferred to CO2 to produce high-energy, reduced sugars.

There are two types of photosynthesis to fix CO2: C3 and C4 photosynthesis. In C3 photosynthesis, CO2 reacts with RuBP (D-ribulose-1,5-bisphosphate) to produce two 3-carbon compounds 3-PGA (3-phosphoglyceric acid) via Calvin-Benson-Bassham cycle (CBB cycle). However, in C4 photosynthesis, CO2 is first fixed to form a 4carbon compound OAA (oxaloacetic acid), which is quickly converted to malic acid.

The 4-carbon acid will be split into a 3-carbon compound and CO2 that will enter the CBB cycle.

In Chlamydomonas reinhardtii, only C3 photosynthesis has been found, while C4 photosynthesis has been found in some marine algae [START_REF] Kaye | The mitochondrial alternative oxidase from Chlamydomonas reinhardtii enables survival in high light[END_REF][START_REF] Gibson | Enzymatic assembly of DNA molecules up to several hundred kilobases[END_REF][START_REF] Mackinder | A repeat protein links Rubisco to form the eukaryotic carbon-concentrating organelle[END_REF] . No matter which type of photosynthesis, global outputs of photosynthesis are triose phosphate (i.e., glyceraldehyde 3-phosphate (G3P)), NADPH, and ATP, which constitute the primary metabolic intermediates in a photosynthetic cell.

The CBB cycle includes three stages. The first stage is the carboxylation step. In this step, CO2 binds to RuBP and produce 3-PGA; The second stage is the reduction stage. Using the energy produced in light reactions, 3-PGA is phosphorylated and reduced to G3P and dihydroxyacetone phosphate. These triose phosphates exit the CBB cycle, becoming the precursors for starch synthesis, or are modified further by the CBB cycle and exit the CBB cycle as the precursors in the shikimate pathway, or stay in the CBB cycle to enter the third stage of the CBB cycle to regenerate the RuBP.

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH)

In the second stage of the CBB cycle, the fate of G3P plays a crucial role in carbon portioning in the cell. GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) is one of the critical enzymes to catalyze the reversible reaction of 1,3-bisphosphoglycerate (BGPA) to G3P. GAPDHs are highly conserved proteins in different organisms and play central roles in carbon metabolism.

Plants contain several isoforms of GAPDH.

In Arabidopsis thaliana, There are four isoforms of GAPDH: GAPA/B, GAPC, GAPCp, and the NADP-dependent non-phosphorylating cytosolic GAPDH (GAPN) [START_REF] Schloss | A Chlamydomonas gene encodes a G protein beta subunit-like polypeptide[END_REF] .

However, GAPN belongs to the aldehyde dehydrogenase superfamily and has no close functional/structural relationship with other phosphorylating GAPDHs [START_REF] Schlesselman | Data Transformation in Two-Way Analysis of Variance[END_REF] .

GAPA/B (encoded by GAPa1; AT3G26650, GAPa2; AT1G12900, and GAPb; AT1G42970) is the chloroplastic GAPDH playing a central role in the Calvin-Benson cycle of CO2 assimilation [START_REF] Genty | The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence[END_REF]74 . GAPC (encoded by GAPc1; AT3G04120 and GAPc2; AT1G13440) is the cytosolic glycolytic GAPDH and plays a role in glycolysis. The GAPC1 knockout mutant showed delayed growth, seed and fruit development alteration phenotypes in Arabidopsis 75 . GAPCps (encoded by GAPCp1; AT1G79530 and GAPCp2; AT1G16300) are mainly localized in plastids 76,77 , while GAPCp1 is also found in cell wall 78 . GAPCps deficiency affects amino acid and sugar metabolism and impairs plant development 79 .

Chlamydomonas reinhardtii contains four distinct isoforms: three phosphorylating and one non-phosphorylating GAPDH. These include: GAP1 (Cre12.g485150), GAP2 (Cre07.g354200), GAP3 (Cre01.g010900) and the NADP-dependent nonphosphorylating cytosolic GAPDH (GAPN1, Cre12.g556600). However, as in Arabidopsis, GAPN1 has no close functional/structural relationship with other phosphorylating GAPDH in Chlamydomonas.

GAP1 is a chloroplastic GAPDH according to PredAlgo prediction 80 and as was later experimentally validated by a fluorescent protein-tagging approach 81 . Its transcription level is highly induced in nitrogen or sulfur depletion conditions [START_REF] Zou | Regularization and variable selection via the elastic net[END_REF] Although the exact function of GAP1 is still unknown, GAP1 showed interaction with FDX1, FDX2 and FDX5 (ferredoxin) and has been shown to be regulated posttranslationally by FDX5 82,83 . GAP2 is the only cytosolic located GAPDH is Chlamydomonas which has been believed plays a role in the glycolysis. However, its exact function has not been experimentally confirmed.

GAP3 is located in the chloroplast in Chlamydomonas and functions like GAPA/B of Arabidopsis. GAP3 plays a central role in the CBB cycle 84 , and its activity could be regulated by interaction with the highly unfolded and flexible protein CP12 and the phosphoribulokinase (PRK) [85][86][87] .

Starch metabolism in Chlamydomonas

The output of the CBB cycle could be used for the alternative carbohydrates metabolism 88,89 . In higher plants and green algae, carbohydrates are formed in the chloroplast, while in red algae, glaucophyte, and dinoflagellate they are formed in the cytosol 90 . Furthermore, different species have different carbohydrate reserve forms.

Green algae and higher plants synthesize amylose (long unbranched chain of Dglucose connected by α 1-4 linkages and very few α 1-6 linkages) and amylopectin (branched chain of D-glucose with both α 1-4 and 1-6 linkages) with ADP-glucose as the donor 91 , while red algae synthesize floridean starch (a hybrid of glycogen and starch) with UDP-glucose as the donor 92 and cyanobacteria synthesis glycogen (α 1-4 linked glucan) 93 . Phaeophyceae (brown algae) and Bacillariophyceae (diatom) synthesis glycogen in both β 1-3 and 1-6 linkages form 94 .

Starch Synthesis in Chlamydomonas

Starch is the primary carbon sink and valuable storage polysaccharide in all the eukaryotic microalgae, although the localization and biosynthesis pathway vary among different phyla. Amylose comprises 10 to 30% of starch in a cell and is synthesized by granule-bound starch synthase (GBSSI) 95 . However, amylopectin comprises 70 to 90% of starch in a cell, and its synthesis requires more enzymatic processing than amylose 96,97 .

In Chlamydomonas reinhardtii, starch accumulates in the chloroplast. Some closely packed starch granules are found surrounding the pyrenoid called starch sheath 98 . The starch sheath has been proved to be essential for pyrenoid function and CCM 99,100 . It prevents the leakage of CO2 in low-CO2 conditions from pyrenoid and minimizes oxygen exposure in the pyrenoid matrix 101 .

Fructose 6-phosphate (F6P), the product of the reaction catalyzed by fructose 1,6bisphosphatase (FBPase), is the branch point for metabolites leaving the CBB cycle and initiating starch synthesis 102 . Next, F6P is converted into glucose 6-phosphate (G6P) by phosphoglucose isomerase (PGI1). G6P is converted to glucose-1-phosphate (G1P) by phosphoglucomutase (PGM). The reaction of G1P with ATP leads to the formation of the ADP-glucose and pyrophosphate catalyzed by ADP-glucose pyrophosphorylase (AGPase). This step is one of the rate-limiting steps of the starch synthesis pathway (Fig. 1.1). After the production of ADP-glucose, starch is synthesized by three classes of enzymes: Starch synthases (SSS), starch debranching enzymes (DBE), and starch branching enzymes (BE).

Starch Degradation in Chlamydomonas

Starch is usually degraded in the dark to provide energy and substrates for different cellular activities. However, starch catabolism is not that clear in Chlamydomonas.

There are two ways to degrade starch in green algae: the hydrolytic and the phosphorolytic pathway 96,[103][104][105] .

Hydrolytic starch degradation generates maltose, mainly mediated by β-amylases (βAMY). This also leads to the generation of malto-oligosaccharides (MOS) as byproducts which are mainly mediated by α-amylases (αAMY) and isoamylase (ISA).

MOS could be modified again by βAMY or processed by disproportionating enzyme1 (DPE1).

Phosphorolytic starch degradation will first generate starch-(P)n by glucan-water dikinases (GWD) and phosphoglucan-water dikinases (PWD). Those phosphorylated glucans will be converted into maltose by βAMY. Maltose generated by starch degradation will be transported into the cytosol by Maltose Excess Protein1 (MEX1) and converted into heteroglycan by disproportionating enzyme2 (DPE2) and starch phosphorylase (PHO). Table 1.1 showed the enzymes that are involved in the starch metabolism in Chlamydomonas.

Strategies to control or increase starch content in microalgae

The depletion of the nutrients, such as nitrogen (N), phosphorus (P), and sulfur (S),

is the widely used way to increase the starch accumulation in microalgae 107,110 . N is essential for proteins, nucleic acids, and chlorophyll synthesis in green algae. The depletion or limitation of N leads to more than 50% (w/w, based on dry weight; DW) in starch-producing in green algae, although algae have slow growth rates and declined photosynthetic efficiency 109 . P is an essential component of biomembrane system synthesis and stabilization. P depletion leads to starch accumulation and lipid accumulation in microalgae 111 . S is widely distributed in polar lipids and coenzymes of green algae. Similar to N and P, S depletion leads to more starch than N or P depletion in Chlorella 110 .

Except for nutrient limitation, other strategies such as temperature changing, specific inhibitors applying, light intensity, and light quality changing. A temperature higher than 30℃ will change the property of the starch fraction and accelerate the conversion of starch to sucrose in Chlorella vulgaris. 112 . By using the specific DNA replication inhibitor 5-fluorodeoxyuridine and protein synthesis inhibitorcycloheximide, green algae cells have been shown to grow to large cell size and accumulate more starch 110,113 .

Naturally, light is also a critical factor in starch accumulation. With the increase of light intensity, the starch content increase. Under high light intensities, the photosynthetic CO2 fixation capacity is enhanced, leading to the accumulation of more starch or lipids as shown for the green algae Dunaliella and Chlorella 108,114 .

Besides light intensity, light quality could change the starch content in microalgae. In an industrial green algae-Chlorella sp. AE10, its total carbohydrate content (mainly starch) can go to 58.5% (w/w per DW) under red light conditions, while it is 45.17%

(w/w per DW) under blue light conditions 115 . In a marine diatom-Isochrysis sp.

CCAP 927/14, its total carbohydrate content (mainly starch) is 34% (w/w per DW) under white light conditions while it is only 22% (w/w per DW) under blue light conditions 116 . However, the mechanism of blue light decrease the starch content is still unclear. Some studies reported that blue light increased the starch degradation 117,118 in Chlorella, but this was not the case in Phaeodactylum tricornutum 119 .

Starch metabolism regulator in Chlamydomonas

Few starch metabolism regulators have been found in Chlamydomonas. Li et al. reported that overexpression of GNAT19, a Gcn5-related N-acetyltransferase, leads to 72% and 118% higher biomass and starch content than its WT, respectively and (Starch Granules Abnormal 2) SAGA2 100 showed a more elongated and thinner starch sheath granules than its WT.

Light Perception

Light is not only the energy source for photosynthetic organisms but also a source of information. Light perceived by different photoreceptors can relay information to the plants about their environment such as light intensity 122 , and length of the day 123 , while recently, photoreceptors have also been found to play the role of temperature sensors 124,125 . Several classes of photoreceptors, including UV-and blue-light receptors and far-red receptors, are involved in the responses of cells to almost every different wavelength of light [START_REF] Feizi | Network deconvolution as a general method to distinguish direct dependencies in networks[END_REF]126 (Fig. 1.2). These photoreceptors convert light, sensed by chromophore molecules, into biological signals controlling gene expression [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF] , photo-orientation, developmental processes [START_REF] Fett | Regulation of Periplasmic Carbonic Anhydrase Expression in Chlamydomonas reinhardtii by Acetate and pH[END_REF]127 , entrainment of the circadian clock 128 , and many other processes [START_REF] Sueoka | Mitotic replication of deoxyribonucleic acid in Chlamydomonas reinhardii[END_REF]129,130 . Phototropins are present in different organisms, including higher plants and algae.

Arabidopsis thaliana contains two phototropins-PHOT1 and PHOT2. They are mainly located in the plasma membrane but can move into the cytosol [147][148][149] . PHOT2 localized weakly to the cytoplasm in the dark and exhibited a blue light-dependent association with the Golgi apparaturs 149 , while PHOT1 could release a fraction to the cytoplasm in a blue light-dependent manner 150 . Phototropins are activated by blue light and control many plant activities, such as temperature sensing 124 , leaf flattening 151 , stomatal opening 142,145,[152][153][154][155] , phototropism 156 , mitochondrial and chloroplast positioning [156][157][158][159][160][161] . Chloroplast avoidance movement is only mediated by PHOT2 strains showed not only the impairment of gametes formation but also the impairment of dark-inactivated gametes reactivation and zygote germination [START_REF] Fett | Regulation of Periplasmic Carbonic Anhydrase Expression in Chlamydomonas reinhardtii by Acetate and pH[END_REF][START_REF] Pérez-Rodríguez | PlnTFDB: Updated content and new features of the plant transcription factor database[END_REF] . PHOT is also involved in carotenoids and Chlorophyll-binding protein synthesis. The PHOT RNAi strains also showed a downregulation of genes encoding glutamate-1-semialdehyde aminotransferase (GSAT), Chlorophyll a-b binding protein (LHCBM6) and phytoene desaturase (PDS), which are critical for photosynthetic function 130 . Moreover, PHOT controls qE by mainly inducing the expression of LHCSR3, together with LHCSR1

and PSBS in excess light conditions [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF][START_REF] Ritchie | Limma powers differential expression analyses for RNAsequencing and microarray studies[END_REF][START_REF] Gorman | Cytochrome f and plastocyanin: their sequence in the photosynthetic electron transport chain of Chlamydomonas reinhardii[END_REF] .

Photoreceptors and carbon metabolism

Interesting links between photoperception and carbon metabolism have been found in higher plants.

In Arabidopsis thaliana, mutants devoid of phytochrome have a reduced CO2 However, in Chlamydomonas reinhardtii, the link between photoperception and carbon metabolism has not been explored so far.

Dissertation Organization

The overall aim of this dissertation was to understand the link between blue light perception and starch accumulation in Chlamydomonas reinhardtii. We first reported the link between photoreceptors and carbon metabolism in Chlamydomonas reinhardtii.

Chapter

Results

PHOT-dependent Genome-wide transcriptional profiling

In order to understand the role of PHOT in global gene expression control in C. reinhardtii, our lab has performed transcriptomic and proteomic analyses on phot and WT under low light (LL, 5 μmol/m 2 /s) and high light (HL, 300 μmol/m 2 /s) conditions.

For the transcriptomics (RNA-seq), samples were collected 1h after the exposure to HL; for the proteomics, cells were exposed to HL for 4h before sampling. In LL conditions, there were 5020 down-regulated and 7380 upregulated genes in phot, while 6021 down-regulated and 6379 upregulated genes in HL conditions.

Experiment design of collecting sample for transcriptomics and proteomics analysis were shown in Fig. 2.1.

Gene ontology analysis of transcriptomics and proteomics data indicated the involvement of PHOT in carbohydrate metabolism in Chlamydomonas (Fig. 2.1). Upper: Cultures were grown in TAP medium until cell density to 2-3 million cells/ml. They were washed with fresh HSM medium and resuspended in HSM to a cell density of 1 million cells/ml. Cells were placed under low light conditions for 16-18 hours. After this period, Cells were sampled as LL samples and start the high light treatment. Samples were collected at 1h and 4h for transcriptomics and 37h, as indicated with green arrows. For each time point, 3 biological replicates were prepared for transcriptomics and proteomics analysis, respectively. Three biological replicates were prepared for omics analysis. Lower: The X-axis is the number of genes annotated with the given GO term; Point size gives the absolute number of genes; Color gives the statistical significance of the observed enrichment as multiple testing adjusted p-value, red means more significant.

Among those differentially expressed genes (DEGs), most genes involved in starch biosynthesis were upregulated in phot under both LL and HL conditions, such as PGM1 (phosphoglucomutase 1), STA1 (ADP-glucose pyrophosphorylase large subunit 1), DPE1 (α-1,4-glucanotransferase), SSS3 (Soluble starch synthase III), starch branching enzymes (SBE1, SBE3, SBE4) and starch debranching enzyme (ISA2, ISA3) (Table 2.1). However, STA6 (ADP-glucose pyrophosphorylase small subunit) was downregulated in LL conditions and upregulated in HL conditions. In starch degradation, beta-amylase and alpha-amylase genes were down-regulated in phot under both LL and HL conditions, while AMYA2 (α-amylase) was upregulated under both LL and HL conditions. However, PWD1 (Phosphoglucan water dikinase) and GWD1 (αglucan water dikinase) were upregulated in phot under both LL and HL conditions (Table 2.2).

Generation of PHOT complemented strains

To address the link between PHOT and starch, we first checked the starch content of the WT (CC125) and phot (PHOT knockout mutant) in TAP medium under LL conditions. We found that phot could accumulate two times more starch than the wild type, although the two strains had identical photosynthetic efficiency under both LL (L1 in Fig. 2.2) and HL (L2 in Fig. 2.2) measured as YII (yield of photosystem II) (Fig. 2.2). To ensure that the high starch content phenotype is linked to the PHOT deletion, the first step was to complement the phot with the PHOT gene.

The phot and pp4 (Full PHOT complement strain) strains used before were generated in the cw15 (cell wall-deficient mutant, flagella-less) genetic background [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF] .

As PHOT is located in the plasma membrane and in the flagella 166 , and the phot strain we used for omics analyses was generated in the CC125 genetic background [START_REF] Li | A pigment-binding protein essential for regulation of photosynthetic light harvesting[END_REF] , we decided to stick to the CC125 background strains in the whole project.

Full-length PHOT CDS was amplified from a plasmid ble-phot preserved in our lab [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF]170 and subcloned into phk330 plasmid through EcoRI/BamHI (Plasmid map is in section 6.3.1, the detail of amplification is in section 6.2.8) and then transformed into phot by electroporation. Cells were plated on Zeocin-containing TAP agar plates.

As PHOT plays a key role in qE regulation [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF] , we decided to screen transformants based on qE phenotype. The Zeocin resistant transformants were picked and grew in the TAP medium under LL (15 μmol/m 2 /s). After two days of growth, the medium was changed into the fresh phototrophic medium-HSM and cells were exposed to HL (300 μmol/m 2 /s) for 4h to induce. One transformant completely rescued the low qE phenotype after HL treatment (Fig. 2.3B). The western blot result showed it is a successful full PHOT complemented strain (Fig. 2.3A), and we named it phot-C 62 . (NPQ measurement setup details in 6.2.4).

In addition, for other investigation goals and to get more PHOT complemented strains, I also tried to express full-length PHOT CDS with Venuse-Flag tag in phot by using pLM005 176 plasmid (Plasmid map is in Section 6.3.1). However, after screening more than 1000 colonies, we did not get any PHOT complemented strain. To solve this problem, we decided to use gDNA rather than CDS. Due to the long PHOT gDNA length (6540bp) and high GC content (68%), the PHOT gDNA was split into two fragments and amplified using different enzymes and primers G3-G6

(Amplification details are in Section 6.2.8). The two fragments of PHOT gDNA were assembled into pLM005 plasmid by Gibson assembly 177 (Plasmid map is in section 6.3.1). With this approach (use of genomic DNA), we got two PHOT complemented lines (phot-C2 and phot-C3) which could rescue the low qE phenotype after screening only 100 colonies (Fig. 2.4). This also suggested that the introns play an important role in enhancing gene expression 178 . Chlorophyll fluorescence was recorded in the dark (labelled as "D"), at 21 (labelled as "L1") and 336 (labelled as "L2") µmol photons m -2 s -1 as indicated in the graphs. (NPQ measurement setup details in 6.2.4)

Total carbohydrate content is a reliable approximation of starch content in

Chlamydomonas

In the starch content measurement method (Section 6.2.6; Fig. So the total carbohydrate content measurement is a reliable and convenient way to indicate the starch content change in Chlamydomonas. Unless otherwise mentioned, total carbohydrate content was used in all the following investigations.

PHOT inhibited starch accumulation in Chlamydomonas

Starch accumulated during the day and degraded during the night, so we checked the starch content of WT, phot, and phot-C strains grown phototrophically (HS medium; HSM) in the 12/12 light/dark cycle.

As Fig. 2.6 showed, phot accumulated around three times more starch than the WT, a phenotype that was fully reversed by the complementation with the PHOT gene (phot-C strain). Interestingly, the synthesis rate in the light and the degradation rate of starch in the dark are similar in these three strains. In addition, we also checked the starch content of phot-C2 and phot-C3 under continuous LL conditions. They behave like phot-C, and their starch content was also similar to the WT (Fig. 2.7).

W T p h o t p h o t -C

PHOT inhibition of starch accumulation is kinase-activity-dependent

To explore whether the kinase activity of PHOT is involved in starch accumulation, we generated the pkin strain-complement with the kinase domain of PHOT (without the LOV domains) in phot by using plasmid ble-kin 170 (Section 6.2). The removal of the LOV domains has been shown to lead to a constitutively active kinase, as has been previously reported in plants 186 and in Chlamydomonas [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF] .

The pkin strain could rescue the qE phenotype of phot as described before [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF] (Fig.

2.9A

). Furthermore, the starch content of pkin went back to WT level (Fig. 2.9B). Moreover, we used another set of phot mutants based on the cw15 background [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF]170 and checked their starch content. In accordance with my data (Fig. 2.7), the cw15 phot mutant accumulated around two times more starch than its WT. Furthermore, complementation with full-length PHOT (pp4) or only PHOT kinase domain (pk1) could rescue this phenotype. Conversely, complementation with LOV domains of PHOT (pl1) and the dead kinase form of PHOT (pk3) failed to rescue the starch phenotype (Fig. 2.10). However, due to the partial complementation of PHOT in pp4 [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF] , the starch content of pp4 stays a little higher than in WT.

Combined, the results of Fig. 2.10 suggest that the inhibition of starch accumulation by PHOT is kinase-activity-dependent.

WT phot pp4 pk1 pl1 pk3

Conclusion

In this chapter, we found that many genes involved in carbohydrate metabolism were upregulated in phot mutant by RNAseq analyses. As the major carbohydrate form in Chlamydomonas is starch, we want to know if there is a link between PHOT and starch in Chlamydomonas. First, we found the high starch content phenotype in phot mutant, and the full PHOT complement strains confirm that the deletion of PHOT leads to the high starch content phenotype. More importantly, by generating the different parts and dead kinase form of PHOT complemented strains, we confirmed that the enzymatically active kinase domain in PHOT is necessary for the PHOT-starch link.

In conclusion, these results provided the first evidence and a novel link between PHOT and starch metabolism in Chlamydomonas.

Chapter 3. Linking blue light perception via PHOT with GAP1 and starch

Abstract

Chapter 3 describes the first signaling cascade pathway linking PHOT with starch accumulation in Chlamydomonas.

In this Chapter, we found a chloroplast localized gene-GAP1 (Cre12.g485150, glyceraldehyde 3-phosphate dehydrogenase) is controlled by PHOT and partially responsible for the high starch content phenotype of phot. Overexpression of GAP1 leads to higher starch content. Moreover, using the Gene Regulatory Network (GRN)

we recently constructed to elucidate the transcriptional responses to the availability of light and carbon source [START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF] , a transcription factor (TF) regulating expression of GAP1 under the control of PHOT was identified. We named this TF GAPR4 (GAPDH Regulator 4). Overexpression of GAPR4 leads to higher starch content and GAP1

expression level in all different light quality conditions.

The manuscript, which includes the GRN we used for predicting GAP1 regulators, is entitled 'Widening the landscape of transcriptional regulation of algal photoprotection" and is presented in section 3.2.8.

Results

PHOT represses GAP1

To get more insights into the starch over-accumulation phenotype of phot, we checked the expression level of genes related to starch metabolism and the Calvin cycle 187 (Fig. 3.1). Interestingly, genes related to starch synthesis and degradation were both upregulated in phot compared with WT. This indicated that the phot mutant synthesizes more starch and consumes more starch. This result is in accord with the previous results in Fig. 2.6. However, one gene encoding one of the three isoforms of GAPDH in Chlamydomonas-GAP1 (Section 1.1.2.5) stands out among these genes.

The GAP1 transcription level is around 100 times higher in phot than in WT (Fig.

3.2A

), which is in accord with our RNA seq results (Table 2.1). Importantly, GAP1

transcription levels in phot-C are similar to WT levels (Fig. 3.2A). Moreover, our proteomics data analysis showed that GAP1 protein level is 30 times and 150 times higher in phot than in WT under HL and LL conditions, respectively (Fig. 3.2B).

Therefore, phototropin inhibits starch accumulation and GAP1 in Chlamydomonas. Glyceraldehyde-3-phosphate dehydrogenases (GAPDHs) are highly conserved proteins in all organisms and play a central role in cellular carbon metabolism (Section 1.1.2.5). As GAP1 is chloroplast located 81 and probably involved in the Calvin cycle and glycolysis, it is worth checking if GAP1 plays a role in PHOTdependent starch metabolism.

Blue light represses GAP1 in a PHOT-dependent way

As PHOT is a blue light photoreceptor, to test the role of GAP1 in the link between PHOT and starch, we decided to check the GAP1 level and starch content under different light qualities (Red, Blue, White) in 12/12 light/dark cycle conditions.

In WT, in the middle of the light phase, GAP1 expression levels were found to be around 100 times higher under red light conditions than under blue light-containing conditions (white light and blue light conditions). However, in phot mutant, GAP1 levels do not vary amongst the different light quality conditions and remain as high as in WT under red light illumination. GAP1 level in phot-C is similar to WT (Fig. 3.3).

So we can conclude that blue light represses GAP1 in a phototropin-dependent way. We also checked the starch content of WT, phot, and phot-C under these conditions.

At the end of the light phase, starch accumulates higher when blue light is eliminated from the light spectrum (red light). This is true for all three strains, although starch levels are significantly higher in the phot. These results imply that blue light is a repressor of starch accumulation (Fig. 3.4).

However, in phot mutant, we still can see a further accumulation under red light, and this implies that perhaps PHOT is not the only mediator of this blue-light inhibition (Fig. 3.4). Some other blue-light photoreceptors or blue-light responsive proteins might also be involved. To summarize, blue light represses GAP1 in a PHOT-dependent way and represses starch accumulation in a PHOT-dependent way and a PHOT-independent way.

Blue light repression of GAP1 is PHOT kinase-activity-dependent

To explore whether the kinase activity of PHOT is involved in GAP1 repression, we checked the GAP1 transcription level in the pkin mutant. The GAP1 transcription level also went back to the WT level in the pkin mutant (Fig. 3.5). We got similar results using another set of phot mutants based on the cw15 background. However, complementation with LOV domains of PHOT (pl1) and the dead kinase form of PHOT (pk3) failed to rescue the GAP1 transcription level (Fig. Combining all the results, it suggested that PHOT represses GAP1 is kinaseactivity-dependent.

W T p h o t p h o t -

3.6).

GAP1 over-expression leads to increased starch contents

To further examine the link between GAP1 and starch accumulation, we decided to generate GAP1 overexpression lines.

Full-length GAP1 CDS was amplified from WT cDNA and cloned into pLM005 176 and pRAM118 plasmid 188 (Amplification details in section 6.2.8, plasmid map in section 6.3). GAP1 CDS was under the control of the PSAD promoter. We got only one GAP1 overexpression line after screening more than 1,000 colonies. Therefore, I decided to change the strategy, and I used GAP1 gDNA rather than CDS; as a result, I got two GAP1-OE expression lines after screening only 100 colonies. This situation is similar to PHOT complementation (Section 2.2.2). After changing from CDS to gDNA, we screened fewer transformants but got more corrected mutants.

Overall, one GAP1 CDS overexpression line (gap1oe-C1) and two GAP1 gDNA overexpression lines (gap1oe-C2 and gap1oe-C3) were generated (Fig. 3.7).

The GAP1 transcription level in these OE lines was at least 12 times higher than in the WT. In these strains, overexpression of GAP1 increased the starch content by a factor of 1.1-1.4, while the disruption of PHOT led to an increase in GAP1

transcription level and starch content by a factor of 90 and 3, respectively (Fig. Combining all the results, it is clear that GAP1 overexpression leads to increased starch accumulation.

GAP1 is not involved in PHOT-dependent LHCSR3 regulation

Another important phot phenotype is the qE phenotype [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF] , so we checked the qE and LHCSR3 levels in three GAP1-OE lines.

LHCSR3 levels did not change in none of the three gap1oe lines, and gap1oe

showed the same qE value as WT (Fig. 3.8). This indicated that GAP1 is not responsible for the PHOT qE phenotype.

Putative GAP1 regulator prediction

To get more insights into how PHOT regulates GAP1, by using our omics data and a mathematical modeling approach [START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF] (The manuscript [START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF] describing this tool is presented in section 3.2.8), we predicted few putative GAP1 regulators. These putative GAP1 regulators encode different family types of TFs, such as bHLH and RWP-RK family TFs.

We first combined data from two publicly available transcriptomics studies of synchronized chemostat WT cultures grown in a 12h/12h light dark scheme and sampled in 30 min to 2h intervals 189,190 with our RNAseq data generated from mixotrophically or autotrophically grown batch cultures of the WT and phot mutant acclimated to LL or exposed to HL; using these data we reconstructed the Gene Regulatory Network 1 (GRN1). We also built the so-called PHOT-specific GRN (GRN2) using the RNAseq data from samples of phot and WT acclimated to LL and after 1h exposure to HL In Table 3.1, we present the 19 predicted GAP1 regulators using GRN1 and 2). A smaller number means higher confidence in the prediction. To validate the putative regulatory links, we took 7 TFs (marked in red in Table 3.1)

and checked their transcription level in the WT and phot under RL and BL conditions by RT-qPCR (Fig. 3.9). GAPR1, GAPR2, GAPR4, and GAPR7 transcription profiles were similar to GAP1 under red light and blue light conditions (Fig. 3.9) (The primers for GAPR3 did not work).

GAPR4 (Cre07.g349152) is the only gene in the top 10 gene list of two approaches predicted results (2 nd position in both approaches, Table 3.1). Furthermore, its expression profile under LL and HL conditions is similar to GAP1 in our RNA-seq data (Fig. 3.10). Moreover, GAPR4 and GAP1 transcription profiles under 12h/12h day/light cycle in published transcriptome datasets 189,190 were similar, e.g. see (Fig. 3.11). GAPR4 is a basic helix-loop-helix proteins domain-containing transcription factor (bHLHs). bHLHs are widely distributed in all three eukaryotic kingdoms and constitute one of the largest families of transcription factors [191][192][193] . bHLHs represent key regulatory components in transcriptional networks controlling many biological processes 193 .

WT

In animals, bHLHs have been involved in sensing environmental signals, regulating the cell cycle and circadian rhythms, and regulating diverse essential developmental processes, including neurogenesis, myogenesis, sex, cell lineage determination, proliferation, and differentiation 191,192 .

In unicellular eukaryotes, such as yeast, bHLH proteins are involved in chromosome segregation, general transcriptional enhancement, and metabolism regulation 194 .

So, we decided to take GAPR4 for further investigation.

GAPR4 -a novel GAP1 regulator under the control of PHOT

To probe the link between GAPR4 and PHOT, we checked GAPR4 and GAP1 transcription levels under different light qualities in the WT, phot, and phot-C.

GAPR4 expression profile is similar to GAP1(Fig. In WT, the GAPR4 transcription level is higher under red light conditions than blue light-containing conditions (white and blue light conditions). However, in phot, GAPR4 levels do not vary amongst the different light quality conditions and remain as high as in WT under red light illumination. GAPR4 level in phot-C is similar to WT (Fig. 3.12). Therefore, GAPR4 is under the control of PHOT.

To test whether GAPR4 is the regulator of GAP1, we generated one GAPR4 overexpression line (Amplification details in 6.2.8), which could increase the GAP1 transcription level and leads to a bit higher starch than WT. (Fig. 3.13). Moreover, the GAPR4 overexpression line leads to a higher GAP1 transcription level in all different light quality conditions (Fig. 3.14). Combining all the results, we conclude that PHOT represses GAPR4 which acts as an activator of GAP1.

Gene regulator network analyses (GRN)

This section presents the manuscript describing the GRN tools used to predict GAP1 regulators. In this manuscript, 1. We compiled a compendium of 158 samples from Chlamydomonas reinhardtii cultures exposed to different light and carbon supplies to infer its gene regulatory network by using an ensemble approach that integrates the results from five benchmarked methods for network inference. Further, we assessed the quality of the resulting light-and carbon-dependent gene regulatory network by employing curated, experimentally confirmed gene regulatory interactions.

2. We used the inferred gene regulatory network to rank candidate transcription factors for their involvement in the regulation of photoprotection and validated two of the identified six candidates in a series of experiments with respective mutants and complementation strains. Specifically, we showed that CCM regulator LCR1 also activates photoprotection and that QER7, a Squamosa Binding Protein, suppresses photoprotection-and CCM-gene expression.

Because QER7 gene expression levels are suppressed by the blue light photoreceptor phototropin, our findings clearly suggest that blue light perception takes part in the regulation of CCM.

3. By combining our findings with the analysis of the inferred gene regulatory network, we demonstrated the existence of regulatory hubs that channel lightand CO2-mediated signals into a common response.

Introduction

Photosynthetic microalgae convert light into chemical energy in the form of ATP and NADPH to fuel the CO2 fixation in the Calvin-Benson cycle 1 . They have evolved to cope with rapid fluctuations in light 2 and inorganic carbon 3 availability in their native habitats. When absorbed light exceeds the CO2 assimilation capacity, the formation of harmful reactive oxygen species can lead to severe cell damage; this is prevented by the activation of photoprotective mechanisms, collectively called non-photochemical quenching (NPQ). NPQ encompasses several processes that are distinguished in terms of their timescales 2 , among which the rapidly reversible energy-quenching (qE) is, under most circumstances, the predominant NPQ component 2,4 . The major molecular effector of qE in Chlamydomonas is the LIGHT HARVESTING COMPLEX STRESS RELATED protein LHCSR3, encoded by the LHCSR3.1 and LHCSR3.2 genes 5 that slightly differ only in their promoters; LHCSR1 can also contribute significantly to qE under conditions where LHCSR3 is not expressed [START_REF] Dinc | LHCSR1 induces a fast and reversible pH-dependent fluorescence quenching in LHCII in Chlamydomonas reinhardtii cells[END_REF][START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF] . PSBS, the key qE effector protein in higher plants [START_REF] Li | A pigment-binding protein essential for regulation of photosynthetic light harvesting[END_REF] is encoded as two highly similar paralogues PSBS1 and PSBS2 in Chlamydomonas 9 . They are only transiently expressed in Chlamydomonas under high light (HL) [START_REF] Correa-Galvis | Photosystem II Subunit PsbS Is Involved in the Induction of LHCSR Protein-dependent Energy Dissipation in Chlamydomonas reinhardtii[END_REF][START_REF] Tibiletti | Chlamydomonas reinhardtii PsbS Protein Is Functional and Accumulates Rapidly and Transiently under High Light[END_REF] and accumulate under UV-B irradiation [START_REF] Allorent | UV-B photoreceptor-mediated protection of the photosynthetic machinery in Chlamydomonas reinhardtii[END_REF] ; their precise contribution in Chlamydomonas photoprotective responses is still unresolved [START_REF] Redekop | PsbS contributes to photoprotection in Chlamydomonas reinhardtii independently of energy dissipation[END_REF] .

Intracellular levels of CO2 are modulated by the availability of its gaseous and hydrated forms 3 in the culture media and the supply of acetate, that is partly metabolized into CO2 [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF][START_REF] Hanawa | Induction of a high-CO2-inducible, periplasmic protein, H43, and its application as a high-CO2-responsive marker for study of the high-CO2-sensing mechanism in Chlamydomonas reinhardtii[END_REF] . Under low CO2, Chlamydomonas activates the CO2-concentrating mechanism (CCM) to avoid substrate-limitation of photosynthesis by raising the CO2 concentration at the site of RuBisCO, where CO2 is assimilated 3 . The CCM mainly comprises of carbonic anhydrases (CAHs) and of inorganic carbon transporters. Almost all CCM-related genes are under the control of the nucleus-localized zinc-finger type nuclear factor CIA5 (aka CCM1) [START_REF] Moroney | Isolation and Characterization of a Mutant of Chlamydomonas reinhardtii Deficient in the CO2 Concentrating Mechanism[END_REF][START_REF] Fukuzawa | Ccm1, a regulatory gene controlling the induction of a carbonconcentrating mechanism in Chlamydomonas reinhardtii by sensing CO2 availability[END_REF][START_REF] Xiang | The Cia5 gene controls formation of the carbon concentrating mechanism in Chlamydomonas reinhardtii[END_REF] , including the Myb Transcription Factor LOW-CO2 -STRESS RESPONSE 1 (LCR1) that controls expression of genes coding for the periplasmic CAH1, the plasma membrane-localized bicarbonate transporter LOW CO2-INDUCED 1 (LCI1), and the low-CO2 responsive LCI6, whose role remains to be elucidated [START_REF] Yoshioka | The novel Myb transcription factor LCR1 regulates the CO2responsive gene Cah1, encoding a periplasmic carbonic anhydrase in Chlamydomonas reinhardtii[END_REF] . CIA5 is also a major qE regulator activating transcription of genes encoding LHCSR3 and PSBS, while repressing accumulation of LHCSR1 protein [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF] .

LHCSR3 expression relies on blue light perception by the photoreceptor phototropin (PHOT) [START_REF] Petroutsos | A blue-light photoreceptor mediates the feedback regulation of photosynthesis[END_REF] , on calcium signaling, mediated by the calcium sensor CAS [START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF] and on active photosynthetic electron flow [START_REF] Petroutsos | A blue-light photoreceptor mediates the feedback regulation of photosynthesis[END_REF][START_REF] Petroutsos | The chloroplast calcium sensor CAS is required for photoacclimation in Chlamydomonas reinhardtii[END_REF][START_REF] Maruyama | Transcriptional regulation of the stressresponsive light harvesting complex genes in Chlamydomonas reinhardtii[END_REF] , likely via indirectly impacting CO2 availability [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF] . The critical importance of CO2 in LHCSR3 expression is demonstrated by the fact that changes in CO2 concentration can trigger LHCSR3 expression [START_REF] Miura | Expression profiling-based identification of CO2-responsive genes regulated by CCM1 controlling a carbon-concentrating mechanism in Chlamydomonas reinhardtii[END_REF][START_REF] Fang | Transcriptome-wide changes in Chlamydomonas reinhardtii gene expression regulated by carbon dioxide and the CO2-concentrating mechanism regulator CIA5/CCM1[END_REF][START_REF] Brueggeman | Activation of the carbon concentrating mechanism by CO2 deprivation coincides with massive transcriptional restructuring in Chlamydomonas reinhardtii[END_REF] even in the absence of light [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF] . Accumulation of LHCSR1 and PSBS mRNA is under control of the UV-B photoreceptor UVR8 [START_REF] Allorent | UV-B photoreceptor-mediated protection of the photosynthetic machinery in Chlamydomonas reinhardtii[END_REF] and PHOT [START_REF] Aihara | Algal photoprotection is regulated by the E3 ligase CUL4-DDB1DET1[END_REF][START_REF] Redekop | Transcriptional regulation of photoprotection in dark-to-light transition-more than just a matter of excess light energy[END_REF] and is photosynthesis-independent [START_REF] Maruyama | Transcriptional regulation of the stressresponsive light harvesting complex genes in Chlamydomonas reinhardtii[END_REF][START_REF] Redekop | Transcriptional regulation of photoprotection in dark-to-light transition-more than just a matter of excess light energy[END_REF] . While LHCSR1 is CO2/CIA5 independent at the transcript level [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF][START_REF] Redekop | Transcriptional regulation of photoprotection in dark-to-light transition-more than just a matter of excess light energy[END_REF] , PSBS is responsive to CO2 abundance and is under partial control of CIA5 [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF] .

A Cullin (CUL4) dependent E3-ligase [START_REF] Aihara | Algal photoprotection is regulated by the E3 ligase CUL4-DDB1DET1[END_REF][START_REF] Gabilly | Regulation of photoprotection gene expression in Chlamydomonas by a putative E3 ubiquitin ligase complex and a homolog of CONSTANS[END_REF][START_REF] Tokutsu | The CONSTANS flowering complex controls the protective response of photosynthesis in the green alga Chlamydomonas[END_REF] has been demonstrated to post-translationally regulate the transcription factor (TF) complex of CONSTANS (CrCO) [START_REF] Gabilly | Regulation of photoprotection gene expression in Chlamydomonas by a putative E3 ubiquitin ligase complex and a homolog of CONSTANS[END_REF] and NF-Y isomers [START_REF] Tokutsu | The CONSTANS flowering complex controls the protective response of photosynthesis in the green alga Chlamydomonas[END_REF] , which bind to DNA to regulate the transcription of LHCSR1, LHCSR3, and PSBS. The putative TF and diurnal timekeeper RHYTHM OF CHLOROPLAST 75 (ROC75) was shown to repress LHCSR3 under illumination with red light [START_REF] Kamrani | ROC75 is an Attenuator for the Circadian Clock that Controls LHCSR3 Expression[END_REF] .

Here, we employed a large compendium of RNAseq data from Chlamydomonas to build a gene regulatory network (GRN) underlying light and carbon responses, and thus reveal the transcriptional regulation of qE at the interface of these responses. The successful usage of RNAseq data to infer GRNs has been demonstrated in many studies [START_REF] Omranian | Gene regulatory network inference using fused LASSO on multiple data sets[END_REF][START_REF] Marbach | Wisdom of crowds for robust gene network inference[END_REF][START_REF] Fang | Decoding the gene regulatory networks in diverse human and mouse conditions[END_REF] , although the data pose some challenges that require careful consideration. All of the developed approaches to infer a GRN quantify the interdependence between the transcript levels of TF-coding genes and their putative targets; the resulting prediction model serves as a proxy for the regulatory strength that the product of the TF-coding gene exerts on its target(s). It is usually the case that the number of observations (samples) is considerably smaller than the number of TFs used as predictors, leading to collinearity of the transcript levels and associated computational instabilities; further, as an artifact of the computational techniques, some of the inferred regulations may be spurious [32][33][START_REF] Schäfer | A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics[END_REF] . To address these issues, here we took advantage of combining the outcome of multiple regularization techniques and postprocessing to increase the robustness of identified interactions [33][START_REF] Schäfer | A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics[END_REF][START_REF] Zou | Regularization and variable selection via the elastic net[END_REF] . In contrast to our approach, the existing predicted GRNs of Chlamydomonas either focused on nitrogen starvation [START_REF] Gargouri | Identification of regulatory network hubs that control lipid metabolism in Chlamydomonas reinhardtii[END_REF] or used a broad RNAseq data compendium, not tailored to inferring regulatory interactions underlying responses to particular cues [START_REF] Lämmermann | Ubiquitin ligase component LRS1 and transcription factor CrHy5 act as a light switch for photoprotection in <em>Chlamydomonas</em&gt[END_REF] . Moreover, these GRNs were not obtained by combining the outcomes from multiple inference approaches, shown to increase accuracy of predictions [START_REF] Marbach | Wisdom of crowds for robust gene network inference[END_REF] , and their quality was not gauged against existing knowledge of gene regulatory interactions.

We used an RNAseq data compendium of 158 samples (Supplementary Table 1) from Chlamydomonas cultures exposed to different light and carbon supply as input to seven benchmarked GRN inference approaches that employ complementary inference strategies [START_REF] Omranian | Gene regulatory network inference using fused LASSO on multiple data sets[END_REF][START_REF] Marbach | Wisdom of crowds for robust gene network inference[END_REF] . We assessed the performance of each approach based on a set of curated TFtarget gene interactions with experimental evidence from Chlamydomonas. Based on this assessment, we integrated the outcome of the five best performing approaches into a unique resource, a consensus network of Chlamydomonas light-and carbon-dependent transcriptional regulation. We used the consensus network to reveal regulators of qE genes and demonstrated the quality of predictions by validating two of the six tested candidates.

We show here that LCR1 regulates not only CCM, as previously reported [START_REF] Yoshioka | The novel Myb transcription factor LCR1 regulates the CO2responsive gene Cah1, encoding a periplasmic carbonic anhydrase in Chlamydomonas reinhardtii[END_REF] but also qE by activating the expression of LHCSR3, and demonstrate that qE-REGULATOR 7 (QER7), belonging to the SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE gene family, is a repressor of qE and CCM-gene expression. Our work consolidates the extensive co-regulation of CCM and photoprotection 7 based on the untargeted assessment of the obtained genome-scale GRN.

Results

Computationally inferred GRN recovered known regulatory interactions underlying qE and CCM in Chlamydomonas

We first aimed to employ published and here generated RNAseq data sets capturing the transcriptional responses of Chlamydomonas to light and acetate availability to infer the underlying GRN. To this end, we obtained data from two publicly available transcriptomics studies of synchronized chemostat wild-type (WT) cultures grown in a 12h/12h light dark scheme and sampled in 30 min to 2h intervals [START_REF] Zones | High-Resolution Profiling of a Synchronized Diurnal Transcriptome from Chlamydomonas reinhardtii Reveals Continuous Cell and Metabolic Differentiation[END_REF][START_REF] Strenkert | Multiomics resolution of molecular events during a day in the life of Chlamydomonas[END_REF] . We combined these with our RNAseq data generated from mixotrophically or autotrophically grown batch cultures of the WT and phot mutant acclimated to low light (LL) or exposed to HL (Methods, Supplementary Table 1). These data sets capture the expected expression patterns of the key genes involved on CCM and qE (Fig. 1a) in response to changes in acetate availability and light intensity.

Specifically, we found strong up-regulation of these genes in the light [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF][START_REF] Redekop | Transcriptional regulation of photoprotection in dark-to-light transition-more than just a matter of excess light energy[END_REF] , and a marked inhibition of LHCSR3.1/2 and CCM genes by acetate as previously described [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF][START_REF] Fett | Regulation of Periplasmic Carbonic Anhydrase Expression in Chlamydomonas reinhardtii by Acetate and pH[END_REF] .

We employed these data together with a list of 407 transcription factors from protein homology studies [START_REF] Pérez-Rodríguez | PlnTFDB: Updated content and new features of the plant transcription factor database[END_REF][START_REF] Jin | PlantTFDB 4.0: Toward a central hub for transcription factors and regulatory interactions in plants[END_REF] (Methods, Supplementary Table 2), as input to seven GRN inference approaches to robustly predict TF-target interactions, as shown in benchmark studies [START_REF] Marbach | Wisdom of crowds for robust gene network inference[END_REF] .

Since there exists no study that experimentally probes TF binding to DNA on a genome-scale, we first curated a list of known, experimentally validated regulatory interactions underlying CCM and qE in Chlamydomonas [START_REF] Fang | Transcriptome-wide changes in Chlamydomonas reinhardtii gene expression regulated by carbon dioxide and the CO2-concentrating mechanism regulator CIA5/CCM1[END_REF][START_REF] Gabilly | Regulation of photoprotection gene expression in Chlamydomonas by a putative E3 ubiquitin ligase complex and a homolog of CONSTANS[END_REF][START_REF] Tokutsu | The CONSTANS flowering complex controls the protective response of photosynthesis in the green alga Chlamydomonas[END_REF][START_REF] Kamrani | ROC75 is an Attenuator for the Circadian Clock that Controls LHCSR3 Expression[END_REF] , to assess the quality of GRNs inferred by the different approaches (Fig. 1b). As a negative control, we considered the lack of effect of the SINGLET OXYGEN RESISTANT 1 (SOR1) TF on PSBS1 transcript levels in diurnal culture [START_REF] Fischer | SINGLET OXYGEN RESISTANT 1 links reactive electrophile signaling to singlet oxygen acclimation in Chlamydomonas reinhardtii[END_REF] . The comparative analysis of the predicted and known interactions demonstrated that two of the applied approaches for GRN inference (i.e. ARACNE [START_REF] Margolin | ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context[END_REF] and global silencing [START_REF] Barzel | Network link prediction by global silencing of indirect correlations[END_REF] ) are unable to recover any literature interactions when using a network density threshold of 10% of all possible TF-TF and TF-target interactions. This poor performance is due either to overtrimming or issues with the validity of the underlying assumptions, as seen in other case studies [START_REF] Omranian | Gene regulatory network inference using fused LASSO on multiple data sets[END_REF] . We therefore considered only the remaining five approaches, namely: Graphical Gaussian Models (GGM), Context Likelihood of Relatedness (CLR), Elastic Net regression, Gene Network Inference with Ensemble of Trees (GENIE3), and Network Deconvolution to infer a consensus GRN by using the Borda count election method [START_REF] Marbach | Wisdom of crowds for robust gene network inference[END_REF]47 (Methods). We then ranked the inferred interactions within each approach and quantified the variability of ranks for the known TF-target gene interactions. We found that the average standard deviation of the ranks of the TF-target gene interactions within an approach is larger than the average standard deviation for the rank of a TF-target gene interaction across the five approaches (Extended Data Fig. 1, Fig. 1b). This observation suggested that the properties of a given TFtarget gene interaction have a stronger influence on its assigned rank than the inference approach used. More specifically, we noted that the regulation of LHCSR genes by the two NF-Y paralogues and the induction of LCR1 by CIA5 are not recovered by any of the used approaches; this is in line with reports showing that CIA5 is constitutively expressed and regulated post-translationally [START_REF] Xiang | The Cia5 gene controls formation of the carbon concentrating mechanism in Chlamydomonas reinhardtii[END_REF] -not reflected in the transcriptomics data. Further, NF-Y factors that rely on complex formation with CrCO to regulate their targets [START_REF] Tokutsu | The CONSTANS flowering complex controls the protective response of photosynthesis in the green alga Chlamydomonas[END_REF] , may also act via unresolved posttranslational mechanisms. Importantly, the regulatory interactions of the CCM effector genes LCI1 and CAH1 by LCR1 are assigned very high ranks (top 1%) by the approaches considered in the consensus GRN (except for GGM); moreover, the experimentally falsified interaction of SOR1 and PSBS1 transcripts [START_REF] Fischer | SINGLET OXYGEN RESISTANT 1 links reactive electrophile signaling to singlet oxygen acclimation in Chlamydomonas reinhardtii[END_REF] is correctly discarded by all approaches (Fig. 1b). In addition, we observed that CLR and GENIE3 demonstrated the best performance with respect to the set of known interactions. For instance, they identified the regulation of LHCSR3.1 by CrCO [START_REF] Gabilly | Regulation of photoprotection gene expression in Chlamydomonas by a putative E3 ubiquitin ligase complex and a homolog of CONSTANS[END_REF][START_REF] Tokutsu | The CONSTANS flowering complex controls the protective response of photosynthesis in the green alga Chlamydomonas[END_REF] and of PSBS1 by NF-YB [START_REF] Tokutsu | The CONSTANS flowering complex controls the protective response of photosynthesis in the green alga Chlamydomonas[END_REF] (Fig. 1b). Generalization of this ranking beyond the known interactions underlying qE and CCM processes is challenging, due to the lack of genome-scale gold standard, and we therefore opted to combine the results of the five approaches, that showed comparable performance, in the consensus GRN (Methods, Supplementary Table 3) to increase robustness of the predictions. Our analyses of the overlap between the consensus and individual GRNs and the enrichment of TF-TF interactions demonstrated the robustness of the inferred interactions (Supplementary Note 1, Extended Data Fig. 2).

Consensus GRN pinpoints LCR1 as a regulator of qE-related genes

59

Using the consensus GRN, we inferred direct regulators of LHCSR and PSBS genes and ranked them according to the score resulting from the Borda method (Methods) [START_REF] Marbach | Wisdom of crowds for robust gene network inference[END_REF][START_REF] De Borda | Mémoire sur les élections au scrutin[END_REF] . Mutants were available for four of the top ten of TFs with strongest cumulative regulatory effect on qErelated genes (Fig. 2a, Supplementary Table 4): Two knock-out mutants of previously uncharacterized genes were ordered from the CliP library [START_REF] Li | A genome-wide algal mutant library and functional screen identifies genes required for eukaryotic photosynthesis[END_REF] , which we termed qE-regulators 4 and 6 (qer4, qer6; see Extended Data Fig. 3 for the genotyping of these mutants). Additionally, we obtained an over-expressor line of the N-acetyltransferase LCI8 [START_REF]UniProt: the universal protein knowledgebase in 2021[END_REF] and the knock-out strain of the known CCM regulator LCR1 [START_REF] Yoshioka | The novel Myb transcription factor LCR1 regulates the CO2responsive gene Cah1, encoding a periplasmic carbonic anhydrase in Chlamydomonas reinhardtii[END_REF] . We tested for a regulatory effect by switching LL-acclimated mutant strains and their respective WT background to HL for 1h and quantified transcript levels of qE-related genes. For qer4, qer6, and lci8-oe we could not observe an effect on the transcript levels of investigated genes after HL exposure (Extended Data Fig. 4). Thus, qer4 and qer6 are considered false positive predictions of the GRN, despite the fact that qer4 accumulated 1.5 times more LHCSR3.1 under LL than the WT. A review of the closest orthologs of LCI8 together with the experimental data indicate that it is likely involved in arginine synthesis [START_REF]UniProt: the universal protein knowledgebase in 2021[END_REF] and wrongly included as histone acetylase in the list of TFs. Interestingly, LCR1, the highest ranking among the tested regulators showed significantly decreased expression of LHCSR3 at both the gene (3 times lower, Fig. 2b) and protein level (4 times lower, Fig. 2c,d) compared to the WT; as a result, lcr1 developed very low NPQ and qE (Fig. 2e). Complementation of lcr1 with the knocked-out gene (strain lcr1-C) restored LHCSR3 gene and protein expression as well as the qE phenotype (Fig. 2b-e).

Interestingly, the lcr1 mutant over-accumulated LHCSR1 and PSBS both at the transcript and at the protein level (Fig. 2b-d); Complementation of lcr1 with the knocked-out gene (strain lcr1-C) restored LHCSR3 gene and protein expression as well as the qE phenotype (Fig. 2b-e).

Because pre-acclimation conditions impact qE gene expression [START_REF] Redekop | Transcriptional regulation of photoprotection in dark-to-light transition-more than just a matter of excess light energy[END_REF] we conducted independent experiments in which cells were acclimated to darkness before exposure to HL and we obtained very similar results. Our data demonstrated that lcr1 showed significantly lower expression of LHCSR3 and higher expression of LHCSR1/PSBS at both the gene (Extended Data Fig. 5a) and protein level (Extended Data Fig. 5b,c), and had lower NPQ and qE (Extended Data Fig. 5d) than the WT, although the higher expression levels of LHCSR1 gene were not rescued by the complementation with the missing LCR1 gene (Extended Data Fig. 5a). Altogether, our data show that LCR1 is a regulator of qE by activating LHCSR3.1 transcription and repressing LHCSR1 and PSBS accumulation.

Further, we revisited the role of LCR1 in regulating CCM genes [START_REF] Yoshioka | The novel Myb transcription factor LCR1 regulates the CO2responsive gene Cah1, encoding a periplasmic carbonic anhydrase in Chlamydomonas reinhardtii[END_REF] by analyzing expression of selected CCM genes in WT, lcr1 and lcr1-C cells shifted from LL or darkness to HL, conditions favoring CCM gene expression [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF] . We first confirmed that under our experimental conditions lcr1 could not fully induce LCI1 (Extended Data Fig. 6a,b) in accordance to the report of the discovery of LCR1 [START_REF] Yoshioka | The novel Myb transcription factor LCR1 regulates the CO2responsive gene Cah1, encoding a periplasmic carbonic anhydrase in Chlamydomonas reinhardtii[END_REF] . Our analyses further showed a statistically significant impairment of lcr1 in inducing genes encoding the Ci transporters LOW-CO2-INDUCIBLE PROTEIN A (LCIA), HIGH-LIGHT ACTIVATED 3 (HLA3), CHLOROPLAST CARRIER PROTEIN 1 (CCP1) and BESTROPHINE-LIKE PROTEIN 1 (BST1) as well as the carbonic anhydrase CAH4, when shifted from LL or dark to HL (Extended Data Fig. 6a,b), indicating that the role of LCR1 in low-CO2 gene expression extends beyond the regulation of gene expression of CAH1, LCI1 and LCI6 17 .

PHOT-specific GRN reveals a novel repressor of qE and CCM

The light-dependent induction of LHCSR3 is predominantly mediated by the blue light photoreceptor PHOT [START_REF] Petroutsos | A blue-light photoreceptor mediates the feedback regulation of photosynthesis[END_REF] . To analyze the PHOT-dependent transcriptional regulators, we first identified the interactions shared by the consensus network and a GRN inferred only by GENIE3, due to its good performance, using the RNAseq data from samples of phot and WT acclimated to LL and after 1h exposure to HL (Methods). This so-called PHOT-specific GRN (Methods, Supplementary Table 5) comprises regulatory interactions underlying the transcriptomic changes observed in the phot mutant while burrowing the statistical power of the RNAseq compendium.

When investigating the top 10 regulators of qE genes in this PHOT GRN the interactions where weighted based on the importance score from GENIE3 (Supplementary Table 6, Fig. 3a). Among these interactions we recovered the previously discussed false positive LCI8 gene (see above, Fig. 3a) (Extended Data Fig. 4). In addition, two previously reported regulators of qE, ROC75 [START_REF] Kamrani | ROC75 is an Attenuator for the Circadian Clock that Controls LHCSR3 Expression[END_REF] and CrCO [START_REF] Gabilly | Regulation of photoprotection gene expression in Chlamydomonas by a putative E3 ubiquitin ligase complex and a homolog of CONSTANS[END_REF][START_REF] Tokutsu | The CONSTANS flowering complex controls the protective response of photosynthesis in the green alga Chlamydomonas[END_REF] , are also included in this list of PHOT-dependent regulators. These observations are in line with an existing hypothesis [START_REF] Aihara | Algal photoprotection is regulated by the E3 ligase CUL4-DDB1DET1[END_REF] suggesting that a CUL4-dependent E3-ligase targeting CrCO ,26 acts downstream of PHOT. ROC75 has been previously reported to act independently of the PHOT signal based on qPCR studies of the mutant grown synchronously under different light spectra [START_REF] Kamrani | ROC75 is an Attenuator for the Circadian Clock that Controls LHCSR3 Expression[END_REF] . In our RNAseq data, gathered under continuous white light, we observed a significant difference in expression levels of ROC75 between WT and phot (log2 fold-change = 1.03, adj. p-value = 1.80*10 -7 ).

The fact that several regulators showed larger regulatory strength than CrCO in the PHOT GRN indicates the existence of yet unreported regulators of qE effector genes in the PHOT signaling pathway. This is in line with existing results [START_REF] Gabilly | Regulation of photoprotection gene expression in Chlamydomonas by a putative E3 ubiquitin ligase complex and a homolog of CONSTANS[END_REF] , showing that the knock-out of CrCO is insufficient to fully abolish light-dependent activation of LHCSR3. Following this reasoning we obtained qer1 and qer7, the available regulator candidates mutants, from the CLiP library [START_REF] Li | A genome-wide algal mutant library and functional screen identifies genes required for eukaryotic photosynthesis[END_REF] , (for genotyping see Extended Data Fig. 3). Our results show higher mRNA and protein levels of LHCSR1 in the qer1 mutant (Extended Data Fig. 7); however, this could not be rescued by ectopic expression of the QER1 gene in the qer1 mutant background (Extended Data Fig. 7). We found significant upregulation of LHCSR3.1 gene expression in the qer7 mutant (1.7 times, Extended Data Fig. 8a) also reflected in higher NPQ (Extended Data Fig. 8b) and qE levels (Extended Data Fig. 8c) which we followed up in more detail. To this end, we ectopically expressed the WT QER7 gene in the qer7 mutant and generated the complemented strain qer7-C that expressed QER7 to levels similar to those WT (Extended Data Fig. 3c). As a result, the qer7-C strain showed reduced LHCSR3 gene expression, NPQ and qE levels as compared with the qer7 mutant (Extended Data Fig. 8a-c). LHCSR1 and PSBS seemed to be unaffected in the qer7 in these LL to HL transition experiments (Extended Data Fig. 8a). As with LCR1, we also performed dark to HL experiments to further characterize the photoprotective responses of qer7; under these conditions, qer7 accumulated significantly more LHCSR1 (1.7 times) and PSBS1 (2.2 times) while LHCSR3 remained unaffected (Extended Data Fig. 8d). As in the LL to HL experiments (Extended Data Fig. 8b,c) qer7 showed more NPQ and qE (Extended Data Fig. 8e,f). Complementation of qer7 with the missing QER7 gene restored all phenotypes (LHCSR1, PSBS, NPQ, qE; Extended Data Fig. 8d-f). These data validate the prediction of QER7 as regulator of qE gene expression (Fig. 3a) and indicate that QER7 regulates different subsets of qE genes depending on the pre-acclimation conditions; LHCSR3 when preacclimated under LL, LHCSR1 and PSBS when pre-acclimation occurs in darkness. Motivated by these findings and given the fact that most of the Chlamydomonas transcriptome, undergoes diurnal changes according to biological function [START_REF] Strenkert | Multiomics resolution of molecular events during a day in the life of Chlamydomonas[END_REF] we decided to address the role of QER7 in regulating qE genes under light/dark cycles. We synchronized WT, qer7 and qer7-C cells in 12h L/12h D cycle and exposed them to HL right after the end of the dark phase. Our results revealed that under these conditions QER7 functions as a repressor of all qE-related genes; the qer7 mutant expresses significantly higher LHCSR3, LHCSR1 and PSBS both at the gene (Fig. 3b) and protein (Fig. 3c,d) level, and exhibits higher NPQ and qE (Fig. 3e), with all phenotypes rescued in the qer7-C complemented line. Previous protein homology studies identified QER7 as Squamosa Binding Protein 50 or bZIP TF [START_REF] Thiriet-Rupert | Transcription factors in microalgae: genome-wide prediction and comparative analysis[END_REF] , and here, we provide the first functional annotation of QER7 as a novel qE regulator.

QER7 co-regulates qE-related and CCM genes

The recent findings that the regulatory role of CIA5 [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF] and LCR1 (Fig. 2 and Extended Data Fig. 5) extends beyond CCM to also control qE-related gene expression, prompted us to also inspect the expression levels of CCM genes in synchronized qer7 cells (Fig. 4a). Indeed, for four of these transcripts (CAH4, LCIA, CCP1, HLA3) we observed a significant upregulation in qer7 after HL exposure that was reversed by complementation with the QER7 gene, indicating that QER7 suppresses expression of CCM genes; the suppression role of QER7 on CCM genes was only observable under HL, conditions that favor CCM gene expression [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF] and not under LL (Fig. 4a). Interestingly, and in line with QER7 acting as suppressor of CCM gene expression (Fig. 4a), we found LCR1 and QER7 among the top 10 regulators of the CCM genes probed by qPCR in the inferred GRNs (Extended Data Fig. 9).

After observing the inhibition of CCM gene transcription by QER7, we investigated the signaling pathway upstream of QER7. To this end, we quantified QER7 gene expression in synchronized phot cultures. As expected, we observe a strong effect of the PHOT knock-out showing that PHOT suppresses QER7 expression (Fig. 4b) implying that blue-light perception via PHOT is involved in the regulation of CCM related gene expression. Interestingly, phot expressed LCR1 to levels similar to those in WT (Fig. 4c). Thus, while sharing part of their target genes, the two TFs mediate different signals. These data demonstrate that the PHOTspecific GRN successfully pinpoints TFs that depend on PHOT activity while retaining the statistical power to recover genuine regulatory interactions.

The two qE regulators that we validated in this study also regulate CCM genes. Therefore, we next investigated to what extent the observed coregulation pattern applies to the global, known transcriptional regulation of low CO2 and light stress responsive genes. To this end, we took advantage of the size of the presented genome-scale GRNs and compiled a list of genes putatively involved in photoprotection (Supplementary Table 7) or the CCM (Supplementary Table 8); we then extracted the 10 TFs exhibiting the strongest regulatory strength on the genes in the compiled lists. We found six (empirical p-value<0.001, Methods) and four (empirical p-value < 0.01) of the top 10 regulators to be shared between these two responses in the consensus (Fig. 5a,b) and the PHOT-specific GRN, respectively (Figure 5c,d). The significant, large number of shared regulators is a strong indication that coregulation of photoprotective and carbon assimilatory processes is a principal feature of Chlamydomonas' transcriptional regulatory program.

Discussion

The molecular actors and structure of the transcriptional regulatory mechanisms that shape Chlamydomonas' response to differential light and carbon availability are largely unknown, although they are paramount to survival of Chlamydomonas and offer valuable targets for biological engineering. Here we set out to elucidate the GRN underlying the response to light and carbon availability by combining the results from five complementary inference approaches and data from 158 RNAseq samples of cultures responding to these cues. The obtained GRN enabled us to identify novel regulators controlling qE. Experimentally testing available mutant strains for six of these candidates we were able to validate two TFs: QER7, suppressing qE-and CCM-gene expression, and LCR1, activating LHCSR3 and suppressing LHCSR1 and PSBS expression. The two TFs belong to independent signalling pathways: LCR1 has been known to be controlled by CIA5 while we found that QER7 is controlled by Phototropin; this last finding adds a new layer of regulation of CCM by blue light sensing. We complemented the finding of the involvement of LCR1 in the regulation of qE related genes with an analysis of the genome-scale co-regulation of CCM and photoprotective genes. Indeed, we observed significant number (six out of the top 10) of regulators targeting both processes in the inferred consensus GRN. Together with previous studies, our results provide evidence for extensive signaling crosstalk between light and carbon sensing and name a set of TFs that likely integrate these disparate signals into a common transcriptional response.

The GRN gives an unbiased representation of the genome-scale regulators acting in light and carbon sensing. Thus, it represents a powerful resource for future dissection of the transcriptional regulation of responses of Chlamydomonas to light and carbon availability.

Methods

Transcriptome analysis

We assembled a compendium of RNAseq data (Supplementary Table 1) that capture regulation of light-dependent processes by combining in-house produced RNAseq measurements with publicly available data from two studies of densely sampled diurnal cultures of Chlamydomonas [START_REF] Zones | High-Resolution Profiling of a Synchronized Diurnal Transcriptome from Chlamydomonas reinhardtii Reveals Continuous Cell and Metabolic Differentiation[END_REF][START_REF] Strenkert | Multiomics resolution of molecular events during a day in the life of Chlamydomonas[END_REF] . For the samples in the acetate time-resolved experiment, adapter sequences were specifically trimmed from raw reads using BBduk 52 (ktrim=r k=30 mink=12 minlen=50). Raw reads of the diurnal transcriptome study from Strenkert et al. [START_REF] Strenkert | Multiomics resolution of molecular events during a day in the life of Chlamydomonas[END_REF] were obtained from NCBI GEO database (GSE112394). Reads were aligned to the Chlamydomonas reference transcriptome [START_REF] Law | Precision weights unlock linear model analysis tools for RNA-seq read counts[END_REF] available from JGI Phytozome (Assembly version 5) using RNA STAR aligner. The BAM files obtained from these measurements were analyzed using HTSeq-count [START_REF] Anders | HTSeq-A Python framework to work with highthroughput sequencing data[END_REF] (standed=reverse) to create raw read count files. The raw read counts from Zones et al. [START_REF] Zones | High-Resolution Profiling of a Synchronized Diurnal Transcriptome from Chlamydomonas reinhardtii Reveals Continuous Cell and Metabolic Differentiation[END_REF] were obtained as .tsv from NCBI GEO (GSE71469). The final data set consists of 158 samples from 62 experimental conditions or time points (Supplementary Table 1). Genes with less than 1 count per million in at least 9 measurements where discarded and the remainder were voom 53 transformed and normalized using library normalization factors based on the TMM [START_REF] Robinson | A scaling normalization method for differential expression analysis of RNA-seq data[END_REF] approach as implemented in the R Bioconductor package edgeR [START_REF] Robinson | A Bioconductor package for differential expression analysis of digital gene expression data[END_REF] .

Transcription factor set from comparative genomics

To reduce the set of parameters in our network model, we compiled transcription factor (TF) annotations for the Chlamydomonas genome based on proteome homology studies. We obtained the proteomes and protein IDs of predicted Chlamydomonas TFs from Pérez-Rodríguez et al. [START_REF] Pérez-Rodríguez | PlnTFDB: Updated content and new features of the plant transcription factor database[END_REF] Since these predictions were built based on the older Chlamydomonas assembly, we first used the conversion table provided by Phytozome to convert JGI4 to Crev5.6 IDs. For the TFs that could not be recovered by this approach we used the Phytozome BLAST tool to align these sequences against the Crev5.6 proteome (BLASTP, E threshold: -1, comparison matrix: BLOSUM62, word length: 11, number of reported alignments: 5). The reported hits were filtered for sequence identity > 97% and gaps ≤ 1 . If sequences mapped multiple times to the same Crev5.6 gene ID, only the hit alignment closest to the N-terminus of the query sequence was kept. The hit was only accepted if the alignment started at least six residues from the N-terminus of the hit sequence. For Crev5.6 loci that had multiple JGI4 TF queries assigned to them the best hit was selected manually. This set was then extended by the TFs found in the study of Jin et al. [START_REF] Jin | PlantTFDB 4.0: Toward a central hub for transcription factors and regulatory interactions in plants[END_REF] and the regulators in the manually curated set of CCM and qE regulatory interactions (Supplementary Tables 7 and8). Using this procedure, we compiled a list of 407 Chlamydomonas TFs (Supplementary Table 2) to be considered as regulators in the inferred networks.

Gene regulatory network inference

The CLR and ARACNE approach were based on all replicate measurements; for all other inference methods the median from each condition was used as input. All input matrices where standardized gene-wise. If not explicitly stated in the respective paragraph the implementations of all GRN inference approaches were applied with their default settings.

Graphical Gaussian Models

The network inferred from a Graphical Gaussian model of gene regulation was obtained using the implementation of the partial correlation estimate from Schäfer et al. [START_REF] Schäfer | A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics[END_REF] as implemented in the R GeneNet package. All interactions between TFs and another gene/TF with non-zero partial correlations were included as network edges.

GENIE3

The random forest-based network from GENIE3 was generated using the R Bioconductor implementation provided by the authors [START_REF] Huynh-Thu | Inferring regulatory networks from expression data using tree-based methods[END_REF] . We used only expression levels of TFs as predictors.

Elastic net regression

A linear regression based network was obtained using the elastic net algorithm [START_REF] Zou | Regularization and variable selection via the elastic net[END_REF] . A model was fit for each gene using the expression levels of all TFs as predictors. The two hyperparameters 𝜆𝜆2 (quadratic penalty) and s (fraction of L1 norm coefficients) were tuned for each gene model using 6-fold cross validation. The 2D parameter space scanned was 𝜆𝜆2={0,0.001,0.01,0.05,0.1,0.5,1,1.5,2,10,100} and 𝑠𝑠={0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9}. The R2 value for each model was calculated as

𝑅𝑅 2 = 1 - ∑(𝑦𝑦 -𝑦𝑦 �) 𝑣𝑣𝑣𝑣𝑣𝑣(𝑦𝑦) (1)
with 𝑦𝑦 marking the vector of observed expression values and 𝑦𝑦̂ the model predictions. Models with a negative 𝑅𝑅2 value were discarded as regularization artifacts. The results of the remaining models were assembled into a network in which interactions were ranked by regression coefficients 𝛽𝛽 normalized by the maximum absolute coefficient.

𝛽𝛽 𝑛𝑛 = 𝛽𝛽 max|𝛽𝛽| (2)

CLR and ARACNE

The implementation of mutual information (MI)-based network inference approaches from the R package minet [START_REF] Meyer | A r/bioconductor package for inferring large transcriptional networks using mutual information[END_REF] was used. Pairwise MI was estimated based on the Spearman correlation as proposed by Olsen et al. [START_REF] Olsen | On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information[END_REF] . Two networks were constructed based on these MI estimates. Using the CLR approach 60 non-significant interactions were removed based on the z-scores calculated from the marginal distributions of MI values for each gene pair. Alternatively, the ARACNE algorithm [START_REF] Margolin | ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context[END_REF] was used to prune the network based on the data processing inequality. For both networks only interactions originating from a TF were taken into consideration and edges were ranked according to the assigned MI value.

Deconvolution and Silencing

For the two networks based on decomposition of the interaction matrix G the Pearson correlation matrix obtained from gene expression values was used as input.

The deconvolution approach introduced by Feizi et al. [START_REF] Feizi | Network deconvolution as a general method to distinguish direct dependencies in networks[END_REF] was implemented as previously described [START_REF] Omranian | Gene regulatory network inference using fused LASSO on multiple data sets[END_REF] . The eigenvalue scaling factor 𝛽𝛽 was initialized as 𝛽𝛽=0.9 and iteratively reduced in increments of 0.05 until the largest eigenvector of the direct interaction matrix generated by deconvolution was smaller than 1. Edges were ranked according to the deconvoluted interaction matrix.

The Silencing approach as described by Barzel et al. [START_REF] Barzel | Network link prediction by global silencing of indirect correlations[END_REF] was implemented in R. The proposed approximation of the direct interaction matrix S in which spurious interactions are silenced relies on the invers of the observed correlation matrix G. In our implementation we used the Moore-Penrose pseudoinverse in case G was close to singular. In the resulting network edges were ranked according to the approximated silenced interaction matrix.

Consensus network construction

To improve network quality [START_REF] Marbach | Wisdom of crowds for robust gene network inference[END_REF] , we built a consensus network integrating the GRN models inferred by the different approaches introduced above. To this end, we used the Borda count election method [START_REF] De Borda | Mémoire sur les élections au scrutin[END_REF] whereby the rank r of an interaction I in the consensus network built on the predictions from k approaches is given by arithmetic mean of the ranks in the individual networks

𝑣𝑣 𝑐𝑐𝑐𝑐𝑛𝑛𝑐𝑐𝑐𝑐𝑛𝑛𝑐𝑐 (𝐼𝐼) = ∑ 𝑟𝑟 𝑖𝑖 (𝐼𝐼) 𝑘𝑘 𝑖𝑖=1 𝑘𝑘 . (3)
Following the reasoning of Feizi et al. [START_REF] Feizi | Network deconvolution as a general method to distinguish direct dependencies in networks[END_REF] in this integration only the top 10% all possible edges in the GRN (625815) were considered from each individual ranking. For an edge that was not assigned a rank by some approaches, the missing ranks were set to 10% of all possible edges plus one.

Using this integration method, we assembled a consensus network based on all approaches to compare predictions from all GRN inference approaches (Extended Data Fig. 1). Due to this comparison and their inability to recover known interactions (Fig. 1b) the rankings derived from ARACNE and Silencing were only considered in Extended Data Fig. 1 and excluded from the final consensus network used for all other analyses. As with the individual networks returned by the different approaches the consensus network (Supplemental Table 3) was trimmed to the top 10% of all possible edges according to the integrated ranks. The weight of edges in the final network was set as 𝑣𝑣 𝑐𝑐𝑐𝑐𝑛𝑛𝑐𝑐𝑐𝑐𝑛𝑛𝑐𝑐 -1

.

PHOT-specific network

To investigate the PHOT-specific regulatory interactions genes that are differentially expressed between phot mutant and wt under low and high light were inferred. To this end, transcript counts of genes with more than 1 count per million in at least four replicates from these conditions (Supplementary Table 1) were tested for differential expression using the R packages limma 62 , DeSeq2 [START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF] , and edgeR [START_REF] Robinson | A Bioconductor package for differential expression analysis of digital gene expression data[END_REF] . Only genes deemed significant by all three tools after Benjamini-Hochberg correction for a false discovery rate of 0.05 were considered differentially expressed with respect to PHOT mutation

In the next step, we focused on the normalized and scaled expression levels from these differentially expressed genes and the previously mentioned conditions, to infer a PHOTspecific GRN using GENIE3. To improve robustness of this network, which was obtained from a comparably sparse data set, we only considered the edges in the intersection with the final consensus network. Again, for both networks only the top 10% of possible edges were taken into account. Therefore, the obtained PHOT-network represents a subnetwork of the final consensus in which edges are weighted by the "PHOT specific" GENIE3 importance measure (Supplementary Table 5).

Identification of major regulators

We compiled a manually curated list of possible target genes known to be involved in the processes of qE (LHCSR1, LHCSR3.1/2, PSBS1/2), photoprotection (Supplementary Table 7), and CCM (Supplementary Table 8). Based on the assumption that major regulators act on several genes important for a biological process, the regulatory strength of a candidate regulator (for the given process) was determined by the sum of edge weights 𝑤𝑤 𝑖𝑖𝑖𝑖 between this regulator and the 𝑘𝑘 genes in the respective target gene set

𝐶𝐶(𝑇𝑇𝑇𝑇) = ∑ 𝑤𝑤 𝑇𝑇𝑇𝑇𝑖𝑖 𝑘𝑘 𝑖𝑖=1 . ( 4
)

Empirical p-value calculation using Monte-Carlo simulation

The one-sided p-value for the overlap between the regulators of CCM and photoprotective genes was approximated by sampling the overlaps of random gene sets. To this end, we compiled two gene sets with the same cardinality as the curated CCM and photoprotective genes. The genes in these sets where randomly sampled without replacement from all targets in the respective networks. The 10 strongest regulators of these two gene sets where then obtained as previously described and the overlap was calculated as our sample statistic. This process was repeated 10,000 times and an empirical p-value was calculated from the number of iterations, 𝑣𝑣, where the overlap was higher or equal to the observed value, and the total number of iterations, n 64 :

𝑝𝑝 = 𝑟𝑟+1 𝑛𝑛+1 . ( 5
)

Strains and conditions

C. reinhardtii strains were grown under 15 µmol photons m -2 s -1 ) in Tris-acetate-phosphate (TAP) media 65 at 23 o C in Erlenmeyer flasks shaken at 125 rpm. For all experiments cells were transferred to Sueoka's high salt medium (HSM) 66 at 1 million cells mL -1 and exposed to light intensities as described in the text and figure legends. For the investigation of the impact of acetate on the genome-wide transcriptome, HSM was supplemented with 20 mM sodium acetate. C. reinhardtii strain CC-125 mt+ was used as WT. The phot (depleted from PHOT1; gene ID: Cre03.g199000), was previously generated [START_REF] Greiner | Targeting of Photoreceptor Genes in Chlamydomonas reinhardtii via Zinc-Finger Nucleases and CRISPR/Cas9[END_REF] and recently characterized [START_REF] Redekop | Transcriptional regulation of photoprotection in dark-to-light transition-more than just a matter of excess light energy[END_REF] . For synchronized cultures, the cells were grown in HSM for at least 5 days under a 12h light/12h dark cycle (light intensity was set at 15 μmol photons m -2 s -1 ; temperature was 18 ℃ in the dark and 23 ℃ in the light). All CLiP mutant strains used in this study and their parental strain (CC-4533) were obtained from the CLiP library (REF); qer1 (LMJ.RY0402.072278), qer4 (LMJ.RY0402.202963), qer6 (LMJ.RY0402.162350), qer7 (LMJ.RY0402.118995). The lcr1 (strain C44), lcr1-C (strain C44-B7) and its parental strain Q30P3 as described in [START_REF] Yoshioka | The novel Myb transcription factor LCR1 regulates the CO2responsive gene Cah1, encoding a periplasmic carbonic anhydrase in Chlamydomonas reinhardtii[END_REF] were a kind gift from Hideya Fukuzawa. Before performing phenotyping experiments, we first confirmed that lcr1 shows no expression of LCR1 and that this is rescued in the lcr1-C strain (Extended Data Fig. 10a). The lci8 overexpressing line was purchased from the Chlamydomonas Resource center; strain CSI_FC1G01, expressing pLM005-Cre02.g144800-Venus-3xFLAG in the CC-4533 background. Overexpression of LCI8-FLAG was verified by immunoblotting against FLAG (Extended Data Fig. 10b).

To complement qer1, a 1152 bp genomic DNA fragment from Chlamydomonas CC-4533 was amplified by PCR using KOD hot start DNA polymerase (Merck) and primers P11 and P12 (Supplementary Table 9). To complement qer7, a 5755 bp fragment DNA fragment from Chlamydomonas CC-4533 was amplified by PCR with Platinum superfii DNA Polymerase (Thermo Fisher Scientific) and primers P13 and P14 (Supplementary Table 9). The PCR products were gel purified and cloned into pRAM118 68 by Gibson assembly [START_REF] Gibson | Enzymatic assembly of DNA molecules up to several hundred kilobases[END_REF] for expression under control of the PSAD promoter. Junctions and insertion were sequenced and constructs were linearized by EcoRV before transformation. Eleven ng/kb of linearized plasmid [START_REF] Mackinder | A repeat protein links Rubisco to form the eukaryotic carbon-concentrating organelle[END_REF] mixed with 400 μL of 1.0 x10 7 cells mL -1 were electroporated in a volume of 120 mL in a 2-mm-gap electro cuvette using a NEPA21 square-pulse electroporator (NEPAGENE, Japan Unless otherwise stated, LL conditions corresponded to 15 µmol photons m -2 s -1 while HL conditions corresponded to 300 µmol photons m -2 s -1 of white light (Neptune L.E.D., France; see [START_REF] Ruiz-Sola | Photoprotection is regulated by light-independent CO2 availability[END_REF] for light spectrum). All experiments were repeated at least three times to verify their reproducibility.

DNA Isolation and genotyping of CLiP mutants.

Total genomic DNA from CLiP mutants and corresponding wild-type strain CC-4533 was extracted according to the protocol suggested by CLiP website (https://www.chlamylibrary.org/). One μl of the extracted DNA was used as a template for the PCR assays, using Phire Plant Direct PCR polymerase (Thermo Fisher Scientific). To confirm the CIB1 insertion site in the CLiP mutants, gene-specific primers were used that anneal upstream and downstream of the predicted insertion site of the cassette (primer pairs P3-P4, P7-P8, P9-P10 and P5-P6 for qer6, qer1, qer7 and qer4 respectively; Supplementary Table 9). While all these primers worked in DNA extracted from WT, they did not work in the DNA extracted from the mutants, with the exception of qer4 (Extended Data Fig. 3), therefore primers specific for the 5' and 3' ends of the CIB1 Cassette were additionally used. All the primers used for genotyping were shown in Supplementary Table 9. We further confirmed the disruption of the genes of interest by quantifying their mRNA accumulation (Extended Data Fig. 3).

mRNA quantification

Total RNA was extracted using the RNeasy Mini Kit (Qiagen) and treated with the RNase-Free DNase Set (Qiagen). 1 μg total RNA was reverse transcribed with oligo dT using Sensifast cDNA Synthesis kit (Meridian Bioscience, USA). qPCR reactions were performed and quantitated in a Bio-Rad CFX96 system using SsoAdvanced Universal SYBR Green Supermix (BioRad). The primers (0.3 μM) used for qPCR are listed in Supplementary Table 10. A gene encoding G protein subunit-like protein (GBLP) [START_REF] Schloss | A Chlamydomonas gene encodes a G protein beta subunit-like polypeptide[END_REF] was used as the endogenous control , and relative expression values relative to GBLP were calculated from three biological replicates, each of which contained three technical replicates. All primers using for qPCR (Supplementary Table 10) were confirmed as having at least 90% amplification efficiency. In order to conform mRNA accumulation data to the distributional assumptions of ANOVA, i.e. the residuals should be normally distributed and variances should be equal among groups, Two-Way Analysis of Variance were computed with log-transformed data Y=logX where X is mRNA accumulation [START_REF] Schlesselman | Data Transformation in Two-Way Analysis of Variance[END_REF] .

Immunoblotting

Protein samples of whole cell extracts (0.5 µg chlorophyll or 10 µg protein) were loaded on 4-20% SDS-PAGE gels (Mini-PROTEAN TGX Precast Protein Gels, Bio-Rad) and blotted onto nitrocellulose membranes. Antisera against LHCSR1 (AS14 2819), LHCSR3 (AS14 2766), ATPB (AS05 085), CAH4/5 (AS11 1737) were from Agrisera (Vännäs, Sweden); antiserum against PSBS was from ShineGene Molecular Biotech (Shanghai, China) targeting the peptides described in Ref. [START_REF] Correa-Galvis | Photosystem II Subunit PsbS Is Involved in the Induction of LHCSR Protein-dependent Energy Dissipation in Chlamydomonas reinhardtii[END_REF] . ATPB was used as a loading control. An anti-rabbit horseradish peroxidase-conjugated antiserum was used for detection. The blots were developed with ECL detection reagent, and images of the blots were obtained using a CCD imager (ChemiDoc MP System, Bio-Rad). For the densitometric quantification, data were normalized with ATPB.

Fluorescence-based measurements

Fluorescence-based photosynthetic parameters were measured with a a pulse modulated amplitude fluorimeter (MAXI-IMAGING-PAM, HeinzWaltz GmbH, Germany). Prior to the onset of the measurements, cells were acclimated to darkness for 15 min. Chlorophyll fluorescence was recorded during 10 min under 570 µmol m -2 s -1 of actinic blue light followed by finishing with 10 min of measurements of fluorescence relaxation in the dark. A saturating pulse (200 msec) of blue light (6000 µmol photons m -2 sec -1 ) was applied for determination of Fm (the maximal fluorescence yield in dark-adapted state) or Fm' (maximal fluorescence in any light-adapted state). NPQ was calculated as (Fm -Fm′)/Fm′ based on [START_REF] Genty | The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence[END_REF] ; qE was estimated as the fraction of NPQ that is rapidly inducible in the light and reversible in the dark.

Figures

Fig. 1: Characterization of the consensus GRN inferred by employing a compendium of RNA-seq data from diverse light and culture conditions. a. Expression levels of representative CCM and qE related genes are plotted over all samples used for network inference (z-scaled log values are depicted). The column annotation gives information on the culture conditions. The values of the rows of the heatmap correspond to the z-scores of representative CCM and qE genes plotted over all samples (columns) used in the network inference. b. The heatmap rows correspond to experimentally validated or falsified (neg) gene regulatory interactions involved in qE and CCM, curated from literature. The heatmap indicates ranking of these interactions by different approaches and the consensus network. Edges are considered highly ranked (depicted in blue) if they are above the 10% network density threshold. Edges ranked below this threshold are depicted in red. Edges that were not included in the given network are marked in grey. ARACNE and Silencing columns were only plotted for comparison and were not used in building the consensus GRN (see Methods). Fig. 2: Consensus GRN for light and acetate responses pinpoints LCR1 as regulator of qErelated genes. a. Dot plot of the relative regulatory strength of the top 10 regulators of qErelated genes in the consensus GRN (see Methods). TFs are marked in green if qE transcript levels were affected in the respective knock-out strain and this effect was reversed by complementation with the missing gene. TFs for which no effect was observed are marked in red. TFs for which no mutant lines were available are plotted in grey. b. WT, lcr1 and lcr1-C cells were acclimated for 16h in LL (15 µmol photons m -2 s -1 ). After sampling for the LL conditions, light intensity was increased to 300 µmol photons m -2 s -1 (HL); samples were taken 1 h (RNA) or 4 h (protein and photosynthetic measurements) after exposure to HL. Shown are relative expression levels of qE-related genes at the indicated conditions normalized to WT LL (n = 3 biological samples, mean ± sd). c. Immunoblot analyses of LHCSR1, LHCSR3, PSBS and ATPB (loading control) of one of the three biological replicates, under the indicated conditions. d. Quantification of immunoblot data of all replicates in panel c after normalization to ATPB. Shown are the HL treated samples; WT protein levels were set as 1. e. NPQ and calculated qE (as an inset) 4h after exposure to HL (n = 3 biological samples, mean ± s.d). The p-values for the comparisons are based on ANOVA Dunnett's multiple comparisons test and as indicated in the graphs (*, P < 0.005, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001). Statistical analyses for panel b and d were applied on log10transformed values. Fig. 3: A PHOT-specific GRN pinpoints QER7 as a suppressor of the expression of qE-related genes. a. Dot plot of the relative regulatory strength of the top 10 regulators of qE-related genes in the PHOT-specific GRN (see Methods). TFs are marked in green if qE transcript levels were affected in the respective knock-out strain and this effect was reversed by complementation with the knocked-out gene, in yellow, if the effect was not reversed by complementation, and in red, if no mutant effect was observed in the mutant. TFs for which no mutant lines were available are plotted in grey. b. WT, qer7 and qer7-C cells were synchronized under 12h light (15 µmol m -2 s -1 )/12h dark cycles. After sampling for the dark conditions (end of the dark phase), cells were exposed to 300 µmol photons m -2 s -1 (HL); samples were taken 1 h (RNA) or 4 h (protein and photosynthetic measurements) after exposure to HL. Shown are relative expression levels of qE-related genes at the indicated conditions normalized to WT LL (n = 3 biological samples, mean ± sd). c. Immunoblot analyses of LHCSR1, LHCSR3 and ATPB (loading control) of one of the three biological replicate set of samples, under the indicated conditions. d. Quantification of immunoblot data of all replicates in panel c after normalization to ATPB. WT protein levels at HL were set as 1. e. NPQ and qE, measured 4h after exposure to HL (n = 3 biological samples, mean ± sd). The p-values for the comparisons are based on ANOVA Dunnett's multiple comparisons test and are indicated in the graphs (*, P < 0.005, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001). Statistical analyses for panel b and d were applied on log10-transformed values. Fig. 1: Characterization of the consensus GRN inferred by employing a compendium of RNA-seq data from diverse light and culture conditions. a. Expression levels of representative CCM and qE related genes are plotted over all samples used for network inference (z-scaled log values are depicted). The column annotation gives information on the culture conditions. The values of the rows of the heatmap correspond to the z-scores of representative CCM and qE genes plotted over all samples (columns) used in the network inference. b. The heatmap rows correspond to experimentally validated or falsified (neg) gene regulatory interactions involved in qE and CCM, curated from literature. The heatmap indicates ranking of these interactions by different approaches and the consensus network. Edges are considered highly ranked (depicted in blue) if they are above the 10% network density threshold. Edges ranked below this threshold are depicted in red. Edges that were not included in the given network are marked in grey. ARACNE and Silencing columns were only plotted for comparison and were not used in building the consensus GRN (see Methods). TFs are marked in green if qE transcript levels were affected in the respective knock-out strain and this effect was reversed by complementation with the knocked-out gene, in yellow, if the effect was not reversed by complementation, and in red, if no mutant effect was observed in the mutant. TFs for which no mutant lines were available are plotted in grey. b. WT, qer7 and qer7-C cells were synchronized under 12h light (15 µmol m -2 s -1 )/12h dark cycles. After sampling for the dark conditions (end of the dark phase), cells were exposed to 300 µmol photons m -2 s -1 (HL); samples were taken 1 h (RNA) or 4 h (protein and photosynthetic measurements) after exposure to HL. Shown are relative expression levels of qE-related genes at the indicated conditions normalized to WT LL (n = 3 biological samples, mean ± sd). c. Immunoblot analyses of LHCSR1, LHCSR3 and ATPB (loading control) of one of the three biological replicate set of samples, under the indicated conditions. d. Quantification of immunoblot data of all replicates in panel c after normalization to ATPB. WT protein levels at HL were set as 1. e. NPQ and qE, measured 4h after exposure to HL (n = 3 biological samples, mean ± sd). Extended Data Fig. 7: Complementation of qer1 with QER1 (qer1-C strain) fails to rescue the LHCSR1 and PSBS phenotypes. WT, qer1 and qer1-C cells were acclimated for 16h in LL (15 µmol photons m -2 s -1 ). After sampling for the dark conditions, light intensity was increased to 300 µmol photons m -2 s -1 (HL); RNA samples were taken 1 h after exposure to HL. Shown are relative expression levels of qE genes at the indicated conditions normalized to WT LL (n = 3 biological samples, mean ± sd). The p-values for the comparisons are based on ANOVA Dunnett's multiple comparisons test on log10-transformed values and are indicated in the graphs (***, P < 0.001, ****, P < 0.0001).

Extended Data Fig. 8: Relative expression of qE-related genes in asynchronous qer7 cells. WT, qer7 and qer7-C cells were acclimated for 16h in LL (15 µmol photons m -2 s -1 ; a-c) or darkness (d-f). After sampling for the LL or dark conditions, light intensity was increased to 300 µmol photons m -2 s -1 (HL) and samples were taken

Conclusion

By taking advantage of proteomics and RNA-seq data, we found that GAP1 encoding the chloroplast form GAPDH is overexpressed in phot mutant. As phototropin is a blue light photoreceptor and the GAP1 transcription level reaches its peak at dusk, we checked the GAP1 expression level under white, blue, and red light conditions. We found that GAP1 is repressed by blue light in a PHOT-dependent way.

GAP1 is a chloroplast form of GAPDH and is probably involved in the Calvin cycle, so we generated GAP1 overexpression lines to check the link between GAP1 and starch. All GAP1 overexpression lines could accumulate at least 1.4 times more starch than WT while not influencing LHCSR3 expression level. These results support the link between PHOT, GAP1, and starch.

To get more insights into how PHOT regulates GAP1, by using our omics data and gene regulatory network (GRN), we found a novel TF-GAPR4 that regulates GAP1.

Overexpression of GAPR4 could upregulate the GAP1 expression level. Section 3.2.8 presents the manuscript describing the GRN tool used to predict the GAP1 regulators. The GRN developed in this manuscript is first based on qE regulator regulation. In this study, we used it to predict GAP1 regulators and experimentally proved the reliability of the prediction. It suggested that this GRN tool can be a powerful and reliable tool to predict putative regulators of genes that may be regulated by light and/or CO2 availability.

In this chapter, we further confirmed the link between PHOT and starch. We proposed a novel pathway involved in starch accumulation: Blue light represses GAPR4 which activates GAP1 in a PHOT-dependent way. PHOT knockout mutant, GAP1 and GAPR4 overexpression lines could accumulate higher starch content than WT.

Chapter 4. A member of the RWP-RK family of transcription factors links PHOT and starch accumulation 4.1 Abstract

Chapter 4 describes the second signaling cascade pathway linking PHOT with starch accumulation in Chlamydomonas.

Blue light could activate RWP5 (a TF belonging to the RWP-RK family of TFs) via PHOT. RWP5, which is usually induced in nitrogen depletion or limitation conditions in Chlamydomonas [START_REF] Zou | Regularization and variable selection via the elastic net[END_REF]195 , is downregulated in phot mutant. Moreover, its deletion leads to high starch content in the cell. Overexpressing RWP5 in phot could partially rescue the high starch content phenotype. These results suggest that RWP5 is involved in PHOT-dependent starch metabolism, and PHOT might also play a role in nitrogen metabolism in Chlamydomonas.

Results

PHOT-dependent Genome-wide transcriptional profiling -Nitrogen metabolism

Nitrogen depletion is the most popular and widely used method to increase starch or lipid accumulation in microalgae. Taking advantage of our RNAseq data, we found that many genes involved in nitrogen metabolism 195 are differentially expressed genes (DEGs) between WT and phot (Fig. 4.1, Table 4.1 & 4.2). Among those differentially expressed genes, we found that several nitrogen metabolism-related genes were upregulated in phot under both LL and HL conditions, such as ammonium transporter (AMT4, AMT6, AMT7), nitrogen import (AOC3, AOC7), NO3 -/NO2 -transporter (NRT1.1, NRT2.6), ammonium fixation (GSN1, GDH1) (Table 4.1). Usually, these genes are upregulated in -N conditions, although AMT6 and AMT7 are downregulated 195 . NAR1.5 for NO3 -/NO2 -transportation and GDH2 for ammonium fixation, downregulated in -N conditions 195 , are also downregulated in phot under both LL and HL conditions (Table 4.2).

However, a known nitrogen regulator-NRR1 (Nitrogen Response Regulator 1) was up-regulated in phot in LL conditions while down-regulated in HL conditions (Table 4.1). The fact that many DEGs between WT and phot are upregulated or downregulated in -N conditions, and that GAP1 is also highly induced in -N conditions [START_REF] Zou | Regularization and variable selection via the elastic net[END_REF] , may suggest that PHOT might be involved in nitrogen metabolism.

Identification of putative downstream effectors of PHOT controlling starch metabolism

To answer the above question, we took advantage of the RNAseq dataset (WT vs phot; LL vs HL) to pinpoint putative regulators of starch metabolism and GAP1 expression.

First, I chose the DEGs with log2FC value > 1 between phot and WT under LL and HL conditions from the RNAseq dataset; this resulted in a list of 2201 genes. After removing the genes that showed the opposite direction of regulation under LL and HL conditions, I got a list of 1957 genes.

For all these genes, I used python to do the web crawling according to gene name or gene number, checked related publications, and known function of each gene (Source code in Section 6.2.12). For unknown genes in C. reinhardtii, I used the homologous genes in Arabidopsis thaliana of these genes and then repeated the checking step. After that, I divided all the genes into different groups depending on the selection criterion. For putative GAP1 regulator selection, I selected GAP1 coexpressed genes (from Phytozome v13) and genes with a similar expression profile as GAP1 in the published RNA-seq dataset 189 . For starch metabolism, I divided genes into two groups: TFs and non-TFs. For the TFs group, I ranked them according to their FC value. As mentioned in Section 4.2.1, we hypothesize that PHOT might be linked to nitrogen metabolism. So I chose the top 100 up-regulated genes and top 100down-regulated genes according to the published datasets of Chlamydomonas cells exposed to nitrogen limitation 109,195 . Within those 200 genes, I found that the top four nitrogen sparing responsive TFs families are RWP-RK, MYB-like DNA-binding protein, Squamosa promoter-binding-link protein, and Acetyltransferase. I check particularly TFs belonging to these four families in our dataset. For Non-TFs, I checked them one by one.

After the selection, I got a list with 24 genes, and we ordered the existing seven CDS mutants lacking the genes listed in Table 4.3, which might act as regulators of GAP1 or starch accumulation. Before performing any experiment with these mutants, we first verified the presence of the transforming cassette in different strains by colony PCR (myb and rwp5 validation will be shown in 4.2.4, validation of other mutants is not shown). All the mutants shown in Fig. 4.2 have been confirmed as being real CDS mutants.

Among these mutants, myb and rwp5 showed significantly higher starch content than WT at t=0d (Fig.

RWP5 and MYB are under the control of PHOT

From our RNA-seq dataset, the MYB transcription level was up-regulated in phot in both LL and HL conditions, while the RWP5 transcription level was down-regulated in phot (Table 4.3). Thus, combining the results (Fig. 4.2) and our RNA-seq dataset, we hypothesized that RWP5 and MYB are under the control of PHOT and involved in starch metabolism.

To check if MYB and RWP5 are under the control of PHOT, we checked the transcription levels of MYB and RWP5 in WT, phot, and phot-C under the same conditions in the RNA-seq experiment was performed. The results confirmed the RNA-seq data; the addition of the phot-C strain in the qPCR experiment confirmed that both MYB and RWP5 are under the control of PHOT (Fig. 4.3).

4.2.4

The rwp5 and myb are starch over accumulators.

To further investigate the role of RWP5 and MYB in starch metabolism in C.

reinhardtii, we took CDS insertion mutants of RWP5 and MYB from CLiP library [START_REF] Yoshioka | The novel Myb transcription factor LCR1 regulates the CO2responsive gene Cah1, encoding a periplasmic carbonic anhydrase in Chlamydomonas reinhardtii[END_REF][START_REF] Petroutsos | A blue-light photoreceptor mediates the feedback regulation of photosynthesis[END_REF] and validated the insertion site.

MYB CDS mutant, LMJ.RY0402.112219 (hereafter myb) has one insertion of the paromomycin resistance cassette (CIB1) in the middle of exon 6 of the MYB gene (Fig. For MYB, using primers designed to amplify the 5' and 3' junction sites of the CIB1 cassette, specific 767-and 944-bp fragments could be detected in the myb mutant (Fig. 4.4). The sequencing analysis of the PCR amplicons confirmed the insertion site (Fig. 6.5). The MYB transcription level of WT and myb has also been checked by RT-qPCR in -N and +N conditions. However, when we tried to check the MYB transcription level in myb using the primers designed to amplify the upstream region of the CIB1 cassette insertion site, we still could see the induction of MYB in

RWP5 is a starch metabolism regulator

To confirm it is the deletion of RWP5 and MYB leads to the high starch phenotype, it is necessary to generate the complementation lines.

For MYB, another lab also found that the starch content in myb was significantly higher than in its WT 197 . However, instead of getting the complement line, they tested two sets of progenies derived from myb and another WT for co-segregation between antibiotic resistance and high starch content; there was no co-segregation 197 .

Therefore, the high starch content of myb might be due to some non-detected gene disruptions.

For RWP5, this gene is 5236 bp with 68% GC content. We first tried to synthesize it. However, due to the gene structure complexity, high GC content, and repeated regions, direct synthesis of the gene could not be achieved.

It is impossible to get RWP5 gDNA by PCR cloning in one step with long primers for the Gibson assembly, so we split the RWP5 gene into three fragments reassembled following PCR amplification. To get each fragment, we tried different enzymes, buffers, programs, chemicals, and programs. However, the cloning efficiency dropped off rapidly due to the multiplicative effect. Fragment 1 from 1bp to 1710bp and fragment 2 from 1691bp to 3411bp were amplified using Platinum and Phusion enzymes. Fragment 3 from 3391bp to 5233bp was amplified using Platinum and KOD enzymes. (Amplification details in section 6.2.8 and Table 4.4, primers in Table 6.2).

Cloning RWP5 gDNA was sequence-verified and assembled into pRAM118 plasmid 198 and pLM005 plasmid 176 by Gibson assembly. After electroporation, we got one complementation line of rwp5 strain (rwp5-C) after screening around 500 transformants. Due to the two mismatched base pairs in the flag region of the pRAM118 plasmid, we can not detect the protein by using the anti-FLAG antibody. So we confirmed the complementation by RT-qPCR (Fig. 4.7).

Moreover, the starch content of rwp5-C went back to the WT level (Fig. 4.7).

Combining all the results, RWP5 is under the control of PHOT, and its deletion leads to high starch content in Chlamydomonas reinhardtii.

RWP5 is involved in the PHOT-dependent starch metabolism

As RWP5 is downregulated in phot under high light conditions (Fig. 4.3 & 4.7) and cannot be induced in -N conditions (Fig. 4.5), it is worth checking if RWP5 is involved in the PHOT-dependent starch metabolism.

To address this question, we overexpressed RWP5 in phot and got two mutants (phot::rwp5-C1 and phot::rwp5-C2) after screening around 1000 transformants.

After confirming the RWP5 expression level by RT-qPCR (Fig. 4.8), we checked the starch content of these two mutants.

Surprisingly, the high starch phenotype of phot in these two mutants was partially rescued (Fig. 4.9). However, the GAP1 transcription levels could not be rescued by the overexpression of RWP5 in phot; they remained high as in phot. These data indicate that RWP5 is involved in PHOT-dependent starch metabolism, and that PHOT-RWP5-starch and PHOT-GAP1-starch are two independent pathways.

RWP5 is not involved in PHOT-dependent photoprotection genes regulation

As RWP5 is under the control of PHOT, we also tested if RWP5 plays a role in PHOT-dependent qE regulation (Fig. 4.12).

As Fig. 4.12 showed, the deletion of RWP5 did not affect the expression of LHCSR3, LHCSR1, PSBS, and qE.

Although MYB is not involved in the PHOT-starch link, we also test if MYB is involved in photoprotection gene regulation. Surprisingly, we found that MYB could accumulate more LHCSR1 and PSBS protein than WT, although myb did not show higher qE (Fig. 4.12). MYB deserves a more profound investigation as a potential PSBS and LHCSR1 regulator. Chlorophyll fluorescence was recorded in the dark (labelled as "D"), at 336 (labelled as "L2") µmol photons m -2 s -1 as indicated in the graphs. (NPQ measurement setup details in 6.2.4).

Conclusion

In this chapter, we found a novel starch metabolism regulator-RWP5 under the control of PHOT.

RWP5 is highly induced in -N conditions 195 . Moreover, RWP5 is downregulated in phot mutant in HL conditions and can not be induced in phot mutant in -N conditions.

Deletion of RWP5 leads to higher starch content than its WT in normal conditions.

By overexpressing RWP5 in phot, the high starch content phenotype of phot could be partially rescued. This suggested that RWP5 is not only a starch metabolism regulator but also a linker between blue light perception and nitrogen metabolism. Furthermore, RWP5 is not involved in the pathway PHOT-GAPR4-GAP1-STARCH presented in chapter 3. Additionally, combining all the results, we also propose that PHOT might play a role in nitrogen metabolism by activating RWP5.

Phylogenetic analysis of BSK1

Before investigating the role of BSK1 in Chlamydomonas, we first took the protein sequence of BSK1 and blast it in Arabidopsis thaliana to find its orthologous proteins.

From the first blast results, we found three BSK1 orthologous proteins in Arabidopsis-HT1, CBC1 and CBC2. Then we did a phylogenetic analysis of BSK1

with 131 proteins in different organisms. ( Details in 6.2.14, Fig. 5.3). The results showed that BSK1 is evolutionarily closer to AtHT1 than to AtCBCs.

BSK1 is involved in the PHOT-dependent starch metabolism

To understand the function of BSK1 in Chlamydomonas, we first checked the BSK1 transcription level in WT, phot, phot-C, and pkin mutants (Fig. 5.5).

The BSK1 transcription level showed no difference in all the cell lines. In addition, we also checked the BSK1 transcription level in cw15 background mutants and got similar results (Fig. 5.5). These results indicated that the BSK1 transcription level is unrelated to the PHOT-dependent regulation. Moreover, our proteomics data showed no change in WT and phot under LL and HL conditions in terms of BSK1 protein abundance (Data not shown). Then we checked the influence of the different phosphorylated forms of BSK1. As mentioned above, BSK1 is phosphorylated at Serine 94 in Chlamydomonas. We decided to generate phospho-specific BSK1 overexpression strains in phot backgrounds.

BSK1, BSK1 S94A, and BSK1 S94D represent the endogenous, phospho-deficient, and phospho-mimetic variants. BSK1 genomic sequence was amplified from genomic DNA using primers G19 and G20, and point mutation was introduced using primers G21, G22, G23, and G24 (Amplification details in section 6.2.8, primers in Table 6.4).

The cloned gene was sequence-verified and assembled into pLM005 plasmid by Gibson assembly.

After transformation, we got six phot::bsk1 S94A -FLAG mutants with different protein expression levels. To determine the biological consequences of the expression of BSK1 S94A in phot, functional analyses were performed aiming at PHOT-starch signaling.

First, we checked the starch content of WT, phot, and six phot::bsk1 S94A mutants (Fig. 5.6). As the expression of BSK1 S94A -Venus-FLAG protein increased, the starch content of six phot::bsk1 S94A mutants gradually decreased (Fig. 5.6). Due to the absence of PHOT, the main BSK1 form in phot is the phosphorylated form. The ectopic BSK1 S94A expression increases the proportion of the de-phosphorylated form of BSK1 in phot and decreases starch content. Interestingly, phot::bsk1 S94D and phot::bsk1 both could accumulate even 1.5 times more starch than phot. However, bsk1-oe showed a similar starch level with WT. This might be due to the presence of PHOT (Fig. 5.7). In phot mutant, BSK1 mainly stays in the phosphorylated form under light conditions due to the absence of PHOT. In WT, because of the presence of PHOT, BSK1 could be normally de-phosphorylated by PHOT, so it will not change the proportion of different phosphorylated forms of BSK1 in WT under light conditions.

Combining all the results, BSK1 is indeed involved in starch metabolism in phot, and its phosphorylation plays a crucial role in this regulation.

GAP1 is under the control of BSK1

To check if BSK1 is involved in PHOT-GAPR4-GAP1-Starch and PHOT-RWP5starch link, we checked GAP1, GAPR4, and RWP5 transcription levels in WT, phot, and different forms of BSK1 overexpression lines.

Surprisingly, the GAP1 expression level went down due to the increasing BSK1 S94A

expression level and increased due to the increasing BSK1 and BSK1 S94D expression levels (Fig. However, RWP5 and GAPR4 transcription level was not affected in all the different BSK1 form overexpression lines (Fig. 5.9). These results indicated that BSK1 is involved in the PHOT-GAP1-starch pathway, and GAPR4 might not be the only TF regulator controlling GAP1.

Conclusion

In this chapter, we reported a novel kinase protein -BSK1, which is dephosphorylated by blue light via PHOT, involved in starch metabolism in

Chlamydomonas.

BSK1 is mainly found in the phosphorylated form in the phot mutant. Interestingly, overexpressing dephosphorylated form of BSK1 in phot could bring its starch content back to WT level, while overexpressing the endogenous and phosphorylated form of BSK1 in phot could even bring starch content to a much higher level. However, overexpressing the endogenous form of BSK1 in WT could not bring the starch level to a higher level. It might be that BSK1 can be dephosphorylated normally due to the presence of PHOT in WT. Moreover, GAP1 is under the control of BSK1, while GAPR4 and RWP5 are not. This also confirmed the link between GAP1 and starch.

123 Chapter 6

Summary and future directions

This thesis reported for the first time the link between blue light photoreceptor PHOT and starch metabolism in Chlamydomonas reinhardtii and three novel signaling cascades behind this link. In Arabidopsis, red and blue light could both induce the expression of GAPa and GAPb, although red light is far less efficient 199 . GAPA and GAPB are the subunits of chloroplastic GAPDH, which plays a central role in the Calvin cycle in Arabidopsis.

In Chlamydomonas, GAP3 is located in the chloroplast and functions like GAPA/B of Arabidopsis 200 . However, we did not see a significant difference in the protein or transcription level of GAP3 between the phototropin mutant and its wild type. In contrast, we found that GAP1 is upregulated in both protein and mRNA levels by the deletion of phototropin (Fig. the wild-type level or even go higher, respectively (Fig. 5.7). Interestingly, overexpression of BSK1 in phot mutant could lead to a higher starch content in white light conditions, but this does not happen in wild type (Fig. 5.7). This confirms that BSK1 dephosphorylation needs not only blue light but also phototropin. Furthermore, the increase in the proportion of BSK1 dephosphorylated form not only bring down the starch content but also bring down the GAP1 transcription level (Fig. 5.7 & 5.8).

This indicated that the GAP1 transcription level, the dephosphorylated form of BSK1 and starch content are positively correlated. It also further confirmed our link between GAP1 and starch.

However, there is still some pending work that would further advance our understanding of the link between PHOT and starch: 2) Could GAPR4 directly bind to GAP1? This could be checked by doing ChIP-qPCR.

phototropin controls starch metabolism and a comprehensive answer to how light quality affects the carbon partitioning in the model green alga Chlamydomonas reinhardtii (Fig. 6.1). In addition, microalgae biomass could be directly catalyzed into biofuels. The two most common types of biofuels are bioethanol and biodiesel, which are made from sugar/starch and lipid/fats, respectively. Our findings will also give some new ideas for improving microalgal bioethanol productivity. For non-synchronized culture, before the experiment, cells were grown in 25-or 50-ml liquid TAP medium in 150ml or 250ml flasks at 23 ℃, 120 rpm/min, 15 μmol/m 2 /s. Cells used for experiments were grown under the same conditions. Low light (LL) was 50 μmol/m 2 /s, while high light (HL) was 300μmol/m 2 /s unless specified.

Cell count and chlorophyll measurement

For cell counts, 480 ml of algal culture was mixed with 20 μl of acetate solution, and cells were counted using Countess II Automated Cell Counter (Thermo Fisher Scientific, US).

Total chlorophyll (chlorophyll a+b) measurement was performed by using methanol extraction. 1 ml of collected culture was centrifugated at 11,000g for 5min and then the supernatant was discarded. Cell pellets were resuspended in 100% methanol and vortexed to extract the pigments. After 10 mins dark incubation, the chlorophyll concentration was determined using a UV-visible spectrophotometer (JASCO, Japan). The absorbance of the methanol extract was measured at 652, 655, 750 nm against a 100% methanol blank. The concentration of total chlorophyll (μg/ml) was calculated using the equation below Two programs were used for record in this study.

Program 1: starting with measurements in the dark (indicated as D below the x-axis of the graphs), followed by measurements at 21 μmol photons m -2 s -1 (indicated as L1 below the x-axis of the graphs) and 336 μmol photons m -2 s -1 (indicated as L2 below the x-axis of the graphs) and finishing with measurements of fluorescence relaxation in the dark.

Program 2: starting with measurements in the dark (indicated as D below the x-axis of the graphs), followed by measurements at 336 μmol photons m -2 s -1 (indicated as L2 below the x-axis of the graphs) and finishing with measurements of fluorescence relaxation in the dark.

YII was calculated as (Fm′ -F)/Fm′, NPQ was calculated as (Fm -Fm′)/Fm′ 212 .

Fm is the maximal fluorescence yield in dark-adapted cells, F and Fm′ is the fluorescence yield in steady-state light and after a saturating pulse in the actinic light, respectively.

Transmission electron microscopy (TEM)

For transmission electron microscopy, the preparation of samples for TEM follows the previous protocol 213 . Sample cells were fixed in 0.1 M acodylate buffer (Sigma-Aldrich), pH 7.4, containing 2.5% glutaraldehyde (TAAB), 2% formaldehyde (Polysciences) for 1 h at room temperature. After the dehydration steps, the cells were infiltrated with ethanol/Epon resin mixture (2/3-1/3 for 1 h and 1/3-2/3 for 1 h) and finally embedded in Epon in a 60 °C oven for 48 h or longer. Ultrathin sections (60 nm) were prepared with a diamond knife on an UC6 Leica ultramicrotome and collected on 200 µM mesh nickel grids before examining on a JEOL 1200 EX electron microscope. Total RNA was extracted using Trizol reagent (Invitrogen) or RNeasy Mini Kit (Qiagen). Two methods were used in this study described in follows:

Total RNA was extracted using Trizol reagent (Invitrogen, US). Around 10 million cells were pelleted by centrifugation at 4,000 g for 5 min. 1 ml of Trizol was added to the pellet, and cells were mixed by pipette up and down. Then cells were vortexed after adding 0.2 ml chloroform and incubated for 5 min at 25°C. The aqueous phase was aspirated from the Eppendorf tube after centrifugation at 13,400 g for 15 min at 4°C. The aspirated sample was precipitated with an equal volume of 100% isopropanol and incubated for 10 min at 25°C. Precipitated RNA was centrifuged at 13,400 g for 5 min at 4°C. The RNA pellet was washed with 75% ethanol, vortexed, and centrifuged at 13,400 g for 5 min in a refrigerated centrifuge. The supernatant was discarded, and the pellet was air-dried for 10 min. Isolated RNA was resuspended in 50 μl of RNAase and DNAase free water and quantified using Micro-Volume Full-Spectrum Fluorospectrometer (NanoDrop 3300, Thermo Scientific, US).

Total RNA was extracted using RNeasy Mini Kit (Qiagen, Germany) and treated with the RNase-Free DNase Set (Qiagen, Germany). 1 μg total RNA was reverse transcribed with oligo dT using Sensifast cDNA Synthesis kit (Meridian Bioscience, USA). qPCR reactions were performed and quantitated in a Bio-Rad CFX96 system using SsoAdvanced Universal SYBR Green Supermix (BioRad, US). The housekeeping gene is G protein subunit-like protein (GBLP) 216 . The primers for RT-qPCR used in this study are shown in Table 6.3 which were synthesized by Thermofisher or IDT. PHOT CDS: The PHOT CDS was amplified by PCR from another plasmid which was preserved in our own lab by using primers G1 and G2 and KOD Hot Start DNA polymerase. The amplified fragment was digested by EcoRI and BamHI restriction enzymes and then was inserted into the EcoRI/BamHI site of vector phk330 60 to produce the transformation vector phk330-phot. Thus, the PHOT CDS is under the control of the Rubisco promoter, followed by a selectable marker of ble gene conferring resistance to zeocin. This plasmid will express PHOT protein and zeocin resistance protein separately in vivo because of the presence of the F2A sequence 217 .

PHOT gDNA: The fragment 1 (1 bp to 3334 bp) and fragment 2 ( 3319 bp to 6536 bp) of PHOT gDNA were amplified from gDNA using the platinum polymerase and primer G3-G6. Two fragments together with HpaI digested pLM005 plasmid and assembled by Gibson assembly. The PHOT gDNA is under the control of the PSAD promoter, followed by the linked Venus-FLAG tag.

GAP1: GAP1 CDS was amplified by PCR using KOD Hot Start DNA polymerase and primers G7 and G8 from WT cDNA. GAP1 gDNA was amplified by PCR from WT gDNA using the platinum polymerase and primers G9 and G10. GAP1

CDS and GAP1 gDNA together with HpaI digested pLM005 or pRAM118 plasmid assembled by Gibson assembly. In particular, the fragment 1 from 1bp to 1710 bp of RWP5 was amplified from gDNA using the Platinum polymerase and primer G13 and G15; the fragment 2 from 1691 bp to 3411 bp of RWP5 was amplified from gDNA using Platinum and Phusion polymerase and primer G16 and G17; the fragment 3 from 3391 bp to 5233 bp of RWP5 was amplified from gDNA using Platinum and KOD polymerase and primers G18 and G14. Extension PCR was performed to ligate the three fragments. The details of the PCR program and conditions used for amplification were shown in Table 4.4.

BSK1: Full-length BSK1 gDNA was amplified from WT gDNA using the Platinum polymerase and primers G19 and G20. To induce point mutation, primers G21-G24

were used to get BSK1 S94A and BSK1 S94D . The mutated base pair are underlined.

All the primers used in gene amplification were shown in Table 6.4.

Immunoblotting

Protein samples of whole-cell extracts (0.5 µg chlorophyll or 10 µg protein-20 µg protein in the case of PSBS detection) were loaded on 4-20% SDS-PAGE gels (Mini-PROTEAN TGX Precast Protein Gels, Bio-Rad) and blotted onto nitrocellulose membranes. Antisera against LHCSR1 (AS14 2819), LHCSR3 (AS14 2766), and ATPB (AS05 085) were from Agrisera (Vännäs, Sweden), Anti-FLAG was from ThermoFisher, US; previously described was antisera against C. reinhardtii PSBS (6).

ATPB was used as a loading control. An anti-rabbit horseradish peroxidaseconjugated antiserum was used for detection. The blots were developed with the ECL detection reagent, and images of the blots were obtained using a CCD imager (ChemiDoc MP System, Bio-Rad) or ImageQuant TM 800 (GE Healthcare, US).

Phylogenetic analysis of BSK1

To identify putative orthologs of the AtCBC1, AtCBC2 and AtHT1, the three fulllength proteins were used as a query to run a BLASTp (Altshul, 1990) (http://tree.bio.ed.ac.uk/software/figtree/).

Statistical analysis

All statistical tests were performed using the computing environment Prism 9

(Graphpad Software, LLC), with a significance level of 0.05. In order to confirm mRNA accumulation data to the distributional assumptions of ANOVA, i.e. the residuals should be normally distributed and variances should be equal among groups, Two-Way Analysis of Variance was computed with log-transformed data Y=logX where X is mRNA accumulation 228 . P < 0.005, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001. CIB1 cassette was highlighted in yellow.

Supplementary data

Phylogenetic tree of BSK1

Voie 3 :

 3 La phosphorylation PHOT-dépendante d'une sérine/thréonine kinase que nous avons nommée Blue Starch Kinase 1 (BSK1 ; orthologue de la kinase HT1 d'Arabidopsis qui contrôle les mouvements stomatiques en réponse au CO2) agit sur les niveaux de GAP1 et d'accumulation d'amidon indépendamment de GAPR4.

  Geiselmann and Dr. Yonghua Li-Beisson. Thank you all for taking the time to evaluate my work. I want to express my deepest gratitude to my supervisor Dr. Dimitris Petroutsos. Without his help, I could not start and finish my Ph.D. study. During my Ph.D., he gave me the freedom to investigate and try different 'crazy' ideas. His patient guidance, daily care, and professional knowledge helped me tremendously to become an independent researcher. I would also thank my co-supervisor Dr. Olivier Bastien for all the support in bioinformatics during my Ph.D. and Dr. Denis Falconet for the help in electron microscopy.

1

 1 The model organism Chlamydomonas reinhardtiiMicroalgae are one of the most diverse eukaryotic organisms occupying broad ecological niches. By converting atmospheric CO2 to organic sugars, they provide much energy to many heterotrophs, including humans, and participate actively in the global carbon cycle, playing a crucial role in different life forms surviving on earth.

Figure 1 . 1 .

 11 Figure 1.1. Scheme of starch metabolism in Chlamydomonas reinhardtii. (Figure modified from Koo et al. 106 )

Figure 1 . 2 .

 12 Figure 1.2. Solar spectrum and photoreceptors in higher plants and in Chlamydomonas reinhardtii. Figure modified from Heijde et al. 131 based on Petroutsos et al. 132

Figure 1 . 3 .

 13 Figure 1.3. Schematic structure of phototropin and phototropin kinase regulation 137

Figure 2 . 1 .

 21 Figure 2.1. Experiment design of collecting sample for transcriptomics and proteomics analysis (Upper) and Gene Ontology (GO) Enrichment Analysis of DEGs between phot and WT (Lower).

Figure 2 . 2 .

 22 Figure 2.2. Y(II) values (A) and starch content (B) of WT and phot cells grown in TAP medium under LL (50 μmol/m 2 /s). A: Just prior to the onset of the measurements, cells were acclimated to darkness for 15 min. Chlorophyll fluorescence was recorded in the dark (labelled as "D"), at 21 (labelled as "L1") and 336 (labelled as "L2") µmol photons m -2 s -1 as indicated in the graphs. YII measurement setup details in 6.2.4. B: The p-values for the comparisons indicated in the graph are based on t-test (***, P < 0.001).

Figure 2 . 3 .

 23 Figure 2.3. Phenotypes of new phot mutant and the phot-C complemented strain 62 . A: Immunoblot blot analyses of WT, phot, and phot-C acclimated to LL (15 μmol/m 2 /s) and after exposure to HL (300 μmol/m 2 /s) for 4 h; the proteins examined were PHOT, ATPB, and LHCSR3; ATPB served as a loading control. B: NPQ of WT, phot, and phot-C strains after exposure to HL for 4 h. Just prior to the onset of the measurements, cells were acclimated to darkness for 15 min. Chlorophyll fluorescence was recorded in the dark (black block), at 336 (blue block) µmol photons m -2 s -1 as indicated in the graphs.

Figure 2 . 4 .

 24 Figure 2.4. Phenotypes of two phot-C complemented strains. A: Immunoblot blot analyses of WT, phot, and phot-C after exposure to HL (300 µmol photons m -2 s -1 ) for 4 h; the proteins examined were PHOT, ATPB, and FLAG; ATPB served as a loading control. B: NPQ values of WT, phot and phot-C1/2 cells. Just prior to the onset of the measurements, cells were acclimated to darkness for 15 min.

Figure 2 . 5 .

 25 Figure 2.5. Starch and total carbohydrate content of WT and phot in TAP medium under LL conditions (50 μmol/m 2 /s).

Figure 2 . 6 .

 26 Figure 2.6. The total carbohydarte content of WT, phot, and phot-C in phototrophic HSM medium under 12/12 light/dark cycle.

Figure 2 . 7 .

 27 Figure 2.7. The total carbohydrate content of WT, phot, phot-C2, and phot-C3 in phototrophic HSM medium under continuous LL (50 μmol/m 2 /s). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (****, P < 0.0001).

Figure 2 . 8 .

 28 Figure 2.8. Transmission electron microscopy pictures of WT, phot, and phot-C under LL. Samples for TEM were collected at the end of the light phase of the 12/12 dark/light cycle. The white regions are starch granules.

Figure 2 . 9 .

 29 Figure 2.9. Phenotype of WT, phot, and pkin. A: NPQ values of WT, phot, and pkin strains after exposure to HL for 4 h. Just prior to the onset of the measurements, cells were acclimated to darkness for 15 min. Chlorophyll fluorescence was recorded in the dark (labelled as "D"), at 21 (labelled as "L1") and 336 (labelled as "L2") µmol photons m -2 s -1 as indicated in the graphs. (NPQ measurement setup details in 6.2.4). B: Total carbohydrate content of WT, phot, and pkin strains in phototrophic HSM medium under continuous LL (50 μmol/m 2 /s) conditions. The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (****, P < 0.0001).

Figure 2 . 10 .

 210 Figure 2.10. The total carbohydrate content (B) of WT, phot, pp4, pk1, pl1, and pk3 in phototrophic medium HSM under continuous LL (50 μmol/m 2 /s) conditions. A: Schematic drawings of the ble::phot constructs for generating pp4, pk1, pk3, pl1 170 . The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (****, P < 0.0001; ns: not significant).

Figure 3 . 1 .

 31 Figure 3.1. The transcription level of starch metabolism-related genes in WT and phot. Samples were collected in phototrophic HSM medium in the middle of the light (6h) under 12/12 dark/light cycle conditions. Fold changes between phot and WT were calculated as log2 FC (phot/WT). GAP, glyceraldehyde-3-phosphate dehydrogenase; PFK, phosphofructokinase; FBP, Fructose-1,6bisphosphatase; PGI, phosphoglucose isomerase; PGM, phosphoglucomutase; HXK, Hexokinase; STA6, ADP-glucose pyrophosphorylase small subunit; SSS, starch synthase; AMY, α-amylase; ISA, isoamylase; PWD, phosphoglucan water dikinase; GWD, α-glucan water dikinase.

Figure 3 . 2 .

 32 Figure 3.2. GAP1 transcription level (A) in WT, phot and phot-C and protein abundance fold change (B) in WT and phot.. A: Samples were collected in the middle of the 12/12 dark/light cycle light phase in phototrophic medium HSM. Values relative to an endogenous control gene (GBLP) were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (*, P < 0.05)..; B: LL-5 μmol/m 2 /s, HL-300 μmol/m 2 /s.

Figure 3 . 3 .

 33 Figure 3.3. GAP1 transcription level in WT, phot and phot-C under 12/12 light/dark cycle conditions. Samples were collected at the middle (6h) of the 12/12 dark/light cycle light phase. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.).

Figure 3 . 4 .

 34 Figure 3.4. The total carbohydrate content in WT, phot, and phot-C under 12/12 light/dark cycle conditions. Samples were collected at the end (12h) of the 12/12 dark/light cycle light phase.

Figure 3 . 5 .

 35 Figure 3.5. The GAP1 transcription level of WT, phot, phot-C2 and pkin in phototrophic HSM medium under continuous LL (50 μmol/m 2 /s). Values relative to GBLP were normalized to wildtype samples (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (****, P < 0.0001).

Figure 3 . 6 .

 36 Figure 3.6. The GAP1 transcription level of WT, phot, pp4, pk1, pl1, and pk3 in phototrophic HSM medium under continuous LL (50 μmol/m 2 /s). Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (****, P < 0.0001; ns, not significant).

Figure 3 . 7 .

 37 Figure 3.7. Immunoblot blot analyses of GAP1-OE lines (Top) and the GAP1 transcription level and total carbohydrate content of WT, phot, and three GAP1-OE lines in phototrophic HSM medium (Bottom). Samples collected under continuous LL conditions (50 μmol/m 2 /s). ATPB as a loading control. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (**, P < 0.01; ***, P<0.001; ****, P < 0.0001).

Figure 3 . 8 .

 38 Figure 3.8. Phenotype of WT and gap1oe lines. A: Immune blot analyses of LHCSR3, ATPB as a loading control. B: NPQ values of for WT and gap1oeC3 after exposure to HL for 4 h. Just prior to the onset of the measurements, cells were acclimated to darkness for 15 min. Chlorophyll fluorescence was recorded in the dark (labelled as "D"), at 336 (labelled as "L2") µmol photons m -2 s -1 as indicated in the graphs. (NPQ measurement setup details in 6.2.4).

Figure 3 . 9 .

 39 Figure 3.9. Putative GAP1 regulators transcription level in WT and phot under red (pink background) and blue light (blue background) conditions (50 μmol/m 2 /s). Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.).

Figure 3 . 10 .

 310 Figure 3.10. GAPR4 (blue) and GAP1 (red) transcripts abundance in RNA-seq dataset.

Figure 3 . 11 .

 311 Figure 3.11. GAPR4 and GAP1 transcription profile from Strenkert et al. 190 .

Figure 3 . 12 .

 312 Figure 3.12. GAPR4 (Left) and GAP1(Right) transcription levels in WT and phot under different light qualities conditions in phototrophic HSM medium in 12/12 light/dark cycle. Samples were collected at 6h after the start of the light phase. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.).

Figure 3 . 13 .

 313 Figure 3.13. The GAPR4 and GAP1 transcription level (A) and total carbohydrate content (B) in WT, phot, gapr4-oe and oe-evc (empty vector overexpression control) in phototrophic HSM medium. Samples collected under continuous LL conditions (50 μmol/m 2 /s). Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (*, P<0.05; ***, P < 0.001; ****, P<0.0001; ns, not significant).

Figure 3 . 14 .

 314 Figure 3.14. The GAP1 transcription level in WT, phot and gapr4-oe under different light qualities conditions in phototrophic HSM medium in 12/12 light/dark cycle. (7h after the start of the light phase)

Fig: 4 :

 4 Fig:4: QER7 suppresses transcription of CCM genes and depends on PHOT. WT, qer7 and qer7-C cells were synchronized under 12h light (15 µmol m -2 s -1 )/12h dark cycles. After sampling at the end of the dark phase, cells were exposed to 300 µmol photons m -2 s -1 (HL) and samples were taken 1 h after HL exposure. a. Relative expression levels of CCM genes at the indicated conditions normalized to WT LL (n = 3 biological samples, mean ± sd). b and c. Relative expression levels of QER7 (b) and LCR1 (c) in synchronized WT, phot and phot-C (phot cells complemented with the PHOT gene) cells. The p-values for the comparisons are based on ANOVA Dunnett's multiple comparisons test on log10-transformed values and are indicated in the graphs (*, P < 0.005, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001).

Fig. 5 :

 5 Fig. 5: Consensus and PHOT-specific GRNs indicate extensive co-regulation of CCM and photoprotective genes. Top: Venn diagram depicting the overlap of the top 10 predicted TFs of the curate CCM and photoprotective (PhPr) genes based on a. the consensus or c. PHOTspecific GRN. Bottom: Network representation of the top 10 TF sets of b. the consensus network or d. the phot GRN (center nodes, same color code as in panel a and the target genes used for prediction (left and right columns of nodes, photoprotection genes are shown in green, qE-related genes in blue, and CCM genes in red. Larger TF nodes indicate higher regulatory strength on target genes. The plotted edge width is proportional to the strength of the regulatory interaction.

Fig. 2 :

 2 Fig. 2: Consensus GRN for light and acetate responses pinpoints LCR1 as regulator of qErelated genes. a. Dot plot of the relative regulatory strength of the top 10 regulators of qErelated genes in the consensus GRN (see Methods). TFs are marked in green if qE transcript levels were affected in the respective knock-out strain and this effect was reversed by complementation with the missing gene. TFs for which no effect was observed are marked in red. TFs for which no mutant lines were available are plotted in grey. b. WT, lcr1 and lcr1-C cells were acclimated for 16h in LL (15 µmol photons m -2 s -1 ). After sampling for the LL conditions, light intensity was increased to 300 µmol photons m -2 s -1 (HL); samples were taken 1 h (RNA) or 4 h (protein and photosynthetic measurements) after exposure to HL. Shown are relative expression levels of qE-related genes at the indicated conditions normalized to WT LL (n = 3 biological samples, mean ± sd). c. Immunoblot analyses of LHCSR1, LHCSR3, PSBS and ATPB (loading control) of one of the three biological replicates, under the indicated conditions. d. Quantification of immunoblot data of all replicates in panel c after normalization to ATPB. Shown are the HL treated samples; WT protein levels were set as 1. e. NPQ and calculated qE (as an inset) 4h after exposure to HL (n = 3 biological samples, mean ± s.d). The p-values for the comparisons are based on ANOVA Dunnett's multiple comparisons test and as indicated in the graphs (*, P < 0.005, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001). Statistical analyses for panel b and d were applied on log10transformed values.

Fig. 3 :

 3 Fig.3:A PHOT-specific GRN pinpoints QER7 as a suppressor of the expression of qE-related genes. a. Dot plot of the relative regulatory strength of the top 10 regulators of qE-related genes in the PHOT-specific GRN (see Methods). TFs are marked in green if qE transcript levels were affected in the respective knock-out strain and this effect was reversed by complementation with the knocked-out gene, in yellow, if the effect was not reversed by complementation, and in red, if no mutant effect was observed in the mutant. TFs for which no mutant lines were available are plotted in grey. b. WT, qer7 and qer7-C cells were synchronized under 12h light (15 µmol m -2 s -1 )/12h dark cycles. After sampling for the dark conditions (end of the dark phase), cells were exposed to 300 µmol photons m -2 s -1 (HL); samples were taken 1 h (RNA) or 4 h (protein and photosynthetic measurements) after exposure to HL. Shown are relative expression levels of qE-related genes at the indicated conditions normalized to WT LL (n = 3 biological samples, mean ± sd). c. Immunoblot analyses of LHCSR1, LHCSR3 and ATPB (loading control) of one of the three biological replicate set of samples, under the indicated conditions. d. Quantification of immunoblot data of all replicates in panel c after normalization to ATPB. WT protein levels at HL were set as 1. e. NPQ and qE, measured 4h after exposure to HL (n = 3 biological samples, mean ± sd). The p-values for the comparisons are based on ANOVA Dunnett's multiple comparisons test and are indicated in the graphs (*, P < 0.005, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001). Statistical analyses for panel b and d were applied on log10-transformed values.

  Fig.3:A PHOT-specific GRN pinpoints QER7 as a suppressor of the expression of qE-related genes. a. Dot plot of the relative regulatory strength of the top 10 regulators of qE-related genes in the PHOT-specific GRN (see Methods). TFs are marked in green if qE transcript levels were affected in the respective knock-out strain and this effect was reversed by complementation with the knocked-out gene, in yellow, if the effect was not reversed by complementation, and in red, if no mutant effect was observed in the mutant. TFs for which no mutant lines were available are plotted in grey. b. WT, qer7 and qer7-C cells were synchronized under 12h light (15 µmol m -2 s -1 )/12h dark cycles. After sampling for the dark conditions (end of the dark phase), cells were exposed to 300 µmol photons m -2 s -1 (HL); samples were taken 1 h (RNA) or 4 h (protein and photosynthetic measurements) after exposure to HL. Shown are relative expression levels of qE-related genes at the indicated conditions normalized to WT LL (n = 3 biological samples, mean ± sd). c. Immunoblot analyses of LHCSR1, LHCSR3 and ATPB (loading control) of one of the three biological replicate set of samples, under the indicated conditions. d. Quantification of immunoblot data of all replicates in panel c after normalization to ATPB. WT protein levels at HL were set as 1. e. NPQ and qE, measured 4h after exposure to HL (n = 3 biological samples, mean ± sd). The p-values for the comparisons are based on ANOVA Dunnett's multiple comparisons test and are indicated in the graphs (*, P < 0.005, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001). Statistical analyses for panel b and d were applied on log10-transformed values.

Fig: 4 :

 4 Fig:4: QER7 suppresses transcription of CCM genes and depends on PHOT. WT, qer7 and qer7-C cells were synchronized under 12h light (15 µmol m -2 s -1 )/12h dark cycles. After sampling at the end of the dark phase, cells were exposed to 300 µmol photons m -2 s -1 (HL) and samples were taken 1 h after HL exposure. a. Relative expression levels of CCM genes at the indicated conditions normalized to WT LL (n = 3 biological samples, mean ± sd). b and c. Relative expression levels of QER7 (b) and LCR1 (c) in synchronized WT, phot and phot-C (phot cells complemented with the PHOT gene) cells. The p-values for the comparisons are based on ANOVA Dunnett's multiple comparisons test on log10-transformed values and are indicated in the graphs (*, P < 0.005, **, P < 0.01, ***, P < 0.001, ****, P < 0.0001).

Fig. 5 :

 5 Fig. 5: Consensus and PHOT-specific GRNs indicate extensive co-regulation of CCM and photoprotective genes. Top: Venn diagram depicting the overlap of the top 10 predicted TFs of the curate CCM and photoprotective (PhPr) genes based on a. the consensus or c. PHOTspecific GRN. Bottom: Network representation of the top 10 TF sets of b. the consensus network or d. the phot GRN (center nodes, same color code as in panel a and the target genes used for prediction (left and right columns of nodes, photoprotection genes are shown in green, qE-related genes in blue, and CCM genes in red. Larger TF nodes indicate higher regulatory strength on target genes. The plotted edge width is proportional to the strength of the regulatory interaction.

  1 h (RNA) or 4 h (photosynthetic measurements) after exposure to HL. a, d. Relative expression of qE and CCM genes at the indicated conditions normalized to WT LL (a) or dark (d) respectively (n = 3 biological samples, mean ± sd). b, e. NPQ and c, f. calculated qE, 4h after exposure to HL (n = 3 biological samples, mean ± s.d. The p-values for the comparisons are based on ANOVA Dunnett's multiple comparisons test and are indicated in the graphs (**, P < 0.01, ***, P < 0.001, ****, P < 0.0001). Statistical analyses for panel a and d were applied on log10transformed values. Extended Data Fig. 9: Reconstructed GRNs recapitulate experimentally found regulators of qE and CCM. Network representation of the top 10 regulators of CCM genes quantified via qPCR (Fig. 4), and qE genes as predicted from a. the concensus or b. PHOT-specific GRN. Regulators are represented by centeral nodes (same color code as in Fig. 5), target qE genes present in the respective network are shown in blue (LHCSR1, LHCSR3.1, LHCSR3.2, PSBS1, PSBS2) and target CCM genes are shown in red (LCI1, LCIA, CAH4, CCP1, HLA3, BST1). Larger TF nodes indicate higher regulatory strength on target genes. The plotted edge width is proportional to the strength of the regulatory interaction.

Figure 4 . 1 .

 41 Figure 4.1. Enzymes related to nitrogen metabolism in microalgae. The upregulated or downregulated enzymes in phot are marked in red and blue, respectively (RNAseq and Proteomics data).

Figure 4 . 2 .

 42 Figure 4.2. The total carbohydrate content of different CLiP mutants. (Left: day 0; Right: day 4 in -N conditions)

Figure 4 . 3 .

 43 Figure 4.3. The transcription level of MYB (Left) and RWP5 (Right) in WT, phot, and phot-C. LL(5 μmol/m2/s) and HL (300 μmol/m2/s) conditions. Values relative to GBLP were normalized to wildtype samples under low light intensity (n = 3 biological samples, mean ± s.d.).

4. 4 )

 4 ; RWP5 CDS mutant, LMJ.RY0402.184466 (hereafter rwp5) also holds the same CIB1 cassette in the middle of exon 5 of the RWP5 gene (Fig.4.5). The site of CIB1 cassette integration in the CLiP mutants had been validated by the insertion site in these two mutants by the LEAP-Seq method[START_REF] Yoshioka | The novel Myb transcription factor LCR1 regulates the CO2responsive gene Cah1, encoding a periplasmic carbonic anhydrase in Chlamydomonas reinhardtii[END_REF] . Using primers annealing 1kb away from the flanking sequence on either side of the insert, no fragment was observed in genomic DNA samples isolated from wild type cc-4533 and mutant strain. It is presumably due to the presence of complex or large insertions, which are typically found in CLiP mutants196 . So, we decided to change the amplification region by trying other primers.

  myb. Then we decided to use primers designed to amplify the downstream region of the CIB1 cassette insertion site and low MYB transcription level in myb mutant in both -N and +N conditions (Fig.4.4). The results showed that myb is more likely a knockdown mutant than a knockout mutant.

Figure 4 . 4 .Figure 4 . 6 .

 4446 Figure 4.4. Characterization of a CLiP mutant-myb (LMJ.RY0402.112219) affected in the MYB gene. A. Insertion map of the CIB1 cassette in MYB gene. Exons were shown in black, introns in line, 5'UTR and 3'UTR in light gray, and primers in arrows. The triangle indicates the insertion site of the CIB1 cassette; B. Validation of the insertion site in myb. Primers used are at the top. The presented PCR results are based on different single colonies, and all colonies showed identical results; C. Transcript levels of MYB, as determined by RT-qPCR in culture under nitrogen replete or depleted conditions using primers binding the 3'UTR region of MYB. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.).

Figure 4 . 7 .

 47 Figure 4.7. The RWP5 transcription level (left) and total carbohydrate content (right) in WT, rwp5, and rwp5-C. Left: Samples collected after 1h high light treatment in phototrophic HSM medium. Right: Cells were grown under the 12/12 light-dark cycle in phototrophic HSM medium. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.). The pvalues for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (*, P<0.05; ***, P < 0.0001).

Figure 4 . 8 .Figure 4 . 9 .

 4849 Figure 4.8. The RWP5 transcription level in WT, rwp5, and two phot::rwp5-C lines. Samples were collected after 1h HL treatment in phototrophic HSM medium. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (**, P<0.01; ***, P < 0.001).

4. 2 . 7 Figure 4 . 10 .Figure 4 . 11 .

 27410411 Figure 4.10. The RWP5 transcription level of WT, phot, phot-C2, and pkin in phototrophic HSM medium after 1h HL treatment. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (****, P < 0.0001; ns, not significant).

Figure 4 . 12 .

 412 Figure 4.12. Phenotype of WT, rwp5, and myb. A: Immune blot analyses of LHCSR3, LHCSR1, PSBS. ATPB as a loading control. B: NPQ values of for WT, rwp5 and myb after exposure to HL for 4 h. Just prior to the onset of the measurements, cells were acclimated to darkness for 15 min.

Figure 5 . 2 .

 52 Figure 5.2. The phosphorylation level of BSK1 in two phosphoproteomics data. A: D, dark; BL, blue light; B: LL, low light; HL, high light. Y axis is the Changes in the phosphorylation levels of BSK1 proteins.

Figure 5 . 3 .

 53 Figure 5.3. Phylogenetic tree of BSK1 by comparing amino acids. (Full tree is in Fig. 6.7)

  HT1 is a negative regulator in CO2-dependent stomatal closure In Arabidopsis 116 thaliana . In ht1 mutants, the stomatal opening was impaired. AtCBC1 and AtCBC2 function in the same pathway as AtHT1 154 .(Section 1.1.8, Fig. 5.4).

Figure 5 . 4 .

 54 Figure 5.4. AtHT1 and AtCBCs function in Blue-light and CO2 signaling in Arabidopsis thaliana 154 .

Figure 5 . 5 .

 55 Figure 5.5. BSK1 transcription level in different cell lines. Samples were collected in phototrophic HSM medium under LL conditions. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.).

Figure 5 . 6 .Figure 5 . 7 .

 5657 Figure 5.6. Immunoblot analysis (A) and total carbohydrate content (B) of phot and six phot::bsk1 S94A mutants under continuous LL conditions in phototrophic HSM medium. ATPB as a loading control. The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test. (****, P < 0.0001; ns, not significant).

Figure 5 . 8 .

 58 Figure 5.8. The GAP1 transcription level of WT, bsk1-oe, phot, phot::bsk1, phot::bsk1 S94A , phot:: bsk1 S94D under continuous LL (15 μE) conditions in phototrophic HSM medium. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (****, P < 0.0001; ns, not significant).

Figure 5 . 9 .

 59 Figure 5.9. The GAPR4 (Left)and RWP5 (Right) transcription level of WT, bsk1-oe, phot, phot::bsk1, phot::bsk1 S94A , phot:: bsk1 S94D under continuous LL(15 μE) conditions and HL (300 μE) conditions in phototrophic HSM medium. Values relative to GBLP were normalized to wild-type samples (n = 3 biological samples, mean ± s.d.). The p-values for the comparisons indicated in the graph are based on ANOVA Dunnett's multiple comparisons test (****, P < 0.0001; ns, not significant).

  3.2). Red light could highly induce the expression level Overexpression of RWP5 in phot mutant could partially bring down the high starch content of phot mutant (Fig.4.9). This confirmed the role of RWP5 in starch metabolism in blue light PHOT-dependent starch metabolism.Taking advantage of phosphoproteomics, we reported the first phototropindependent dephosphorylation protein -BSK1 in Chlamydomonas. BSK1 is an ortholog of Arabidopsis HT1 and CBCs, and it could be dephosphorylated by blue light (Fig.5.2 & 5.3). This indicated its possible role in starch metabolism in Chlamydomonas. By overexpressing the dephosphorylated form of BSK1 and phosphorylated form of BSK1 in the phot mutant, the starch content could go back to

Figure. 6 . 1

 61 Figure. 6.1 Putative model of phototropin controls starch metabolism in Chlamydomonas reinhardtii.

  photosynthetic parameters were measured with a pulse modulated amplitude fluorimeter (MAXI-IMAGING-PAM, HeinzWaltz GmbH, Germany). Prior to the onset of the measurements, cells were acclimated to darkness for 15 min. Chlorophyll fluorescence was recorded under different intensities of actinic light.

  GAPR4 gDNA: GAPR4 gDNA was amplified by PCR from WT gDNA using the platinum polymerase and primers G11 and G12. GAPR4 gDNA together with HpaI digested pLM005 plasmid and assembled by Gibson assembly. RWP5: RWP5 gDNA was amplified by PCR using KOD Hot Start DNA Polymerase, Phusion HotstartII polymerase, and Platinum superfii DNA Polymerase.

  on the NCBI non-redundant sequence database 219 . The alignment of the 131 protein sequences was carried out at the amino acid level using Muscle 220 . During the process, alignments was curated using both the multiple alignment editing softwares ClustalX 2.0 221 and Jalview 2 222 . The phylogenetic relationship was inferred using the maximum likelihood method PhyML 223 . The evolutionary model selection was done with MEGA 7 224 under the Bayesian information criterion 225 . According to the model selection process, the best evolutionary was GTR+G+I 226 . The maximum likelihood tree was evaluated with aLRT 227 , a non-parametric branch support based on a Shimodaira-

  Figure. 6.2. pHK330 plasmid map

Figure 6 . 5 .

 65 Figure 6.5. Sequencing results using the Multiple Sequence Alignment by CLUSTALW-myb.CIB1 cassette was highlighted in yellow.

Figure 6 . 6 .

 66 Figure 6.6. Sequencing results using the Multiple Sequence Alignment by CLUSTALW-rwp5.

Figure 6 . 7 .

 67 Figure 6.7. Full Phylogenetic tree of BSK1. (methods in 6.2.14, triangle nodes is the collapse of other proteins due to the paper size limitation)

Table 1 .1 Enzymes involved in starch metabolism in Chlamydomonas 104-109

 1

		Synthesis (S)
	Enzyme	or Degradation	Gene Name	Gene ID
		(D)
	ADP-glucose pyrophosphorylase	S	AGP2	Cre16.g683450
		S	STA1	Cre13.g567950
		S	STA6	Cre03.g188250
	alpha glucosidase	S	AGL1	Cre03.g194700
	alpha-amylase	S	AMYA3	Cre08.g384750
		S	AMYA1	Cre08.g385500
		S	AMYA2	Cre08.g362450
	beta-amylase	S	AMYB1	Cre06.g307150
		S	AMYB2	Cre06.g270350
		S	AMYB3	Cre01.g044100
	4-alpha-glucanotransferase	S	DPE1	Cre02.g095126
	phosphoglucomutase	S	GPM1	Cre06.g278210
		S	GPM2	Cre01.g012600
	glycosyl transferase	S	GTR11	Cre03.g198200
		S	GTR12	Cre04.g214650
		S	GTR12	Cre06.g302050
		S	GTR7	Cre12.g524000
	hexokinase	S	HXK1	Cre02.g117500
	Fructan fructosyltransferase	S	FFT4	Cre12.g488000
	phosphoglucose isomerase	S	PGI1	Cre03.g175400
	starch phosphorylase	S	PHOA	Cre07.g336950
		S	PHOB	Cre12.g552200
	starch branching enzyme	S	SBE1	Cre06.g289850
		S	SBE2	Cre06.g270100
		S	SBE3	Cre10.g444700
	NAD-dependent epimerase/dehydratase	S	SNE4	Cre06.g278143
	soluble starch synthase	S	SSS1	Cre04.g215150
		S	SSS2	Cre03.g185250
		S	SSS3	Cre13.g579598
		S	SSS4	Cre16.g665800
		S	SSS6	Cre12.g521700
		S	STA3	Cre06.g282000
	4-alpha-glucanotransferase	S	STA11	Cre03.g181500
	alpha,alpha-trehalase	S		Cre03.g195600
	UDP-glucose 6-dehydrogenase	S		Cre12.g532450
	UDP-glucose dehydrogenase	S	UGD1	Cre07.g357200
		S	UGD2	Cre06.g278185
	UDP-glucose pyrophosphorylase	S	UGP1	Cre04.g229700
	fructose-1,6-bisphosphate aldolase	D	FBA1	Cre01.g006950
		D	FBA2	Cre02.g093450
		D	FBA3	Cre05.g234550
		D	FBA4	Cre02.g115650
	fructose-1,6-bisphosphatase	D	FBP1	Cre12.g510650
	dihydrolipoyl dehydrogenase	D	GCSL	Cre18.g749847
	phosphoglucomutase	D	GPM1	Cre06.g278210
		D	GPM2	Cre01.g012600
	hexokinase	D	HXK1	Cre02.g117500
	phosphofructokinase family protein	D	PFK1	Cre06.g262900
		D	PFK2	Cre12.g553250
	phosphofructokinase	D	PFK3	Cre11.g467557
		D	PFK3	Cre11.g467552

  162,163 . Likewise, PHOT1 alone plays a role in mediating the rapid inhibition of hypocotyl growth 164 and promoting the destabilization of specific transcripts under high light intensities 165 .

	Arabidopsis thaliana 168 . The Arabidopsis phot1 phot2 double mutant is almost totally
	deficient in phototropism, chloroplast positioning and blue light-induced stomatal
	opening 156,169 . All the above phenotypes could be rescued by expressing
	Chlamydomonas PHOT in the phot1 phot2 double mutant of Arabidopsis 168 .
	In Chlamydomonas reinhardtii, PHOT has been reported to respond to low fluence
	blue light, mediating regulation of eyespot development 170 , sexual life cycle 40,41 ,
	chlorophyll and carotenoid biosynthesis 130 . Eyespot size is significantly larger when
	PHOT is absent; this phenotype is fully rescued by the complementation with the
	missing gene. Interestingly, the phenotype can partially be rescued by
	complementation with a truncated PHOT lacking the kinase domain, indicating for the
	first time a kinase-independent function of PHOT 170 . Furthermore, channelrhodopsin-
	1 (ChR1) is also under the control of PHOT 170 . Blue light can induce the pregametes
	mature into gametes in the sexual life cycle of Chlamydomonas. The PHOT RNAi
	Unlike in Arabidopsis thaliana, there is only one single phototropin gene (PHOT,
	Cre03.g199000) in Chlamydomonas reinhardtii. It is located in the plasma membrane
	and flagella 166 . Its protein sequence is similar to Arabidopsis thaliana phototropins,
	which showed 38% and 35% identity with PHOT2 and PHOT1, respectively 167 .
	Moreover, phototropin from Chlamydomonas reinhardtii is also functional in

Chapter 2. Phototropin inhibits starch accumulation in Chlamydomonas reinhardtii 2.1 Abstract

	Chlamydomonas. The PHOT knockout mutant could accumulate more starch than its
	WT in normal conditions. Moreover, the inhibition of starch accumulation requires
	the kinase activity of PHOT.
	Chapter 3 describes the first signaling cascade pathway linking PHOT with starch
	accumulation in Chlamydomonas. Blue light, via PHOT, represses the bHLH domain-In this chapter, I present the establishment of a novel link between blue light
	containing transcription factor (TF) GAPR4 (GAP1 Regulator 4), an activator of photoreceptor-phototropin (PHOT) and starch metabolism in Chlamydomonas
	GAP1. Both GAP1 and GAPR4 overexpression lines could lead to higher starch reinhardtii. The experimental data showed that a PHOT knockout mutant (phot) over-
	content. The manuscript reporting the application of Gene Regulatory Network tools accumulates starch. The complementation of the phot with the PHOT gene fully
	to predict the GAPR4 is also included in this chapter. rescued this phenotype showing that PHOT is indeed responsible for regulating starch
	Chapter 4 describes the second signaling cascade pathway linking PHOT with in Chlamydomonas.
	starch accumulation in Chlamydomonas. Blue light, via PHOT, activates the RWP5
	TF (belonging to the RWP-K family of TFs), a repressor of starch accumulation in a
	GAP1-independent manner. RWP5 is usually induced in -N conditions.
	Overexpression of RWP5 in the PHOT knockout mutant could partially rescue the
	high starch content phenotype of the PHOT knockout mutant.
	Chapter 5 describes the third signaling cascade pathway linking PHOT with starch
	accumulation in Chlamydomonas. We reported the first PHOT-dependent
	dephosphorylated protein in Chlamydomonas. PHOT-dependent dephosphorylation of
	a serine/threonine kinase-BSK1 (Blue-light Starch Kinase 1) mediates GAP1
	expression level and starch accumulation in a GAPR4-independent manner.
	Overexpression of dephosphorylated form of BSK1 could rescue the high starch
	content phenotype of the PHOT knockout mutant.
	2 describes the first and novel link between blue light photoreceptor-PHOT
	and starch metabolism in Chlamydomonas. PHOT inhibits starch accumulation in

Table 2 .1 Upregulated DEGs involved in phot under LL conditions (Calvin-Benson cycle, Starch metabolism) Primary class Secondary class Locus ID Gene name phot LL /wt LL FC (log2) phot HL/wt HL FC (log2)

 2

	Calvin-	Cre12.g485150	GAP1	0.93	2.17
	Benson

Primary class Secondary class Locus ID Gene name phot LL /wt LL FC (log2) phot HL/wt HL FC (log2)

	cycle
			Cre01.g029300	TPIC	0.85	1.66
			Cre07.g354250	PGK2	0.83	1.55
			Cre02.g115650	FBA4	0.82	0.80
			Cre07.g354200	GAP2	0.34	1.34
			Cre02.g093450	FBA2	0.31	-0.45
			Cre12.g554800	PRK1	0.12	1.17
			Cre01.g006950	FBA1	0.09	1.23
	Starch metabolism	synthesis	Cre16.g683450	AGP2	0.32	2.70
			Cre06.g272050	GPM1	0.60	0.43
			Cre13.g564000	GPM3	0.46	-0.52
			Cre17.g698850	ISA2	0.86	2.04
			Cre03.g207713	ISA3	0.39	0.79
			Cre01.g012600	PGM1	1.76	1.75
			Cre03.g199700	PGM11	1.55	0.61
			Cre12.g518950	PGM13	1.37	0.23
			Cre05.g236150	PGM14	0.64	-0.39
			Cre17.g711200	PGM16	0.30	-0.12
			Cre10.g431850	PGM18	1.27	0.54
			Cre10.g440250	PGM18	0.05	-0.92
			Cre12.g487150	PGM2	0.43	-0.09
			Cre03.g182300	PGM7	0.50	-0.25
			Cre14.g625150	PGM8	1.30	1.01
			Cre12.g527400	PGM9	0.34	0.93
			Cre06.g289850	SBE1	1.58	1.32
			Cre10.g444700	SBE3	0.22	0.44
			Cre08.g373450	SBE4	0.87	1.79
			Cre04.g215150	SSS1	1.39	2.03
			Cre13.g567950	STA1	0.15	1.31
			Cre06.g282000	STA3	0.58	1.30
			Cre12.g552200	STA4	0.39	1.51

Primary class Secondary class Locus ID Gene name phot LL /wt LL FC (log2) phot HL/wt HL FC (log2)

	degradation	Cre08.g362450	AMYA2	1.12	2.28
		Cre03.g181500	DPE1	0.85	1.16
		Cre07.g319300	GWD1	0.05	1.69
		Cre17.g719900	PWD1	0.78	1.12

Table 2 .2 Down-regulated DEGs involved in phot under LL conditions (Calvin-Benson cycle, Starch metabolism) Primary class Secondary class Locus ID Gene name phot LL /wt LL FC (log2) phot HL/wt HL FC (log2)

 2

	Calvin-
	Benson		Cre01.g032650	TAL1	-2.14	1.00
	cycle
			Cre05.g234550	FBA3	-1.16	0.66
			Cre03.g187450	RPI1	-0.98	-0.74
			Cre12.g511900	RPE1	-0.84	0.37
			Cre03.g185550	SEBP1	-0.83	1.66
			Cre01.g010900	GAP3	-0.58	0.69
			Cre02.g080200	TRK1	-0.41	1.38
			Cre12.g556600	GAPN1	-0.33	-1.02
			Cre12.g510650	FBP1	-0.12	0.49
			Cre02.g120100	RBCS1	-0.11	1.45
			Cre02.g116450	RPE2	-0.09	0.86
	Starch metabolism	synthesis	Cre07.g331300	AGP3	-1.02	-0.12
			Cre05.g232550	GPM4	-0.33	-0.64

  Synechocystis185 , and Chlamydomonas 120 .

	Scenedesumus 181 ,	Chlorella 182 ,	Porphyridium 183 ,	Dunaliella	salina 184 ,

2.2B), removing the ethanol after extraction is among the most time-consuming steps in determining starch in algae. Starch measurement will take half a day for 24 samples. As the main carbohydrate form in Chlamydomonas is starch and in order to check more samples in a shorter time, I investigated whether total carbohydrate content (TCC) will be a good proxy for starch content.

The phenol-sulfuric acid method 179,180 is a quick and reliable way to measure the total carbohydrate content, widely used in different microalgae species, such as I measured the starch content and total carbohydrate content of WT and phot samples. As Fig.

2

.5 shows, the results based on total carbohydrate content and on starch content are in very good agreement, showing very similar differences (in starch or carbohydrates) between WT and phot. Because total carbohydrate content is only marginally higher than starch content, these results additionally confirm that starch is indeed the dominant carbohydrate accumulating in Chlamydomonas.

Table 3 .1 Putative GAP1 regulators predicted results.

 3

	Locus ID	Gene Name	GRN1	GRN2	Check order (GAPRx)
	Cre01.g000050	RWP14	7
	Cre01.g001600		1		GAPR1
	Cre02.g079200		3
	Cre02.g118250	SWB1		6	GAPR2
	Cre03.g189000			10
	Cre03.g197100			3
	Cre05.g240600		9
	Cre06.g262800		4		GAPR3
	Cre06.g266850			4
	Cre06.g269100		10
	Cre07.g323000	TDP1	6
	Cre07.g349152		2	2	GAPR4
	Cre07.g354500	HSF2	8
	Cre08.g383000			8
	Cre09.g397475			7
	Cre09.g399289			1
	Cre11.g480950			9	GAPR5
	Cre13.g606750		5		GAPR6
	Cre16.g672300			5	GAPR7

  3.12).

	GAPR4	transcription level	2 4 6 8 10		GAP1	transcription level	10 0 10 1 10 2 10 3	White Blue Red
			0				10 -1
			W T p h o t p h o t -C	W T p h o t p h o t -C	W T p h o t p h o t -C		W T p h o t p h o t -C	W T p h o t p h o t -C	W T p h o t p h o t -C

  In this manuscript, I am the co-first author responsible for all the

	wet lab experiment work, including the mutant phenotyping, mutant generating, and
	experiment	designing.	This	manuscript	is	available	on	bioRxiv 63
	https://www.biorxiv.org/content/10.1101/2022.02.25.482034v3 and currently under
	review in Nat Comms.

Table 4 .1 Upregulated DEGs involved in phot under LL conditions

 4 (Nitrogen metabolism)

	Primary class	Secondary class	Locus ID	Gene name	phot LL /wt LL FC (log2)	phot HL/wt HL FC (log2)
	Nitrogen metabolism	Import	Cre01.g023650	AOC7	1.58	1.52
			Cre02.g108550	AOC3	1.36	1.95
			Cre06.g298750	AOT4	0.88	0.36
			Cre02.g112000	AOT7	0.82	1.46

Table 4 .2 Down-regulated DEGs involved in phot under LL conditions

 4 (Nitrogen metabolism)

	Primary class	Secondary class	Locus ID	Gene name	phot LL /wt LL FC (log2)	phot HL/wt HL FC (log2)
	Nitrogen metabolism	NH4 fixation	Cre12.g514050	GSF1	-0.18	-1.07
			Cre02.g113200	GLN1	-0.22	1.09
			Cre12.g530650	GLN2	-1.04	-0.90
			Cre05.g232150	GDH2	-3.65	-1.43
		NO metabolism	Cre07.g315400	NOA2	-0.06	0.46
			Cre09.g400550	NOA1	-0.19	-0.45
		NO3 NO2 transporter	Cre12.g541250	NAR1.5	-1.69	0.18

Table 4 .3. List of predicted putative starch or GAP1 regulators

 4

	Gene ID	Name in this study	Annotation	phot LL/WT LL FC (log2)	phot HL/WT HL FC (log2)
	Cre06.g285600	RWP5	RWP-RK domain-containing protein	-2.61	-4.37
			SAM-dependent
	Cre11.g475550	UMM10	methyltransferases	-0.68	-2.95
			superfamily protein
	Cre02.g085150	SBP	Squamosa promoter binding protein-like 1	2.24	2.26
	Cre04.g218050	RWP8	RWP-RK domain-containing protein	1.55	2.11
	Cre01.g034350	MYB	MYB domain-containing protein	2.05	1.66
	Cre03.g207800	ADH5	Alcohol dehydrogenase	-2.21	-3.84
			Transient receptor
	Cre09.g398400	TRP5	potential ion channel	-7.2	-5.1
			protein

Table 4 .4. RWP5 gDNA amplification strategy

 4 Y means I could get the correct size band. N means I did not get the correct size band.

	Name	Template	Enzyme	add	Tm value	GET?
	RWP5-F1	gDNA	Platinum			Y
		gDNA	KOD			N
		gDNA	Phusion+HF buffer			N
		gDNA	Phusion+GC buffer			N
		gDNA	Platinum	5%DMSO		N
		gDNA	KOD	5%DMSO		N
		gDNA	Phusion+HF buffer	5%DMSO		N
		gDNA	Phusion+GC buffer	5%DMSO		N
	RWP5-F2	gDNA	Platinum			N
		gDNA	KOD			N
		gDNA	Phusion+HF buffer			N
		gDNA	Phusion+GC buffer			N
		gDNA	Platinum	5%DMSO		Y
		gDNA	KOD	5%DMSO		N
		gDNA	Phusion+HF buffer	5%DMSO		N
		gDNA	Phusion+GC buffer	5%DMSO		N
		gDNA	Platinum	2.5MBetaine		N
		gDNA	KOD	2.5MBetaine		N
		gDNA	Phusion+HF buffer	2.5MBetaine		Y
		gDNA	Phusion+GC buffer	2.5MBetaine		Y
	RWP5-F3	gDNA	Platinum			N
		gDNA	KOD			N
		gDNA	Phusion+HF buffer			N
		gDNA	Phusion+GC buffer			N
		gDNA	Platinum	5%DMSO		Y
		gDNA	KOD	5%DMSO		Y
		gDNA	Phusion+HF buffer	5%DMSO		N
		gDNA	Phusion+GC buffer	5%DMSO		N
	RWP5-F1+F2	F1+F2	Platinum			N
		F1+F2	KOD			N
		F1+F2	Phusion+HF buffer			N
		F1+F2	Phusion+GC buffer			N
		F1+F2	Platinum	5%DMSO		N
		F1+F2	KOD	5%DMSO		Y
		F1+F2	Phusion+HF buffer	5%DMSO		N
		F1+F2	Phusion+GC buffer	5%DMSO		N
		F1+F2	Platinum	2.5MBetaine		N
		F1+F2	KOD	2.5MBetaine		N

*:

  In higher plants-Arabidopsis, blue light could stimulate stomatal opening via activating PHOT by inducing the starch degradation in the guard cells. In this study, we showed that PHOT deletion could lead to a high starch content in green algae-Kamiya et al. reported that blue light could induce starch breakdown in Chlorella 117 .

	Chlamydomonas and suggested a novel role of PHOT in regulating carbon
	partitioning in green algae.

However, starch content did not decrease in Phaeodactylum in blue light acclimation conditions

119

. In this study, RNA-seq and RT-qPCR analysis showed that the expression of genes related to starch synthesis or degradation is both up-regulated in phot mutant (Table

2.1 & 2.2

, Fig. 3.1). As Fig. 2.6 showed, starch synthesis and degradation rate in WT and phot are similar, lt indicated that the high starch phenotype has no connection with the circadian rhythm. All these results indicated that PHOT is indirectly connected with starch metabolism, and blue light inhibits starch accumulation is not via a single mechanism-promoting starch degradation or starch synthesis in Chlamydomonas. By generating the mutant pkin, which could constantly express the activated form of the PHOT kinase domain, we further confirmed that enzymatically active kinase is necessary for the blue light to inhibit the starch accumulation via PHOT (Fig. 2.10).

Table 6 .2. primers for colony PCR and screening in this study

 6

	Primer Name	Sense Primer
	P1	GACGTTACAGCACACCCTTG
	P2	GCACCAATCATGTCAAGCCT
	P3	TTCCCTGCCGCTGCAACA
	P4	AGTCAAGGTGGGTTCCAGTG
	P5	GTTGCAAATGATCACAACGC
	P6	CAAAGAGGAGGACGAGGATG
	P7	CACATGTGTTGTCCTTTGGC
	6.2

.7.2 RNA extraction and cDNA synthesis

Table 6 .3. RT-qPCR primers used in this study

 6

	Gene ID	Gene Name	Sense Primer	Anti-sense Primer
	Cre08.g385500	AMYA1	GCATGGCAATGGCCGAGTA	AAAGCTTTCTCGTTGTGCCG
	Cre06.g307150	AMYB1	GAATCGACCGCAGAATCGCA	GTGCATAGCCGTTATCGCTG
	Cre07.g338451	FBP1	GTGTGTATGTTGGCTGTGGC	GAGACACTTACGGCGAGACA
	Cre12.g510650	FBP2	GGGAGGCTGCATGGGATAC	GCAAATTGGTCGACAAGGCT
	Cre01.g010900	GAP3	ATGGGACAAGGGACTTGCAG	GTGCACCTACGACTCTACCG
	Cre07.g332300	GWD2	TTGAATGACGAGCACAGGCT	GGGCGTGAAAGCGTGAAAAT
	Cre17.g698850	ISA2	CGAGTGCGAGTAAGACCTCG	CCACAACGTATGCAAACCCG
	Cre12.g553250	PFK2	TGAAAGGCGGAGTTCTGTGT	TCAGAACGTGCGTGCTAGAG
	Cre10.g450500	PWD	GCATCCACTTGGTCAGGGAA	CTACAGCGCGGATGCTATGA
	Cre04.g215150	SSS1	TATGTCCGGCTCGATAGGGT	ATGACAACCCCACCACTGAC
	Cre06.g282000	STA3	AGCCATCGACTACATCGAGC	CCCCTTGCCTCCTTTCTCTG
	Cre03.g188250	STA6	GCACAGCTGCATAGAAGCAC	CTCGGTCCAAATCTCTGGGG
	Cre02.g080200	TRK1	AAGAGCTCTGTCGGAACTGC	GCCGTTACAGGCGTTCAATC
	Cre01.g032650	TAL1	GGTGGACAGCAGCCAACCT	TGGAGGCGAGCTTGATGTAG
	Cre06.g283050	LHCA1	GCGAGGGACCAAGTCAATCA	CAGTTGTTAACACGCACGCA
	Cre17.g720250	LHCB4	TCGGAGTTCGTGGTGATTGG	CTCCGAGCTAACCTCATCGG
	Cre01.g010900	GAP3	ACTGAGGAGTGGACCTGTGA	CCATCGCCCTCCCCATTAAG
	Cre07.g354200	GAP2	ATTGGGAAGTGCATACGGGG	GCACATGCGTGTCACAAACT
	Cre12.g485150	GAP1	GATCGGCATCAACGGCTTCG	CGGCATCAATGAATGGGTCG
	Cre12.g556600	GAPN1	GACAAGGAGTCCTACACGCT	AGGCCCGGCAAAGACAAATC
	Cre01.g016600	PSBS1	TAAACCGTGTATTGGAACTCCG	CTCTGCACGCGGCGTGTT
	Cre01.g016750	PSBS2	GGTTACGCACGTTATCACTTGT	CTCTCTGCACGCGTTGTACG
	Cre08.g367500	LHCSR3.1	CACAACACCTTGATGCGAGATG	CCGTGTCTTGTCAGTCCCTG
	Cre08.g367400	LHCSR3.2	TGTGAGGCACTCTGGTGAAG	CGCCTGTTGTCACCATCTTA
	Cre08.g365900	LHCSR1	GAGTCTGAGATCACCCACGG	CCGATCTGCTGGAAGTGGTA
	Cre01.g001600	GAPR1	CGGGGCAATTAGTGTCCAGA	ATTGCGGAAGCTTGGACTGA
	Cre02.g118250	GAPR2	GAAGGATGCCGAGGACTGAG	GGGAATGCAGCCAACCCTAA
	Cre06.g262800	GAPR3	ATCATGGCGAACATGCTGGA	CACGGTCAGGGACTTGGC
	Cre07.g349152	GAPR4	CACGTGATAGTAAAGCGCGG	GACAGAGGCGTTGCGAATCT
	Cre11.g480950	GAPR5	AAGCGCACCTGTTGCGTAAT	AAACAACAGGCCCAGAAAGC

8 Gene cloning and Plasmid construction 6.2.8.1 Gene cloning and Plasmid construction PHOT

  , GAP1, GAPR4, RWP5, BSK1, BSK1 S94A , and BSK1 S94D gDNA or CDS were generated by amplifying from gDNA or cDNA, or plasmid using different PCR enzymes.

Table 6 .4 Primers for gene cloning is this study Primer Name Primer Sequence G1 CCCGGATCCATGGCAGGGGTGC

 6

	G2	ttagaattctcagtagttgtcgaacgccg
	G3	gctactcacaacaagcccagttATGGCAGGGGTGAGCAC
	G4	gagccacccagatctccgttGTAGTTGTCGAACGCCGCG
	G5	AATCACGTCTCCCACGTGTTG
	G6	ATGGGGGCTGGGGTGTC
	G7	GCAGGAGATTCGAGGTTATGGCCCTCCTCGCCC
	G8	CCTTGGACACCATGTTCACCTTGACGGCGGCC
	G9	gctactcacaacaagcccagttATGGCCCTCCTCGCCCA
	G10	gagccacccagatctccgttCACCTTGACGGCGGC
	G11	gctactcacaacaagcccagttATGGAGCTGGAGGACGCG
	G12	gagccacccagatctccgttGTGGGCGCGCGG
	G13	gctactcacaacaagcccagttATGCTAGACACTTTCGACAGCCT

Acknowledgments

Acknowledgments

We are grateful to Prof. Hideya Fukuzawa for sending us lcr1, lcr1-C and their respective WT strains and to Prof. Peter Jahns for the antibody against PSBS.

Funding:

The authors would like to thank the following agencies for funding: The Human Frontiers Science Program through the funding of the project RGP0046/2018 (DP, ZN); the French National Research Agency in the framework of the Young Investigators program ANR-18-CE20-0006 through the funding of the project MetaboLight (DP); the French National Research Agency in the framework of the Investissements d'Avenir program ANR-15-IDEX-02, through the funding of the "Origin of Life" project of the Univ. Grenoble-Alpes (DP, YY); the French National Research Agency through the funding of the Grenoble Alliance for Integrated Structural & Cell Biology GRAL project ANR-17-EURE-0003 (DP, MAR-S), the Prestige Marie-Curie co-financing grant PRESTIGE-2017-1-0028 (MAR-S); the International Max Planck Research School 'Primary Metabolism and Plant Growth' at the Max Planck Institute of Molecular Plant Physiology (MA, ZN).

(Marker painting by Yizhong-Grenoble, France, 15 th December 2019)

Abstract

Availability of light and CO2, substrates of microalgae photosynthesis, is frequently far from optimal. Microalgae activate photoprotection under strong light, to prevent oxidative damage, and the CO2 Concentrating Mechanism (CCM) under low CO2, to raise intracellular CO2 levels. The two processes are interconnected; yet, the underlying transcriptional regulators remain largely unknown. By employing a large transcriptomics data compendium of Chlamydomonas reinhardtii's responses to different light and carbon supply we reconstructed a consensus genome-scale gene regulatory network from complementary inference approaches and used it to elucidate the transcriptional regulation of photoprotection. We showed that the CCM regulator LCR1 also controls photoprotection, and that QER7, a Squamosa Binding Protein, suppresses photoprotection-and CCM-gene expression under the control of the blue light photoreceptor Phototropin. Along with demonstrating the existence of regulatory hubs that channel light-and CO2-mediated signals into a common response, our study provides a unique resource to dissect gene expression regulation in this microalga.

Competing interests: Authors declare that they have no competing interests.

Supplementary Materials for

Widening the landscape of transcriptional regulation of algal photoprotection Authors Marius Arend 1,2 † , Yizhong Yuan 3 † , M. Águila Ruiz-Sola 3 ‡ , Nooshin Omranian 1,2,4 , Zoran Nikoloski 1,2,4* , Dimitris Petroutsos 3 * † these authors contributed equally to this work * Corresponding Authors: nikoloski@mpimp-golm.mpg.de (Z.N.), dimitris.petroutsos@cea.fr (D.P.)

This PDF file includes:

Supplementary Note Extended Data Figs. 1

to 10

Other Supplementary Materials for this manuscript include the following:

Tables S1 to S10 (to be downloaded as excel file) Supplementary Note Simulation studies [START_REF] Marbach | Wisdom of crowds for robust gene network inference[END_REF] indicated that the top 10% of the edges in the consensus network are enriched in positive interactions if the underlying approaches perform better than guessing. Thus, a higher overlap with the consensus networks is expected to result in improved predictions. We found that the interactions from the network deconvolution approach exhibited the largest overlap with the consensus network, while GGM assigns the highest rank to consensus interactions (Extended Data Fig. 2a). Repeating this analysis by including the two worse performing approaches (i.e. ARACNE and Silencing) resulted in no qualitative changes in the overlaps (Extended Data Fig. 2b) and only 7.47% of difference to the original consensus network, demonstrating the robustness of the inferred interactions. Considering the 10% network density threshold, we also inspected the proportion of TF-TF interactions in the different networks and their consensus; we found that it ranges from 2.55% in the GENIE3 network to 2.77% in the network deconvolution approach (Extended Data Fig. 2c). Since the fully connected network containing all possible TF-TF and TF-target interactions has a relative TF-TF content of 2.52%, these findings suggest an enrichment of TF-TF interactions in GRNs inferred by all five approaches considered in the consensus. To confirm the CIB1 insertion site, gene-specific primers were used that anneal upstream and downstream of the predicted insertion site of the cassette (primer pairs P3-P4, P7-P8, P9-P10 and P5-P6 for qer6, qer1, qer7 and qer4 respectively; Supplementary Table 9). Pairs of primers used are indicated at the bottom of the agarose gels used to separate the PCR products. Note that the PCR product of P5-P6 is indicated by an arrow. c. Relative expression levels of predicted qE regulator genes in the different CLiP mutants after exposure to HL (300 µmol photons m -2 s -1 ) for 1h.

Extended Data

Extended

Supplementary Table Captions

Supplementary Table 1: RNAseq data sets used in this study; HSM: Sueoka's high salt medium (1); HSM -new trace = modified Sueoka's high salt medium with trace elements as described in (2); Rep= biological replicates (1) Sueoka, N. Mitotic replication of deoxyribonucleic acid in Chlamydomonas reinhardii. Proc. Natl. Acad. Sci. U.S. A. 46, 83-91 (1960).

(2) Kropat, J.et al. A Revised Mineral Nutrient Supplement Increases Biomass and Growth Rate in Chlamydomonas Reinhardtii. Plant J. 2011, 66 (5), 770. Moreover, GAP1 is also under the control of BSK1.

Supplementary

Overall, this work describes for the first time a PHOT-dependent phosphorylation protein in Chlamydomonas and investigates its role in starch metabolism.

Results

PHOT-dependent phosphoproteome analyses

In order to explore the early PHOT-dependent phosphorylation events, our team performed a phosphoproteomics analysis of WT and phot after an overnight acclimation to darkness and subsequent exposure to blue light (100 μE) for 5 min.

Based on the analysis, 6273 unique phosphopeptides from 2618 phosphoproteins were identified. From the trend analysis that has been performed across the conditions, 12 clusters (from A to L) with notable trends have emerged.

Among different clusters, cluster H contains the phosphopeptides, which increase in abundance only in blue-light exposed WT samples. Cluster I contains phosphopeptides that decrease in abundance after blue-light exposure in the WT but not in the phot.

In Cluster H and I, we found several phosphoproteins with interesting biological significance, which may mediate some of the PHOT-initiated signaling cascades.

Among them, in cluster I, one of the phosphoproteins that attracted our attention is the gene product of Cre16.g659400 which encodes a Ser/Thr protein kinase of around 61.33 kDa in size (Fig. 5.1). We named this protein BSK1 (Blue-light Starch Kinase 1). The reason will be explained in the following sections. Therefore, BSK1 is an interesting candidate worth investigating whether it is involved in any PHOT-mediated pathways.

of GAP1. Moreover, the deletion of PHOT will bring the GAP1 expression level up close to the level of WT under red light conditions (Fig. 3.3). This indicated that, the induction of GAP1 in red light is mainly due to the absence of blue light.

Although the exact function of GAP1 in Chlamydomonas is still unknown, we found that overexpressing GAP1 could increase the starch content (Fig. 3.7). This gave the first role of GAP1 in starch metabolism in Chlamydomonas: GAP1 is involved in the PHOT-dependent starch metabolism. Moreover, by using GRN-based predictions [START_REF] Love | Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[END_REF] , we reported a novel TF regulator of GAP1-GAPR4, which is under the control of PHOT (Fig. 3.12). Overexpression of GAPR4 could lead to a higher GAP1 expression level in different light quality conditions and also a higher starch content than its WT (Fig. 3.13).

In addition, when we tried to generate PHOT complementation lines and GAP1-OE lines with venus-tag, we first used the CDS of each gene, but it failed or with a meager success rate. Baier reported that intron-containing algal transgenes could enhance the gene expression in Chlamydomonas reinhardtii 178,201 . In our case, after switching to the genomic sequence, the success rate increased at least 20 times (from 0.1% to 2%). We think it is the introns' role in gene expression regulation. However, due to the high GC content of the Chlamydomonas genome and the longer length of gDNA than CDS, some researchers still prefer using CDS to avoid costly and technically challenging. Emrich-Mills recently reported a recombineering pipeline that could be a good solution for getting gDNA sequence in Chlamydomonas 202 .

Light quality has been proved to affect nitrogen metabolism in different organisms.

Blue light could induce the enzyme activities of nitrogen metabolism involved in

Malus domestica, such as glutamate synthase (GOGAT) and nitrate reductase (NR) 203 .

In Arabidopsis, the light control of asparagine synthetase (ASN1) and glutamine synthetase (GLN2), which are involved in nitrogen assimilation, has been proved to operate via phytochrome and by blue light 204 . In Chlamydomonas, blue light could activate nitrate reductase in vivo [205][206][207] . Taking advantage of proteomics and RNAseq, we found that many genes or enzymes involved in nitrogen metabolism have been affected in phot mutant (Table 4. 1 & 4.2, Fig. 4.1). Motivated by this, we found a novel starch metabolism regulator-RWP5. RWP5 could be activated by blue light via PHOT, and the deletion of RWP5 leads to higher starch content (Fig. 4.3 & 4.7).

3) How does the TF RWP5 control the starch metabolism? Which gene or protein is directly under the control of RWP5? Due to the two bp mismatch in the venus-flag region of our plasmid, the rwp5 complementation line we generated in this study has no tag. However, this could be checked by generating a new rwp5 complementation line using RWP5-tag (e.g. VENUS, FLAG, HIS) construction and then proceeding with ChIP-seq. In summary, this study provided evidence and three pathways to describe how

Material and methods

Algal Strains

The following algal strains were obtained from the Chlamydomonas Resource

Center and other labs (https://www.chlamycollection.org/) or generated in this study (Table 6.1). All the strains were maintained on solid TAP agar plates with or without appropriate antibiotics at 18℃ and 5 μmol/m 2 /s.

Growth Conditions and Medium

All the experiments in this study were performed using standard (Tris Acetate

Starch and total carbohydrate content measurement

The starch content was determined by using Starch Assay Kit (Sigma-Aldrich, US) as described in the instruction. The result was calculated according to the standard curve made with glucose solution after starch digestion. Before starch digestion, samples were prepared as follows: 1 ml algal cells were pelleted by centrifugation at 11,000g for 5 min and then resuspended in 1ml 80% ethanol to incubate at 80℃ for 5 min. Another equal volume of 80% ethanol was added and the supernatant was discarded by centrifugation at 1,000g for 10 min. The previous step was repeated, the supernatant was carefully removed. After that there was 1 hour of waiting time to allow for the complete ethanol volatilization. Samples were now ready for starch digestion.

Total carbohydrate content was measured by the phenol-sulfuric acid method 182,214 . 0.1 ml of the sample was added to a 2 ml Eppendorf tube, spun down, and the supernatant was discarded. Then 0.1 ml of 5% phenol (v/v) solution and 0.1 ml of water were added and mixed. After mixing, 0.5 ml of concentrated sulfuric acid was added rapidly and mixed well by vortexing. After cooling down the samples for 10 min at room temperature to allow color development, 200 μl of the solution was pipetted into 96-well microplates (Greiner Bio-One, 655101) and the absorbance was measured at 488 nm with TECAN. The result was calculated based on a standard curve made with different concentrations of glucose solution (in the range from 0 μg/ml to 500 μg/ml).

Nucleic Acid Extraction

Genomic DNA extraction

Genomic DNA was extracted using Gentra Puregene Tissue Kit (Qiagen, Germany) following the manufacturer's online protocol (https://www.qiagen.com/cn/resources/resourcedetail?id=9a9d082d-26c5-40e1-8f7a-f6197d90ba91&lang=en).

For colony PCR, crude genomic DNA was extracted by using EDTA-method 215 .

All the primers for colony PCR and screening are shown in Table 6.2 which were synthesized by Thermo-fisher or IDT.

Primer Name

Primer Sequence G14 gagccacccagatctccgttATCCTCCTGCGGCACCT

Gibson Assembly

The Gibson assembly methods were modified from Gibson 177 . Fifteen fmol of each fragment were mixed together to a total volume of 5μl. and then 15μl isothermal

Gibson assembly reaction mix were added and the resulting solution was mixed well by pipetting. After a 60 min-long incubation at 50°C the reaction products were ready to be used for bacteria transformation.

All PCR products were gel extracted and purified before Gibson assembly. The Gene of interest was mixed with a purified and linearized pLM005 or pRam118 plasmid, cloned in-frame with a C-terminal Venus-3Xflag tag. All the plasmid sequence has been confirmed by sequencing before using for transformation.

Bacterial Transformation

The bacterial transformation was performed using lab protocol as follows; 50μl of thawed competent bacteria and 1 -5μl of DNA (usually 10pg -10ng) were added to a pre-chilled Eppendorf tube and mixed gently. The suspensions were kept on ice for 30 min, and after that, the mixture was heated at 42°C for 45s. Following the heat-shock step, the tubes were kept on ice for another 2 min. Then add 1ml LB medium to the mixture and grow them in a shaking incubator for 1h at 37°C. Plate some or all the transformation onto a 10 cm LB agar plate containing the appropriate antibiotic.

Incubate plates at 37°C overnight. For the plasmid size more than 8kbp, we used Ecoli. DH10β, otherwise using DH5a.

Transformation of Chlamydomonas reinhardtii

The transformation was performed by electroporation, which follows the protocol of Zhang et al. 218 with modification. Cells for transformation were collected at 1-2h before the end of the light phase in a synchronized (12h light/ 12h dark) culture.

Before using for transformation, 30 min of heat shock (40 ℃) was applied in a thermomixer with gentle mixing at 350 rpm. After 30 min recovery at RT, cells were ready for the transformation. For three reactions 11ng/kb linearized plasmid was mixed with 400 μl of 1.0 x10 7 Chlamydomonas reinhardtii cells/ml and electroporated in a volume of 125 ml in a 2-mm-gap electro cuvette using a NEPA21 square-pulse electroporator, using two poring pulses of 250 and 150 V for 8 msec each, and five transfer pulses of 50 msec each starting at 20 V with a "decay rate" of 40% (i.e., successive pulses of 20, 12, 7.2, 4.3, and 2.6 V). Electroporated cells were immediately transferred to a 15ml centrifugation tube containing 9ml TAP plus 40mM sucrose. After overnight incubation under low light, cells were collected by centrifugation and spread on TAP agar plates which contain the appropriate antibiotic (10μg/ml paromomycin or 7.5μg/ml zeocin or 10μg/ml hygromycin B).

Transformants typically appear after 7 days.

Complementation strains screening

The plasmids were transformed into different strains using electroporation (described above), and then the putative antibiotic-resistant transformants were