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. Employing a large transcriptomic data compendium of _Chlamydomonas reinhardtii’s_ responses to different light and carbon supply, we reconstruct a consensus

genome-scale gene regulatory network from complementary inference approaches and use it to elucidate transcriptional regulators of photoprotection. We show that the CCM regulator LCR1 also

regulation in this microalga. SIMILAR CONTENT BEING VIEWED BY OTHERS LIGHT-INDEPENDENT REGULATION OF ALGAL PHOTOPROTECTION BY CO2 AVAILABILITY Article Open access 08 April 2023 CENTRAL

TRANSCRIPTIONAL REGULATOR CONTROLS PHOTOSYNTHETIC GROWTH AND CARBON STORAGE IN RESPONSE TO HIGH LIGHT Article Open access 06 June 2024 SYSTEMS-WIDE ANALYSIS REVEALED SHARED AND UNIQUE

RESPONSES TO MODERATE AND ACUTE HIGH TEMPERATURES IN THE GREEN ALGA _CHLAMYDOMONAS REINHARDTII_ Article Open access 13 May 2022 INTRODUCTION Photosynthetic microalgae convert light into

chemical energy in the form of ATP and NADPH to fuel the CO2 fixation in the Calvin–Benson cycle1. They have evolved to cope with rapid fluctuations in light2 and inorganic carbon (Ci)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

timescales2, among which the rapidly reversible energy-quenching (qE) is, under most circumstances, the predominant NPQ component2,4. The major molecular effector of qE in the green model

microalga _Chlamydomonas reinhardtii_ (hereafter _Chlamydomonas_) is the LIGHT HARVESTING COMPLEX STRESS RELATED protein LHCSR3, encoded by the _LHCSR3.1_ and _LHCSR3.2_ genes5 that slightly

differ only in their promoters; LHCSR1 can also contribute significantly to qE under conditions where LHCSR3 is not expressed6,7. PSBS, the key qE effector protein in higher plants8 is

encoded in two highly similar paralogues _PSBS1_ and _PSBS2_ in _Chlamydomonas_9. They are only transiently expressed in _Chlamydomonas_ under high light (HL)9,10 and their gene products

accumulate under UV-B irradiation11; while their precise contribution in _Chlamydomonas_ photoprotective responses is still a matter of ongoing research, current understanding is that PSBS

proteins contribute to photoprotection during HL acclimation of _Chlamydomonas_ through both NPQ-independent and NPQ-dependent mechanisms12. Intracellular levels of CO2 are modulated by the

availability of its gaseous and hydrated forms3 in the culture media and the supply of acetate, which is partly metabolized into CO27,13. 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 assimilated3. 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)14,15,16, including the Myb Transcription Factor LOW-CO2 -STRESS RESPONSE 1 (LCR1) that controls the 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 elucidated17. CIA5 is also a major qE regulator activating transcription of genes

encoding LHCSR3 and PSBS, while repressing accumulation of LHCSR1 protein7. LHCSR3 expression relies on blue light perception by the photoreceptor phototropin (PHOT)18, on calcium signaling,

mediated by the calcium sensor CAS19 and on active photosynthetic electron flow18,19,20, likely via indirectly impacting CO2 availability7. The critical importance of CO2 in LHCSR3

expression is demonstrated by the fact that changes in CO2 concentration can trigger LHCSR3 expression21,22,23 even in the absence of light7. Accumulation of _LHCSR1_ and _PSBS_ mRNA is

under control of the UV-B photoreceptor UVR811 and PHOT24,25 and is photosynthesis-independent20,25. While LHCSR1 is CO2/CIA5 independent at the transcript level7,25, PSBS is responsive to

CO2 abundance and is under partial control of CIA57. A Cullin (CUL4) dependent E3-ligase24,26,27 has been demonstrated to post-translationally regulate the transcription factor (TF) complex

of CONSTANS (CrCO)26 and NF-Y isomers27, 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 light28. 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 studies29,30,31, 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) used in building the model is considerably smaller than the number of TFs used as predictors,

leading to collinearity of the transcript levels and associated computational instabilities; furthermore, as an artifact of the computational techniques, some of the inferred regulations may

be spurious32,33,34. To address these issues, here we took advantage of combining the outcome of multiple regularization techniques and post-processing to increase the robustness of

identified interactions33,34,35. In contrast to our approach, the existing predicted GRNs of _Chlamydomonas_ either focused on nitrogen starvation36 or used a broad RNAseq data compendium,

not tailored to inferring regulatory interactions underlying responses to particular cues37. Moreover, these GRNs were not obtained by combining the outcomes from multiple inference

approaches, shown to increase accuracy of predictions30, and their quality was not gauged against existing knowledge of gene regulatory interactions. We used an RNAseq data compendium of 158

samples (Supplementary Data 1) from _Chlamydomonas_ cultures exposed to different light and carbon supply as input to seven benchmarked GRN inference approaches that employ complementary

inference strategies29,30 to identify activating and inhibiting regulatory interactions. We assessed the performance of each approach based on a set of curated TF-target 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

experimentally validating two of the six tested candidates. We show here that LCR1 regulates not only CCM, as previously reported17, 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 photoprotection7 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 in-house 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 12 h/12 h light-dark scheme and sampled in 30 min to 2 h intervals38,39. 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 Data 1). These data sets capture the expected expression patterns of the key genes involved in

CCM and qE (Fig. 1a, Supplementary Fig. 1) in response to changes in acetate availability and light intensity. Specifically, we found strong upregulation of these genes in the light7,25, and

a marked inhibition of _LHCSR3.1/2_ and CCM genes by acetate as previously described7,40. We employed these data together with a list of 407 transcription factors from protein homology

studies41,42 (Methods, Supplementary Data 2), as input to seven GRN inference approaches to robustly predict TF-target interactions, as shown in benchmark studies30. The employed methods

infer both activating and inhibiting TF-target interactions. Benchmarking of GRNs usually relies on ground truth data obtained from ChIPseq or transcriptomic profiling of TF mutants. Since

to date, no such comprehensive data set exists for _Chlamydomonas_ we manually curated a list of known, experimentally validated regulatory interactions underlying CCM and qE in

_Chlamydomonas_22,26,27,28, to assess the quality of GRNs inferred by the different approaches. As negative control, we included the reported lack of effect of the SINGLET OXYGEN RESISTANT 1

(SOR1) TF on _PSBS1_ transcript levels in diurnal culture43 as well as a set of four TFs, designated TF1-4, whose knock-out or overexpression did not affect _LHCSR3.1_ transcript levels

(Supplementary Note 1, Supplementary Fig. 2 and Fig. 1b). When we assessed the predicted ranks, as a measure of confidence assigned to the positive and negative ground truth data, we found

that two approaches were clear outliers, showing sensitivity of 0%. More specifically, ARACNE44 and global silencing45 are unable to recover any positive literature interactions when using a

network density threshold of 10% of all possible TF-TF and TF-target interactions (most prominently observable in the case of LCR1 interactions). Possible reasons for this finding are

over-trimming or issues with the validity of the underlying assumptions, as seen in other case studies46. Since the presence of most gene regulatory interactions is dependent on

environmental stimuli, it is considerably easier to experimentally validate the presence of TF-target interactions than to show that they do not take place47. Thus, in inferring a consensus

GRN we considered the five approaches that were able to recover positive interactions, namely: Graphical Gaussian Models (GGM), Context Likelihood of Relatedness (CLR), Elastic Net

regression, Gene Network Inference with Ensemble of Trees (GENIE3), and Network Deconvolution (Methods). To obtain insights into the performance of these approaches, we next 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 (Supplementary Fig. 3, Fig. 1b). This observation suggested that the properties of a

given TF-target 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-translationally16—not reflected in the transcriptomics data. Further, NF-Y factors that rely on complex formation with CrCO to regulate their targets27, may also act via unresolved

post-translational mechanisms. As mentioned in the introduction, some of the employed approaches use regularization techniques, mitigating effects of collinearity and low sample number, this

comes at the cost of increasing the number of false negatives, another reason for the high number of unrecovered interactions previously reported in the literature. 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, only for the interaction of TF1 and LHCSR3.1 we observe false positives, originating from spurious interactions. The interactions of the other four TFs (Supplementary Note 1,

Supplementary Fig. 2) are correctly discarded by all approaches, indicating the robustness of the employed 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 CrCO26,27 and of _PSBS1_ by NF-YB27 (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 Data 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 2, Supplementary Fig. 4).

CONSENSUS GRN PINPOINTS LCR1 AS A REGULATOR OF QE-RELATED GENES 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)30,48. Mutants were available for four of the top ten of TFs with the strongest cumulative regulatory effect on qE-related genes (Fig. 2a,

Supplementary Data 4): Two knock-out mutants of previously uncharacterized genes were ordered from the CliP library49, which we termed _qE-regulators 4_ and _6_ (_qer4_, _qer6_; see

Supplementary Fig. 5 for the genotyping of these mutants). Additionally, we obtained an overexpressor line of the N-acetyltransferase LCI821 and the knock-out strain of the known CCM

regulator LCR117. We tested for a regulatory effect by switching LL-acclimated mutant strains and their respective WT background to HL for 1 h and quantified transcript levels of qE-related

genes. Since both the paralogs of _PSBS_ as well as of _LHCSR3_ show correlation >0.96 over all RNAseq samples used in this study and they additionally have very similar expression

profiles quantified by RT-qPCR50, we only probed the transcripts of _LHCSR3.1_ and _PSBS1_ via qPCR in the validation assays. For _qer4_, _qer6_, and _lci8-oe_ we did not observe an effect

on the transcript levels of investigated genes after HL exposure (Supplementary Fig. 6). 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 synthesis51 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 (three times lower, Fig. 2b) and protein level (four times lower, Fig. 2c, d) compared to the WT; as a result, _lcr1_ developed very low NPQ and qE

(Fig. 2e). 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 expression25 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 (Supplementary Fig. 7a) and protein level (Supplementary Fig. 7b, c), and had lower NPQ and qE (Supplementary Fig. 7d) than the

WT, although the higher expression levels of the _LHCSR1_ gene were not rescued by the complementation with the missing _LCR1_ gene (Supplementary Fig. 7a). Altogether, our data show that

LCR1 is a regulator of qE by activating _LHCSR3.1_ transcription and repressing LHCSR1 and PSBS accumulation. Furthermore, we revisited the role of LCR1 in regulating CCM genes17 by

analyzing the expression of selected CCM genes in WT, _lcr1_ and _lcr1-C_ cells shifted from LL or darkness to HL, conditions favoring CCM gene expression7. We first confirmed that under our

experimental conditions _lcr1_ could not fully induce _LCI1_ (Supplementary Fig. 8a, b) in accordance with the report of the discovery of LCR117. 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), and BESTROPHINE-LIKE PROTEIN 1 (BST1) as

well as the carbonic anhydrase CAH4, when shifted from LL or dark to HL (Supplementary Fig. 8a, 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 The light-dependent induction of LHCSR3 is predominantly mediated by the blue light

photoreceptor PHOT18. To analyze the PHOT-dependent transcriptional regulators, we first inferred a GRN based solely on the RNAseq from samples of _phot_ and WT acclimated to LL and after 1 

h exposure to HL (data set PH, Supplementary Data 1) using only the GENIE3 approach, reported to show good performance30,46 and which is among the best-performing approaches in our consensus

network. Since the PH experiment contains a low number of samples (12 samples, 4 conditions) the inferred GRN will inevitably suffer from the effects of low statistical power and high

collinearity. To mitigate these effects, we decided to only include interactions that are also present in the benchmarked consensus network. By determining the intersection of the two

networks, we obtained a GRN that resolves regulatory interactions underlying the transcriptomic changes observed in the _phot_ mutant, while borrowing the statistical power of the whole

RNAseq compendium. We refer to the resulting network as PHOT-specific GRN (Methods, Supplementary Data 5). When investigating the top 10 regulators of qE genes in this PHOT-specific GRN the

interactions where weighted based on the importance score from GENIE3 (Supplementary Data 6, Fig. 3a). Among the interactions in this list of PHOT-dependent regulators, we recovered two

known regulators of qE, namely, ROC7528 and CrCO26,27 (Fig. 3a). These observations are in line with an existing hypothesis24 suggesting that a CUL4-dependent E3-ligase targeting CrCO26 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 spectra28. 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-specific GRN indicates the existence of yet unreported regulators of qE effector genes in

the PHOT signaling pathway. This is in line with existing results26, 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 library49 (for genotyping see Supplementary Fig. 5). Our results show higher mRNA levels

of _PSBS1 _in the _qer1_ mutant (Supplementary Fig. 9); however, this could not be rescued by ectopic expression of the _QER1_ gene in the _qer1_ mutant background (Supplementary Fig. 9). We

found significant upregulation of _LHCSR3.1_ gene expression in the _qer7_ mutant (1.7 times, Supplementary Fig. 10a) also reflected in higher NPQ (Supplementary Fig. 10b) and qE levels

(Supplementary Fig. 10c) 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 (Supplementary Fig. 5c). As a result, the _qer7-C_ strain showed reduced _LHCSR3_ gene expression, NPQ, and qE levels as compared with the

_qer7_ mutant (Supplementary Fig. 10a–c). _LHCSR1_ and _PSBS_ seemed to be unaffected in the qer7 in these LL to HL transition experiments (Supplementary Fig. 10a). 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 (Supplementary Fig. 10d). As in the LL to HL experiments (Supplementary Fig. 10b, c) _qer7_ showed more NPQ and qE (Supplementary Fig. 

10e, f). Complementation of _qer7_ with the missing _QER7_ gene restored all phenotypes (_LHCSR1_, _PSBS_, NPQ, qE; Supplementary Fig. 10d–f). These data validated the prediction of QER7 as

regulator of qE gene expression (Fig. 3a) and indicated 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 changes39 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 12 h L/12 h 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 not only at the gene (Fig. 3b) but also at the 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 Protein52 or bZIP TF53, and here, we provide the first functional annotation of QER7 as a novel qE regulator.

QER7 CO-REGULATES QE-RELATED AND CCM GENES Our findings that the regulatory role of CIA57 and LCR1 (Fig. 2 and Supplementary Fig. 7) 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 five of these transcripts (_HLA3_, _CAH4_, _BST1_, _LCI1_,

_LCIA_) 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 expression7 and not in the dark (Fig. 4a). We subsequently checked if this regulation

is also captured in the GRNs. To this end, we used the CCM genes included in Fig. 4a as target genes and predicted the top 10 regulators of these genes using the same method as for the qE

regulators (Methods). Interestingly, we found LCR1 (Supplementary Fig. 11a, c) among the top regulators of both, CCM and qE genes, in the consensus network and QER7 in the PHOT-specific

network (Supplementary Fig. 11b, d). Led by this observation, we investigated the signaling pathway upstream of QER7. To this end, we quantified _QER7_ gene expression in synchronized _phot_

cultures and observed that _QER7_ is overexpressed in the _phot_ mutant, suggesting that PHOT suppresses _QER7_ expression (Fig. 4b). In contrast to _QER7_, _LCR1_ expression levels were

WT-like in the _phot_ mutant (Fig. 4b), and the same was true for the _qer7_ mutant that also expressed _LCR1_ to WT levels (Fig. 4a). Further validation that LCR1 and QER7 act on different

pathways comes from the fact that although LCR1 is controlled by CIA517, QER7 is not (Fig. 4c). Thus, while sharing part of their target genes, the two TFs, LCR1, and QER7, mediate different

signals. We captured this distinction in our PHOT-specific network, further underlining the power of the inferred networks. Our findings that QER7 represses CCM gene expression (Fig. 4a)

naturally raised the question of whether the _qer7_ mutant has altered CCM function, i.e. altered capacity of the cells to accumulate inorganic carbon (Ci). To address this question, we

compared WT and _qer7_ cells for their affinity for Ci, under conditions where CCM is not fully induced (LL) and after inducing CCM by acclimation to HL, which leads to mRNA accumulation of

CCM-related genes (see for example Fig. 4a but also our previous study7). Under these conditions no difference could be observed between WT and _qer7_ (Supplementary Fig. 12, Supplementary

Data 7). Our data suggest that PHOT, by repressing _QER7_ (Fig. 4b), is also involved in the regulation of CCM-related gene expression. We investigated this further by quantifying CCM gene

expression in WT, _phot_ mutant and the complemented line _phot-C_, in the samples collected from the experiment presented in Fig. 4b, i.e. synchronized photoautotrophic cultures shifted to

HL right after the end of the dark phase. We first analyzed expression of qE genes that was found as expected18,24,25 to be under control of PHOT (Supplementary Fig. 13a). We then analyzed

expression of the five CCM genes found to be repressed by QER7 (Fig. 4a); out of those, _CAH4_ and _HLA3_ were down-regulated in the _phot_ mutant exposed to HL after the end of the dark

phase in synchronized cultures (Supplementary Fig. 13b) and this phenotype was fully rescued in the _phot-C_ complemented line, suggesting a potential involvement of PHOT in regulating

expression of CCM-related genes. Nevertheless, the affinity of _phot_ for Ci was not different than this of WT (Supplementary Fig. 14, Supplementary Data 7), in line with previous work

reporting CCM to be induced to very similar extent under blue or red illumination54. Thus, under our experimental conditions, the role of PHOT and QER7 in regulating CCM is restricted to the

transcriptional level. Since the PHOT-QER7 pathway acts independently of the LCR1 pathway, both regulating the same subset of CCM genes we tested, it may be not so surprising that neither

PHOT nor QER7 impact the affinity for Ci; in both _qer7_ and _phot_ mutants LCR1 levels are unaffected and therefore the control of LCR1 on CCM may mask any potential effect that PHOT or

QER7 might have. Since many known CCM regulatory mechanisms act post-transcriptionally55, it is conceivable, that the transcriptional regulation of PHOT and QER7 on their own are not

sufficient and rely on integration with other simultaneous signals to clear all roadblocks for full CCM induction under HL. GENOME-SCALE GRN INDICATES THAT PHOTOPROTECTION AND CCM ARE

CO-REGULATED The two qE regulators that we validated in this study also regulate CCM genes. Therefore, we next investigated to what extent the observed co-regulation 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 Data 8) or the CCM (Supplementary Data 9); 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, Supplementary Data 10) and the PHOT-specific GRN, respectively (Fig. 5c, d, Supplementary Data 11). The significant, large number of shared regulators is a strong

indication that co-regulation 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. In the network inference process for this study,

we carefully choose approaches and integration procedures developed to address the inherent difficulties of RNAseq data sets (e.g., collinearity and high number of variables compared to

samples). Together with the many post-transcriptional layers of regulation in eukaryotic cells, the task of recovering the true GRN, nevertheless, remains a major challenge of the field of

systems biology and this study is no exception. We were able to experimentally validate two of the six novel qE regulators that the GRN predicted and for which KO mutants were available:

QER7, suppressing LHCSR3, LHCSR1, and PSBS expression (Fig. 3, Supplementary Fig. 10), and LCR1, activating LHCSR3 and suppressing LHCSR1 and PSBS expression (Fig. 2, Supplementary Fig. 7).

Both TFs also regulate expression of CCM genes, LCR1 as previously reported17, QER7 as demonstrated in this study (Fig. 4), suggesting that the processes of qE and CCM are co-regulated. From

the physiological point of view, the interconnection of qE and CCM comes as no surprise; exposure to HL boosts CO2 fixation rates and results in depletion of intracellular Ci, reflected,

for instance, in the expression levels of the CO2-responsive marker RH17; therefore, HL-exposed cells need to activate not only qE, to protect against photooxidative stress, but also CCM to

sustain photosynthetic CO2 fixation. On the other hand, photooxidative damage is exacerbated by CO2-limitation; when CO2 fixation decreases, photosynthetically generated electrons accumulate

in the electron transport chain potentially leading to reactive oxygen species generation56. Therefore, acclimation to low-CO2 availability needs to include not only activation of CCM to

elevate CO2 levels at the site of fixation, but also of protection against photooxidative damage. It was recently shown that overexpression of the bZIP transcription factor BLZ8, resulting

in enhanced CCM via overexpression of HLA3, CAH7, and CAH8, conferred enhanced oxidative tolerance triggered by alkaline stress57. Although the qE capacity of the BLZ8 overexpressing lines

was not assessed, this work suggests that CCM and protection from oxidative stress are physiologically interconnected. We complemented the findings of the involvement of LCR1 and QER7 in the

regulation of qE and CCM-related genes with an unbiased analysis of the genome-scale co-regulation of CCM and photoprotective genes. In this way we observed a significant number of

regulators targeting both processes in the inferred consensus GRN as well as the PHOT-specific GRN (Fig. 5). This finding is in line with several experimental studies: _LHCSR3_ mRNA has been

reported to accumulate under low CO221,22 while exposure to HL has been reported to trigger CCM protein58 and mRNA7 accumulation. The Chloroplast Calcium Sensor protein CAS initially found

to be a qE regulator19 turned out to also regulate CCM59, and similarly, CIA5, the master regulator of CCM gene expression14,15,16 is also controlling qE gene and protein expression7. Our

results indicate a set of TFs (Fig. 5, Supplementary Data 10, 11) that likely act downstream of these different signals and integrate them into a common transcriptional response. Their

further characterization is a promising avenue for future research. We found QER7 in the list of PHOT-specific regulators and validated its role experimentally: Expression of _QER7_ was

repressed by PHOT (Fig. 4b) and was CIA5-independent (Fig. 4c), while expression of _LCR1_ is regulated by CIA517 and was found to be PHOT-independent (Fig. 4b). Thus, we showed that

filtering the consensus network by differential expression data of mutants indeed successfully captures this context-specific regulatory interaction. As far as qE is concerned, qE genes are

overexpressed in the _qer7_ mutant (Fig. 3b), lacking the repressor QER7, and down-regulated in _phot_ mutant (Supplementary Fig. 13a), overexpressing the QER7 repressor; as for CCM, QER7

represses expression of all five CCM genes we investigated (Fig. 4a), with two of them, _CAH4_ and _HLA3_, being down-regulated in the _phot_ mutant under HL (Supplementary Fig. 13b).

Further work is needed to obtain a global understanding of the role of phototropin on the transcriptional regulation of Chlamydomonas CCM, including its observed role in controlling some of

the CCM genes in the dark (Supplementary Fig. 13b); nevertheless, this suggested link forms an interesting parallel with the convergence of phototropin- and CO2-mediated signals recently

shown to control stomata opening, responsible for CO2/O2 exchanges, in the model plant Arabidopsis60. In summary, we presented three valuable sets of PHOT-specific and general regulators:

(i) a set of regulators of qE for which we validated available mutants (Figs. 2a and 3a, Supplementary Data 4, 6), (ii) a set of regulators of the core CCM genes analyzed via qPCR in Fig. 4,

in which we recovered QER7 and LCR1 as coregulators of expression for qE and CCM genes (Supplementary Fig. 11), and (iii) a set of regulators (Fig. 5, Supplementary Data 10, 11) of genes

putatively involved in photoprotection or CCM (Supplementary Data 8, 9), which depicted significant co-regulation at a global scale. Led by these predictions we experimentally showed that

QER7 acts as a repressor of qE and CCM gene expression, LCR1 is a regulator of qE with a more expanded role on regulating CCM as previously thought and finally introduced a

photoreceptor-mediated layer of regulation of CCM gene expression. These results clearly demonstrated that the generated GRN represents a powerful resource for future dissection of the

transcriptional regulation of responses of _Chlamydomonas_ to light and carbon availability. To allow easy access to this resource, we published an R-shiny webtool

(https://github.com/arendma/GRN_web) to query the networks for arbitrary regulators and target genes. We expect that the webtool will prompt more concerted, community-wide efforts in

resolving the interactions between other pathways that integrate different environmental cues in _Chlamydomonas_. METHODS TRANSCRIPTOME ANALYSIS We assembled a compendium of RNAseq data

(Supplementary Data 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 Chlamydomonas38,39. For the samples in the acetate time-resolved experiment, adapter sequences were specifically trimmed from raw reads using BBduk61 (ktrim = r k

 = 30 mink = 12 minlen = 50). Raw reads of the diurnal transcriptome study from Strenkert et al.39 were obtained from NCBI GEO database (GSE112394). Reads were aligned to the Chlamydomonas

reference transcriptome62 available from JGI Phytozome (Assembly version 5) using RNA STAR aligner. The BAM files obtained from these measurements were analyzed using HTSeq-count63

(stranded=reverse) to create raw read count files. The raw read counts from Zones et al.38 were obtained as.tsv from NCBI GEO (GSE71469). The final data set consists of 158 samples from 62

experimental conditions or time points (Supplementary Data 1). Genes with less than 1 count per million in at least 9 measurements where discarded and the remainder were voom62 transformed

and normalized using library normalization factors based on the TMM64 approach as implemented in the R Bioconductor package edgeR65. 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.41 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.42 and the regulators in the manually curated set of CCM and qE regulatory interactions (Supplementary Data 

8, 9). Using this procedure, we compiled a list of 407 Chlamydomonas TFs (Supplementary Data 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.34 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 authors66. We used only expression levels of TFs as predictors. ELASTIC NET REGRESSION A linear regression-based network was

obtained using the elastic net algorithm35. A model was fit for each gene using the expression levels of all TFs as predictors. The two hyperparameters _λ_2 (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 _λ_2 = {0, 0.001, 0.01, 0.05, 0.1, 0.5, 1, 1.5, 2, 10,

100} and _s_ = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}. The _R_2 value for each model was calculated as $${R}^{2}=1-\frac{\sum

\left({{{{{\bf{y}}}}}}-\hat{{{{{{\bf{y}}}}}}}\right)}{{{{{\mathrm{var}}}}}\left({{{{{\bf{y}}}}}}\right)},$$ (1) with \(y\) denoting the vector of observed expression values and _y_̂ the

model predictions. Models with a negative coefficient of determination (R2) 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 _β_ normalized by the maximum absolute coefficient: $$\widetilde{{{{{{\boldsymbol{\beta }}}}}}}=\frac{1}{\mathop{{{\max

}}}\limits_{i}\{{{{{{\rm{|}}}}}}{{{{{{\boldsymbol{\beta }}}}}}}_{i}{{{{{\rm{|}}}}}}\}}{{{{{\boldsymbol{\beta }}}}}}.$$ (2) CLR AND ARACNE The implementation of mutual information (MI)-based

network inference approaches from the R package minet67 was used. Pairwise MI was estimated based on the Spearman correlation as proposed by Olsen et al.68. Two networks were constructed

based on these MI estimates. Using the CLR approach69 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 algorithm44 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 were used as input. The deconvolution approach introduced by Feizi and co-workers70 was implemented as previously described46. The

eigenvalue scaling factor _β_ was initialized as _β_=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.45 was implemented in R. The

proposed approximation of the direct interaction matrix _S_ in which spurious interactions are silenced relies on the inverse 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 quality30, 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 method48 whereby the rank _r_ of an interaction _I_ in the consensus network built on the predictions from _k_ approaches is given by the arithmetic mean of the ranks in

the individual networks $${{{{{{\bf{r}}}}}}}_{{{{{{\rm{consens}}}}}}}\left(I\right)=\frac{\mathop{\sum }\limits_{i=1}^{k}{{{{{{\bf{r}}}}}}}_{i}(I)}{k}.$$ (3) Following the reasoning of

Feizi et al.70 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 (Supplementary Fig. 4b). Due to this comparison and their sensitivity of 0% (Fig. 1b) the rankings derived from ARACNE and Silencing were only considered in

Supplementary Fig. 4b 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 Data 3) was trimmed to the top 10% of all possible edges according to the integrated ranks. For predictions of regulators the weight of edges in the final network was set as

\({r}_{{{{{\rm{consens}}}}}}^{-1}\), denoting the reciprocal of the interaction rank. 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 one count per million in at least four

replicates from these conditions (Supplementary Data 1) were tested for differential expression using the R packages limma71, DeSeq272, and edgeR65. 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 PHOT-specific 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 Data 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 Data 8), and CCM (Supplementary Data 9). 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 _w__ij_ between this regulator and the _k_ genes in the

respective target gene set $$C\left({TF}\right)=\mathop{\sum }\limits_{j=1}^{k}{w}_{{TFj}}.$$ (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, _r_, where the overlap was higher or equal to the observed value, and the total number of iterations, _n_73: $$p=\frac{r+1}{n+1}.$$ (5)

STRAINS AND CONDITIONS _C. reinhardtii_ strains were grown under 15 µmol photons m−2 s−1) in Tris-acetate-phosphate (TAP) media74 at 23 °C in Erlenmeyer flasks shaken at 125 rpm. For all

experiments cells were transferred to Sueoka’s high salt medium (HSM)75 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_ mutant

(depleted from _PHOT1_; gene ID: Cre03.g199000)_, was_ previously generated76 and recently characterized together with its complemented line _phot-C_25. The _cia5_ mutant (defective in

_CIA5_, aka CCM1; geneID: Cre02.g096300; Chlamydomonas Resource Center strain CC-2702), was previously generated14 and was used along with its complemented _cia5-C_ (ref. 7). For

synchronized cultures, the cells were grown in HSM for at least 5 days under a 12 h light/12 h dark cycle (light intensity was set at 15 μmol photons m−2 s−1; temperature was 18 °C in the

dark and 23 °C 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 in17 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

(Supplementary Fig. 15a). 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 (Supplementary Fig. 15b). 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 Data 12). 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 Data 12). The PCR products were gel

purified and cloned into pRAM11877 by Gibson assembly78 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 plasmid79 mixed with 400 μL of 1.0 × 107 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). The electroporation parameters were set as follows: Poring Pulse (300 V; 8 ms length; 50 ms interval; one pulse; 40% decay rate; + Polarity),

Transfer Pluse (20 V; 50 ms length; 50 ms interval; five pulses; 40% decay rate; +/- Polarity). Transformants were selected onto solid agar plates containing 20 μg/ml hygromycin and screened

for fluorescence by using a Tecan fluorescence microplate reader (Tecan Group Ltd., Switzerland). Parameters used were as follows: YFP (excitation 515/12 nm and emission 550/12 nm) and

chlorophyll (excitation 440/9 nm and 680/20 nm). Transformants showing high YFP/chlorophyll value were further analyzed by real time qPCR. 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 ref. 7 for light spectrum). 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 Data 12). 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_ (Supplementary Fig. 5), 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 Data 12. We further confirmed the disruption of the genes of interest by quantifying their mRNA accumulation (Supplementary Fig. 5).

RNASEQ ANALYSIS For RNA sampling, CC-125 cells grown in TAP at 5 µmol photons m−2 s−1 were transferred to HSM at 1 million cells mL−1 and were left to acclimate to the new medium for 24 h

always at 5 µmol photons m−2 s−1. At time point 0, while maintaining the light intensity at 5 µmol photons m−2 s−1, cells were either supplied with 20 mM of sodium acetate or remained in HSM

and were samples at _t_ = 15, 240 and 1440 min. Additionally, at time points 240 min and 1440 min part of the cultures (HSM and HSM supplied with acetate) were exposed to 300 µmol photons 

m−2 s−1 and were sampled after 15 min and 60 min exposure to high light (AC experiment; Supplementary Data 1). In an independently designed experiment _phot_ and CC-125 cells acclimated for

24 h in HSM and 5 µmol photons m−2 s−1 at a cell density of 1 million cells mL−1, were sampled right before and after 60 min of exposure to 300 µmol photons m−2 s−1 (PHOT experiment;

Supplementary Data 1). Freshly collected samples were lysed using Tri-reagent (Sigma). After double chloroform extraction the aqueous phase was transferred to a RNeasy Mini-Kit column

(Quiagen) and processed according to the manufacturers guide. Libraries were generated using the TruSeq LT libraries stranded mRNA protocol and sequenced on a Illumina HiSeq 4000 device.

RT-QPCR ANALYSIS 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 quantified in a Bio-Rad CFX96 system using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad). The

primers (0.3 μM) used for qPCR are listed in Supplementary Data 13. A gene encoding G protein subunit-like protein (GBLP)80 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 Data 13) 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 \(Y={{\log }}X\) where \(X\) is mRNA accumulation81.

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, 1:15000 dilution), LHCSR3 (AS14 2766, 1:15000 dilution), ATPB (AS05 085, 1:15000 dilution) were from Agrisera (Vännäs,

Sweden). Antiserum against PSBS was from ShineGene Molecular Biotech (Shanghai, China) targeting the peptides described in Ref. 9 (used at a dilution of 1:1000). ATPB was used as a loading

control. An anti-rabbit horseradish peroxidase-conjugated antiserum was used for detection at 1:10000 dilution. Mouse monoclonal antibody against FLAG was purchased from Sigma-Aldrich

(F3165, St. Louis, MO, USA) and was used at a dilution of 1:15000. An anti-mouse horseradish peroxidase-conjugated antiserum (Jackson Immuno Research Europe LTD) was used as a secondary

antibody for 3xFLAG immunoblotting (1:10000 dilution). 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

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 s−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 (_F_m—_F_m′)/_F_m′ based on82; qE was estimated as the fraction of NPQ that is rapidly inducible in the light and reversible in the dark.

CO2-DEPENDENT O2 EVOLUTION The measurements were performed in accordance with83 with minor modifications. Cells in photoautotrophic conditions (Sueoka’s high salt medium; HSM75), shaken in

Erlenmeyer flasks at 125 rpm and 23 °C were shifted from LL (overnight at 15 µmol m−2 s−1) to HL (4 h at 300 µmol m−2 s−1) in order to induce the CCM. Cells were suspended in 4 ml 25 mM

HEPES-KOH buffer (pH 7.3), at 25 µg chlorophyll per mL and were briefly sparged with nitrogen gas to remove the dissolved inorganic carbon (Ci). The cells were then transferred to an oxygen

respiration vial (Pyro Science GmbH, Aachen, Germany) and were illuminated at 300 μmol photons m−2 s−1 for about 10–20 min, until no net oxygen evolution was seen, an indication that the

internal Ci was depleted. Ci concentration in the cell suspension was then increased by stepwise injecting NaHCO3 with a microsyringe. O2 evolution was measured using a fiber optic probe

(FireStingO2, Pyro Science GmbH, Aachen, Germany). Cumulative concentration of NaHCO3 after each addition were as follows: 25, 50, 100, 250, 500, 1000, 2000 μM. K1/2(Ci), the Ci

concentration needed for half maximal rate of oxygen evolution, and Vmax were calculated by non-linear curve fitting to the Michaelis-Menten equation, using Prism Graph (GraphPad Software,

LLC). STATISTICS AND REPRODUCIBILITY Public and in-house generate RNAseq data contained three biological replicates for each sampled condition except for the study of Zones et al.38 hat

reported two biological replicates per conditions. All other experimental results presented in this study are based on three biological replicates as indicated in the respective sections. No

statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The Investigators were not blinded to allocation

during experiments and outcome assessment. REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. DATA

AVAILABILITY The in-house RNAseq data generated in this study (Supplemental Data 1) have been deposited to the GEO database under accession codes GSE227473 (phot mutant screen) and GSE227281

(HSM and acetate light stress time course). Other previously published RNAseq data used in this study38,39 are available in the GEO database under accession codes GSE112394 and GSE71469.

The consensus and PHOT-specific GRN generated in this study are provided in edge list format in the Supplemenary Information (Supplemental Data 3, Supplemental Data 4). To allow easy access

to the information, we developed an R-shiny webtool that allows to query arbitrary TFs and target genes for regulatory interactions. The R-shiny webtool can be accessed at

https://github.com/arendma/GRN_web84. The source data underlying Figs. 1–5 and Supplementary Figs. 1–3, 5c, 6–10, 12–15 are provided as a Source Data file. The Source Data file also contains

uncropped and unprocessed scans of the western blots of Figs. 2c and 3c, Supplementary Figs. 1b and 15b. Exact _p_ values are also included in this file. All biological material described

in this study is available upon request. Source data are provided with this paper. CODE AVAILABILITY The code used to infer the GRNs in this study is available at

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ACKNOWLEDGEMENTS We are grateful to professor Hideya Fukuzawa for sending us _lcr1_, _lcr1-C,_ and their respective WT strain and to Professor Peter Jahns for the antibody against PSBS. We

thank Michael P. Edwards and Qiao Wen for their feedback and contribution to GRN_code github repository. We would like to express our gratitude towards Arne Neumann for implementing the R

shiny application. The authors would like to thank the following agencies for funding: The Human Frontiers Science Program through the funding of the project RGP0046/2018 (D.P., Z.N.); the

French National Research Agency in the framework of the Young Investigators program ANR-18-CE20-0006 through the funding of the project MetaboLight (D.P.); 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 (D.P., Y.Y.); the French

National Research Agency through the funding of the Grenoble Alliance for Integrated Structural & Cell Biology GRAL project ANR-17-EURE-0003 (D.P., M.A.R.-S.), the Prestige Marie-Curie

co-financing grant PRESTIGE-2017-1-0028 (M.A.R.-S.); the International Max Planck Research School ‘Primary Metabolism and Plant Growth’ at the Max Planck Institute of Molecular Plant

Physiology (M.A., Z.N.). FUNDING Open Access funding enabled and organized by Projekt DEAL. AUTHOR INFORMATION Author notes * M. Águila Ruiz-Sola Present address: Instituto de Bioquímica

Vegetal y Fotosíntesis, Universidad de Sevilla-CSIC, 41092, Sevilla, Spain * These authors contributed equally: Marius Arend, Yizhong Yuan. AUTHORS AND AFFILIATIONS * Bioinformatics Group,

Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany Marius Arend, Nooshin Omranian & Zoran Nikoloski * Systems Biology and Mathematical Modeling Group,

Max-Planck-Institute of Molecular Plant Physiology, 14476, Potsdam, Germany Marius Arend, Nooshin Omranian & Zoran Nikoloski * Bioinformatics and Mathematical Modeling Department, Center

of Plant Systems Biology and Biotechnology, 4000, Plovdiv, Bulgaria Marius Arend, Nooshin Omranian & Zoran Nikoloski * University of Grenoble Alpes, CNRS, CEA, INRAE, IRIG-LPCV, 38000,

Grenoble, France Yizhong Yuan, M. Águila Ruiz-Sola & Dimitris Petroutsos Authors * Marius Arend View author publications You can also search for this author inPubMed Google Scholar *

Yizhong Yuan View author publications You can also search for this author inPubMed Google Scholar * M. Águila Ruiz-Sola View author publications You can also search for this author inPubMed 

Google Scholar * Nooshin Omranian View author publications You can also search for this author inPubMed Google Scholar * Zoran Nikoloski View author publications You can also search for this

author inPubMed Google Scholar * Dimitris Petroutsos View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS M.A., Y.Y., M.A.R.-S. performed

experiments. M.A., Y.Y., D.P. analyzed and visualized data. M.A. implemented consensus GRN inference. M.A., Y.Y., N.O., Z.N., D.P. designed study and planned research. M.A., Y.Y., Z.N., D.P.

wrote the manuscript. CORRESPONDING AUTHORS Correspondence to Zoran Nikoloski or Dimitris Petroutsos. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests.

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