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Central transcriptional regulator controls photosynthetic growth and carbon storage in response to high light – Nature Communications

Time Stamp: June 5, 2024 8:00 PM
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Transcriptomic interaction analysis identifies a circadian transcription factor during high light response

The TG1 and TG2 isolated variants are designated the same species P. celeri on the basis of their exact match between 18 S rDNA sequences⁶. Genetic variation underlying phenotypic divergence between the strains was characterized by comparing whole genome shotgun sequence data from TG1 DNA against one copy of the phased diploid genome assembly, designated “p0” and available at NCBI accession codes JAACMV010000001.1 through JAACMV010000015.1 [https://www.ncbi.nlm.nih.gov/nuccore/1811192891]. Variant analyses indicated greater divergence from the reference TG2 assembly than originally suspected. Combined single nucleotide polymorphisms and insertions/deletions indicated nearly 2% divergence from the TG2 reference across the 13.7 Gb haploid genome length (Table 1). This sequence homology permits the shared species identity, but as clearly differentiated strains or cultivars. The number of variants (over 200,000 unique) with putative effects on gene encoding and regulatory regions precluded the possibility of assigning a genomic variant basis to the observed phenotypes (Supplementary Table 1).

Table 1 Picocholorum celeri TG1 genomic variants summary

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To further deconvolute the genetic basis of the isolates’ respective response to HL and phenotypic differences, we carried out gene expression analysis upon transition and acclimation to HL intensity. Both isolates were cultured under non-light-limited, dilute cell conditions (<0.3 µg/mL chlorophyll) allowing for homogeneous exposure of 1000 µE light (referred to as HL as described in materials and methods) without self-shading effects. We characterized temporal gene expression changes as cultures acclimated from low light (LL) to HL over the course of 4 h. The relative transcript abundances of TG1-WT and TG2-WT biological replicates differed significantly during this acclimation to HL growth conditions relative to the LL acclimated state established by 48 h of growth at 60 µE (Fig. 1). Both isolates demonstrated strong transcriptional responses to increased light with as much as 73% of annotated coding sequence transcript levels significantly changed after 1 h in HL (p < 0.05). The proportion of light responsive genes in P. celeri was larger than similarly treated photoautotrophs Chlamydomonas reinhardtii, Chromochloris zofingiensis, and Arabidopsis thaliana (Supplementary Table 2)^26,27,28,29. This drastic shift could be attributed to the extremely small and streamlined P. celeri genome being largely devoted to light response. Large magnitude changes in transcript levels were limited to a small set representing <9% of transcripts (L2FC > 3).

**Fig. 1: Gene expression variation between TG1-WT and TG2-WT upon exposure to high light intensity.**

To specifically look for unique and differential gene expression behavior in the light response of both isolates, several different analyses were carried out to isolate the interaction effect between light and strain for the 1 h time point. The majority of significantly changed transcript levels after 1 h in HL ( | L2FC | > 1.0; p < 0.05) were down regulated relative to the LL levels in each of the wild type strains (Fig. 1A, B). The most highly induced transcript at this time was the same for both strains, encoding a carotene biosynthesis-related protein (CBR; EMRE3EUKT4063781). CBR expression likely reflects a strong response of the photosynthetic machinery to dissipate excess light until the physiological HL acclimation is complete, as was similarly implicated in another high light tolerant green algae, C. ohadii³⁰. This theme of transcripts being similarly regulated between the strains was carried throughout the transcriptome. In fact, there was not a single transcript which was changed in the opposite direction between TG1 and TG2 after applying the same fold change and significance thresholds (Fig. 1C). With a carefully-designed likelihood ratio test on the full transcriptome, we were able to identify several transcripts that exhibited a statistically significant interaction effect in their expression response to high light transition and genetic background (Fig. 1D). The single most significant gene identified in this analysis (EMRE3EUKT4064225), was annotated as a transcription factor encoding a homolog of late elongated hypocotyl/circadian clock associated like (LHY/CCA) and was strongly separated from the rest of the transcriptome. The translated protein sequence indicated a close relationship with A. thaliana homologs for CCA1 and LHY containing the conserved domain related to the SANT family of MYB-like DNA binding sequences (Supplementary Fig. 1) hereafter referred to as Picochlorum CCA1. The variation in TG2 and TG1 CCA1 gene transcription may be in part due to sequence variant sites apparent in an alignment of the genomic loci (Supplementary Fig. 2A), including a 9 bp insertion in the TG2 promoter region 590 bp upstream of the translation start site as well as a deletion and insertion event within 170 bp downstream of the stop codon, which may lie beyond the 3′ UTR as RNA-seq read data coverage extends to ~165 bp. The coding sequence of the TG1 CCA1 gene contains up to 20 predicted non-synonymous sequence variants with respect to the TG2 genomic locus. Only two changes in amino acid residues lie within the single identifiable conserved functional domain region, neither of which alter the conserved residues defining the SANT domain structure (Supplementary Fig. 2B). We hypothesize that this transcription factor acts as one of the core regulatory factors that underly the respective observed strain phenotype (Fig. 1D).

Regulatory genome-wide transcriptional analysis suggests control over carbon metabolism and circadian response

We tested the impact of the genome-wide regulatory CCA1 function in P. celeri as related to both growth and carbon allocation dynamics by creating a genetically engineered variant of TG1 with the TG2-WT CCA1 gene elements transformed into a TG1-WT background using an expression vector containing the TG2 CCA1 coding sequence under control of the TG2 native promoter and terminator regions (Supplementary Fig. 3). The cell line with the highest specific growth rate in HL conditions was selected from a screen of transformants for further analysis and designated TG1-MYB99. After genomic integration and both RT-PCR and phenotypic confirmation of the transformant line, we included this strain alongside the WT isolates for further regulatory and response analysis of the mutant. The insertion site of the TG2-like CCA1 transgene in the TG1-MYB99 line was associated with NIPA2 magnesium transporter coding sequences (Supplementary Table 3). It was not possible to map unambiguously due to multiple NIPA2 gene copies, however, evaluation of transcript levels indicated that the insertion likely disrupted the EMRE3EUKT4068533 gene which was compensated for by elevated transcription rate of the remaining copies in the TG1-MYB99 genome (Supplementary Fig. 4).

A four-hour time course transcriptomics analysis during transition from LL to HL was conducted to determine the transcriptional regulatory alterations in the TG1-MYB99 strain. All strains demonstrated a biphasic acclimation to the increased light intensity, with the largest shift in gene expression occurring 30 min and 1 h after HL and gene expression trending back towards LL levels by the fourth hour (Fig. 2A). The differential transcription dynamics of CCA1 were further validated by light-shift time course results, with a persistent down-regulation in the TG1-WT cultures while TG2-WT maintained the intermediate level observed under LL illumination (Fig. 2B). The CCA1 gene reaches maximum transcriptional expression around dawn in both land plants and the marine microalgae, Ostreococcus tauri, when entrained to diel light cycles¹⁰. We postulate that TG2-WT strain may be a circadian mutant relative to the TG1-WT strain, as the rapid suppression of CCA1 transcription in response to HL resembles patterns reported at dawn in other systems. The time course of CCA1 gene expression dynamics of the TG1-MYB99 strain confirm the successful transfer and expression of TG2 gene elements, starting with high-expression levels as for WT TG1 and upon HL transition shifting to follow the continued high expression as observed for TG2 (Fig. 2B). The global transcriptomics response over the 4 h time course similarly positions the TG1-MYB99 strain as intermediate between TG1 and TG2, with a significant fraction of the transcriptome following closely the expression patterns observed in TG2 (Fig. 2C). Over-representation statistical analysis of the set of 49 genes that closely matched CCA1 expression dynamics between the cell lines implicated important functions related to biotin metabolism (fatty acid biosynthesis, amino acid catabolism), broad central carbon metabolism, and circadian rhythms (Fig. 2D). Further representation of transcriptional dynamics of the respective major pathways’ gene expression is included as heat maps (Supplementary Figs. 5 and 6).

**Fig. 2: Transcriptional responses of *Picochlorum celeri* TG1 and TG2 isolates and engineered TG1-MYB99 strain, in response to high intensity light shift.**

Only a small set of genes shared transcriptional dynamics with CCA1 among the strains in response to shifting from LL to HL (Fig. 2C). LHY, CCA1, and related transcription factor binding sites (TFBS) were significantly enriched in the upstream regulatory regions of the 49 cluster 21 genes relative to the regulatory sequences of all other genes across the P. celeri genome (Supplementary Data 1). Similarly, TFBSs for REVEILLE family transcription factors were enriched at nearly identical levels. This is likely to represent consolidated functionality of the CCA1 gene in P. celeri, which has a severely reduced quantity of circadian clock-related genes relative to plants like A. thaliana¹¹. Expression of genes annotated with functions related to carbon metabolism was enriched among the set containing CCA1 (Fig. 2D), and widespread alteration in expression of genes related to carbon fixation was observed in the mutant line (Supplementary Fig. 6).

Instationary metabolic flux analysis confirms metabolic control nodes for central carbon allocation to carbohydrates

To assess the respective biomass composition response to light transition, we quantified the primary macromolecular biochemical composition of harvested algal biomass after acclimation to low or high light. Experimental biomass composition data was measured for protein, carbohydrates, and FAME. However, experimentally measured composition does not often satisfy mass balance³¹ and must be corrected due to factors such as high ash content and underestimation of components. To account for the missing fractions of biomass that we did not measure experimentally, chlorophyll content was assumed from P. celeri⁶, while DNA and RNA content was assumed from previous work in algae²⁴. FAME and carbohydrate content was corrected and subsequently all data were scaled to satisfy mass balance as described in Supplementary Information, INST-MFA assumptions. Carbon allocation varied dramatically between the cell lines in response to HL conditions, with TG1-WT accumulating as much as 44.9% of its total biomass as carbohydrate, while TG2-WT only reached 16.0% after 48 h of growth in HL. The TG1-MYB99 mutant line cells, remarkably, apportioned over 56% of their biomass to carbohydrate content after the same 48-h acclimation period to HL growth (Table 2). This phenotype contrasts with the intermediate expression level of CCA1 in the mutant line with respect to the WT P. celeri cultures. To quantify metabolite fluxes through central nodes of carbon assimilation and storage, we applied ¹³C labeling enabled INST-MFA of TG1 and TG2, and the TG1-MYB99 mutant. This quantitative analysis aims to elucidate key differences in metabolism. Because ¹³C-labeled CO₂ is challenging to introduce in an immediate and step-change manner (instantaneous change to ¹³C from ¹²C for subsequent carbon fixation) that allows for following the ¹³C tracer over a time course¹⁵, we used a bolus of ¹³C labeled bicarbonate (1 g L^-1 NaH¹³CO₃) in a series of physiologically-controlled experiments (see Supplementary Information for more in depth discussion and experimental results). A step-change is crucial since several fast labeling metabolites such as those involved in the CBB cycle incorporate the label in the order of a few seconds^8,15,16. The sampling time points were chosen from 30 s to 10 min such that labeling dynamics for fast labeling metabolites were able to be fully captured. To prevent metabolite leakage by quenching methods such as cold methanol quenching³², we rapidly filtered cells and quenched in a liquid N₂ bath within 10 s of the timepoints.

Table 2 Picocholorum celeri strains growth rate and biomass composition for the same experiments

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The INCA 2.0 software package³³ was used to calculate the flux values based on time course labeling data (Supplementary Data 2), biomass composition (Supplementary Table 4), and specific growth rate (Supplementary Table 5). The metabolic network and atom transitions were taken and adapted from previous work in algae^8,24,34 and is reported in Supplementary Data 3. The same metabolic network was used for all three strains and includes compartmentalization and dilution parameters to account for inactive metabolite pools or metabolite channeling¹⁷. The glyoxylate shunt is not modeled since it is unlikely to exist based on the P. celeri genome. BLAST results revealed that the genome for P. celeri has poor matches for the genes malate synthase and isocitrate lyase that are known to facilitate the glyoxylate shunt in algae. There is evidence that the glyoxylate shunt has been lost in several clades of algae³⁵. The fitted models estimated CO₂ uptake from the sink demands and was not supplied as an input parameter. TG1, TG1-MYB99, and TG2 had a CO₂ uptake of 5.96, 6.99, and 9.59 mmol gBiomass⁻¹ h⁻¹ respectively. The model fits were assessed by evaluating the SSR. The best fitted models for TG1, TG1-MYB99, and TG2 had SSR = 226 [160, 237.8], SSR = 244.2 [213.6, 302.2], and 297.3 [213.6, 302.2]. To identify differences in flux partitioning at different metabolic nodes, the fluxes were normalized to CO₂ uptake rate as shown in Fig. 3. (Fluxes and their 95% confidence intervals are available in Supplementary Data 4; measured and fitted mass isotopomer distribution (MID) profiles are plotted in Supplementary Figs. 7–9.)

**Fig. 3: Flux maps determined in TG1, TG1-MYB99, and TG2 under continuous HL conditions (1000 µE).**

Although there are variances between the net fluxes in TG1, TG1-MYB99, and TG2, key differences in metabolic points of control can be identified in Fig. 3, which shows the flux after normalizing to uptake of 100 units of CO₂. TG1 and TG1-MYB99 have a high carbohydrate content which is consistent with the higher flux from F6P in the chloroplast to G6P followed by conversion to G1P and subsequently carbohydrates compared to TG2. The normalized flux from 3PGA as the first product formed after carbon fixation to F6P is not significantly different in the three strains, and a flux difference is only observed in subsequent conversion of F6P to carbohydrates. The conversion of F6P to G6P is catalyzed by the reversible G6P isomerase (EMRE3EUKT4065295) followed by transfer of the phosphate group to position 1 to form G1P catalyzed by phosphoglucomutase (EMRE3EUKT4067541). This is subsequently converted to ADPG by G1P-adenylyl-transferase (EMRE3EUKT4064735, EMRE3EUKT4063059). Transcriptomics suggests that the expression of these enzymes is at least 2-fold higher in TG1 and TG1-MYB99 compared to TG2. This higher expression contributes to the higher flux to carbohydrates in these strains and may suggest that their expression is co-regulated. Expression of PGI and G1P adenylyl-transferase is similar in both TG1 and TG1-MYB99 whereas, in the case of PGM, TG1 has ~2.5-fold higher expression while TG1-MYB99 has a ~4 fold higher expression compared to TG2. This indicates that CCA1 expression further increases carbohydrate accumulation through the control over PGM. This is in line with the observations in C. reinhardtii where increased PGM1 expression was implicated in starch accumulation³⁶. Increased flux through overexpression of these enzymes diverts flux carbon away from regeneration of precursors for photosynthetic carbon fixation via the CBB cycle and may result in TG1 and TG1-MYB99 having a lower net carbon fixation compared to TG2.

The different biomass composition of the strains (Table 2 and Supplementary Table 6) necessitates a distinct carbon allocation. TG2, which has a higher protein content demands more precursors from the TCA cycle whereas TG1 and TG1-MYB99 produce carbohydrates and require higher glycolytic flux in the chloroplast. Further, lipid production occurs in the chloroplast from PEP imported from the cytosol. The precursor demands are reported in Supplementary Table 7. In green algae, the upper half of glycolysis is chloroplastic while the lower half is in the cytosolic compartment³⁷. Thus, metabolite transport across the chloroplast where carbon is fixed has a crucial link to biomass composition. We hypothesized that TG2, which has a higher protein and lipid content will have a higher flux to the cytoplasm through the triose phosphate transporter (EMRE3EUKT4065742) to supply metabolic precursors for amino acid biosynthesis and back into the chloroplast through the phosphoenolpyruvate transporter (EMRE3EUKT4068085) to supply acetyl-CoA for lipid biosynthesis and PEP for synthesis of aromatic amino acids by the shikimate pathway. This is confirmed by the flux data which shows that TG2 has a 40% and 70% increased transport to the cytoplasm compared to TG1 and TG1-MYB99, respectively, whereas the transport back into the chloroplast is 60% higher in TG2 compared to both TG1 and TG1-MYB99. This is consistent with recent work in C. reinhardtii where TPT3 null mutants showed a reduced growth phenotype coupled with an increase in carbohydrate accumulation³⁸. Interestingly, transcriptomics revealed that expression of the triose phosphate transporter (EMRE3EUKT4065742) had exactly the opposite trend with the lowest abundance in TG2 and highest in TG1-MYB99, possibly pointing to a post-transcriptional regulation. In the case of the PEP transporter (EMRE3EUKT4068085), TG1-MYB99 exhibited significantly increased transcript abundance from the similar levels observed in TG2 and TG1. This could indicate that in P. celeri, transport between the chloroplast and cytoplasm is not limited by transcript abundance and may be influenced at the protein level or through post-translational modifications. These observations are consistent with carbohydrate accumulation in the form of starch, as a metabolic carbon storage and overflow mechanism, and is a key feature of diel and circadian cycles of plants and algae such as A. thaliana and C. reinhardtii^39,40. Non-additive alterations in CCA1 and LHY expression have been linked to improved agronomic traits in allotetraploid A. thaliana hybrid plants that presented increased starch, chlorophyll content, and growth compared to parental lines⁴¹.

In addition to these nodes, carbohydrate accumulation as starch, as expected, was shown to have a significant effect on ¹³C labeling dynamics of amino acids. A reduction in amino acid pool sizes was observed for most amino acids in TG1 and TG1-MYB99 (Supplementary Fig. 10). Even though it takes a higher flux to achieve the same labeling for a larger pool size, all the amino acids showed faster incorporation of ¹³C label in 10 min in TG2 (Supplementary Fig. 11). This higher flux to amino acids contributes to the protein rich biomass composition of TG2 and could be necessary for faster growth.

Tricarboxylic acid (TCA) metabolism regulation in P. celeri suggests incomplete cycle upon high light acclimation

The fluxes through the TCA cycle in all the strains show no significant flux from alpha-ketoglutarate (AKG) to succinate and subsequently to fumarate (Fig. 3, Supplementary Data 4). This is also reflected in the labeling of succinate (Supplementary Figs. 7–9) which does not label significantly in the 10 min timescale. This observation indicates that P. celeri may prefer to operate a linear branch from fumarate to AKG under HL acclimated conditions. This has been previously observed in fast growing cyanobacteria such as UTEX 2973 and S. elongatus 7942^16,25,42 and may be due to very low expression of Succinyl-CoA ligase which catalyzes the synthesis of succinate from Succinyl CoA (EMRE3EUKT4065470). The ¹³C-labeling dynamics of other TCA cycle metabolites measured such as fumarate, malate, citrate, and AKG indicate significant labeling in all strains compared to succinate further indicating that a complete TCA cycle may not be essential under HL conditions in P. celeri (Supplementary Figs. 7–9). This could potentially be a mechanism in fast growing strains under high light conditions to prevent loss of CO₂ from the conversion of AKG to succinate via the succinyl-CoA as energy demands in the form of NADH or ATP are met by light-dependent reactions.

Even though these three strains seemingly operate an incomplete TCA cycle under HL acclimated conditions, there exist a few key differences between protein-rich TG2 and carbohydrate-rich TG1 and TG1-MYB99. Since several amino acids draw flux from the TCA cycle, TG2 drives roughly a two-fold normalized flux compared to TG1 and TG1-MYB99 (Fig. 3, Supplementary Data 4). The higher flux to the TCA cycle results in a higher loss of CO₂ in TG2 due to decarboxylation. Interestingly, TG2 can increase RuBisCO carboxylase and PEP carboxylase flux to recapture the CO₂ lost as evidenced by the normalized flux through RuBiscCO and PEP carboxylase (Fig. 3). Higher TCA cycle flux is achieved by a higher transcription of the mitochondrial dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex (EMRE3EUKT4066919, identified by sequence similarity by BLAST) in TG2 compared to TG1 and TG1-MYB99. Expression of EMRE3EUKT4066919 is ~50% and ~86% higher in TG2 compared to TG1 and TG1-MYB99 respectively. In addition to a higher flux to the TCA cycle via pyruvate dehydrogenase, TG2 shows a significantly higher anaplerotic TCA cycle flux than TG1 and TG1-MYB99 via the PEP carboxylase (EMRE3EUKT4066235) reaction which converts PEP to oxaloacetate. Although TG2 has the highest anaplerotic flux to meet the TCA cycle’s energy demands, PEP carboxylase expression is the highest in TG1-MYB99, followed by TG2, and then TG1. This may be due to regulation of PEP carboxylase at the post translation modification level as reported previously in algae and plants^43,44. Malate and fumarate label rapidly in all strains indicating the activity of PEP carboxylase (Supplementary Figs. 7–9). Fumarate hydratase or fumarase (EMRE3EUKT4065827), which can reversibly convert fumarate to malate, is active in all the strains and characterized by a high exchange flux resulting in rapid labeling of fumarate (Supplementary Figs. 7–9) and has the highest expression in TG2, followed by TG1 and TG1-MYB99.

Interestingly, the higher flux into the TCA cycle from PEP carboxylase in TG2 results in a net flux from oxaloacetate to malate in TG2, which is then catalyzed back to pyruvate by NADP-dependent malic enzyme. This results in a cycle that causes the net production of NADPH by the malic enzyme reaction. Whereas, in case of TG1 and TG1-MYB99, malate dehydrogenase catalyzes the forward reaction from malate to oxaloacetate and malic enzyme flux is negligible. In terms of transcription of NADP-dependent malic enzyme (EMRE3EUKT4065957), expression is similar in TG1 and TG2, while it is 23% lower in TG1-MYB99. Metabolism of malate is influenced by the redox status of cell as well as the cellular requirements for ATP and NADPH of the cell^45,46,47,48. These differences indicate that TG2 may have a higher NADPH demand which necessitates a higher malic enzyme flux especially since the flux through the oxidative pentose phosphate pathway, which also produces NADPH is negligible. It is not surprising that this is the case since several fast-growing cyanobacteria and algae have been found to have negligible flux through this pathway which results in loss of CO₂^16,18_. A comparison of the absolute and normalized fluxes for strains in this study and previous work is algae and plants is shown in Supplementary Figs. 12 and 13.

Estimation of ATP and NADPH demand in P. celeri supports cellular energetics balance

Due to distinct biomass composition and metabolic demands for these strains, the cellular ATP and NADPH demands were calculated based on metabolic fluxes from our model (Supplementary Data 5). The ATP and NADPH demands for biosynthetic reactions were calculated based on reactions listed in Supplementary Table 7. The ATP maintenance cost was assumed to be 2.85 mmol g^-1 h^-1 as reported in C. reinhardtii since data is unavailable for P. celeri⁴⁹. Our estimates show that TG2 consumes ATP/NADPH in the ratio 1.58 whereas this number is 1.71 and 1.69 for TG1 and TG1-MYB99 respectively. These estimates are close to the ATP/NADPH demand of 1.6 that has been suggested previously⁵⁰. Our data may indicate that the malic enzyme shuttle from malate to pyruvate has a higher flux in TG2 (Fig. 3), resulting in the net production of NADPH, acts as an additional overflow pathway to supply NADPH to balance cellular energetics⁵¹, and is essential in TG2 to meet its higher NADPH demand.

Furthermore, the ATP/NADPH ratio is of great importance since an imbalance between the synthesis and consumption of ATP and NADPH is directly linked to photosynthesis⁵². Linear electron flow (LEF) produces ATP/NADPH in the ratio 1.29 based on the coupled electron-proton reactions that power ATP synthase to generate 3 ATP per 14 protons pumped⁵³. The CBB cycle, on the other hand, requires a higher demand for ATP/NADPH in the ratio 1.5⁵⁴. This imbalance in production and consumption has been suggested as a point of engineering in photosynthesis and forms the basis of the argument that cyclic electron flow (CEF), or an alternative flux of electrons to generate more ATP, is important to balance the ATP/NADPH synthesis and consumption^54,55,56 with energy demands required to respond to environmental perturbations. Based on these estimates, we hypothesize that TG1 and TG1-MYB99 have a higher cyclic/alternative electron flow compared to TG2. To test this, we utilized photophysiology experiments which enable the characterization of electron flow in the photosynthetic apparatus.

Acclimation responses of the photosynthetic apparatus and related mechanisms can often underly the ability of photoautotrophs to resist abiotic stresses including excessive light intensities. P. celeri TG1 and TG2 cells exhibited remarkably low non-photochemical quenching (NPQ) response after acclimation to high light, and particularly when measured using a pulse amplitude modulated (PAM) fluorometry instrument under 1000 µE actinic light. We developed custom protocols using a fast repetition rate fluorometer (FRRf) that could deliver much higher actinic light intensities to induce quenching responses (Supplementary Fig. 14). Both wild type strains and the TG1-MYB99 mutant exhibited similar NPQ responses to increasing actinic light, only reaching ~0.5 following complete saturation of photosystems with 2025 µE of background illumination (Fig. 4). Other algae species and plants like A. thaliana can reach >2.0 NPQ calculated on the classical scale⁵⁷ that does not have a specified upper limit, as opposed to the PhiNPQ framework that cannot exceed 1⁵⁸.

**Fig. 4: Non-photochemical quenching (NPQ) responses of high light acclimated cell cultures.**

We probed the cells’ photophysiology further by applying inhibitors of documented mechanisms for alternative electron flow. The plastid terminal oxidase (PTOX) protein acts in many photoautotrophs to oxidize plastoquinone (PQ) pools, and its activity can be inhibited by the application of octylgallate (OG)^59,60. TG2-WT cultures demonstrated a reliance on PTOX activity based on significant suppression of NPQ response across most light levels when treated with OG, while TG1-WT exhibited a minimal impact from the inhibition (Fig. 4A). This may be indicative of a higher capacity of TG2 cells to respond rapidly to extreme light intensity increases, as PTOX can directly relieve PQ reduction pressure but at the cost of ATP and NADPH production along with reactive oxygen species generation⁶¹.

The activity of PTOX may be unlikely to facilitate long term acclimation to extreme light, as its oxidation of over-reduced plastoquinone pools comes at the cost of harmful superoxide generation by a side reaction⁶². Alternatively, CEF does offer a sustainable mechanism to relieve over-reduction of the photosynthetic electron transport system by altering lumen proton gradients effecting pH sensitive mechanisms like NPQ, and proton motive force. We found that only TG1-WT cells significantly responded to antimycin A (AA) treatment during NPQ experiments (Fig. 4B), indicating an appreciable level of CEF activity. The AA sensitive CEF pathway has been observed across plant and algae species, with CEF deficient mutant in A. thaliana displaying impaired NPQ responses⁶³. The P. celeri genome contains homologous sequences to two key proteins in the AA sensitive CEF mechanism. Transcription of a PGRL1B annotated gene (EMRE3EUKT4068276; Supplementary Fig. 15), a transmembrane protein thought to be present in thylakoids, was suppressed in TG2-WT during HL acclimation, while TG1-WT maintained high levels of expression. This protein was shown to moderate the electron transfer from ferredoxin back to the PQ pool in plant systems, and implicated in sustaining CEF⁶⁴. Remarkably, nearly all the photophysiology metrics derived from FRRf measurements did not vary significantly among the cell lines. One exception was observed in the second order electron transfer time constant being significantly lower in the TG2 cells than TG1 (Supplementary Table 8; p = 0.0354) at 269.00 ± 8.66 µs and 421.33 ± 59.47 µs, respectively. This rate constant was calculated at an intermediate value of 382.00 ± 103.36 µs in the TG1-MYB99 cells, though the difference in rate from each wild type cell line was not statistically significant.

The P. celeri wild type and mutant cell lines discussed herein represent a unique and accessible study system for interrogating and testing relationships between the critical agronomic yield traits of biomass productivity and carbon allocation and storage. The majority of algae strains with documented productivities high enough to support industrial biofuel operations require induction of stress conditions to elicit accumulation of either lipid or carbohydrate products to reach maximum valorization⁶⁵. This approach does reduce growth and productivity if applied too long, and so necessitates careful planning, monitoring, and timely control. Several studies have shown that genetic engineering and other approaches to control specific aspects of plant and algae physiology can improve yield traits while minimizing inhibition of growth or photosynthetic efficiency^66,67. TG1-WT and TG2-WT cells provide evidence that growth rates and carbon allocation dynamics can be influenced strongly by a central regulatory factor in the form of CCA1 in the context of acclimation responses to HL exposure. The mixed and opposing phenotypes observed in the TG1-MYB99 mutant cell line are indicative of complex and indirect regulation. While these can be partially interpreted by gene clustering and transcription factor binding site (TFBS) analyses, more powerful and direct tools were needed for evaluation of biochemical outcomes of the genetic manipulation. Fluxomic results, along with requisite assessments of biomass composition, provide a powerful method for evaluating the effects of genetic engineering on some of the most important phenotypic factors affecting P. celeri productivity and valorization potential.

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Source: https://www.nature.com/articles/s41467-024-49090-7

Central transcriptional regulator controls photosynthetic growth and carbon storage in response to high light – Nature Communications

Transcriptomic interaction analysis identifies a circadian transcription factor during high light response

Regulatory genome-wide transcriptional analysis suggests control over carbon metabolism and circadian response

Instationary metabolic flux analysis confirms metabolic control nodes for central carbon allocation to carbohydrates

Tricarboxylic acid (TCA) metabolism regulation in P. celeri suggests incomplete cycle upon high light acclimation

Estimation of ATP and NADPH demand in P. celeri supports cellular energetics balance

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