Green plants and algae rely on sunlight to transform light energy into chemical energy in a process known as photosynthesis. However, too much light can damage plants. Green plants prevent this by converting the extra absorbed light into heat. Both the absorption and the dissipation of sunlight into heat occur within so called light harvesting complexes. These are protein structures that contain pigments such as chlorophyll and carotenoids.

The process of photoprotection starts when the excess of absorbed light generates protons (elementary particles with a positive charge) faster than they can be used. This causes a change in the pH (a measure of the concentration of protons in a solution), which in turn, modifies the shape of proteins and the chemical identity of the carotenoids. However, it is still unclear what the exact mechanisms are.

To clarify this, Troiano, Perozeni et al. engineered the light harvesting complex LHCSR3 of the green algae Chlamydomonas reinhardtii to create mutants that either could not sense changes in the pH or contained the carotenoid zeaxanthin. Zeaxanthin is one of the main carotenoids accumulated by plants and algae upon high light stress. Measurements showed that both pH detection and zeaxanthin were able to provide photoprotection independently. Troiano, Perozeni et al. further found that pH and carotenoids controlled changes to the organisation of the pigment at two separate locations within the LHCSR3, which influenced whether the protein was able to prevent photodamage. When algae were unable to change pH or carotenoids, dissipation was less effective. Instead, specific molecules were produced that damage the cellular machinery.

The results shed light onto how green algae protect themselves from too much light exposure. These findings could pave the way for optimising dissipation, which could increase yields of green algae by up to 30%. This could lead to green algae becoming a viable alternative for food, biofuels and feedstock.

Sunlight is the essential source of energy for most photosynthetic organisms, yet sunlight in excess of the organism’s photosynthetic capacity can generate reactive oxygen species (ROS) that lead to cellular damage. To avoid damage, plants respond to high light (HL) by activating photophysical pathways that safely convert excess energy to heat, which is known as nonphotochemical quenching (NPQ) (Rochaix, 2014). While NPQ allows for healthy growth, it also limits the overall photosynthetic efficiency under many conditions. If NPQ were optimized for biomass, yields would improve dramatically, potentially by up to 30% (Kromdijk et al., 2016; Zhu et al., 2010). However, critical information to guide optimization is still lacking, including the molecular origin of NPQ and the mechanism of regulation.

Green algae is a sustainable alternative for biofuels and livestock feed (Lum et al., 2013; Wijffels and Barbosa, 2010). In Chlamydomonas (C.) reinhardtii, the model organism for green algae, light-harvesting complex stress-related (LHCSR) is the key gene product for NPQ. LHCSR contains chlorophyll (Chl) and carotenoid (Car) held within its protein scaffold. Two isoforms of LHCSR, LHCSR1 and LHCSR3, are active in NPQ, although LHCSR3 is accumulated at higher levels and so has the major role (Dinc et al., 2016; Maruyama et al., 2014; Peers et al., 2009; Tokutsu and Minagawa, 2013). While the photophysical mechanism of quenching in light-harvesting complexes has not been determined, the primary proposals involve Chl-Car interactions (Liao et al., 2010; Ma et al., 2003; Ruban et al., 2007; Son et al., 2020a; Son et al., 2020b; de la Cruz Valbuena et al., 2019).

NPQ is triggered by a proton gradient across the thylakoid membrane that forms through a drop in luminal pH (Horton et al., 1996). Lumen acidification generally occurs when the light available causes an imbalance between the proton generation and the capacity of the photosynthetic apparatus to use protons for ATP production (Joliot and Finazzi, 2010). The C-terminus of LHCSR3 contains a number of luminal residues that are protonated upon the pH drop to trigger quenching (Ballottari et al., 2016; Liguori et al., 2013). The pH drop also activates the enzymatic conversion of the Car violaxanthin (Vio) to zeaxanthin (Zea) (Eskling et al., 1997). Along with LHCSR, other homologous light-harvesting complexes are likely involved in quenching (Nicol et al., 2019). In C. reinhardtii, the CP26 and CP29 subunits, which are minor antenna complexes of Photosystem II (PSII), have been implicated in NPQ (Cazzaniga et al., 2020). In higher plants, Zea has been reported to be involved in NPQ induction by driving light-harvesting complexes into a quenched state and/or by mediating interaction between light-harvesting complexes and PsbS, nonpigment binding subunits essential for NPQ induction in vascular plants (Sacharz et al., 2017; Ahn et al., 2008; Jahns and Holzwarth, 2012). Similarly, Zea binding to LHCSR1 in the moss Physcomitrella patens and LHCX1 (a LHCSR homolog) in the microalga Nannochloropsis oceanica has been shown to be essential for NPQ (Pinnola et al., 2013; Park et al., 2019). Finally, in C. reinhardtii, a reduction of NPQ in the absence of Zea has been reported (Niyogi et al., 1997). In contrast, recent work has shown Zea to be unnecessary for NPQ induction or related to highly specific growth conditions (Bonente et al., 2011; Tian et al., 2019; Vidal-Meireles et al., 2020). Thus, the contribution of Zea to quenching in green algae remains under debate.

Because of the complexity of NPQ and the large number of homologous light-harvesting complexes, the individual contributions and mechanisms associated with LHCSR3, pH, and Zea have been challenging to disentangle, including whether they activate quenching separately or collectively. With the power of mutagenesis, the contribution of LHCSR3, and the dependence of this contribution on pH and Zea, can be determined. However, in vivo experiments leave the molecular mechanisms of LHCSR3 and its activation obscured. In vitro experiments, and particularly single-molecule fluorescence spectroscopy, are a powerful complement to identify protein conformational states (Gwizdala et al., 2016; Krüger et al., 2010; Kondo et al., 2017; Schlau-Cohen et al., 2014; Schlau-Cohen et al., 2015). A recent method to analyze single-molecule data, 2D fluorescence correlation analysis (2D-FLC) (Ishii and Tahara, 2013a; Kondo et al., 2019) quantifies the number of conformational states and their dynamics, including simultaneous, distinct processes. Thus, the conformational changes associated with NPQ can be resolved.

Here, we apply a combined in vivo and in vitro approach to investigate NPQ in C. reinhardtii. We use mutagenesis, NPQ induction experiments, and fluorescence lifetime measurements on whole cells and single LHCSR3 complexes to show that pH and Zea function in parallel and that either parameter can activate full quenching and prevent ROS accumulation. The pH-dependent quenching in LHCSR3 is controlled by the protonation of residues in the C-terminus as shown by mutagenesis to remove these residues. The Zea-dependent quenching is activated even at neutral pH both in vitro and in vivo. Based on the in vitro results, we find two likely quenching sites, that is Chl-Car pairs within LHCSR3, one regulated by pH and the other by Zea. The two quenching processes act in combination to provide different time scales of activation and deactivation of photoprotection, allowing survival under variable light conditions.

Roles of pH and Zea in fluorescence intensity in vivo and in vitro. To disentangle the contributions of LHCSR, pH, and Zea, both in vivo and in vitro measurements were performed on different C. reinhardtii genotypes. Wild-type (WT) strains (4A⁺ and CC4349), a strain depleted of LHCSR3 and LHCSR1 subunits (npq4 lhcsr1; Figure 1—figure supplements 1 and 2; Ballottari et al., 2016), a strain unable to accumulate Zea due to knock out of the enzyme responsible for xanthophyll cycle activation (npq1; Figure 1—figure supplement 3; Li et al., 2016), and a mutant constitutively accumulating Zea due to knock out of the zeaxanthin epoxidase enzyme (zep; Figure 1—figure supplement 3; Baek et al., 2016; Niyogi et al., 1997) were characterized in vivo. A mutant depleted of both LHCSR subunits (npq4 lhcsr1) was chosen rather than a mutant missing only LHCSR3 (npq4) due to the partial ability of LHCSR1 to substitute for LHCSR3 in its absence (Girolomoni et al., 2019).

To assess the ability of these phenotypes to undergo quenching of Chl fluorescence, the NPQ levels were measured in vivo after cells were acclimated to HL (500 μmol m⁻²s⁻¹) for several generations to induce LHCSR expression (WT, npq1, and zep strains) and then exposed to strong light treatment (1500 μmol m⁻²s⁻¹) for 60 min to induce maximum drop in luminal pH and Zea accumulation (WT, npq1, zep, and npq4 lhcsr1 strains; data for xanthophyll cycle activation shown in Figure 1—figure supplement 3). The NPQ induction kinetics are shown in Figure 1. In the WT strains, the maximum NPQ level was reached after 10 min of illumination and fully recovered in the dark (Figure 1A, black), despite a strong accumulation of Zea (Figure 1—figure supplement 3). The recovery of the NPQ traces to similar levels for all mutants acclimated to HL demonstrated that the photodamage, known as photoinhibition, was limited, if any, for these cells during the 60 min of illumination. Zea-dependent quenching and the reduction in fluorescence emission due photoinhibition have a similar relaxing time. The low level of these two processes observed in our NPQ measurements also suggests that the major component of NPQ in C.reinhardtii is Zea-independent, in agreement with previous results (Bonente et al., 2011). In contrast, in the case of low-light (LL) acclimated samples, the NPQ induced by WT and npq1 was much lower compared to the HL acclimated cells, with a significant fraction of NPQ that did not recover in the dark (Figure 1—figure supplement 4). This suggests the possible induction of photoinhibition in LL acclimated cells exposed to strong light treatment for 60 min. In the npq4 lhcsr1 strain, which lacks LHCSR subunits, a null NPQ phenotype was observed in both HL and LL acclimated cells (Figure 1A, purple). These results confirm that LHCSR subunits are responsible for light-dependent NPQ in C. reinhardtii as previously reported (Peers et al., 2009; Ballottari et al., 2016). Consistent with this conclusion, LHCSR accumulation in LL acclimated samples, which exhibit less NPQ, was strongly reduced compared to the corresponding HL samples (Figure 1—figure supplement 2).

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Figure 1—source data 1

Table of statistical parameters for the histograms of the single-molecule fluorescence intensity shown in Figure 1.

The median (med) and standard deviation (std) are given for each sample. The 95% confidence intervals for each value were determined by bootstrapping the samples 10,000 times.

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In the npq1 strain, which lacks Zea, no reduction of the maximum level of NPQ was observed compared to its background, the 4A+ WT strain in HL (Figure 1A, blue, black). The similar level and timescales of onset and recovery for NPQ in these two strains suggest a minor role, if any, for Zea in light-activated quenching. In the zep strain, which constitutively accumulates Zea, a strong reduction of the NPQ level was observed compared to both WT strains (Figure 1A, red). To understand why, first accumulation of the LHCSR subunits was measured (Figure 1—figure supplements 1 and 2). However, similar LHCSR3 content was found in the WT strains, npq1 and zep mutants. In the case of LHCSR1, similar accumulation was observed in the 4A+ WT strain and zep mutant, while no trace of this subunit was detectable in the CC4349 WT strain. Second, the extent of proton motive force as compared to WT was measured through the electrochromic shift (Bailleul et al., 2010). However, although proton transport into the lumen was reduced in the zep strain at low actinic light, it was similar at the higher irradiance used for measurement of NPQ (Figure 1—figure supplement 6). Therefore, neither differences in LHCSR accumulation nor in proton transport are the cause of the reduced NPQ phenotype in the zep mutant. The PSII maximum quantum yield measured though the photosynthetic parameter Fv/Fm was significantly reduced in the zep mutant compared to the WT case (Figure 1—figure supplement 7), suggesting quenching may be occurring even in dark adapted samples.

In order to investigate the effect of pH and Zea at the level of the LHCSR3 subunit, single-molecule fluorescence spectroscopy was used to measure individual complexes in vitro. As an initial comparison, we determined the fluorescence intensity from single LHCSR3. Each period of constant intensity was identified (representative intensity traces in Figure 1—figure supplement 5) and the average intensity for each period was calculated. The fluorescence intensities for each period were used to construct histograms as shown in Figure 1B–D. Histograms were constructed for LHCSR3 with Vio (LHCSR3-Vio) at high and low pH, which mimic the cellular environment under LL and HL conditions, respectively (statistical parameters in Figure 1—source data 1). The fluorescence intensity decreases, generally along with the fluorescence lifetime, as quenching increases. As shown in Figure 1B, upon a decrease in pH from 7.5 to 5.0, the median fluorescence intensity of LHCSR3-Vio decreased by ~5 counts/10 ms due to an increase in the quenched population, reflecting additional quenching of the excitation energy absorbed. This is consistent with the conclusion from the in vivo NPQ experiments that quenching can occur without Zea under HL conditions.

Activation of quenching in LHCSR3 was previously suggested to be related to protonation of putative pH-sensing residues present at the C-terminus (Ballottari et al., 2016; Liguori et al., 2013). To assess the role of these pH-sensing residues in pH-dependent quenching, a mutant of LHCSR3 lacking this protein portion (stop-LHCSR3) was produced (Figure 1—figure supplement 8). Stop-LHCSR3 was also measured using single-molecule spectroscopy (Figure 1—figure supplements 9 and 10). Upon the same pH decrease that induced quenching in LHCSR3-Vio, stop-LHCSR3 with Vio (stop-LHCSR3-Vio) exhibited similar fluorescence intensity where the median intensity even increases, primarily due to increased heterogeneity in the fluorescence emission at low pH as seen through the standard deviation of the two distributions (Figure 1C, Figure 1—source data 1). The data show that the mutants have lost the ability to activate quenching channels upon a pH drop, highlighting the sensing role of the residues of the C-terminus of LHCSR3.

Single-molecule measurements were also performed on LHCSR3 with Zea (LHCSR3-Zea) at high and low pH. Under both conditions, as shown in Figure 1D, LHCSR3-Zea in vitro showed a decrease in median fluorescence intensity by ~10–12 counts/10 ms compared to LHCSR3-Vio. The pH-independence of these histograms is consistent with the in vivo NPQ measurements in the zep mutants where HL, and the associated pH drop in the lumen, does not change quenching levels (Figure 1A). However, the lower intensity points to the existence of a quenching process that requires only Zea, consistent with in vivo fluorescence lifetime measurements discussed below.

Roles of pH and Zea in fluorescence lifetime in vitro

The single-molecule fluorescence intensities are time averages, and so we also analyzed the fluorescence emission from single LHCSR3 through a photon-by-photon method, 2D fluorescence lifetime correlation (2D-FLC) analysis. This method uses the associated lifetime data, and is more appropriate to analyze this data as the lifetime decays exhibit complex kinetics (de la Cruz Valbuena et al., 2019). Applying the 2D-FLC analysis to single-molecule data identifies fluorescence lifetime states, which correspond to protein conformations with different extents of quenching, and rates of transitions between states, which correspond to switches between the protein conformations (Kondo et al., 2019).

To determine the number of lifetime states, the distributions of lifetime values were constructed (Figure 2A–D). In a lifetime distribution, lifetime states appear as peaks with varying profiles. Traditional lifetime fitting requires an a priori assumption of the number of exponential terms required to fit a decay curve. In contrast, construction of a lifetime distribution does not require prior assignment of the number of decay timescales, which is particularly important when there are multiple different lifetimes as is the case for LHCSR3 (de la Cruz Valbuena et al., 2019). The lifetime distribution also allows analysis of multi-exponential lifetimes, even for the low signal-to-background regime of single-molecule data. The initial lifetime distribution for each sample was calculated by first performing an inverse Laplace transform of all the lifetime data (time between excitation and emission), which was recorded on a photon-by-photon basis (Figure 2—figure supplement 1). Photon pairs separated by a series of delay times were identified, and a 2D inverse Laplace transform was performed for the photon pairs associated with each delay time (see Materials and methods for details). The final lifetime distributions (Figure 2A–D) were determined by fitting the data in order to optimize the lifetime distributions and to generate the correlation functions, which are discussed in more detail below. For each of the LHCSR3 samples, two lifetime states were observed in the distributions, an unquenched state (~2.5 ns) and a quenched state (~0.5 ns).

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Table of parameters estimated through the global fit to the correlation functions using the 2D-FLC analysis.

The number of dynamic components, fluorescence lifetime states, intensity of each lifetime state, population of each state, and transition rates between states were estimated by global fitting of the correlation functions as shown in Figure 2—figure supplement 1 using the model function described in the Materials and methods and the fluorescence lifetime distributions shown in Figure 2A–D. The fluorescence intensity is a relative intensity that is normalized by the total measurement time for each sample and scaled to set the maximum intensity to 1. The free-energy differences were calculated as described in the Materials and methods.

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Pigment binding properties of recombinant LHCSR3 WT and stop-LHCSR3 refolded in vitro.

Binding pigments are reported referred to 7 Chlorophyll. The results reported are representative of two independent experiments. Errors are reported as standard deviation.

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The dynamics of the lifetime states were investigated through the auto- and cross-correlation functions for the lifetime states of each sample (Figure 2—figure supplement 2). The correlation function is a normalized measure of how similar the photon emission time, that is, the lifetime, is as time increases (Nitzan, 2006). Therefore, an auto-correlation function for a given lifetime state contains the timescales for transitions out of the state and a cross-correlation function contains the timescales for transitions between the states (anti-correlation) and similar behavior of the states (correlation) due to processes throughout LHCSR3, such as photobleaching. The auto- and cross-correlations were globally fit to estimate the parameters in the correlation model function given by Equation 1 in the Materials and methods, which includes the number of lifetime states, the brightness of each state, the population of each state, the rates of transitions between states, and the number of separate processes that transition between states, referred to as dynamic components. The parameters extracted from the fits are given in Figure 2—source data 1 for all samples.

The best fits to the data included three dynamic components, where each component arises from distinct emissive states with separate conformational dynamics within single LHCSR3 (Figure 2—figure supplement 2, Figure 2—source data 1). Correlation-based analysis of the photon fluctuations is a well-established tool to identify the number of independent emissive processes (Schwille and Haustein, 2002; Mets, 2001), and was adapted to determine the number of dynamic components (Kondo et al., 2019). The cross-correlation for all LHCSR3 samples begins above zero (Figure 2—figure supplement 2), which appears in the presence of multiple dynamic components (Kondo et al., 2019). The Chl a have the lowest energy levels, and, due to their significantly lower energy than the Chl b energy levels, primarily give rise to the emissive states. Because three components were observed within single LHCSR3, they indicate multiple Chl a emissive sites within each LHCSR3, consistent with previous models of LHCs (Mascoli et al., 2019; Mascoli et al., 2020; Krüger et al., 2010; Krüger et al., 2011). Thus, the dynamic components reflect conformational dynamics that switch between unquenched and quenched lifetime states at different places within LHCSR3.

The rate constants for the transitions between the lifetime states within each component were also extracted from the fit, primarily based on the dynamics of the cross-correlation functions (Figure 2—source data 1). Two of the components exhibit rapid dynamics, which arise from conformational changes that vary the extent of quenching of the Chl a emitters. The timescales of the transitions for one component are tens of microseconds and those for the other are hundreds of microseconds, which are both timescales that would be hidden in traditional single-molecule analyses. The third dynamic component is static at <0.01 s. Due to the lack of dynamics, we assigned the component to emitters far from, and thus unaffected by, quenchers for the unquenched state and partially photobleached complexes for the quenched state.

Finally, the relative populations of the lifetime states for each component were also determined within the model. Assuming a Boltzmann distribution (see Materials and methods), the relative rate constants were used to determine the equilibrium free-energy difference between the states for each component (Figure 2—source data 1). The free-energy barrier for a transition between states is related to the rate of the transition, which was used to approximate the barrier height (Kondo et al., 2019). These free-energy differences and barrier heights were then combined to construct illustrative free-energy landscapes, which are shown in Figure 2 for the two dynamic components.

We examined the dependence of the two dynamic components on pH, Zea and the C-terminal tail, which contains the pH-sensing residues. Figure 2E and F show the pH-dependence of the free-energy landscapes for the slower (hundreds of microseconds) dynamic component in LHCSR3-Vio and LHCSR3-Zea, respectively. In both cases, a decrease in pH from 7.5 to 5.0 stabilizes the quenched state. In LHCSR3-Vio, the quenched state is stabilized by a decrease in the transition rate from the quenched to the unquenched state, corresponding to a higher barrier in the free-energy landscape. In LHCSR3-Zea, the decrease in the transition rate from the quenched to the unquenched state is also accompanied by an increase in the transition rate from the unquenched to the quenched state, further stabilizing the quenched state relative to the unquenched one. In stop-LHCSR3-Vio, however, no change in the population of the quenched state is observed upon a decrease in pH (Figure 2G), reflecting the expected pH-independence of the sample.

Figure 2I and J show the Zea-dependence of the free-energy landscapes of LHCSR3 for the faster (tens of microseconds) dynamic component at pH 7.5 and pH 5.0, respectively. At both pH levels, conversion from Vio to Zea stabilizes the quenched state via a decrease in the transition rate from the quenched to unquenched state. At pH 5.0, the transition rate to the quenched state increases as illustrated by the lower barrier, which would enable rapid equilibration of population into the quenched state. The Zea-dependent behavior is maintained for stop-LHCSR3 (Figure 2K), where the quenched state is still stabilized in the presence of Zea. 

Roles of pH and Zea in fluorescence lifetime in vivo

Quenching mechanisms were further investigated in vivo by measuring fluorescence emission lifetimes at 77K of whole cells acclimated to LL or HL, as traditional NPQ measurements can be affected by artifacts (Tietz et al., 2017). Under these conditions, the photochemical activity is blocked and by following the emission at 680 nm it is possible to specifically investigate the kinetics of PSII, the main target of NPQ. Cells were either grown in LL or HL, which determines the level of LHCSR protein (Figure 1—figure supplements 1 and 2) and the fluorescence lifetime was recorded before and after exposure to 60 min of HL, which induces ΔpH and determines the level of Zea. These light conditions, combined with the genotypes generated, enabled studies that partially or fully separated the contributions of the different components of NPQ.

Whole cell fluorescence lifetime traces show that LHCSR is necessary for the primary light-dependent component of NPQ in C. reinhardtii trigged by lumen acidification, in agreement with previous findings (Peers et al., 2009; Ballottari et al., 2016). WT cells and npq1 cells, which lack Zea, acclimated to HL show a faster fluorescence decay, or an increase in quenching, after exposure to 60 min of HL (Figure 3A, gray bars, fluorescence decays and fits to data shown in SI). For npq4 lhcsr1 cells, which lack LHCSR, similar fluorescence decay kinetics were measured regardless of light treatment (Figure 3A, purple), which is comparable to the unquenched kinetics of WT cells. WT and npq1 cells grown in control light (low LHCSR content) remain unquenched, even after exposure to 60 min of HL (Figure 3—figure supplements 1 and 2). These results are consistent with the NPQ measurements shown in Figure 1A. Similar to WT, npq1 cells grown in HL show a faster fluorescence decay after exposure to 60 min of HL (Figure 3A, blue bars). While the results from WT show a role for pH and/or Zea in light-induced quenching in LHCSR3, the results from the npq1 strain show that quenching can occur without Zea, that is, induced by the pH drop alone.

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In vivo 77K fluorescence lifetime decays from TCSPC where individual sheets demarcate different genotypes acclimated to either low light (LL) or high light (HL) with the individual data from two independent biological and technical replicates after exposure to 60’ of HL or in the dark.

For each measurement, the first column is the time (ns) and the second column is photon counts.

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77K time resolved fluorescence analysis and average fluorescence decay lifetimes of whole cells.

Kinetics of Figure 3—source data 1 were fitted with a three-exponential decay function using Vinci two software from ISS. Fractions (fi) and time constants (τi) are reported. Average fluorescence lifetimes were calculated as Σfiτi. The results reported are two independent experiments with two independent biologic replicates.

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Figure 3—source data 3

Time resolved fluorescence analysis and average fluorescence decay lifetimes of isolated monomeric (b2) and trimeric (b3) light-harvesting complexes.

Kinetics of Figure 3—figure supplement 5—source data 1 were fitted with a two-exponential decay function using Vinci two software from ISS. Fractions (fi) and time constants (τi) are reported. Average fluorescence lifetimes were calculated as Σfiτi. Errors are reported as standard deviation (n = 2).

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Figure 3—source data 4

In vivo SOSG fluorescence emission kinetics where individual sheets demarcate different genotypes acclimated to either low light (LL) or high light (HL).

For each measurement, the first column is the time at which the fluorescence (arbitrary units) was measured. The following columns are the measured fluorescence of replicate experiments as well as the standard deviation for the specific sample.

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The zep mutant, which constitutively accumulates Zea, presented a similar decay among all samples, regardless of HL or LL acclimation or light treatment, that was much faster, or more quenched, compared to the decay of dark-adapted WT (Figure 3A, red, Figure 3—figure supplements 1 and 2). These results indicate quenching upon Zea accumulation alone, consistent with the reduced Fv/Fm observed in this mutant (Figure 1—figure supplement 7). This result is also consistent with the pH-independent quenching observed through the single-molecule fluorescence shown in Figure 1D and Figure 2E and F. However, the quenching observed in the zep mutant was essentially unchanged in LL vs. HL acclimated zep cells suggesting that the Zea-dependent quenching observed in zep mutants is a more general process as opposed to one that occurs solely in LHCSR3 as quenching is observed even in the cells acclimated to LL that lack LHCSR3.

To investigate the generality of this quenching, monomeric or trimeric light-harvesting complexes were isolated from the zep mutant after exposure to 60 min of HL, which induces maximum Zea accumulation. These complexes had a twofold higher content of Zea compared to the same fraction isolated from WT (CC4349) under the same conditions (Figure 3—figure supplement 5). The light-harvesting complexes isolated from the zep mutant also showed a 10% decrease in the fluorescence lifetime, suggesting that Zea-dependent quenching is at least somewhat shared with other light-harvesting complexes (Figure 3—figure supplement 6, Figure 3—figure supplement 5—source data 1, and Figure 3—source data 3). In contrast, no major differences in quenching properties were found in monomeric and trimeric LHC complexes isolated from WT cells before or after exposure to 60 min of HL, consistent with previous findings from higher plants and other green algae (Xu et al., 2015; Girolomoni et al., 2020).

This work leverages in vivo and in vitro experimental approaches to investigate NPQ mechanisms in C. reinhardtii and the molecular parameters responsible for their activation. In higher plants, both lumen acidification and Zea accumulation have been long understood to play a role in the induction of NPQ. While lumen acidification was thought to play a similar role in C. reinhardtii, here we characterize the impact of Zea accumulation, which had previously been elusive. We also identify the likely conformational dynamics associated with both pH and Car composition.

Source data files have been provided for Figures 1A and 3. Single-molecule photon emission data for Figures 1B-D and 2 has been deposited on https://zenodo.org/ and is available at https://doi.org/10.5281/zenodo.4306869.

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Author details

1. Julianne M Troiano

   Department of Chemistry, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Federico Perozeni

   Competing interests

   No competing interests declared

2. Federico Perozeni

   Department of Biotechnology, University of Verona, Verona, Italy

   Contribution

   Data curation, Investigation, Methodology, Writing - review and editing

   Contributed equally with

   Julianne M Troiano

   Competing interests

   No competing interests declared

3. Raymundo Moya

   Department of Chemistry, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Data curation, Investigation, Visualization, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3923-8000

4. Luca Zuliani

   Department of Biotechnology, University of Verona, Verona, Italy

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

5. Kwangyrul Baek

   Department of Life Science, Hanyang University, Seoul, Republic of Korea

   Contribution

   Resources

   Competing interests

   No competing interests declared

6. EonSeon Jin

   Department of Life Science, Hanyang University, Seoul, Republic of Korea

   Contribution

   Resources

   Competing interests

   No competing interests declared

7. Stefano Cazzaniga

   Department of Biotechnology, University of Verona, Verona, Italy

   Contribution

   Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2824-7916

8. Matteo Ballottari

   Department of Biotechnology, University of Verona, Verona, Italy

   Contribution

   Conceptualization, Resources, Supervision, Investigation, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   matteo.ballottari@univr.it

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8410-3397

9. Gabriela S Schlau-Cohen

   Department of Chemistry, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Visualization, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   gssc@mit.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7746-2981

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