Dissecting the contributions to non-photochemical quenching in a land plant under fluctuating light

Domain: chemistry
Published: 2026-03-09
Editorial outcome: major_revision

Abstract
Photosynthetic organisms have evolved multiple non-photochemical quenching (NPQ) processes, providing
photoprotection by safely dissipating excess excitation energy. These processes involve various molecular players
functioning on overlapping timescales from seconds to days, making it challenging to isolate and quantify their
individual kinetics. In this study, we perform whole-leaf chlorophyll fluorescence lifetime and xanthophyll
concentration measurements on wild-type and various newly characterized NPQ mutants of Nicotiana
benthamiana, a vascular land plant. Based on these measurements, we construct a fluorescence lifetime-based
quantitative kinetic model that disentangles individual photoprotection components and, when integrated
additively, accurately predicts wild-type and mutant NPQ behaviors under various light-dark regimes.
Additionally, the model quantifies the per-molecule quenching effectiveness of various xanthophylls and the
contributions of six quenching components (qE , qE qNE qE qZ, and qI) across different genotypes. It also
V A, Z, L,
suggests improved overall quenching efficiency at specific VDE:ZEP:PsbS overexpression stoichiometries,
aligning with previous studies and supporting translational efforts to optimize photoprotection and enhance crop
yields under dynamic light environments.

Introduction
The initial step in photosynthesis involves the absorption of a photon and excitation of chlorophyll (Chl) to its
singlet excited state (¹Chl*) within light-harvesting complexes (LHCs), facilitating electronic energy transfer to
the reaction center (RC) to drive charge separation1. However, under high light (HL) conditions, the
photosynthetic electron transport chain can become saturated. When 1Chl* lacks an adequate electron acceptor,
formation of the longer-lived triplet state of chlorophyll (3Chl*) via intersystem crossing can occur2. These triplet
states, when interacting with molecular oxygen, produce reactive oxygen species (ROS), which pose a threat to
thylakoid membrane integrity by damaging essential pigments, proteins, and lipids3,4. To prevent or mitigate such
damage, oxygenic photosynthetic organisms have evolved multiple sophisticated photoprotection pathways,
collectively known as NPQ, that safely dissipate excess excitation energy as heat5,6.

To date, five distinct types of NPQ have been discovered, with various genetic mutants deepening our
understanding of land plant NPQ at the molecular level. Among these types of NPQ, energy-dependent quenching
(qE) occurs on a seconds-to-minutes timescale and accounts for the majority of NPQ induction and relaxation in
response to dynamic light intensity changes7. qE involves the coordinated action of a pH-sensing protein,
photosystem II subunit S (PsbS/RNPQ4)8,9, and xanthophyll carotenoids (Cars), primarily zeaxanthin (Zea, Z) and
lutein (Lut, L). Under HL conditions, violaxanthin de-epoxidase (VDE/NPQ1) converts violaxanthin (Vio, V) to
antheraxanthin (Ant, A) and subsequently to Zea, a process reversed by zeaxanthin epoxidase (ZEP/NPQ2) under
low light/dark conditions10,11. Lut, produced via lycopene epsilon cyclase (LUT2), independently and additively
contributes to NPQ along with Zea12,13, however the relative contributions of each Car to qE are still extensively
debated 11,12,14,15.

The longer-lasting Zea-dependent quenching (qZ) functions independently of PsbS, which is required for
qE16. Unlike qE and qZ, which involve specific Cars, state transitions in light-harvesting complex II (qT) regulate
light harvesting by redistributing excitation energy between photosystem I (PSI) and photosystem II (PSII)17.
Prior tests have shown negligible qT-associated changes in 1Chl* fluorescence lifetimes under short-term,
fluctuating light conditions18, likely because qT does not directly quench 1Chl* and thus contributes minimally to
the PSII Chl a fluorescence quenching in the organism studied here, Nicotiana benthamiana, as supported by
prior tests showing negligible qT-associated changes in 1Chl* fluorescence lifetimes under our experimental
conditions18. The most recently discovered chloroplast lipocalin-dependent sustained antenna quenching (qH)
has, to date, only been detected under long-term cold and HL conditions20–22, and is therefore also unlikely to be
relevant under rapid, fluctuating light conditions. The most slowly relaxing NPQ component, photoinhibition
(qI), occurs when photoprotection is insufficient, resulting in the hours to days long photoinactivation of PSII19.
Collectively, these NPQ processes involve diverse molecular players that function either additively or
independently across overlapping timescales to acclimate to varying illumination conditions in nature. This
makes it challenging to disentangle and study their individual kinetics and contributions in vivo23.

The specific molecular interactions and mechanisms involved in these NPQ processes are still extensively
debated. In a recent review, Bassi and Dall’Osto6 describe four distinct mechanisms that involve either Chl–Chl
or Chl–Car interactions, where the role of the Cars may be direct24–26 or as an allosteric effector (e.g. in Chl–Chl
interactions)27–29. Evidence of the former includes proposed mechanisms suggesting Cars’ role as direct quenchers
include charge transfer between Chl and Car that quenches Chl Qy30–33 and excitonic interactions that may
produce a low-lying energy level of a Car, which relaxes rapidly to the ground state either directly or via transfer
to a Car S state 34,35. Other models propose, however, exclusively support the latter hypothesis where Cars do not
function as direct quenchers, but as allosteric effectors that induce conformational changes in LHCs to facilitate
energy dissipation36–38. Structural and spectroscopic studies support Zea-dependent but structurally mediated
pathways contributing to quenching, suggesting that Zea may enable or stabilize specific protein conformations
that facilitate NPQ without directly quenching Chl excited states36,39. Recent studies have also proposed the
formation of PSI-PSII supercomplexes and associated excitation spillover from PSII to PSI as a potential
quenching mechanism involving Zea and PsbS40–42, though its physiological relevance in N. benthamiana remains
to be fully established.

Given the ongoing debate on the molecular mechanisms (which may all occur with various contributions
in parallel), we have constructed a kinetic model for NPQ that is as agnostic as possible to the precise molecular
mechanisms while incorporating the known factors of ΔpH activation of VDE and PsbS and the VAZ cycle. The
time-dependent contributions of the various actors emerge naturally from the model and may help to advance the
discussions of NPQ mechanisms. A complete list of assumptions underlying the construction of this model,
including those that were deliberately excluded, is provided in the Supplementary Information (Supplementary
Methods).

There have been a number of previous modeling studies of NPQ kinetics, and qE in particular43–50. These
models have generally been based on the VAZ cycle, with Zea and protonated PsbS acting as the sole quenching-
related components, most commonly represented by the 4-state 2-site quenching model proposed by Jahns and
Holzwarth14, which takes into account both open and closed RCs and quenched and unquenched PSII antennas. In
the most relevant previous studies51,52, a xanthophyll cycle-based kinetic model of short-term photoprotection was
developed in the alga Nannochloropsis oceanica. This model, utilizing Chl fluorescence lifetime and xanthophyll
concentration measurements, captured the dynamics of xanthophyll interconversion and quenching activity, and
further highlighted Zea as the most effective photoprotective Car, followed by Ant and Vio. The model also
predicted the behavior of two mutants that were not included in the parameterization of the model52. These
findings provided valuable insights into the kinetics of fast-reacting photoprotection and implications for
optimizing photosynthetic efficiency.

However, this model had a number of significant limitations: First, the model was based on a definition
of NPQ capacity analogous to the generally used definition based on fluorescence intensity (𝑁𝑃𝑄 = 𝐹𝑚 − 𝐹𝑚’ / 𝐹𝑚’ , or
𝑁𝑃𝑄 (𝑡) = 𝜏 𝑑𝑎𝑟𝑘(𝑡=0)−𝜏(𝑡) / 𝜏(𝑡) ) and, in the latter, calculated using Chl fluorescence lifetimes measured in
“snapshots” over time as quenching in excess light or relaxation in darkness occurs. Here 𝜏 (𝑡 = 0) denotes
dark
the amplitude-weighted average lifetime of the initial dark snapshot, and τ(t) denotes the lifetime at the
corresponding snapshot sequence time t. While convenient for modeling, normalization by the initial dark-
acclimated lifetime 𝜏 (𝑡 = 0) forces the assumption of zero initial quenching in the darkness, which may not
dark
hold in mutants with impaired NPQ recovery or altered thylakoid protein composition, such as zep or lut2
mutants. This limits the comparability of the 𝑁𝑃𝑄 parameter across different genotypes with differing initial
𝜏
fluorescence lifetimes. Additionally, its dependence on τ(t) becomes nonlinear at high quenching levels, hence its
relationship to true quenching dynamics becomes unclear. In particular, the model assumes that 𝑁𝑃𝑄 is directly
𝜏
proportional to the concentrations of quenching species, which is not true for 𝑁𝑃𝑄 levels > 2. To address this
𝜏
issue, we formulated the model in this study to directly fit the measured Chl fluorescence lifetimes, allowing for a
more fundamental and accurate representation of NPQ capacity.

Second, Nannochloropsis oceanica lacks specific pigments found in land plants, such as Lut and
chlorophyll b (Chl b), and it relies on the phylogenetically and functionally distinct algal protein LHCXI, rather
than PsbS, to initiate qE53–55. Consequently, the model is not directly applicable to land plants. This limitation is
particularly significant given the long-standing debate regarding the roles of Zea and Lut as quenchers in qE, as
well as their potentially distinct quenching kinetics and contributions26. In addition, while the previous model
incorporated qZ, it did not include qI—the slowest NPQ component—which is present at substantially higher
relative magnitudes in plants but is challenging to disentangle from other NPQ components23,56. Therefore,
developing a new lifetime-based model tailored to land plants that includes both the Lut-dependent qE quenching
pathway and qI is essential to provide deeper insights into these debates and improve our capability to predict and
engineer NPQ in plants.

To that end, we selected N. benthamiana, an allotetraploid vascular land plant, as our model system due
to its compatibility with previous related studies conducted using transient absorption spectroscopic
measurements, which facilitate investigation of potential qE quenching mechanisms26 and assessment of exciton
diffusion length in relation to NPQ levels in isolated thylakoids57—measurements that are challenging to perform
in Arabidopsis thaliana. This choice could enable both kinetic and mechanistic analysis of NPQ in land plants
when combined with complementary techniques across a series of studies. In the present study, however, to
preserve physiological function as much as possible, we utilized fresh, intact leaves of N. benthamiana to
systematically investigate the NPQ response using a comprehensive set of single and double NPQ mutants26
(Table 1) across three regular/irregular actinic light sequences. By combining five critical mutants, each lacking
specific components of the NPQ pathways, we construct a lifetime-based quantitative kinetic model of NPQ
focused on the largest components in land plants: qE, qZ, and qI. Model parameters are directly fitted to the
experimental Chl fluorescence lifetimes, guided by xanthophyll concentrations measured via high-performance
liquid chromatography (HPLC), with all measurements performed in intact leaves as described in more detail in
the Methods. Parameterized using four single mutants and one double mutant under fluctuating light, this
approach enables us to isolate the activation and relaxation kinetics of NPQ as well as the contributions of each
NPQ components individually while capturing their interplay—the model accurately predicts NPQ responses in
the four single mutants under constant light, as well as the wild type (WT) and four additional double mutants
under three different illumination sequences. Furthermore, the model enables comparisons of per-molecule
quenching effectiveness of the xanthophylls and further predicts quenching kinetics upon overexpression of VDE,
PsbS, and/or ZEP in various stoichiometries. Altogether, these findings highlight potential strategies to modulate
key enzyme expression and activity, offering valuable insights for enhancing NPQ efficiency and improving crop
productivity58,59.

Results
An NPQ kinetic model based on chlorophyll fluorescence lifetime

We aimed to construct a minimal model of NPQ dynamics in land plants derived from known biochemical
processes. Our kinetic model incorporates three key components: a dynamic xanthophyll cycle, Lut activation,
and a simplified phenomenological representation of qI. The model incorporates 13 chemical species: free
xanthophylls Vio (V)/Ant (A)/Zea (Z), protein-xanthophyll bound complexes PV/PA/PZ, and an additional
protein-Lut complex PL, which are activated to be quenching complexes (QX) in light, as well as the key
xanthophyll cycle enzymes VDE and ZEP. A phenomenological representation of qI is given by modeling 𝛼 ,
the ratio of damaged RCs to the whole pool. A simplified schematic of this model is shown in Figure 1A and a
𝑞𝐼
detailed description is provided in the Supplementary Methods section.

To address the limitations of our previous N. oceanica model described in the Introduction and expand
the model to represent land plant NPQ, we present an alternative approach to directly model quenching using the
fundamental definition of fluorescence lifetime. The total fluorescence decay rate, 𝜏 (𝑡), is determined by several
𝐹
competing processes, including the non-radiative decay and the intrinsic fluorescence decay of Chl a, qE, qZ, and
qI. Assuming the quenching rate is linearly dependent on the concentration of quenching species, we define the
fluorescence lifetime of Chl a via a Stern-Volmer type approach60 as:

1 / 𝜏𝐹 = 𝜅𝑟,𝑛𝑟 + 𝜅𝑞𝐸[𝑄𝑋] + 𝜅𝑞𝑍[𝑍] + 𝜅𝑞𝐼[𝐼]

Where 𝜅 𝜅 𝜅 represent the quenching rate constants of qE, qZ, and qI, respectively, and 𝜅 accounts for
𝑞𝐸, 𝑞𝑍, 𝑞𝐼 𝑟,𝑛𝑟
all the other radiative and non-radiative de-excitation processes. While this phenomenological formulation can
include multiple alternative quenching mechanisms, it enables effective kinetic fitting of fluorescence lifetimes,
and it was chosen for its simplicity, interpretability, and capacity to capture the dynamic contributions of multiple
quenching species. Furthermore, since QV, QA, QZ, and QL may exhibit different per-molecule quenching
effectiveness, we further distinguish their contributions within qE:

𝑘𝑞𝐸[𝑄𝑋] ≈ 𝜅𝑄𝑉[𝑄𝑉] + 𝜅𝑄𝐴[𝑄𝐴] + 𝜅𝑄𝑍[𝑄𝑍] + 𝜅𝑄𝐿[𝑄𝐿] = ∑ 𝜅𝑄𝑋[𝑄𝑋]
𝑋

This yields the final time-dependent expression for the Chl a fluorescence lifetime:

1 / 𝜏𝐹(𝑡) = 𝑘𝑟,𝑛𝑟 + ∑ 𝜅𝑄𝑋[𝑄𝑋] + 𝜅𝑞𝑍[𝑍] + 𝜅𝑞𝐼𝛼𝑞𝐼
𝑋

This formulation allows for direct quantification of quenching rates and per-molecule effectiveness of the
individual quenchers while accounting for genotypic variations in 𝜏 (𝑡 = 0). In this model, 𝜅 𝜅 and 𝜅
dark 𝑞𝑋, 𝑞𝑍, 𝑞𝐼
are additional fitting parameters, while 𝑘 is calculated directly from 𝜏 (𝑡 = 0) for each mutant. Details of
𝑟,𝑛𝑟 dark
the model implementation and fitting are given in Methods and Supplementary Information (Supplementary
Methods).

Characterization of xanthophyll cycling in novel NPQ mutants of N. benthamiana

To systematically disentangle the individual quenching pathways found in N. benthamiana, we characterized a
series of mutants, each deficient in one or more NPQ components. Three such mutants were characterized
previously: the psbs1psbs2 mutant that lacks qE (hereafter npq4), the lut2-1 lut2-2 mutant that lacks lutein-
dependent qE (hereafter lut2), and the vde1 vde2 mutant that lacks Zea-dependent qE and qZ (hereafter npq1)26.
In this study, we generated and characterized single and double CRISPR-generated mutants deficient in one or
both ZEP orthologs. ZEP is necessary for the production of Vio, Ant, and neoxanthin, the latter being an
important precursor for the stomata-regulating plant hormone abscisic acid79. Correspondingly, knockouts of ZEP
should constitutively accumulate Zea even in the dark. However, the lack of epoxidized Vio in the zep1zep2
double knockout in N. benthamiana significantly reduced plant viability, with only one of eleven T0
transformants producing viable, stable progeny (Supplementary Table 5). In contrast, both zep1 and zep2
mutants were able to grow under ambient greenhouse conditions with more modest differences in growth. While
the zep2 mutant was indistinguishable from WT plants, the zep1 mutant exhibited stunted growth, frilled leaf
edges, and reduced leaf turgor. We found that each ortholog contributes additively to Zea epoxidation, with ZEP1
being the dominant copy. Interestingly, knockout of ZEP2 (hereafter zep2) resulted in a moderate knockdown
phenotype, with WT-like growth and a near-native Vio and Neo composition and sufficient constitutive Zea to
saturate Zea-dependent qE capacity (Supplementary Figure 1), unlike the non-native composition and slowed,
humidity-dependent growth found in the A. thaliana npq2 point mutant10. This zep2 mutant provided quantitative
and temporal variation in Zea-dependent qE and qZ that was necessary and sufficient for phenotypic modeling,
without the pleiotropic impacts on xanthophyll composition that are present in existing A. thaliana mutants.

The presence or absence of these traits within the qE, qZ, and qI parameters that represent the vast
majority of WT land plant NPQ are included in Table 1. These mutants enabled a stepwise model training
approach, where parameters were progressively fitted, starting with the simplest NPQ system (npq4npq1, which
contains only qI) and gradually incorporating additional quenching components to reconstruct the full WT model
(Fig. 1B). Whole-leaf pigment profiles were quantified using HPLC under dark-acclimated and HL-acclimated
conditions for all genotypes used in this study (Supplementary Figure 2). WT, npq4, zep2, lut2, and zep2lut2
mutants exhibited robust de-epoxidation of Vio in response to HL, and furthermore, zep2, npq4zep2 and zep2lut2
retained more residual accumulation of Ant and Zea in the dark and after HL due to reduced ZEP activity. On the
contrary, npq1 and its double mutants (npq1npq4, npq1lut2) displayed no significant changes in pigment profiles
before and after HL exposure, indicating a complete loss of VDE function61–63. Additionally, lut2 and its derived
double mutants (npq4lut2, npq1lut2, zep2lut2) lacked detectable Lut and exhibited a considerably expanded VAZ
pool size, consistent with previous reports64.

Training the NPQ model with chlorophyll fluorescence lifetimes

Whole-leaf Chl fluorescence lifetime measurements were conducted using time-correlated single-photon counting
(TCSPC), as described in detail in the Methods section. The genotypes npq4npq1, npq4, npq1, lut2, and zep2 are
the minimal set sufficient to fit all parameters, integrating all major photoprotective pathways in order to
reconstruct the complete WT model of 𝜏 (Fig. 1B). Fluorescence lifetime data was collected using a 5HL-10D-
𝐹
5HL actinic light sequence (Fig. 2A–E). In general, fluorescence lifetimes decreased during the light period,
indicating quenching activity, and increased to varying extents in npq4, npq1, lut2, and zep2 during the following
dark period, reflecting partial NPQ recovery. Among these, lut2 and zep2, both possessing functional VDE and
therefore having Zea as a major contributor to quenching, exhibited shorter lifetimes compared to npq1, which
lacks Zea. Conversely, npq4npq1 and npq4, both deficient in PsbS, showed less quenching and minimal recovery.

Our model fitting results (Fig. 2A–E) successfully captured the contributions of individual quenching
components—npq4npq1 for qI, npq4 for qZ, and npq1 for Vio-dependent qE (qE )and Lut-dependent qE (qE )
V L
—as well as the combined effects of multiple quenchers (lut2, zep2). Except for the initial Vio concentration,
which was derived from HPLC results, all fitting parameters remained consistent across mutants. While the qI
component was derived from fitting npq4npq1 lifetime data under the 5HL-10D-5HL sequence (Fig. 2A), an
additional control measurement performed using only laser excitation without actinic light on npq4npq1 (laser
baseline) was accurately captured by the qI-only model (Supplementary Figure 3). This indicates although the
laser baseline modestly contributes to detectable quenching, this effect is accounted for in our model within the qI
parameters via the positive 𝛼 accumulation rate in darkness in npq4npq1, which was modeled
𝑞𝐼
phenomenologically due to the unresolved mechanistic basis of qI, yet still offers insight into the temporally
resolved contribution of qI.

In npq4 (Fig. 2B), the relatively slow activation and relaxation dynamics were attributed to the slower qI
and qZ kinetics. In npq1 (Fig. 2C), the NPQ components qI, qE ,and qE exhibited rapid activation, contributing
V L
to a sharp decrease in lifetime during both the initial and second illumination periods, although the model slightly
underestimated Lut activation rates. Partial recovery in the dark period was attributed to the rapid deactivation of
qE . Similarly, lut2 (Fig. 2D) exhibited substantial contributions to activation from qE , as well as Ant-dependent
L V
qE (qE ), and Zea-dependent qE (qE ), with lingering qZ causing a prolonged relaxation phase in the dark. In
A Z S
zep2 (Fig. 2E), the rapid initial NPQ activation was largely driven by pre-existing Zea, leading to a strong qE
Z
followed by a slower qZ response. Partial recovery in darkness resulted from qE
Z
deactivation, while qZ recovery
remained slow. Similar observations and model fitting accuracy were observed under the 3HL-1D-1HL-3D-9HL-
3D sequence (Supplementary Figure 4).

To ensure consistency of rate parameters across genotypes, fluorescence lifetime datasets were fitted in
batches. In the absence of Lut, which plays a critical role in maintaining trimeric LHCII stability, lut2 mutants
have altered LHCII pigment compositions (e.g. replacement of Lut by Vio, Ant, and Zea) and xanthophyll
binding affinities, which will affect excitation energy quenching26,63. As a result, kinetic rate parameters for PX
formation specific to lut2 were used and marked by an asterisk (*). Additionally, to account for genotypic
differences in pigment composition, initial pigment concentrations 𝑉 were fitted independently for each
0
genotype.

The kinetic parameters obtained from npq1, npq4, lut2, zep2, npq1npq4, and WT under HL conditions
are summarized in Supplementary Table 1. In zep2, the conversion of Zea to Ant and Ant to Vio was reduced
b reduced
because of lower ZEP activity due to knockout of the less dominant of two ZEP paralogs, thus ZEP function was
not entirely eliminated. Large rate constants for the formation of protein-xanthophyll complexes PX, denoted by
𝑋+𝑃 ⇌ 𝑃𝑋, reflect a rapidly equilibrating process, whereas the formation from PX to quenching complexes QX
occurred at a much slower rate, influencing NPQ induction dynamics. QL shows a similar per-molecule
activation rate to QV but substantially lower than QZ and QA. However, given its high concentration relative to
the VAZ pigments, the contribution of QL to overall quenching is nevertheless significant. The rate constants
associated with pigment binding and dissociation were found to differ in lut2; in particular, the X⇌PX
equilibrium shifts to the left for Vio, Ant, and Zea, consistent with reduced LHCII trimer stability, and the ratio of
binding affinities between Vio, Ant, and Zea changes from approximately 2:3.5:7 to 1:1:3, consistent with
alterations in VAZ binding affinities. The total number of VAZ binding sites, [P] , is approximately halved in
tot
lut2, consistent with the importance of Lut as a structural component of the LHCII trimer.

Quenching rate constants for each xanthophyll species are provided in Supplementary Table 2. The
quenching rate (𝑘 ) for a given species X is defined as 𝑘 = κ [𝑋], where κ represents quenching rate
𝑋 𝑋 𝑄𝑋 𝑄𝑋
constant and [X] represents the concentration of the quenching species (in mmol/mol Chl). These rate constants
(κ ) reflect intrinsic differences in quenching ability between the pigments once activated. Though 𝜅 is
𝑄𝑋 𝑞𝑍
substantially lower than 𝜅 , [Z] typically exceeded [QZ] by an order of magnitude, so the overall quenching
𝑄𝑍
contributions of qZ and qE are similar. The quenching rate constants for Lut and Zea are also similar, with Lut
Z
being slightly higher. 𝜅 acts on the fractional quantity 𝛼 ; thus, the rate of photoinhibition does not exceed 2.82
𝑞𝐼 𝑞𝐼
(mmol/mol Chl-1 s-1) in this model.

Predictions made by the NPQ model

To assess the predictive capabilities of our kinetic model, we performed fluorescence lifetime measurements on
WT and an additional set of N. benthamiana mutant leaves under three distinct illumination sequences, as
described above. Parameters obtained by fitting the previous five mutants were integrated without modification to
construct a complete WT model (only [V] was fitted separately). Across all illumination sequences, the
0,WT
model successfully captures the overall kinetics of NPQ induction and relaxation in WT leaves (Fig. 3A-C). In
particular, the model captures the WT characteristics of slower initial activation of NPQ, partial recovery in dark,
and fast activation upon re-illumination. Consistent with our current understanding of N. benthamiana
photoprotection, the model predicts a strong reliance on qE and qZ under continuous and fluctuating HL
conditions, reflecting the dominant role of these components in rapid adjustment to fluctuating light and
acclimation to intense light stress.

While the model accurately reproduces NPQ dynamics in the 5HL-10D-5HL and 20HL sequences, it
slightly underestimates NPQ activation during the first ~8 min of the irregular 3HL-1D-1HL-3D-9HL-3D
sequence. This discrepancy likely arises from biological variability, such as differences in VAZ pool sizes,
growth conditions, or leaf-to-leaf variation. Such factors, which may contribute to batch-dependent discrepancies,
are further discussed in the Discussion.

Beyond WT, the model effectively captures NPQ dynamics in the four single mutants npq1, npq4, lut2,
zep2 measured under 20-min HL (Supplementary Figure 5), as well as an additional set of double mutants,
including zep2lut2, npq1lut2, npq4lut2, npq4zep2 in the 5HL-10D-5HL sequence (Fig. 3D-G), with only a slight
overestimation of quenching in npq4zep2 during the later phases of the illumination sequence. This
overestimation may result from differences in ZEP levels and total VAZ pool sizes between zep2 and npq4zep2
mutants, as indicated by the HPLC pigment profiles discussed above, or it could simply result from biological
variation, as described further in the Discussion. The model also gives reasonable predictions of NPQ behavior in
these four double mutants under the 3HL-1D-1HL-3D-9HL-3D sequence (Supplementary Figure 6). These
findings highlight the model’s robustness in integrating multiple quenching components.

The agreement between experimental data and model predictions for WT, four single mutants (under
20HL), and four double mutants under different illumination sequences support the validity of the kinetic
parameters obtained from fitting and the model’s underlying assumptions. In particular, the model accurately
predicts WT induction and relaxation dynamics from a direct sum of the individual quenching pathways
associated with qE , qE qE qE qZ, and qI, suggesting that the effects of these components can be
V A, Z, L,
distinguished from each other and may be considered additive. The generalizability of the kinetic parameters to
single mutants (20HL) and double mutants further supports our hypothesis that the contributions to different NPQ
components are distinguishable, and that each of the quenching components operates largely independently so
that even double knockouts retain the NPQ capabilities from the remaining pathways, although all four qE
components (qE , qE qE andqE ) require PsbS. Additionally, the model makes accurate predictions of lut2
V A, Z, L
double mutants using the lut2-specific PX binding rates, consistent with the effect of Lut on the stability of LHCII
trimeric structure26,63.

Methods
Plant Material and Growth Conditions

Transgenic Nicotiana benthamiana (accession Nb-1) lines were generated via Agrobacterium-mediated
transformation by the Ralph M. Parsons Foundation Plant Transformation Facility at UC Davis
(https://ptf.ucdavis.edu/), as described in a previous work26. The newly characterized zep2 mutant was generated
using the same approach; the corresponding guide RNAs are listed in Supplementary Table 3, together with
other previously reported mutants used in this study26. Higher-order quadruple mutants were generated by
crossing homozygous, transgene-free double mutants (e.g. npq4 or psbs1 psbs2 crossed with npq1 or vde1 vde2).
Progeny were phenotypically screened and genotyped to identify homozygous mutants with the appropriate
mutations, as described below. N. benthamiana plants were grown with a 10-hour daylength in a south-facing
greenhouse. Seeds were germinated directly on a mixture of four parts Sunshine Mix #1 (Sungro) and one part
perlite. Plants were fertilized with JR Peter’s Blue 20-20-20 fertilizer monthly.

Genotyping of CRISPR/Cas9 Edits

Multiplexed CRISPR/Cas9 mutagenesis was employed to knockout N. benthamiana orthologs of key A. thaliana
NPQ proteins. Orthologs of candidate NPQ genes (NPQ4/PSBS, NPQ1/VDE, NPQ2/ZEP, and LUT2) were
identified using the N. benthamiana draft genome sequence v1.0.1 (Sol Genomics Network)75 and A. thaliana
protein sequences as queries. Gene structure was largely similar across pairs of genes, excluding LUT2-2, which
was manually assembled by splicing two draft contigs (Supplementary Figure 7A). Guide RNA (gRNA) spacers
were cloned into a modified pCAMBIA2300 backbone for CRISPR/Cas9 mutagenesis and an A. thaliana U6-26
promoter drives expression of candidate gRNAs. Candidate gRNA were identified using CRISPR-P
(crispr.hzau.edu.cn)76 and two high-scoring candidates for each gene were chosen depending (1) on their ability to
target both N. benthamiana orthologs and (2) sequence similarity to the orthologous A. thaliana gene downstream
of the chloroplast transit peptide sequence. Each set of 2 gRNAs was synthesized as a gBlock (Integrated DNA
Technologies), interspersed with a gRNA scaffold and tRNA linker to allow for polycistronic gRNA expression
as previously described77 (Supplementary Figure 7BI).

Genomic DNA from putative knockout lines was isolated and genotyped by Phire Plant Direct PCR
Master Mix (ThermoScientific™, Catalog #F160L) using the supplied dilution buffer. DNA was amplified by
PCR using primers that spanned the two gRNA target sites for each gene of interest, with primer pairs specific to
one of the two highly similar paralogs. In our previous study, we reported the gene models and gRNA target-site
positions for the N. benthamiana NPQ genes used for mutagenesis in Fig. S126, together with the corresponding
gene annotations. Here, we present an updated gene model, including ZEP1 and ZEP2, and the corresponding
gRNA target sites in Supplementary Figure 7. Previously generated mutant lines and their knockout alleles are
reported in Tables S1–S3 of our previous study26. PCR products were purified and sequenced by Sanger
sequencing using the primers listed in Supplementary Table 4 (newly reported in this study) and, for previously
generated genotypes, in Table S4 of our prior study26.

The knockout alleles for single trait and higher order mutants (newly reported in this study) are listed in
Supplementary Table 5 and Supplementary Table 6, respectively. Higher-order mutants were generated by
crosses of homozygous single trait mutants that lacked Cas9, which mitigated the concern for off-target mutations
or need for back-crossing. All four genes were genotyped for each higher-order mutant to confirm the
homozygous knockouts as described.

Segregation of the Cas9 transgene was determined via changes in Chl fluorescence after antibiotic
treatment as previously described78. Briefly, floated leaf punches were incubated in 50 µg/mL kanamycin (from a
50 mg/mL stock in Milli-Q H O) for 3-4 days under 100 μmol m-2 s-1 fluorescent light (14 h light/10 h dark; 27°C
2
day/25°C night). Kanamycin sensitivity was quantified using an Imaging-PAM Maxi (Walz) by measuring Fv/Fm
after 30 min dark acclimation in biological duplicate. Transgene-free (kanamycin-sensitive) plants showed a
~0.3–0.4 decrease in Fv/Fm, whereas transgenic plants maintained near-WT Fv/Fm (~0.6–0.8) across at least two
replicates per plant screened (Supplementary Figure 7C).

As mentioned in the Results section, ZEP is necessary for the production of Vio, Ant, and neoxanthin, the
latter being an important precursor for the stomata-regulating plant hormone abscisic acid79. Correspondingly,
knockouts of ZEP should constitutively accumulate Zea even in the dark. However, mutations in single and
double mutants of ZEP also corresponded to significant changes in plant fitness and overall physiology. The
zep1zep2 mutant was only able to grow under the high-humidity conditions used for germination and was unable
to maintain turgor pressure or grow past the seedling stage necessary for seed set (Supplementary Table 5). In
contrast, both zep1 and zep2 mutants were able to grow under ambient greenhouse conditions with more modest
differences in growth. While the zep2 mutant was indistinguishable from WT plants, the zep1 mutant exhibited
stunted growth, frilled leaf edges, and reduced leaf turgor. Given the near lethality of the zep1zep2 double
knockout, only one of eleven T0 transformants, ZEP-ko-4, produced viable, stable progeny (Supplementary
Table 5). Raw Sanger sequencing chromatograms confirming edits in zep1, zep2, and zep1zep2 lines (file
identifiers correspond to progeny in Supplementary Table 5) are available at
https://doi.org/10.5281/zenodo.16755870.

High-performance Liquid Chromatography of Whole-leaf Extracted Pigments

Leaves were harvested from N. benthamiana plants that had been dark-acclimated overnight and were cut into
equal-sized disks (~0.5 cm). These leaf disks were exposed to dark or light conditions (~1500 µmol m⁻² s⁻¹) under
a white-light LED panel. Each leaf disk was flash-frozen in liquid nitrogen, collected in tubes containing Lysing
Matrix D beads and homogenized using a High-Speed Homogenizer (6.0 m/s, 1 × 40 s; FastPrep-24 5G™; MP
Biomedical). Pigments were extracted twice with 200 µL 100% ethanol until the remaining leaf debris, pelleted
after 24,100 g centrifugation for 5 mins, turned white to ensure complete extraction. To minimize pigment
degradation and solvent evaporation, the extraction process was performed on ice or at 4°C and in the dark.
Approximately 400 µL of the pigment solution was collected from each sample, filtered through a 0.2 µm nylon
filter, and concentrated to ~150 µL under nitrogen flow for 5 minutes before loading into an HPLC vial. The total
leaf-pigment profile, including neoxanthin, violaxanthin, antheraxanthin, lutein, chlorophyll-b, zeaxanthin,
chlorophyll-a, and β-carotene, was obtained by performing high-performance liquid chromatography (1100
HPLC, Agilent) with a combination of solvents consisting methanol, methyl-tert-butyl ether and water80 for
Supplementary Figure 1 or a C30 column (YMC Carotenoid, 5 µm, YMC America) with a gradient of solvents
consisting methanol, methyl-tert-butyl ether and water81 for Supplementary Figure 2. Pigments were quantified
against standard dilution series.

Whole-leaf Fluorescence Lifetime Measurements and Analysis

We used a lab-constructed time-correlated single photon counting (TCSPC) setup, similar to a previously
described design23,82, to measure Chl fluorescence lifetimes and quantify the quenching kinetics of the leaf
samples. A diode laser (Coherent Verdi G10) operating at ~532nm was used to pump a Ti:sapphire oscillator
(Coherent, Mira900f, 76 MHz), generating pulses centered at ~808nm. The laser beam was then directed into a
beta-barium borate crystal, thereby frequency-doubled to ~404 nm to selectively excite the Chl a Q band. A
x
beam splitter directed a portion of the beam to a sync photodiode (Becker-Hickl, PHD-400) to provide time
references to the TCSPC card (Becker-Hickl, PSC-150). The remaining beam, serving as the excitation source,
was directed at the adaxial leaf surface at an angle of approximately 70°, carefully avoiding the leaf stem and
petioles.

Prior to the measurements, plants were dark-acclimated overnight, and water was added to a custom leaf
holder with a water reservoir to maintain leaf hydration throughout the entire measurement. The excitation beam
was adjusted to 1.0 mW to saturate the RCs. The leaves were treated by one of the three 20-minute actinic light
sequences using a Leica KL1500 LCD light source, with high light (HL) set at approximately 1500 µmol m⁻² s⁻¹.
The sequences were designated as following, with the numbers indicating the duration of each high light (HL) or
dark (D) period in minutes.

20HL: continuous high light for 20 minutes.
5HL-10D-5HL: 5 minutes of high light, 10 minutes of dark , followed by another 5 minutes of high light.
3HL-1D-1HL-3D-9HL-3D: a more complex sequence combining shorter intervals of high light and dark.

Chl a Qy band fluorescence emission was selected at 680 nm by a monochromator (HORIBA Jobin-
Yvon; H-20) and detected by a microchannel plate (MCP)-photomultiplier tube (PMT) detector (Hamamatsu
R3809U MCP-PMT. The system's instrument response function (IRF) had a full-width half-maximum (FWHM)
of ~37 ps, sufficient to resolve sub-nanosecond fluorescence lifetimes. A LabVIEW program was applied to
control a series of shutters and coordinate the excitation beam, actinic light, and detector accordingly to the
sequences.

Fluorescence decay profiles were recorded with a laser excitation and detector integration time of 1
second (termed a "snapshot"), collected every 15 seconds to ensure sufficient temporal resolution. A total of 83
snapshots were taken over any type of the 20-minute sequences, including 3 initial snapshots in the dark-
acclimated state. From each 1-second integrated decay, a 0.2-second timestep with the longest lifetime was
selected for analysis to further ensure that the RCs were fully saturated82. Each fluorescence decay profile was
fitted with a bi-exponential decay function and the amplitude-weighted average lifetime was calculated as
follows:

𝜏 = ∑ 𝑖 𝐴𝑖𝜏𝑖 / ∑ 𝑖 𝐴𝑖

Here, A and τ denote the amplitudes and fluorescence lifetimes of the ith fitting component, respectively. The
i i
fitting accounts for the convolution of the fluorescence signal with the instrument response function (IRF), using
the following equation:

𝐼(𝑡) = ∫ 𝐼𝑅𝐹(𝑡′)∑ 𝑖 𝐴𝑖 𝑒 −(𝑡−𝑡′)/𝜏𝑖 𝑑𝑡′

where 𝐼(𝑡) is the measured fluorescence intensity, 𝐼𝑅𝐹(𝑡′) is the instrument response function, and A and τ are
i i
as defined above.

Note that the measured Chl fluorescence decay results from the averaging of tens of thousands of rate
constants. As we showed in previous work83, a decay curve arising from 30,000 rates can be perfectly fit by a sum
of two exponentials, though these components do not correspond to any specific underlying rates or combinations
thereof. The amplitude-weighted average lifetime values obtained from each snapshot were directly used in
constructing and testing the NPQ model. Each lifetime thus corresponds to a single data point in the figures. A
representative example of the raw fluorescence decay profile, bi-exponential fit, and IRF is shown in
Supplementary Figure 8.

Modeling details

NPQ dynamics are modeled by a set of biochemical pathways describing the VAZ cycle and a light-dependent
quencher activation mechanism. Each interconversion between species shown in the NPQ model schematic, as
shown in the Results, is treated as an elementary reaction step described within a set of differential equations.
Rate kinetics are determined by rate constants and initial concentrations (mmol/mol Chl a) of violaxanthin and
membrane protein binding sites, which are parameters fit to experimental data.

The fit quality of a parameter set, 𝛿2(𝜃), is quantified by performing a least-squares residual fit to
experimental lifetime data for a given D-L sequence S:

𝛿2(𝜃) = ∑ 𝑁snaps 𝑖=1 (𝜏 exp(𝑡𝑖,𝑆𝑖)−𝜏 model(𝑡𝑖,𝑆𝑖,𝜃)) 2

For a given parameter set, the kinetic differential equation system is solved with MATLAB ode23t and
𝜏 (𝑡)and 𝜿2(𝜃) are calculated directly from the solution set. particleswarm from the MATLAB Global
𝐹
Optimization Toolbox is used to perform particle swarm optimization searching parameter space for fit quality
minima. The output Θ is refined using patternsearch and fmincon. 95% confidence intervals were obtained for
the final parameter set via refitting to experimental data and bootstrapping to estimate lower and upper bounds.

Full details of the kinetic scheme and the complete parameter set with 95% confidence intervals are given
in the Supplementary Information (Supplementary Methods).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This report presents an extensive body of work concerning the generation of various tobacco mutants, each deficient in specific proteins associated with non-photochemical chlorophyll fluorescence quenching (NPQ). The authors undertook chlorophyll fluorescence lifetime measurements across all mutant lines, charting both the induction and relaxation kinetics of NPQ. They proceeded to construct a kinetic model designed to incorporate the various components—hypothetically corresponding to discrete phases of NPQ—with the intention of elucidating the respective roles of xanthophylls (notably lutein and zeaxanthin) and the PsbS protein in the plant's response to fluctuating light conditions. While the experimental ambition is commendable, one cannot help but observe that similar Arabidopsis mutants have already been extensively developed and scrutinised in the past. Regrettably, this current study appears to overlook a significant corpus of prior findings. Notably, there remains no compelling in vivo evidence that zeaxanthin or lutein are directly responsible for quenching chlorophyll fluorescence. The mechanism attributed to the so-called qZ component of NPQ is, as yet, not fully resolved. Indeed, several reports suggest that the observed quenching is more consistent with qI-type behaviour— dependent upon residual proton accumulation in the PSII antenna system, rather than any direct engagement by carotenoids. Furthermore, the qI component itself remains a complex and multifaceted phenomenon. Unless it can be unequivocally attributed to irreversible damage to reaction centres (RCII), it must be considered a conflation of both photoinhibitory damage and regulatory quenching within the antenna. More broadly, the manuscript presents a somewhat partial narrative, both in interpretation and citation. Several pivotal contributions to the mechanistic understanding of NPQ, most notably the kinetic analyses and spectroscopic investigations of Horton, Holzwarth, and Jahns, are conspicuously absent from the discussion. The model fitting, in several instances, appears inadequate, and the exclusion of alternative kinetic frameworks (such as those developed by van Grondelle and colleagues) is a notable omission. Finally, one is left to question the practical utility of the modelling approach adopted. Its level of formalism, while impressive, does not appear to yield predictive power beyond that already demonstrated in seminal work such as the Science article by Kromdijk et al. (Long and Niyogi), wherein overexpression of PsbS, VDE, and ZEP was shown to confer tangible physiological benefit. In conclusion, while the effort is appreciable and the experimental execution technically competent, the manuscript suffers from conceptual oversights, selective engagement with the literature, and methodological opacity. A more balanced and integrative approach would be required for the work to reach its full scholarly potential.", "reviewer_2": "The authors have developed a relatively simple model to estimate chlorophyll fluorescence lifetime based on the dynamics of quencher molecules and have successfully reproduced the activation and inactivation kinetics of NPQ under fluctuating light. Compared to the fluorescence intensity measurement by the PAM method, which has been commonly used to assess NPQ activity in plants, the new method provides a more accurate estimate of the concentration changes of multiple quencher molecules. The widespread use of this method will enable more accurate and precise phenotypic analysis of mutant plants and is expected to contribute to plant breeding. The authors have shown that photosynthetic productivity might be improved by enhancing the three kye enzyme activities of NPQ in specific ratios. Although the prediction has not been validated by creating overexpressing strains, the value of this paper is enhanced by presenting predictions that can be demonstrated or disproven by future research. I hope this manuscript will be published after miner revisions described below. 1. The explanation of the fluorescence lifetime model is dispersed into three sections: results, methods, and supplementary information, making it difficult to understand the entire model. I would like the supplemental materials to fully describe this methodology, including the portions already described in the Results and Methods. 2. The manuscript includes many kinds reaction rates and rate constants, but does not well explain what they mean. Even though it is possible to guess what the subscripts f, b, and eq mean, it is unkind that they are never explained in the text. 3. The schematic of stepwise fitting shown in Figure 1B does not seem accurate. lut2 and zep2 have different antenna compositions, so the qE-VSZ columns should be independent of each other, and lut2 should be marked with an * to clarify the difference.", "reviewer_3": "The ms by Lam et al describes a modelling approach where different subcomponents of NPQ are separated from chlorophyll fluorescence lifetime data in wt and mutants of Nicotiana, grown under a few conditions. The kinetic modelling builds in part on previous work, including analyses of various mutants, and a set of double mutants are generated and included in the study. The claim of the authors is that they construct a “fluorescence lifetime-based quantitative kinetic model that disentangles individual photoprotection components and, when integrated additively, accurately predicts wild-type and mutant quenching/recovery behaviors under various light-dark regimes” I have several problems with the ms. I am not a modeller and may not understand all details well enough but from what I can see the approach rely on assumptions that may not necessarily be true, at least where there is far from a consensus on the matter. The NPQ field is hard to navigate in and very entrenched with fundamental disagreements between the key persons in the field concerning the photophysical mechanism of the quenching reaction. Some, like these authors, believe the quenching reaction is a direct quenching of the excited state of chl to the ground state mediated by a carotenoid. Others have different views, there exist also a lot of data in the literature that qE occurs through an interaction between LHCII and PsbS and that some kind of protein conformational change leads to quenching through chl-chl interactions. That camp tends to believe that zeaxanthin, and perhaps other carotenoids, in a yet unclear and perhaps more indirect way affect this change. The jury is still out on this subject but the authors do not even mention that their assumption is not accepted by many other experts in the field. Since the whole ms rely on this assumption, the fact that it is not consensus cannot simply be ignored. I myself do not know what to believe in – maybe the view of the authors is true – but a ms on the subject in a highly respected journal should not without comments/discussion rely on assumptions that many others think are wrong. In addition: - Related to what written above: Since only a direct quenching to the electronic ground state is considered, and a “Stern-Volmer” analog description is used to describe it, doesn´t the design of this study simply exclude any other possible quenching models? An interaction between the excited state of the quenched Chl-protein complex and the quencher molecule could give rise to a new fluorescing species with different emission spectrum and lifetime. Fluorescence is detected at only wavelength (680 nm) and the kinetic analysis only rely on average lifetimes, wouldn´t such a species be invisible is this analysis? - Also concerning quenching mechanisms: In addition to the ones discussed and analysed here, qH was identified in the lab of the authors. It has also been discussed that spillover from PSII to PSI may occur (for example in https://academic.oup.com/pcp/article/64/8/858/7148152, https://www.biorxiv.org/content/10.1101/2024.04.18.590023v2 and https://www.biorxiv.org/content/10.1101/2025.01.26.634902v1). Cyclic electron flow may also influence NPQ. Do the authors believe that these processes are irrelevant for their analysis? If so, motivate. - Is there evidence that all these postulated protein PX complexes are formed in the membrane, and that the action mechanism for at least some of the carotenoid is only indirect in the membrane, without forming such PX complexes? - More than 45 parameters - quenching efficiencies for various carotenoids, interconversion rates etc. - are fitted in the model. Doesn´t this mean a severe risk of overfitting given the amount of independent experimental data? And what does this mean for the error range of the parameters? The number are sometimes given in tables with five digits, which cannot be appropriate. These aspects should be discussed. - A 1 mW beam of laser pulsed measuring light is used. I assume that the same leaf spot has been exposed for ca 20 minutes. If I understand it correctly, this measuring light is much higher than the actinic light which would explain why even the npq1/npq4 mutant has quite substantial quenching (ca 0.7). It is likely that in wt and other mutants, the measuring light may cause even bigger effects. I think the authors need to discuss this issue. - The model performs well for WT under fluctuating light but less well under the 3HL-1D-1HL-3D-9HL-3D regime, or for example in zep2. What is the explanation for this? - The model assumes a halved number of pigment binding sites in the lut2 mutant to approximate LHC structural changes. However, alterations in VAZ binding affinities and LHCII trimer stability may occur in the absence of lutein may have nonlinear effects on NPQ kinetics - The model also assumes that quenching contributions are additive across mechanisms which may not necessarily be true. I guess this could be tested directly by comparing the modelled sum of two single mutant behaviours with the observed double mutant behaviour. Discrepancies here could reveal pleiotropic interactions or structural compensation that the model does not account for. Without this validation, the assumption of linear additivity remains a speculation. - The authors talk about a “lifetime-based quantitative kinetic model that disentangles individual photoprotection components” and lifetime snapshots have been taken. But as far as I understand in their model they only use average lifetimes, not the multi-exponential kinetics. Is it then really a lifetime-based quantitative kinetic model? As lifetimes are proportional to the fluorescence yield, wouldn´t it be possible to reach the same or similar conclusions from stead-state fluorescence measurements, just that the calculated rates in that case would be proportional and not absolute? If so, this analysis does really not make use of the extra information obtained from ultrafast time-resolved experiments. The same lab has previously modelled the photoprotection in Nannochloropsis oceanica. In the introduction of this ms they lists a number of “significant limitations” of that model due to various assumptions. Although I acknowledge the skills of the lab and the complexity of this analysis, I am worried that some of the assumptions they have made here, and the issues mentioned above, reduces the value of this ms and the validity of the claims. Maybe the authors can present good explanations and good discussion on these points, but I am quite sceptic. There are many papers on NPQ mechanisms which suffer from the same weakness; making assumptions that others do not buy. It is not easy when there is no consensus in the field about general assumptions but to move forward it is important to interpret data in the light of the whole body of literature, and this is where I think this ms fall short. Therefore I cannot recommend publication, at least not in the current form.", "reviewer_4": "In this submission, the authors attempt to use a fluorescence lifetime modelling approach to understand the contributions of different components of NPQ in wildtype and mutant Nicotiana benthamiana plants. This review is written from the perspective of someone who has spent a lot of time measuring NPQ in whole plants but using PAM-style approaches and with little experience of modelling. I am therefore reading this as an interested reader rather than an expert and my review is delivered on this basis. I assume more expert reviewers have also been consulted. The paper opens by mentioning the existence of 5 mechanisms of NPQ, though I was a little unclear from the introduction what they consider these to be. I assume that they are identifying Lut quenching as a distinct NPQ mechanism, or are they including this in qE, not quite clear from the wording. Also, I am not clear which are the canonical and non-canonical mechanisms. And is reaction center quenching, associated with Ca2+ release, included in your count or canon? Anyway, they then go on to develop a model that only considers 3 mechanisms of quenching. This could perhaps be more clearly explained in the introduction. The main experimental approach involves the measurements of the decay kinetics of fluorescence under different conditions. I was a little unclear about the details of how these measurements were made, but I assume this is measuring the fluorescence decay following application of 404 nm flashes (duration?) and these decays were fitted with a bi-exponential decay function. It is not clear how good the fit is across different plants and conditions, as no examples are shown. The overall strategy of the modelling made sense to me – mutants with varying lesions in NPQ were fitted using their model and the parameters estimated were used predictively to compare to wild type and further mutants including in additional light regimes. It is always a concern with modelling that it is always possible to find a model to fit data, but using this in a predictive way gives confidence. The initial fits and predictions are not perfect but seem to capture the main processes occurring. Given the number of simplifications required this is reasonably convincing. In terms of applying the models to predict the optimal solution for optimising NPQ, I was not totally sold on the usefulness of the approach, though I think this is a useful thought experiment. In the discussion, the authors are honest about the limitations of their approach, especially in terms of the need to consider biological variability. Whether this experimental approach will ever be as accessible as traditional PAM style measurements I do not know, but this does have the potential to provide useful information in at least some circumstances.", "reviewer_5": "In this study, Lam et al. explore the specific contributions of NPQ components in Nicotiana benthamiana under fluctuating light conditions. By integrating chlorophyll fluorescence lifetime measurements on intact leaves with HPLC pigment profiling, the authors construct a kinetic model that quantifies the activity and molecular efficiency of distinct mechanisms (qE, qZ, qI) and of individual xanthophylls (Zea, Lut, Ant). Through the use of single and double mutants affecting specific NPQ pathways (some pre-existing and others generated in this study), they deconvolute overlapping processes and fit the model through parameter optimization based on experimental fluorescence decay data. The model computes the contributions of each quencher to energy dissipation over time and accurately predicts NPQ dynamics under complex light regimes. This represents an important methodological advancement, offering a predictive framework to guide genetic strategies aimed at improving light use efficiency and photoprotection. Nevertheless, many biological complexities are simplified. The fluctuating light conditions were selected a priori, and the parameter values used in the model may be influenced by various factors. For example, in natural environments characterized by unpredictable sunflecks and alternating low/high light conditions, the model could potentially underestimate or overestimate NPQ dynamics. Or even the approximations used for qI. In fact, the authors themselves acknowledge the main limitations (lines 362 to 376). Nonetheless, the model is coherent with experimental data and provides a solid foundation for further refinement. In the natural environment, the sudden changes in light intensity are accompanied by strong variations in the spectra of actinic light. A short paragraph could be dedicated to this factor of complexity and its effect on measurements. Additional points: - A sensitivity analysis of kinetic parameters (k) across NPQ components would improve robustness and highlight critical points to be implemented to cope with large biological variability. - In Fig. S3, quenching induced by the laser in the absence of actinic light reaches ~0.6 ns after 20 minutes, which is non-negligible. How frequently are lifetime snapshots taken per induction curve? Reducing snapshot density might minimize laser pulse-induced quenching, especially during dark phases, and improve the accuracy of recovery kinetics; a potential trade-off. - Line 334: please verify consistency with respect to Fig. 4A. The major components are Lut and Ant, not Vio."}, "author_response": "For clarity, we have color-coded this response letter as follows:\nBlack: Reviewer comments\nBlue: Our responses\nRed: Direct citations of the manuscript and underlining relavant changes made in the revised\nmanuscript. (Line numbers refer to the no tracked-changes version; page numbers are consistent\nacross both versions.)\nGeneral Response\nWe thank all five reviewers for their careful reading of our manuscript. In reading the reviews it\nbecame clear that we did not adequately describe the motivation for our approach and\nspecifically what we assume and what we don’t assume. We agree with reviewer 3 that “The\nNPQ field is hard to navigate in and very entrenched with fundamental disagreements between\nthe key persons in the field concerning the photophysical mechanism of the quenching reaction.”\nThis is why our model does not assume specific quenching mechanisms and makes what we\nbelieve to be the minimal number of assumptions necessary to create a closed self-consistent\nmodel. In addition, we were less than precise in our use of the word “mechanism”. In what\nfollows and in the revised manuscript we reserve “mechanism” to refer only to specific\nmolecular processes such as energy, charge transfer or the induction of structural change\nallosterically. We refer to the phenomenologically defined components of NPQ (qE, qZ, qI etc.)\nas “types of NPQ”, “components of NPQ”, or “contributions to NPQ”.\nSpecifically, what we assume is listed below followed by the list of assumptions we did not\nmake. We have added these lists to the SI “Extended methods” section and summarized the key\npoints when we introduce the modeling approach in the main text.\nAssumptions Needed\n1. NPQ in plants is mainly contributed by qE (VAZ, and lutein), qZ, and qI. In our study,\nqT, qH, and chloroplast movement have negligible effects.\n2. Each NPQ contribution contributes independently to the Chl fluorescence quenching, and\ncan be therefore modelled separately.\n3. Quenching is modeled using a Stern-Volmer–like relation between fluorescence lifetime\nand the concentrations of quencher molecules (QV, QA, QZ, QL, qI). The Stern-Volmer\nrelation has been the standard method of modeling fluorescence quenching since its\nintroduction in 191945.\n4. qZ depends on the concentration of Zea in a light-independent manner.\n5. Lut is assumed to be always protein-bound when present.\n1\n6. The activation of protein-xanthophyll complexes (PX) to quenching complexes (QX) is\nneeded for the qE type of quenching to be activated. While the total pool of xanthophyll-\nbinding sites P is fixed and shared among Zea, Vio, Ant, and Lut, the\nformation/dissociation kinetic rates are unique to each xanthophyll (Zea, Vio, Ant, Lut).\n[qI Model]\n7. The qI component is modeled phenomenologically as irreversible photodamage to PSII\nreaction centers, accumulating over time with a rate proportional to excitation (i.e.,\ninversely to lifetime).\n8. The reverse/repair process is assumed to be negligible within our timescale.\n9. Quenching observed during laser-only exposure is attributed to qI and is assumed to be\nsufficiently captured by fitting qI parameters in npq4npq1 data.\n[Data Validation]\n10. Time-resolved fluorescence snapshots are assumed to sufficiently capture the dynamics\nof all the NPQ contributions, with average lifetimes representing total quenching at each\ntime point. The average lifetime is directly proportional to the fluorescence quantum\nyield. We have shown elsewhere that fluorescence decays of a model thylakoid\nmembrane57 containing roughly 30,000 rates can be fit very well with two exponentials,\nbut those two components do not relate to the underlying microscopic dynamics in any\ndirect way.\n[lut2 specifically]\n11. Considering the structural changes in the absence of lutein, the number of protein-\nxanthophyll binding sites ([P]_tot) is assumed to be different between WT and the lut2\nmutant. [P]_tot in lut2 was approximated by modeling to be ~50% of [P]_tot in WT.\nAssumptions Not Needed\n1. By fitting the lifetime values directly, we do not assume zero initial quenching in all the\nmutants as was done by Short et al.37.This is important for mutants such as zep2.\n2. Despite utilizing some widely accepted biochemical processes6 involved in induction of\nqE (including the VAZ cycle), the model does not make assumptions on the actual\nphysical mechanism of how these quenching processes happen. The model never claims\nto resolve ultrafast excited-state dynamics. For example, the model doesn’t assume direct\nquenching (by energy transfer/charge transfer) of Chl excited states by Car.\n3. The model does not resolve or define the exact mechanistic basis of qZ or qI. It only\ndifferentiates them kinetically from qE based on mutant behavior and pigment profiles.\n4. Calculating per-molecule quenching efficiencies does not reflect direct mechanistic\ninterpretation. It reflects the general impact on NPQ/lifetime caused by each species.\n2\n[Other NPQ components]\n5. While qT mutants were tested (e.g., stn7 mutants) and found to have negligible influence\non our system over the timescale we studied, we do not reject or adopt any specific\nquenching mechanism. We do not claim these (qT, qH, Chloroplast movement/thylakoid\nmembrane structural adjustment) are irrelevant to NPQ in general.\n[Fitting Interpretation]\n6. The model does not assume that NPQ component contributions (e.g., qE, qZ, qI) are\nadditive in general. However, when making predictions, the model does compute overall\nNPQ as a sum of individual quenching components (see Assumption 2 - each mechanism\nis independent). However, our approach of fitting simpler mutants and directly applying\ntheir rates to complex genotypes, such as zep2 and WT, with great accuracy shows that\nNPQ components can be considered as additive.\n7. The model captures most of the NPQ dynamics in different illumination sequences. We\ndesigned different irregular sequences that presented a more difficult challenge for the\nmodel to make predictions.\nThe meaning of equations (1) and (2) in the main text and S1-S8 in the SI is, taking zeaxanthin\nas a specific example, that the extent of rapid NPQ (qE) depends on the amount of zeaxanthin\nbound to light-harvesting proteins and, for some components of NPQ, activation of quenching by\nPsbS. Beyond the proportionality of NPQ to the concentration of zeaxanthin, which is well\nestablished experimentally, and the requirement for activation of quenching via protonated PsbS,\nno further assumptions are made as to the molecular mechanism of zeaxanthin’s role or the\nmechanism by which PsbS activates quenching other than requiring a ΔpH across the thylakoid\nmembrane.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis report presents an extensive body of work concerning the generation of various tobacco\nmutants, each deficient in specific proteins associated with non-photochemical chlorophyll\nfluorescence quenching (NPQ). The authors undertook chlorophyll fluorescence lifetime\nmeasurements across all mutant lines, charting both the induction and relaxation kinetics of\nNPQ. They proceeded to construct a kinetic model designed to incorporate the various\ncomponents—hypothetically corresponding to discrete phases of NPQ—with the intention of\nelucidating the respective roles of xanthophylls (notably lutein and zeaxanthin) and the PsbS\nprotein in the plant's response to fluctuating light conditions.\nResponse: We thank the reviewer for their thoughtful and critical evaluation of our manuscript.\nWe appreciate the recognition of the experimental scope and the effort to engage with the\nconceptual framework of our model. These comments have helped us identify areas where our\n3\nassumptions, scope, and citations could be more clearly articulated. Below we respond to each of\ntheir comments in detail.\n1. While the experimental ambition is commendable, one cannot help but observe that similar\nArabidopsis mutants have already been extensively developed and scrutinised in the past.\nResponse: We appreciate the reviewer’s question and the opportunity to clarify our rationale for\nselecting Nicotiana benthamiana as the model organism in this study. Our choice was motivated\nby several practical and scientific considerations.\nFirst, we note that fluorescence measurements do not allow detailed questions of molecular\nmechanisms to be fully addressed. Transient absorption experiments are required for this level\nof investigation However, Arabidopsis thylakoids have proven difficult to use for these types of\nmeasurements. To the best of our knowledge, neither we nor others have successfully acquired\nhigh-quality, time-resolved transient absorption data from Arabidopsis thylakoid preparations.\nAlso, the exceptional amount of biomass capable of being produced via N. benthamiana also\ndramatically improves biochemical experiment capacity that is often constrained by chloroplast\nand thylakoid yield. In contrast, we have obtained experimental evidence in isolated thylakoids\nfor chlorophyll to zeaxanthin energy transfer26 as well as intensity-cycling transient absorption\ndata42 from five N. benthamiana NPQ mutants also described in this study. Although NPQ\nvalues are lower in thylakoids than in intact leaves, these measurements allow us to extract semi-\nquantitative estimates of photoprotection generated by a specific amount of fluorescence\nquenching. This makes N. benthamiana a uniquely suitable platform for linking fluorescence-\nbased modeling with spectroscopic validation. We revised the manuscript shown below:\n(Main text Page 6 Line 118-123) “To that end, we selected Nicotiana benthamiana as\nour model system due to its compatibility with transient absorption spectroscopic\nmeasurements, which facilitate investigation of potential qE quenching mechanisms26 and\nassessment of exciton diffusion length in relation to NPQ levels in isolated thylakoids42—\nmeasurements that are challenging to perform in Arabidopsis thaliana. This choice could\nenable both kinetic and mechanistic analysis of NPQ in land plants when combined with\ncomplementary techniques.”\nSecond, as mentioned in the Introduction, while our previous modeling efforts in\nNannochloropsis offered important conceptual insights, that system lacks the complexity of\nhigher plant NPQ regulation and is not representative of crop physiology. Developing the first\nland plant-specific NPQ model, even in a non-crop model organism, therefore fills a critical gap\nin the field.\nFurthermore, the allotetraploid nature of N. benthamiana opens up new quantitative routes for\ntitrating NPQ gene expression and downstream phenotypes. For example, we specifically\nhighlighted our use of the zep2 mutant (retaining functional ZEP1) in this study, and have refined\nthe text to emphasize its novelty.\n4\n(Main text Page 8-9 Line 179-189) “In this study, we generated and characterized single\nand double mutants deficient in one or both ZEP orthologs. Vio is a precursor to abscisic\nacid, an essential plant hormone for stomatal regulation and drought tolerance. The lack\nof epoxidized Vio in the zep1zep2 double knockout in N. benthamiana significantly\nreduced plant viability, with only one of eleven T0 transformants producing viable, stable\nprogeny (see Methods). Each ortholog contributes additively to Zea epoxidation, with\nZEP1 being the dominant copy. Interestingly, knockout of ZEP2 (hereafter zep2) resulted\nin a moderate knockdown phenotype, with WT-like growth and a near-native Vio and\nNeo composition and sufficient constitutive Zea to saturate Zea-dependent qE capacity\n(Fig. S1), unlike the non-native composition and slowed, humidity-dependent growth\nfound in the A. thaliana npq2 point mutant10. This zep2 mutant provided quantitative and\ntemporal variation in Zea-dependent qE and qZ that was necessary and sufficient for\nphenotypic modeling, without the pleiotropic impacts on xanthophyll composition that\nare present in existing A. thaliana mutants.”\nThe aforementioned intensity-cycling transient absorption study also utilizes a newly\ncharacterized psbs1 mutant with reduced qE capacity, similarly deficient in one of two PsbS\northologs, which has expanded our capacity to interrogate the biophysical basis of NPQ. It is our\nhope that these mutants, parallel to those in Arabidopsis but with interesting use cases, can be a\nhelpful resource to the NPQ research community.\nFinally, as mentioned in the discussion, N. benthamiana shares close genetic similarity with N.\ntabacum, a species in which VPZ (VDE, PsbS, ZEP) overexpression has already been shown to\nimprove biomass and photosynthetic performance52. We thus view N. benthamiana as an ideal\nmodel for bridging mechanistic understanding with translational relevance, particularly in the\ncontext of improving crop photoprotection through NPQ engineering.\n2. Regrettably, this current study appears to overlook a significant corpus of prior findings.\nNotably, there remains no compelling in vivo evidence that zeaxanthin or lutein are directly\nresponsible for quenching chlorophyll fluorescence.\nResponse: We thank the reviewer for raising this point. As noted in the general response, our\nmodel does not assume or require a specific direct quenching mechanism between carotenoids\nand Chl*. While some studies support the possibility of energy or charge transfer from carotenoids24-26,30-35, our\nmodel focuses on NPQ kinetics as well as the model’s predictive capability, and remains\nagnostic to the photophysical mechanism. We now clarify this distinction and the limits of our\ninterpretation in the Introduction of the revised manuscript. There is a good deal of evidence\n(Demmig-Adams et al., Zeaxanthin Synthesis, Energy Dissipation, and Photoprotection of\nPhotosystem II at Chilling Temperatures 1. Plant Physiology 90, 894–898 (1989).) that the\nextent of qE is linearly related to the zeaxanthin content of leaves and this is what we assume.\n(Main text Page 4 Line 74-86) “The specific molecular interactions and mechanisms\ninvolved in these NPQ processes are still extensively debated. In a recent review, Bassi\nand Dall’Osto6 describe four distinct mechanisms that involve either chlorophyll–\n5\nchlorophyll or chlorophyll–carotenoid interactions, where the role of the carotenoids may\nbe direct24–26 or as an allosteric effector (e.g. in Chl–Chl interactions)27–29. Other\nproposed mechanisms include charge transfer between Chl and Car that quenches Chl\nQy30–33 and excitonic interactions that may produce a low-lying energy level of a Car,\nwhich relaxes rapidly to the ground state either directly or via transfer to a carotenoid S1\nstate 34,35.\nGiven the ongoing debate on molecular mechanisms (which may all occur in parallel), we\nhave constructed a kinetic model for NPQ that is as agnostic as possible to the precise\nmolecular mechanisms while incorporating the known factors of ΔpH activation of VDE\nand PsbS and the VAZ cycle. The time-dependent contributions of the various actors\nemerge naturally from the model and may help to advance the discussions of NPQ\nmechanisms.”\n3. The mechanism attributed to the so-called qZ component of NPQ is, as yet, not fully resolved.\nIndeed, several reports suggest that the observed quenching is more consistent with qI-type\nbehaviour—dependent upon residual proton accumulation in the PSII antenna system, rather than\nany direct engagement by carotenoids.\nFurthermore, the qI component itself remains a complex and multifaceted phenomenon. Unless it\ncan be unequivocally attributed to irreversible damage to reaction centres (RCII), it must be\nconsidered a conflation of both photoinhibitory damage and regulatory quenching within the\nantenna.\nResponse: We appreciate this important question. As noted, the model does not claim a\nmechanistic separation, but rather a kinetic distinction based on behavior in specific mutant\nbackgrounds (e.g., npq4, npq1npq4).\nIn our model, qI is modeled phenomenologically by fitting npq4npq1 data, representing a slow,\nirreversible process consistent with photoinhibition. While we do not attempt to resolve the\nunderlying molecular nature of qI—acknowledging that it likely includes both damage-related\nand regulatory components—our goal was to quantitatively capture its kinetics under fluctuating\nlight. We have added discussion to acknowledge this complexity and the limitations of our\ncurrent treatment in our revised manuscript.\n(Main text Page 10 Line 221-224) “This indicates although the laser baseline modestly\ncontributes to detectable quenching, this effect is accounted for in our model within the\nqI parameters via the positive 𝛼 accumulation rate in darkness in npq4npq1, which was\n𝑞𝐼\nmodeled phenomenologically due to the unresolved mechanistic basis of qI, yet still\noffers insight into the temporally resolved contribution of qI.”\n4. More broadly, the manuscript presents a somewhat partial narrative, both in interpretation and\ncitation. Several pivotal contributions to the mechanistic understanding of NPQ, most notably the\n6\nkinetic analyses and spectroscopic investigations of Horton, Holzwarth, and Jahns, are\nconspicuously absent from the discussion.\nResponse: We thank the reviewer for highlighting this oversight. In the revised manuscript, we\nhave expanded our Introduction to include key foundational studies from these authors, please\nsee Comment #2 of Reviewer #1. However, as we have noted, the extensive debate in the\nliterature as to molecular mechanisms, while important, is not central to our model. Regarding\nthe two sites, independent quenching mechanisms in each site introduced by Holzwarth and\nJahns (Barbara Demmig-Adams et al., Non-Photochemical Quenching and Energy Dissipation in\nPlants, Algae and Cyanobacteria. (Springer Netherlands, Dordrecht, 2014).), we note that this is\nconsistent with the additivity of contributions that our model suggests. We have added comments\non this point in the Introduction of the revised text, also mentioned in our response to Comment\n#2 of Reviewer #1.\n5. The model fitting, in several instances, appears inadequate, and the exclusion of alternative\nkinetic frameworks (such as those developed by van Grondelle and colleagues) is a notable\nomission.\nResponse: We understand the reviewer’s concern for the quality of model fitting in some\ninstances. While the model visually appears to underperform for certain mutants (e.g. npq4zep2\nin Fig 3G and Fig S6D and lut2 in Fig S5C), we find the calculated RMSD values of fit quality\n(𝛿2(𝜃) defined in Methods) obtained for each mutant to be within an acceptable limit (<1 s-1).\nFurthermore, given that these discrepancies occur only in model predictions, rather than for\nsequences used for fitting, we anticipate and accept a certain level of error. Small deviations\nfrom model predictions are likely the result of pleiotropic interactions or specific mutant\nbehaviors beyond the scope of our model.\nWe further thank the reviewer for highlighting past works on NPQ kinetic modeling by van\nGrondelle. We note that our work bears no contradiction to their previous studies. Their kinetic\nframework places emphasis on the state of PSII (as either “quenched” or “unquenched” and with\n“open” or “closed” reaction centers) without directly parameterizing any biochemical pathways\nor chemical species60. Furthermore, in our experiments, we excite Chl a using a saturating laser\npulse (1.0 mW) to ensure the reaction centers are closed during measurement; thus, in our\ncontext their model reduces to the interconversion of PSII between quenched and unquenched,\nwhich is effectively a maximal simplification of our model.\nNevertheless, their approach to modeling quantum yield (proportional to average lifetime) as a\nlinear combination of fractional species concentrations and their quantum yields is intriguing.\nHowever, this approach restricts the characterization of species concentrations to fractions of a\nwhole (constant) pool, which limits the ability to compare kinetics across different genotypes,\nwhich can exhibit large variations in the size of the total xanthophyll pool. As such, we consider\nsuch a framework inadequate for capturing the complex dynamics of NPQ in N. benthamiana.\n7\n(Main text Page 18-19 Line 418-425) “Lastly, to focus on the rapid NPQ components\nthat occur in the PSII, our experimental approach involves closing all RCs using laser\npulses in the region of the leaf from which chlorophyll fluorescence is detected. In\nnatural environments, however, RCs are likely to exist in both open and closed states,\nwhich may influence NPQ behavior. This situation was previously modeled using PAM\nfluorometry by Snellenburg et al.60. Recent work from our group has shown that NPQ\nreduces the range of excitation migration and limits the number of accessible RCs42.\nThus, combining the approach of Snellenburg et al. with our current method would\nintroduce a complex dependence on membrane morphology.”\n6. Finally, one is left to question the practical utility of the modelling approach adopted. Its level\nof formalism, while impressive, does not appear to yield predictive power beyond that already\ndemonstrated in seminal work such as the Science article by Kromdijk et al. (Long and Niyogi),\nwherein overexpression of PsbS, VDE, and ZEP was shown to confer tangible physiological\nbenefit.\nResponse: We thank the reviewer for this question. The work by Kromdijk et al. demonstrated\nthat overexpressing NPQ-related proteins (VDE, ZEP, PsbS) improved plant productivity, and\nincluded PAM-based NPQ measurements and representative pigment quantification52. However,\nit did not provide a predictive framework to simulate NPQ behavior under different light regimes\nor varying expression ratios. In contrast, our model enables quantitative simulation of NPQ\nkinetics across mutants and conditions, and makes testable predictions about how different levels\nof enzyme overexpression affect induction, relaxation, and overall quenching efficiency. These\nfindings emphasize that the specific stoichiometry of overexpression is essential, and that\nstoichiometry is unknown for different species. This issue may reflect the differences in success\nin the application of the VPZ genetic engineering strategy across tobacco, Arabidopsis, potato,\nand soybean, reviewed in Croce et al.56. We agree that further experimental validation is needed,\nand we now emphasize this in the revised Discussion section.\n(Main text Page 17 Line 378-389) “The complete WT model further provides a\npredictive framework for … Building on prior empirical studies, our model enables\npredictive simulation of NPQ behavior under different expression levels and\nstoichiometries of these key enzymes.”\n(Main text Page 17 Line 395-399) “In these modeled VPZ overexpression mutants, an\nincrease in the relative contribution from qE , qE ,qE ,andqE underlie the\nV A Z L\nimprovements in the NPQ induction and recovery dynamics. While this thought\nexperiment offers helpful insight, future experimental work with VPZ mutants of N.\nbenthamiana or related species will be necessary to validate these predictions and help\nrealize their potential for field application.”\nMoreover, we emphasize that our model uniquely predicts the temporal evolution of quenching\ncontributions from individual NPQ pathways, including qE , qE ,qE ,qE , qZ, and qI,\nV A Z L\n8\nthroughout the course of light exposure (see Movie S1), a temporal resolution not currently\nachievable with existing studies.\n7. In conclusion, while the effort is appreciable and the experimental execution technically\ncompetent, the manuscript suffers from conceptual oversights, selective engagement with the\nliterature, and methodological opacity. A more balanced and integrative approach would be\nrequired for the work to reach its full scholarly potential.\nResponse: We acknowledge that our initial manuscript would have benefited from clearer\narticulation of the model’s scope, assumptions, and relationship to existing literature, but most of\nthe shortcomings noted by the reviewer relate to the misconception of how the model is\nconstructed and what it is based upon. In response, we have made substantial revisions to clarify\nour modeling framework, explicitly distinguish between assumptions made and not made, and\nbroaden the discussion to engage more fully with key mechanistic studies in the field. We hope\nthat these revisions properly address the reviewer’s concerns.\nReviewer #2 (Remarks to the Author):\nThe authors have developed a relatively simple model to estimate chlorophyll fluorescence\nlifetime based on the dynamics of quencher molecules and have successfully reproduced the\nactivation and inactivation kinetics of NPQ under fluctuating light. Compared to the\nfluorescence intensity measurement by the PAM method, which has been commonly used to\nassess NPQ activity in plants, the new method provides a more accurate estimate of the\nconcentration changes of multiple quencher molecules. The widespread use of this method will\nenable more accurate and precise phenotypic analysis of mutant plants and is expected to\ncontribute to plant breeding. The authors have shown that photosynthetic productivity might be\nimproved by enhancing the three key enzyme activities of NPQ in specific ratios.\nAlthough the prediction has not been validated by creating overexpressing strains, the value of\nthis paper is enhanced by presenting predictions that can be demonstrated or disproven by future\nresearch.\nI hope this manuscript will be published after miner revisions described below.\nResponse: We thank the reviewer for their positive and encouraging assessment of our\nmanuscript. We are especially grateful for their recognition of the methodological contribution\nand the potential of our model to advance phenotypic analysis and predictive understanding of\nNPQ. We also appreciate the helpful suggestions for improving clarity and presentation. Below,\nwe respond to each of the comments in turn.\n1. The explanation of the fluorescence lifetime model is dispersed into three sections: results,\nmethods, and supplementary information, making it difficult to understand the entire model. I\nwould like the supplemental materials to fully describe this methodology, including the portions\nalready described in the Results and Methods.\nThank you for this helpful suggestion. In response, we have reorganized and expanded the\nSupplementary Information “Extended Methods” section to provide a complete and self-\ncontained description of the fluorescence lifetime-based kinetic model. This now includes all\nrelevant equations, assumptions, and methodological explanations that were previously scattered\nacross the main text and Methods. We hope this revision will improve accessibility for readers.\n2. The manuscript includes many kinds reaction rates and rate constants, but does not well\nexplain what they mean. Even though it is possible to guess what the subscripts f, b, and eq\nmean, it is unkind that they are never explained in the text.\nResponse: We appreciate this observation and apologize for the oversight. We have revised both\nthe main text and Supplementary Information (Table S1 and “Extended Methods”) to clearly\ndefine all rate constants and subscripts upon their first use. We hope this improves clarity and\nreadability.\n(Main text Page 16 Line 350-351) “(\n𝑘𝑞𝑥𝑓\n, where 𝑘 /𝑘 are the forward and\n𝑞𝑥𝑓 𝑞𝑥𝑏\n𝑘𝑞𝑥𝑓+𝑘𝑞𝑥𝑏\nbackward rates of QX formation, respectively), and (3) the quenching rate constant of\nQX (𝜅 ).”\n𝑄𝑋\n(Figure S1) “Subscripts “f” and “b” denote forward and backward rates and superscripts\n“L” and “D” denote light and dark conditions, respectively. Additionally, subscripts “va”\nand “az” denote conversion from V to A and from A to Z, while subscripts “za” and\n“av” denote conversion from Z to A and A to V, respectively.\n(SI “Extended Methods” Page 20) “To reduce the number of parameters needed to fit\nthe model, we take VDE concentration as a ratio: 𝛼 (𝑡) =\n[𝑉𝐷𝐸𝑎 ]\nwhere subscript\n𝑣𝑑𝑒𝑎 [𝑉𝐷𝐸𝐿 ]\n𝑎,𝑒𝑞\n“eq” denotes the equilibrium condition.”\n3. The schematic of stepwise fitting shown in Figure 1B does not seem accurate. lut2 and zep2\nhave different antenna compositions, so the qE-VSZ columns should be independent of each\nother, and lut2 should be marked with an * to clarify the difference.\nResponse: We thank the reviewer for pointing this out. We have revised Figure 1B to more\naccurately reflect the structural differences between lut2 and zep2, and now clearly mark with an\nasterisk (*) the qE-VAZ fitting component associated with lut2 to indicate the mutant’s distinct\nxanthophyll-binding profile. Note that some of the rate parameters involved in the VAZ\ncomponent of qE are shared between lut2 and zep2, so in Figure 1B, the qE-VAZ section is not\nentirely independent between lut2 and zep2, hence the overlap.\nReviewer #3 (Remarks to the Author):\nThe ms by Lam et al describes a modelling approach where different subcomponents of NPQ are\nseparated from chlorophyll fluorescence lifetime data in wt and mutants of Nicotiana, grown\nunder a few conditions.\ninterpretations, and broader context within the NPQ field. We address each point below in detail,\nwith clarifications and revisions to the manuscript to reflect the reviewer’s helpful suggestions.\nWe would like to clarify that all plants used in this study were grown under the same\nenvironmental conditions as described in the Methods section. However, they were exposed to\ndifferent actinic light sequences during measurements in order to resolve the distinct kinetic\nbehaviors of various NPQ components.\nThe kinetic modelling builds in part on previous work, including analyses of various mutants,\nand a set of double mutants are generated and included in the study. The claim of the authors is\nthat they construct a “fluorescence lifetime-based quantitative kinetic model that disentangles\nindividual photoprotection components and, when integrated additively, accurately predicts wild-\ntype and mutant quenching/recovery behaviors under various light-dark regimes”\n1. I have several problems with the ms. I am not a modeller and may not understand all details\nwell enough but from what I can see the approach rely on assumptions that may not necessarily\nbe true, at least where there is far from a consensus on the matter. The NPQ field is hard to\nnavigate in and very entrenched with fundamental disagreements between the key persons in the\nfield concerning the photophysical mechanism of the quenching reaction. Some, like these\nauthors, believe the quenching reaction is a direct quenching of the excited state of chl to the\nground state mediated by a carotenoid.\nResponse: As noted in the general response, our model does not assume or require a specific\ndirect quenching mechanism between carotenoids and Chl*. While some experimental evidence\nsupports such mechanisms (e.g., via excitation energy transfer or charge transfer), our model\nremains agnostic to the precise molecular mechanism of quenching. Rather than resolving\nultrafast dynamics or structural details, our approach focuses on phenomenologically separating\nquenching contributions based on genotypic and pigment dynamics, rather than assuming or\nattributing them to a specific photophysical pathway. We revised the Introduction to make this\nclearer, please see response to Comment #2 of Reviewer #1.\n2. Others have different views, there exist also a lot of data in the literature that qE occurs\nthrough an interaction between LHCII and PsbS and that some kind of protein conformational\nchange leads to quenching through chl-chl interactions.\nResponse: We thank the reviewer for highlighting this important perspective. We agree that\nalternative mechanistic hypotheses for qE—such as PsbS-induced conformational changes or\nChl-Chl interactions—are widely discussed in the field. We have revised the manuscript, shown\nin our response to Comment #2 of Reviewer #1, to explicitly acknowledge these ongoing debates\nand clarify that our model does not aim to resolve such molecular details (see the list of\nassumptions in the general response). The observed lifetime changes we attribute to qE reflect\nschematic (Fig. 1A).\nIn addition, LHCII aggregation is known to give rise to far-red fluorescence emission that has\nbeen attributed to Chl-Chl charge-transfer states27 and also is observed in vivo during qE\n(Miloslavina et al., Quenching in Arabidopsis thaliana Mutants Lacking Monomeric Antenna\nProteins of Photosystem II*. Journal of Biological Chemistry 286, 36830–36840 (2011);\nMarulanda Valencia, W. & Pandit, A. Photosystem II Subunit S (PsbS): A Nano Regulator of\nPlant Photosynthesis. Journal of Molecular Biology 436, 168407 (2024).). As noted in the\ngeneral response, our model does not explicitly consider Chl-Chl interactions, nor do we\nconsider any specific physical mechanism of quenching. If Chl-Chl interactions or other\nmechanisms contribute to quenching of Chl excited states, they will contribute to the decrease of\nfluorescence lifetime, and therefore be captured by the TCSPC measurement. Their effects might\nbe captured empirically by our framework, but not mechanistically resolved.\n3. That camp tends to believe that zeaxanthin, and perhaps other carotenoids, in a yet unclear and\nperhaps more indirect way affect this change. The jury is still out on this subject but the authors\ndo not even mention that their assumption is not accepted by many other experts in the field.\nSince the whole ms rely on this assumption, the fact that it is not consensus cannot simply be\nignored. I myself do not know what to believe in – maybe the view of the authors is true – but a\nms on the subject in a highly respected journal should not without comments/discussion rely on\nassumptions that many others think are wrong.\nResponse: As mentioned in the general response, our kinetic model neither assumes direct\nquenching by carotenoids nor excludes indirect pathways. To clarify, we now explicitly\nacknowledge the lack of consensus in the field and the range of mechanistic interpretations in\nour revised manuscript. We also include additional citations to relevant literature from differing\nviewpoints, as shown in response to Comment #2 of Reviewer #1.\n4. In addition:\n- Related to what written above: Since only a direct quenching to the electronic ground state is\nconsidered, and a “Stern-Volmer” analog description is used to describe it, doesn´t the design of\nthis study simply exclude any other possible quenching models?\nResponse: Please see assumption 3 in the general response. The Stern-Volmer-type relationship\nin our model simply describes a process (not necessarily returning to the ground state) that\ncompetes with the normal fluorescence decay and is proportional to the concentration of a\nquencher. It is intended as a phenomenological tool, describing the correlation between quencher\nconcentration and fluorescence lifetime. It does not imply a specific molecular quenching\nmechanism. This approach was chosen for its simplicity, interpretability, and experimental\n12\ntractability. We clarify this in the revised manuscript and also cited foundational work45 that\nsupport this description in modeling.\n(Main text Page 8 Line 160-163) “While this phenomenological formulation can include\nmultiple alternative quenching mechanisms, it enables effective kinetic fitting of\nfluorescence lifetimes, and it was chosen for its simplicity, interpretability, and capacity\nto capture the dynamic contributions of multiple quenching species.”\n5. An interaction between the excited state of the quenched Chl-protein complex and the\nquencher molecule could give rise to a new fluorescing species with different emission spectrum\nand lifetime. Fluorescence is detected at only wavelength (680 nm) and the kinetic analysis only\nrely on average lifetimes, wouldn´t such a species be invisible is this analysis?\nResponse: We thank the reviewer for raising this point. Our detection wavelength is centered at\n680 nm to monitor Chl a fluorescence. The excited state species described by reviewer 3 would\nreduce excited Chl a population, as a competing pathway that is incorporated into Stern-Volmer\nexpression. Thus, any mechanism that reduces Chl a fluorescence will be captured in our lifetime\nmeasurements. The use of amplitude-weighted average lifetime at 680 nm represents a collective\neffect resulting from quenching of Chl a by all the species and quenching pathways, we revised\nthe Methods section to clarify this.\n(Main text Page 22-23 Line 511-515) “Note that the measured chlorophyll fluorescence\ndecay results from the averaging of tens of thousands of rate constants. As we showed in\nprevious work65, a decay curve arising from 30,000 rates can be perfectly fit by a sum of\ntwo exponentials, though these components do not correspond to any specific underlying\nrates or combinations thereof. The amplitude-weighted average lifetime values obtained\nfrom each snapshot were directly used in constructing and testing the NPQ model.”\n6. Also concerning quenching mechanisms: In addition to the ones discussed and analysed here,\nqH was identified in the lab of the authors. It has also been discussed that spillover from PSII to\nPSI may occur (for example\nin https://academic.oup.com/pcp/article/64/8/858/7148152, https://www.biorxiv.org/content/10.1\n101/2024.04.18.590023v2\nand https://www.biorxiv.org/content/10.1101/2025.01.26.634902v1). Cyclic electron flow may\nalso influence NPQ. Do the authors believe that these processes are irrelevant for their analysis?\nIf so, motivate.\nResponse: We thank the reviewer for this thoughtful question. We do not claim that these\nprocesses are irrelevant to NPQ in general. However, based on our experimental design and\nsome prior testing, their contribution was found to be negligible under the conditions used in this\nstudy. For example, we previously examined qT using stn7 and npq4stn7 mutants in Arabidopsis\nleaves and observed no significant differences in fluorescence lifetimes within our timescale\n(shown in Lam et al.18 and some unpublished data). qT tends to contribute more significantly to\n13\nalgae (Steen et al., Interplay between LHCSR proteins and state transitions governs the NPQ\nresponse in Chlamydomonas during light fluctuations. Plant, Cell & Environment 45, 2428–2445\n(2022).).\nqH is activated under cold and high-light stress, which does not apply to our growth or\nexperimental conditions21-22. Our main purpose in this study is to provide a quantitative but\nsimplified NPQ model to eventually help provide guidance on understanding the kinetic\nbehaviors of the key quenching components as well as crop yield improvement, therefore making\nqE, qZ, and qI the key components to incorporate, to balance between predictability of the model\nand the conciseness and usability of the model.\nIn addition, both qT and CEF are unlikely to contribute considerably under the high light\nintensity and short timescales relevant to this study (Ramakers et al., Unravelling the different\ncomponents of nonphotochemical quenching using a novel analytical pipeline. New Phytologist\n245, 625–636 (2025)). While cyclic electron flow can influence ΔpH, its impact would likely be\nsimilar across all mutants tested, as PSII was saturated by the laser pulses.\nWe now clarify this rationale in the Introduction of the revised manuscript.\n(Main text Page 4 Line 63-70) “Since qT does not directly quench 1Chl*, it contributes\nminimally to the PSII Chl a fluorescence quenching in the organism studied here,\nNicotiana benthamiana, as supported by prior tests showing negligible qT-associated\nchanges in 1Chl* fluorescence lifetimes under our experimental conditions18 … The most\nrecently discovered chloroplast lipocalin-dependent sustained antenna quenching (qH)\noccurs under cold and high light conditions20–22, and is therefore unlikely to be relevant\nunder the typical growth and experimental conditions used for N. benthamiana or target\ncrops.”\n(Main text Page 6 Line 126-127) “we construct a lifetime-based quantitative kinetic\nmodel of NPQ focused on the largest components in land plants: qE, qZ, and qI.”\n7. Is there evidence that all these postulated protein PX complexes are formed in the membrane,\nand that the action mechanism for at least some of the carotenoid is only indirect in the\nmembrane, without forming such PX complexes?\nResponse: We appreciate this insightful question. In our model, PX and QX represent abstract\nstates of pigment–protein association and activation, rather than asserting specific structural\nassemblies. Beyond the proportionality of NPQ to the concentration of xanthophylls, the PX →\nQX transition reflects the requirement for quenching activation via protonated PsbS. No further\nassumptions are made as to the molecular mechanism of xanthophyll’s role or the mechanism by\nwhich PsbS activates quenching.\nIn addition, while carotenoids can indeed form protein-bound complexes and act as direct or\nindirect quenchers6,24-26,30-35, membrane-dispersed carotenoids primarily function as antioxidants\nand likely contribute minimally to NPQ on the timescales studied here (eg., Carotenoids. eds.\nWise, R. R. & Hoober, J. K., (Springer Netherlands, Dordrecht, 2006). doi:10.1007/978-1-4020-\n14\n4061-0_16; Strzalka et al., Carotenoids and Environmental Stress in Plants: Significance of\nCarotenoid-Mediated Modulation of Membrane Physical Properties. Russian Journal of Plant\nPhysiology 50, 168–173 (2003).). In contrast, carotenoids located in close proximity to Chl\nwithin protein complexes are thought to play a more significant role in quenching (e.g., Liguori\net al., From light-harvesting to photoprotection: structural basis of the dynamic switch of the\nmajor antenna complex of plants (LHCII). Sci Rep 5, 15661 (2015)).\n8. More than 45 parameters - quenching efficiencies for various carotenoids, interconversion\nrates etc. - are fitted in the model. Doesn´t this mean a severe risk of overfitting given the amount\nof independent experimental data? And what does this mean for the error range of the\nparameters? The number are sometimes given in tables with five digits, which cannot be\nappropriate. These aspects should be discussed.\nResponse: We appreciate this concern. While the model contains multiple parameters, most are\nshared across genotypes, and the model is trained on diverse datasets across mutants and light\nsequences. Importantly, it was able to accurately predict behavior in genotypes that were not\nused for parameter fitting with the same set of parameters, demonstrating generalizability. We\nhave added a sensitivity analysis (based on refitting the experimental data and computing\nbootstrapped 95% confidence intervals) to identify which parameters most influence model\nbehavior (Table S1 and Table S2). We reduced the number of significant digits in our parameter\ntables to align with these error margins. We have found that in general, the lower and upper\nbounds of the parameter set are within an acceptable range and increase our confidence in our\nmean fitted values.\n9. A 1 mW beam of laser pulsed measuring light is used. I assume that the same leaf spot has\nbeen exposed for ca 20 minutes. If I understand it correctly, this measuring light is much higher\nthan the actinic light which would explain why even the npq1/npq4 mutant has quite substantial\nquenching (ca 0.7). It is likely that in wt and other mutants, the measuring light may cause even\nbigger effects. I think the authors need to discuss this issue.\nResponse: Each snapshot involves 1 second of laser exposure, acquired every 15 seconds (83\ntotal over 20 minutes). We revised the Methods section to clarify these technical details.\n(Main text Page 22 Line 497-500) “Fluorescence decay profiles were recorded with a\nlaser excitation and detector integration time of 1 second (termed a \"snapshot\"), collected\nevery 15 seconds to ensure sufficient temporal resolution. A total of 83 snapshots were\ntaken over any type of the 20-minute sequences, including 3 initial snapshots in the dark-\nacclimated state.”\n(Main text Page 23 Line 514-516) “The amplitude-weighted average lifetime values\nobtained from each snapshot were directly used in constructing and testing the NPQ\nmodel. Each lifetime thus corresponds to a single data point in the figures.”\nlight and modeled the result (Figure S3). Interestingly, the qI model with fitted parameters from\nactinic-light experiments accurately predicted the laser-only quenching baseline, suggesting that\nthis slow component is accounted for. The contribution of the qI in various mutants was shown\nin Figure 4 and Movie S1.\n10. The model performs well for WT under fluctuating light but less well under the 3HL-1D-1HL-3D-9HL-3D regime, or for example in zep2. What is the explanation for this?\nResponse: Given the number of experiments needed to collect these data, it was necessary to use\ndifferent batches of plants. We consider that this discrepancy likely reflects biological variability\nacross experimental batches, as we noted in the manuscript. The 3HL-1D-1HL-3D-9HL-3D\nsequence was intentionally designed to be more complex, and subtle differences in pigment\npools or enzyme levels may contribute to the observed variation. We have already elaborated on\nthis point in the Discussion in the original manuscript.\n(Main text Page 18 Line 401-405) “Biological variation introduces uncertainty in\nparameter estimation, as differences in growth conditions and leaf age affect pigment\npools, enzyme activity, and consequently, fluorescence lifetime and xanthophyll\ninterconversion rates—even within the same genotype. These factors likely contribute to\nthe model’s slight NPQ underestimation in the 3HL-1D-1HL-3D-9HL-3D sequence (Fig.\n3C).”\n11. The model assumes a halved number of pigment binding sites in the lut2 mutant to\napproximate LHC structural changes. However, alterations in VAZ binding affinities and LHCII\ntrimer stability may occur in the absence of lutein may have nonlinear effects on NPQ kinetics\nResponse: We agree that the removal of lutein may cause multiple structural and kinetic changes.\nIn the model, [P]_tot in lut2 was fitted separately from [P]_tot in WT. Additionally, the rate\nconstants associated with pigment binding and dissociation were separately fitted in lut2 and\nwere found to differ from WT; in particular, the X⇌PX equilibrium shifts to the left for all\nxanthophylls, consistent with reduced LHCII trimer stability, and the ratio of binding affinities\nbetween V, A, and Z differs in lut2 versus WT, consistent with alterations in VAZ binding\naffinities. We have clarified this point in the main text.\n(Main text Page 11 Line 250-254) “The rate constants associated with pigment binding\nand dissociation were found to differ in lut2; in particular, the X⇌PX equilibrium shifts\nto the left for Vio, Ant, and Zea, consistent with reduced LHCII trimer stability, and the\nratio of binding affinities between Vio, Ant, and Zea changes from approximately 2:3.5:7\nto 1:1:3, consistent with alterations in VAZ binding affinities.”\nWhile more detailed modeling of structural compensation is possible, we chose a minimal\napproach for tractability given the main goal of our study.\n16\n12. The model also assumes that quenching contributions are additive across mechanisms which\nmay not necessarily be true. I guess this could be tested directly by comparing the modelled sum\nof two single mutant behaviours with the observed double mutant behaviour. Discrepancies here\ncould reveal pleiotropic interactions or structural compensation that the model does not account\nfor. Without this validation, the assumption of linear additivity remains a speculation.\nResponse: As noted in the general response, our model does not assume additivity a priori.\nRather, it was a conclusion drawn from the model’s ability to predict wild-type and double\nmutant behavior from integrating each of the relevant quenching components, with parameters\nobtained from the five essential genotypes used for fitting.\n13. The authors talk about a “lifetime-based quantitative kinetic model that disentangles\nindividual photoprotection components” and lifetime snapshots have been taken. But as far as I\nunderstand in their model they only use average lifetimes, not the multi-exponential kinetics. Is it\nthen really a lifetime-based quantitative kinetic model?\nResponse: We quote Papageorgiou and Govindjee (Barbara Demmig-Adams et al., Non-\nPhotochemical Quenching and Energy Dissipation in Plants, Algae and Cyanobacteria.\n(Springer Netherlands, Dordrecht, 2014), p21.) “Note that change in fluorescence intensity can\nbe simply due to change in the concentration of Chl; thus, measurement of fluorescence lifetime\nis crucial in reaching firm conclusions.”, the Chl fluorescence decay results from averaging\ntogether many tens of thousands of rates. As we showed in previous57, a decay curve arising\nfrom 30,000 rates can be perfectly fit by two exponential components but the two decay\ncomponents have no relation to any underlying rates of combination of rates. The weighted\naverage lifetime is directly proportional to the fluorescence yield and seems the most reasonable\nand feasible way to represent the overall degree of quenching. We revised our manuscript\naccordingly, please refer to our responses to Comment #5 of Reviewer #3.\n14. As lifetimes are proportional to the fluorescence yield, wouldn´t it be possible to reach the\nsame or similar conclusions from steady-state fluorescence measurements, just that the\ncalculated rates in that case would be proportional and not absolute? If so, this analysis does\nreally not make use of the extra information obtained from ultrafast time-resolved experiments.\nResponse: Our model is indeed based on fluorescence lifetimes, which are directly proportional\nto the absolute fluorescence quantum yield. As such, both snapshot lifetime measurements and\nhypothetical snapshot quantum yield measurements would, in principle, be equally valid for the\nmodeling framework we present. However, at present, there is no technique available that allows\ntrue snapshot quantum yield measurements with sufficient time resolution.\nWe believe the reviewer may be referring to steady-state fluorescence yield measurements\nobtained via PAM fluorometry when suggesting that similar results could be obtained without\n17\nlifetime data. While PAM is widely used and powerful for many applications, it differs from our\napproach in key ways:\n1. PAM measures relative fluorescence changes (e.g., with Fo, Fv/Fm) rather than absolute\nfluorescence quantum yield value, and therefore these fluorescence changes could not be\nproportionally convertible with lifetime.\n2. PAM is sensitive to the concentration of Chl which could be affected by many factors\nacross measurements on various samples. Lifetime does not.\n3. PAM NPQ parameters do not scale linearly with absolute quenching capacity at high\nNPQ levels, limiting their interpretability in the quantitative kinetic model.\n15. The same lab has previously modelled the photoprotection in Nannochloropsis oceanica. In\nthe introduction of this ms they lists a number of “significant limitations” of that model due to\nvarious assumptions. Although I acknowledge the skills of the lab and the complexity of this\nanalysis, I am worried that some of the assumptions they have made here, and the issues\nmentioned above, reduces the value of this ms and the validity of the claims. Maybe the authors\ncan present good explanations and good discussion on these points, but I am quite sceptic.\nThere are many papers on NPQ mechanisms which suffer from the same weakness; making\nassumptions that others do not buy. It is not easy when there is no consensus in the field about\ngeneral assumptions but to move forward it is important to interpret data in the light of the whole\nbody of literature, and this is where I think this ms fall short. Therefore I cannot recommend\npublication, at least not in the current form.\nResponse: We appreciate the reviewer’s candid feedback and acknowledge the importance of\ncritically evaluating modeling assumptions, especially in a field where mechanistic consensus\nremains elusive. We hope the lists of assumptions made and not made along with the substantial\nclarifications to the manuscript adequately address these concerns.\nReviewer #4 (Remarks to the Author):\nIn this submission, the authors attempt to use a fluorescence lifetime modelling approach to\nunderstand the contributions of different components of NPQ in wildtype and mutant Nicotiana\nbenthamiana plants. This review is written from the perspective of someone who has spent a lot\nof time measuring NPQ in whole plants but using PAM-style approaches and with little\nexperience of modelling. I am therefore reading this as an interested reader rather than an expert\nand my review is delivered on this basis. I assume more expert reviewers have also been\nconsulted.\nResponse: We thank the reviewer for their thoughtful and constructive feedback, and especially\nfor engaging with our manuscript from the perspective of an experienced NPQ researcher with a\nthe manuscript. Below we address each of the reviewer’s comments in turn.\n1. The paper opens by mentioning the existence of 5 mechanisms of NPQ, though I was a little\nunclear from the introduction what they consider these to be. I assume that they are identifying\nLut quenching as a distinct NPQ mechanism, or are they including this in qE, not quite clear from\nthe wording.\nResponse: We appreciate the reviewer highlighting this point. In the revised manuscript, we have\nclarified that the five types of NPQ referenced are: energy-dependent quenching (qE),\nzeaxanthin-dependent quenching (qZ), photoinhibition (qI), state transitions (qT), and sustained\nquenching (qH). Among these, our model includes only qE (quenching by both VAZ and lutein),\nqZ, and qI, as they account for the dominant NPQ responses under the conditions tested. In other\nwords, lutein-dependent quenching is treated as a subcomponent of qE. We have revised the\nIntroduction to more clearly distinguish between the types of NPQ included in our model and\nthose excluded, along with brief justifications. As noted in the general response, we also revised\nour use of the term “mechanism” to “types of NPQ” or “NPQ components” (e.g., qE, qZ, qI) to\navoid misunderstanding.\n(Main text Page3-4 Line 49-70) “To date, five distinct types of NPQ have been\ndiscovered, with various genetic mutants deepening our understanding of land plant NPQ\nat the molecular level. Among these types of NPQ, energy-dependent quenching\n(qE)…The longer-lasting Zea-dependent quenching (qZ) functions independently of\nPsbS… state transitions in light-harvesting complex II (qT)… Since qT does not directly\nquench 1Chl*, it contributes minimally to the PSII Chl a fluorescence quenching in the\norganism studied here, Nicotiana benthamiana, as supported by prior tests showing\nnegligible qT-associated changes in 1Chl* fluorescence lifetimes under our experimental\nconditions18. The most slowly relaxing NPQ component, photoinhibition (qI)… The most\nrecently discovered chloroplast lipocalin-dependent sustained antenna quenching (qH)\noccurs under cold and high light conditions20–22, and is therefore unlikely to be relevant\nunder the typical growth and experimental conditions used for N. benthamiana or target\ncrops.”\n2. Also, I am not clear which are the canonical and non-canonical mechanisms.\nResponse: We thank the reviewer for raising this point. In our initial wording, we referred to qE\nand qZ as \"canonical NPQ mechanisms” because they are well-defined in terms of their\nmolecular actors, specifically carotenoids such as zeaxanthin and lutein, have well-established\ngenetic and biochemical bases, and account for the vast majority of detectable NPQ under the\nshort actinic light regimes used in this study. However, we now realize that this terminology may\nbe confusing. To avoid ambiguity, we have revised the manuscript to clarify our use of\nterminology.\n19\n(Main text Page 3-4 Line 61-63) “Unlike qE and qZ, which involve specific carotenoids,\nstate transitions in light-harvesting complex II (qT) regulate light harvesting by\nredistributing excitation energy between photosystem I (PSI) and photosystem II\n(PSII)17.”\n3. And is reaction center quenching, associated with Ca2+ release, included in your count or\ncanon?\nResponse: We thank the reviewer for raising this aspect. While our model focuses on qE, qZ, and\nqI, we do not exclude the possibility that other mechanisms—such as reaction center\nquenching—may contribute under certain conditions. If such a mechanism leads to detectable\nquenching of Chl a, it would be reflected in the observed lifetime decay and grouped under the\nbroadly defined photoinhibition category qI, as it occurs on a similar timescale. However, as\nnoted in the general response, our current model does not aim to mechanistically resolve these\nprocesses, and therefore does not explicitly include Ca²⁺-dependent RC quenching. We have\nadded clarification in the revised manuscript explaining that the model intentionally simplifies\nthe system to enable quantification of dominant, well-characterized NPQ components. Please\nrefer to Response 6 to Reviewer 3.\n4. Anyway, they then go on to develop a model that only considers 3 mechanisms of quenching.\nThis could perhaps be more clearly explained in the introduction.\nResponse: We agree with this comment and have tried to be more explicit in the Introduction.\nThe primary aim of our study is to develop a quantitative yet simplified model of NPQ that\ncaptures the essential kinetic features of major quenching components, with the broader goal of\ninforming strategies for improving crop productivity. To achieve a balance between biological\naccuracy and model usability, we focused on the three dominant NPQ components—qE, qZ, and\nqI—which account for the majority of dynamic quenching responses under our experimental\nconditions. Other mechanisms, such as qT and qH, were excluded based on their minimal\ncontributions in our system and timescale, as well as to maintain conceptual clarity and\ntractability. We have revised the Introduction to make this scope and rationale more explicit, as\nalso detailed in the previous comments.\n5. The main experimental approach involves the measurements of the decay kinetics of\nfluorescence under different conditions. I was a little unclear about the details of how these\nmeasurements were made, but I assume this is measuring the fluorescence decay following\napplication of 404 nm flashes (duration?) and these decays were fitted with a bi-exponential\ndecay function.\nof the fluorescence lifetime measurements are now more clearly presented in the revised\nMethods section titled “Whole-leaf Fluorescence Lifetime Measurements and Analysis.” We\nhave revised this section to enhance clarity and ensure that the experimental setup is more\naccessible to readers unfamiliar with time-resolved techniques. Please also refer to our responses\nto Comment #9 of Reviewer #3.\n6. It is not clear how good the fit is across different plants and conditions, as no examples are\nshown.\nResponse: The fitting of fluorescence decay curves is a well-established method in our\nlaboratory64, where we compared snapshot-based and PAM-based measurements). The quality of\nthe fits is maintained across all the genotypes by collecting a consistent number of photons per\nsnapshot, ensuring robust signal-to-noise ratios. Additionally, the short instrument response\nfunction (~36 ps) of our setup provides sufficient time resolution to resolve fluorescence\nlifetimes ranging from several nanoseconds to approximately 100 picoseconds across all\ngenotypes under all the conditions examined.\nIn response to the reviewer’s suggestion, we now include a representative example of\nfluorescence decay fitting in the Supplementary Information (Fig. S7) to illustrate the quality of\nthe fits.\n(Main text Page 23 Line 516-517) “A representative example of the raw fluorescence\ndecay profile, bi-exponential fit, and IRF is shown in Figure S7.”\n7. The overall strategy of the modelling made sense to me – mutants with varying lesions in NPQ\nwere fitted using their model and the parameters estimated were used predictively to compare to\nwild type and further mutants including in additional light regimes. It is always a concern with\nmodelling that it is always possible to find a model to fit data, but using this in a predictive way\ngives confidence. The initial fits and predictions are not perfect but seem to capture the main\nprocesses occurring. Given the number of simplifications required this is reasonably convincing.\nResponse: Indeed, one of our main goals was to test whether a simplified kinetic model,\ngrounded in Chl fluorescence lifetime and pigment dynamics, could capture the essential features\nof NPQ behavior across genotypes and conditions. We are encouraged that the overall strategy\nwas clear and convincing to the reviewer.\n8. In terms of applying the models to predict the optimal solution for optimising NPQ, I was not\ntotally sold on the usefulness of the approach, though I think this is a useful thought experiment.\nResponse: We appreciate the reviewer’s thoughtful and cautious perspective. The simulations\ninvolving overexpression of VDE, PsbS, and ZEP were intended as a conceptual\n21\ndemonstration—an initial attempt to illustrate the predictive potential of our model. We also\nfully acknowledge that the current version of the model has limitations, particularly in its\napplicability to complex and variable natural conditions. Our goal in including these simulations\nwas not to make definitive physiological claims, but rather to explore how the model might be\nused to guide hypotheses and inform experimental design. As acknowledged in the Discussion,\nexperimental validation of these predictions remains an important next step. We are actively\nworking on this in ongoing and future studies. Please refer to Response 6 to Reviewer 1.\n9. In the discussion, the authors are honest about the limitations of their approach, especially in\nterms of the need to consider biological variability. Whether this experimental approach will ever\nbe as accessible as traditional PAM style measurements I do not know, but this does have the\npotential to provide useful information in at least some circumstances.\nResponse: We fully acknowledge the practical limitations of our current model and experimental\napproach, and we remain committed to further refining the methodology to improve its usability\nand relevance under more complex and natural conditions. While our model is fundamentally\nbased on chlorophyll fluorescence lifetime measurements, which offer distinct advantages in\nquantifying NPQ, we also fully recognize that PAM-based approaches are more widely adopted\nin the field. Bridging the gap between these two types of measurements is an important future\ngoal, and we hope that continued development of our model methodology might facilitate greater\ninteroperability and accessibility for broader applications.\nReviewer #5 (Remarks to the Author):\nIn this study, Lam et al. explore the specific contributions of NPQ components in Nicotiana\nbenthamiana under fluctuating light conditions. By integrating chlorophyll fluorescence lifetime\nmeasurements on intact leaves with HPLC pigment profiling, the authors construct a kinetic\nmodel that quantifies the activity and molecular efficiency of distinct mechanisms (qE, qZ, qI)\nand of individual xanthophylls (Zea, Lut, Ant). Through the use of single and double mutants\naffecting specific NPQ pathways (some pre-existing and others generated in this study), they\ndeconvolute overlapping processes and fit the model through parameter optimization based on\nexperimental fluorescence decay data. The model computes the contributions of each quencher\nto energy dissipation over time and accurately predicts NPQ dynamics under complex light\nregimes. This represents an important methodological advancement, offering a predictive\nframework to guide genetic strategies aimed at improving light use efficiency and\nphotoprotection.\nResponse: We thank the reviewer for recognizing the methodological novelty and predictive\npotential of our kinetic model. We also appreciate the constructive suggestions and fully agree\nthat the biological complexity of NPQ requires further refinement of our framework. Below,\nwe respond in detail to each of the reviewer’s comments.\n22\n1. Nevertheless, many biological complexities are simplified. The fluctuating light conditions\nwere selected a priori, and the parameter values used in the model may be influenced by various\nfactors.\nResponse: We agree that our current model simplifies certain aspects of natural light\nenvironments. To partially address this, we tested the model across a range of light sequences,\nincluding both continuous (20HL), regular (5HL-10D-5HL) and irregular (3HL-1D-1HL-3D-\n9HL-3D) regimes, designed to capture variation in NPQ activation and relaxation kinetics\ncontributed by different components. These were selected to provide controlled but progressively\nmore challenging conditions for model prediction. We acknowledge that in natural environments\nwhere light conditions are highly unpredictable, further refinement is needed, and we added a\nfew more sentences to discuss this limitation explicitly in the revised manuscript (Discussion\nsection).\n(Main text Page 18 Line 407-418) “In addition, the model also simplifies the\nillumination treatments by using three designed regular and irregular HL-to-D\nillumination sequences. These were chosen to capture variation in NPQ activation and\nrelaxation kinetics contributed by different components, with progressively more\nchallenging conditions for model prediction, providing a controlled platform to\ninterrogate NPQ mechanisms before extending the model to more complex light regimes.\nHowever, natural sunlight exhibits more unpredictable and unpatterned HL-to-low-light\n(LL) fluctuations58, and more gradual transitions … Incorporating more stochastic\nillumination patterns, HL-LL transitions, and light intensity-dependent rate constants may\nimprove the physiological relevance of this framework for understanding NPQ regulation\nunder natural light fluctuations, if not offset by increased phenotypic variability.”\n2. For example, in natural environments characterized by unpredictable sunflecks and alternating\nlow/high light conditions, the model could potentially underestimate or overestimate NPQ\ndynamics. Or even the approximations used for qI. In fact, the authors themselves acknowledge\nthe main limitations (lines 362 to 376). Nonetheless, the model is coherent with experimental\ndata and provides a solid foundation for further refinement.\nResponse: We agree with the reviewer that this is a significant consideration. Indeed,\nenvironmental light variability introduces layers of complexity that our current model does not\nyet fully capture. We are fully aware of this limitation as we described in our discussion.\nIncorporating light intensity dependence would increase physiological realism but also\nsignificantly increase model complexity and experimental burden. Nevertheless, incorporating\nfield-realistic light inputs and further parameter refinement are important next steps for\nadvancing the model’s applicability, and remain a priority for our future work.\n3. In the natural environment, the sudden changes in light intensity are accompanied by strong\n23\nvariations in the spectra of actinic light. A short paragraph could be dedicated to this factor of\ncomplexity and its effect on measurements.\nResponse: In our understanding of responses to spectral changes (change of UV A/B and far-red\nlight amplitude) accompanying sun flecks, the response of plants such as shade avoidance occurs\non longer timescales than those studied in our work. (Sellaro et al., Making the most of canopy\nlight: shade avoidance under a fluctuating spectrum and irradiance. J Exp Bot 76, 712–729\n(2024). gives a recent review)\n4. Additional points:\n- A sensitivity analysis of kinetic parameters (k) across NPQ components would improve\nrobustness and highlight critical points to be implemented to cope with large biological\nvariability.\nResponse: We appreciate this recommendation. In response, we have performed a sensitivity\nanalysis of all kinetic parameters and included the results (via refitting the data several times and\nbootstrapping 95% confidence intervals) in the revised Supplementary Information (Table S1\nand S2). This analysis identifies which parameters most strongly influence the model’s outputs\n(indicated by a narrow range between lower and upper bound) and highlights potential targets for\nfuture refinement or experimental focus. We are grateful for the reviewer’s insight, which we\nbelieve has strengthened our study.\n5. In Fig. S3, quenching induced by the laser in the absence of actinic light reaches ~0.6 ns after\n20 minutes, which is non-negligible. How frequently are lifetime snapshots taken per induction\ncurve? Reducing snapshot density might minimize laser pulse-induced quenching, especially\nduring dark phases, and improve the accuracy of recovery kinetics; a potential trade-off.\nResponse: The laser exposure and signal integration time is 1 second (termed a “snapshot”),\nrepresented by each of the data points in the figures. The frequency of the measurement is 1\nsnapshot per 15 seconds. We revised the Methods section to clarify more technical details of the\nmeasurements. Please refer to our responses to Comment #9 of Reviewer #3.\nWe agree that laser-induced quenching is non-negligible and have performed laser-only controls,\nwhich confirmed this. We agree that even reducing snapshot/measurement frequency could help\nreduce this laser-induced quenching, and we did various testing that proves this. However, even\nif we reduce the frequency, the laser baseline reduces but still is not negligible. We therefore\ndecided to use “1 snapshot per 15 s” measurement frequency to prioritize NPQ dynamic\nresolution, and incorporate laser baseline, which turned out to be qI, in our model to account for\nthis phenomenon.\n6. Line 334: please verify consistency with respect to Fig. 4A. The major components are Lut\nand Ant, not Vio.\ncorresponding description for Fig. 4A in the main text to accurately reflect that Lut and Ant are\nthe dominant contributors between t=0 and t=3 min, not Vio.\n(Main text Page 16 Line 369-371) “In WT, from t=0 to t=3 min (Figure 4A-B), Lut and\nAnt are the major quenching contributors due to their rapid activation capabilities.\nBetween t=3 and t=5 mins, quenching contributions from Ant and Zea are similar in\nmagnitude.”\nFor clarity, we have color-coded this response letter as follows:\nBlack: Reviewer comments\nBlue: Our responses\nRed: Direct citations of the manuscript (Line numbers refer to the pdf version of the final edits\nof the manuscript.)\nReviewer #1 (Remarks to the Author):\nThe authors replied to my comment point-by-point:\n1. Choice of mutants\nThe authors argue that thylakoids are more suitable for transient absorption (TA) measurements,\nand I am prepared to accept that point in principle. However, the material has not been properly\ncharacterized with respect to electron transport and the resulting proton gradient (ΔpH) that\ndrives NPQ. Both the kinetics and amplitude of these processes should be reported. Without such\ndata, neither the measurements nor the resulting model can be considered reliable. Furthermore,\nreference 26 does not, in my view, provide higher-quality data than comparable work in\nArabidopsis, nor does it present convincing evidence that zeaxanthin directly receives excitation\nenergy from chlorophyll.\nResponse: It is well established that ΔpH generated through electron transport is a primary driver\nof NPQ induction. This relationship has been validated in numerous studies across model\nsystems, including both Arabidopsis and Tobacco species. For example, Cardol et al. (2010,\nBiochimica et Biophysica Acta, doi:10.1016/j.bbabio.2009.10.002) explored the influence of\nproton motive force across the thylakoid membrane on NPQ onset in tobacco; Miyake et al.\n(2005a, Plant Cell Physiology, doi:10.1093/pcp/pci067) examined the role of PSI/PSII electron\nfluxes in determining NPQ magnitude; and Miyake et al. (2005b, Plant Cell Physiology,\ndoi:10.1093/pcp/pci197) investigated the effects of light intensity on cyclic electron flow and its\nrelationship to NPQ. Therefore, our work builds upon this foundational understanding, rather\nthan re-demonstrating it specifically for N. benthamiana.\nAlso note that the kinetics of ΔpH activation are considered fast enough on the timescale of our\nexperiment to be effectively instantaneous, hence ΔpH is phenomenologically modeled as an\non/off switch of constant amplitude. Since all experiments are performed at the same light\nintensity, this is a reasonable simplifying assumption to make to avoid overparameterization of\nthe model.\nWe would also like to re-emphasize that our model does not presuppose a specific physical\nmechanism of Zea-mediated quenching. Instead, it captures the kinetics of NPQ components in a\nmanner consistent with the availability of individual quenchers. We explicitly state that Zea’s\nrole in our model should be interpreted functionally, based on the timing and extent of its\n1\ncontribution to quenching, rather than as evidence of a particular photophysical mechanism such\nas direct energy transfer from Chl to Zea.\nWith respect to the spectroscopic evidence, we note that high-quality data demonstrating direct\nZea-mediated quenching via excitation energy transfer through the Qy band are limited even in\nArabidopsis. If the reviewer does not find reference 26 sufficiently convincing, we respectfully\nsubmit that, to our knowledge, no higher-quality spectroscopic dataset currently exists in\nArabidopsis or in equivalent/more intact systems.\n2. Zeaxanthin as a quencher\nThe authors appear unwilling to engage with the full scope of research on the relationship\nbetween zeaxanthin and NPQ published over the past few decades. Instead, they continue to\npromote the long-standing and now outdated view originating with Demmig-Adams et al. some\n35 years ago. This selective treatment of the literature risks perpetuating a simplified narrative\nthat does not reflect current understanding.\nResponse: We thank the reviewer for pointing out the alternative theories of zeaxanthin’s\ncontribution to quenching. In response, we have added the following sentences to the\nIntroduction of the main text:\n(Main text Page 5 Line 81-85) “Other models propose, however, that carotenoids\nfunction not as direct quenchers, but as allosteric effectors that induce\nconformational changes in LHCs to facilitate energy dissipation36–38. Structural and\nspectroscopic studies support Zea-dependent but structurally mediated pathways\ncontributing to quenching, suggesting that Zea may enable or stabilize specific\nprotein conformations that facilitate NPQ without directly quenching Chl excited\nstates36,39.”\nWe would also like to clarify (as discussed in the first response letter and in Model Assumptions\nin the Extended Methods) that our model assumes the quenching rate constant depends linearly\non the concentration of QX for qE or the concentration of Zea for qZ. This framework is agnostic\nto the specific mechanism by which carotenoids contribute to quenching, whether through direct\nenergy transfer or through indirect mechanisms such as allosteric modulation. To make this\nclearer for readers, we have revised our wording throughout the entire manuscript. For example,\nwe changed “quenchers” to “contributors to quenching” or “effectors of quenching” and\n“quenching efficiency” to “quenching effectiveness,” to better reflect the agnostic nature of our\nmodel.\nAdditionally, in response to this comment and to Comment 5, we apologize for omitting prior\nwork on modeling NPQ. We found a number of relevant references (43–50; see the revised\nmanuscript’s Reference list) which are now included in a new paragraph in the Introduction as\nfollows:\n(Main text Page 5 Line 96-100) “There have been a number of previous modeling\nstudies of NPQ kinetics, and qE in particular43–50. These models have generally been\nbased on the VAZ cycle, with Zea and protonated PsbS acting as the sole quenching-\n2\nrelated components, most commonly represented by the 4-state 2-site quenching\nmodel proposed by Jahns and Holzwarth14, which takes into account both open and\nclosed RCs and quenched and unquenched PSII antennas.”\nWe discuss the connections between the current work and references 43-50 in a new paragraph\nin the Discussion Section:\n(Main text Page 15 Line 326-341) “In this work, our experimental protocol closes\nthe RCs. We allow each component of the VAZ cycle to act as an inducible\ncontributor to quenching, with differing activation rates and quenching\neffectiveness. We also include qI and qZ as well as the contributions of Lut, which\nis an important contributor to qE at short timescales and the major component for\nthe npq1 mutant which was also studied by Morales et al.45. As Stirbet et al. point\nout50, several existing models assume that the VAZ cycle (in particular, Zea) and\nPsbS protonation are the only components involved in qE quenching (the “4 state 2-\nsite” model), simplifying NPQ dynamics but reducing the ability to capture complex\nphotoprotective systems. These models also commonly make the simplifying\nassumption that the total pool of [Vio] + [Zea] is constant over short timescales.\nHowever, this limits the ability to compare kinetics across genotypes, which can\nexhibit large variations in the size of the total xanthophyll pool even after dark\nadaptation (Figure S1, S2). The memory effect explored by Matuszynska et al. is an\nintrinsic part of our model43, as it was in our earlier algal model51,52, and is unraveled\nin greater mechanistic detail here, demonstrating the importance of the Ant\nintermediate to NPQ memory during dark periods. Thus, our work here expands\nupon the existing body of qE modeling studies by providing a detailed molecular\npicture of photoprotection, connecting lifetime dynamics directly to specific\nmolecular contributors and kinetic rates, and capturing multiple NPQ components\n(qE, qI, qZ).”\nWe provide further comments comparing existing models with ours in our response to Comment\n5.\n3. qI component\nThe model presented does not question the mechanistic origins of qI but rather addresses only its\nkinetic behavior. This raises the question: what is the real utility of such a model for\nphysiologists? Without a mechanistic framework, a purely kinetic description of such a complex\ncomponent offers limited biological insight.\nResponse: Indeed, the mechanistic basis of qI involves a complex and not yet fully understood\nset of molecular processes, including photodamage, D1 turnover, and possibly sustained\npigment-protein aggregation. However, elucidating these mechanistic details lies outside the\nscope of our current study, which focuses on quantifying and separating the kinetics of\noverlapping NPQ components across genotypes.\nto this work, it has been challenging to disentangle qI contributions across different mutants or\nspecies, particularly due to its overlap with qE and qZ. Our model provides a framework to\nquantitatively resolve qI dynamics, as demonstrated in Figure 4 and Movie S1, which reveal\ndistinct time-dependent contributions of qI in wild-type and mutants.\nImportantly, isolating the kinetics of qI is foundational to the structure of our model. Without\naccounting for qI, it would not be possible to reliably separate the faster components, such as qE\nand qZ, especially since the qI contribution and onset vary significantly across genotypes and\nevolve over time. Assuming identical qI behavior in all mutants would introduce substantial\ninaccuracies. Thus, including qI in the model is essential to unlock more mechanistic insights\ndownstream, such as the per-molecule quenching effectiveness of each component, the time-\nresolved NPQ contributions, and the predictive capacity for NPQ phenotypes across mutants,\nwhich constitute the central findings of our study.\nIn addition, a kinetic approximation of qI is also essential because the principle behind NPQ is\nthat an increase in quenching will reduce photodamage (qI may not represent the entirety of\nphotodamage, but remains a useful proxy). Thus, mutants with higher NPQ capacities will have\nlower accumulations of qI, as we see demonstrated in our model (Fig 4). Modeling the quantity\nof qI is thus not only useful for reliably capturing different NPQ kinetics but also provides an\nexplanation for why mutants differ in NPQ recovery—some sustain more photodamage than\nothers—and allows direct observation of the impact on NPQ on photoinhibition levels. We have\nadded the following sentences to the manuscript to provide additional clarification.\n(Main text Page 18 Line 405-406) “Thus, although qI is not modeled\nmechanistically, we are still able to observe the effects of NPQ activity on the\naccumulation of photodamage.”\n(Main text Page 18 Line 414-416) “qI accumulates at an increased rate and acts as\nthe major quenching contributor, explaining the lack of NPQ recovery in darkness”\nFinally, we note that although the exact molecular mechanism of qI remains under investigation,\nour model provides a practical and extensible starting point for quantitatively analyzing qI using\naccessible chlorophyll fluorescence lifetime data. As new mechanistic details emerge, this\nmodeling framework can be readily adapted to incorporate such refinements and may be helpful\nfor testing various mechanistic hypotheses.\nRegarding biological insight, we note that our earlier modeling work (Zaks et al., PNAS 2012)\nhas been cited 183 times according to Google Scholar, and has been incorporated by others into\nphysiological crop growth models such as BioCro. BioCro is a model that predicts plant growth\nover time given crop-specific parameters and environmental data as input (BioCro.org). We\nbelieve our current model will be even more useful, as it incorporates a more complete set of\nNPQ components, provides specific molecular contributions and biochemical rates from\nfluorescence lifetime-based modeling, predicts experimental results more accurately, and offers\nguidance for the design of overexpression mutants.\n4\n4. Kinetic aspects of NPQ\nEven within the narrow kinetic focus, the treatment of NPQ dynamics in this manuscript remains\nincomplete. The relevant kinetic aspects of NPQ have not been adequately reviewed, and the\nanalysis feels partial at best.\nResponse: See response to Comment 1 and 3. Without further specificity, it is difficult to\nrespond to this comment. There are indeed entrenched positions on the mechanisms of NPQ, but\nwithout putting detailed models out for discussion by the broader community it is difficult to see\na route to progress.\n5. Suitability for Nature Communications\nFrom the discussion surrounding the modelling work of Snellenburg et al., it is evident that this\nproject is highly specialized and riddled with unresolved controversies. As such, it seems more\nappropriate for a specialist journal in photosynthesis research, rather than a broad-audience outlet\nsuch as Nature Communications. The current scope and accessibility do not justify publication in\na general high-impact venue.\nResponse: Please see our response to Comment 2.\nTurning specifically to the Snellenburg et al. work mentioned by Reviewer 1, the current paper\nand Snellenburg et al. are not contradictory, but rather they approach the task of modeling NPQ\nfrom entirely different angles. The emphasis of the Snellenburg et al. model is on characterizing\nthe state of PSII using purely kinetic/mathematical parameters. They do not attempt to\ncharacterize biochemical pathways in any detail as that is not the goal of their study.\nSnellenburg et al. modeled the evolution of PAM fluorescence by fitting population fractions and\nquantum yields to four PSII states. Their framework describes how much of PSII becomes\nquenched under given conditions. In contrast, our model reveals the involvement of different\nxanthophylls in promoting quenching and their relative effectiveness at the molecular level. By\ndirectly connecting lifetime changes to specific quenchers and kinetic rates, our approach\nprovides a mechanistic picture of how photoprotection proceeds in photosynthetic systems.\nBy exciting Chl a with a saturating laser pulse we reduce the problem to one in which reaction\ncenters are closed. If we didn't do that, the reaction centers would be either open or closed (an\nadditional factor to model), in which case the work by Snellenburg et al. would become useful.\nOur assumption of reaction centers being closed is not an \"unsolved controversy\" but rather a\ndirect result of our experimental design.\nThe work by Snellenburg et al. also makes assumptions, namely that the pool of xanthophylls is\nconstant and importantly that the amount of violaxanthin and zeaxanthin are constant over the\ncourse of the experiment. This is a simplifying assumption, as the authors write: \"the fitting\nresults show that given the short duration of the experiments this extra complexity is not needed\nto adequately describe the data.\" The authors readily acknowledge that in reality, V to Z\ninterconversion is taking place.\nThus, there is no contradiction or unresolved controversy between the two works, as Reviewer 1\nseems to suggest.\ncommunity, the lifetime-based modeling framework itself is broadly applicable: it offers an\nintuitive and quantitative tool for disentangling dynamic quenching processes, even for a\ncomplex light-harvesting system. Moreover, by organizing these mechanisms into modular\ncomponents, our manuscript helps general audiences understand how the photosynthetic\napparatus functions as an integrated, dynamically regulated system.\nRegarding the manuscript’s suitability for Nature Communications, please also see our response\nto Comment 3 of Reviewer 3.\n6. Authors’ own admissions\nThe authors themselves acknowledge that their approach requires further experimental validation\nand, in all likelihood, improved quality in the kinetic fitting of their data. This further reinforces\nmy view that the manuscript, in its current form, is not ready for publication in its intended\njournal.\nResponse: Please see response to Comment 5.\nReviewer #1 (Remarks on code availability):\nn/a\nReviewer #2 (Remarks to the Author):\nPhotoprotection mechanisms collectively referred to as non-photochemical quenching (NPQ)\nremain incompletely understood due to the large number of underlying components and their\ndiversity among species. In this study, the authors established a convenient technique to monitor\nchlorophyll fluorescence lifetime changes accompanying the induction and relaxation of NPQ\nunder fluctuating light conditions. Furthermore, they proposed a simplified NPQ model,\ncomposed of only the essential elements, that accurately reproduces these fluorescence lifetime\ndynamics. I recommend the prompt publication of this manuscript.\nCompared with the commonly used PAM-based parameter, (Fm-Fm')/Fm’, the newly proposed\nτF(t) has two distinct advantages: it is less influenced by residual NPQ present before the onset\nof measurements, and it exhibits higher linearity with respect to changes in quencher\nconcentration. The authors applied model fitting to fluorescence lifetime data obtained from both\nwild-type plants and multiple NPQ mutants. Beginning with a double mutant containing the\nfewest NPQ components and progressively incorporating additional factors, they ultimately\ndetermined the parameter set representing all NPQ factors in wild-type plants. The validity of\nthis NPQ model was confirmed by its ability to predict, with high accuracy, the fluorescence\nlifetime dynamics of independent NPQ mutants under fluctuating light conditions. The authors\nfurther suggest that this model could provide insights into improving photosynthetic responses\nand potentially contribute to crop yield enhancement.\nneither redundancy nor omission. Consequently, the model does not aim to fully replicate the\ncomplex behavior of NPQ in natural environments, nor does it present novel discoveries\nregarding NPQ mechanisms. The authors explicitly state that these were not the primary\nobjectives of their work.\nSince the inception of NPQ research over half a century ago, non-invasive PAM measurements\nhave greatly advanced the field. The approach proposed in this manuscript—combining\nfluorescence lifetime measurements with a streamlined NPQ model—offers the potential to\ndetect subtle alterations in minor NPQ components that PAM-based analyses cannot resolve.\nThis advance is expected to be valuable for future photosynthesis research and for practical\napplications such as crop breeding. The publication and subsequent broad validation of this\nmethod across diverse environments and species may eventually establish it as an alternative to\nconventional PAM-based techniques.\nResponse: We thank the reviewer for their positive assessment of our work.\nAs the reviewer noted and we discussed in Sylak-Glassman et al. Photosynth Res 127, 69-76\n(2015)71, PAM measures the total amount of fluorescence and may not always be related to the\nrate of fluorescence quenching, because of chloroplast movement, damage etc. In contrast,\nfluorescence lifetime measurements directly reflect the rate of excited-state quenching and can\nthus be quantitatively modeled using a kinetic framework like the one we present here. This\nsame point has also been made by Papageorgiou and Govindjee, as quoted in our previous\nresponse (Barbara Demmig-Adams et al., Non-Photochemical Quenching and Energy\nDissipation in Plants, Algae and Cyanobacteria. (Springer Netherlands, Dordrecht, 2014), p21.).\nWe agree, however, that PAM measurements have been and will continue to be highly valuable\nfor NPQ studies. We hope that continued development of our modeling methodology will\nfacilitate greater interoperability and accessibility, helping to bridge the gap between these two\ntypes of measurements for broader applications.\nReviewer #3 (Remarks to the Author):\n1. The ms has been modified in several ways to satisfy the concerns of me and the other\nreviewers. I think that the authors have done a good job in improving the explanation of what\nthey have done and why. In particular, I appreciate the list of assumptions made and not made in\nthe Extended methods section. But as not everyone immediately reads the SI, there should a\nspecific reference to this information in the main text, there may be others that like me need this\ndescription.\nResponse: We appreciate the suggestion and have added a specific comment in the main text\npointing to the list of assumptions made and not made in the SI.\n(Main text Page 5 Line 93-95) “A complete list of assumptions underlying the\nconstruction of this model, including those that were deliberately excluded, is\nprovided in the Supplementary Information (Extended Methods).”\n7\n2. Also, the\nreferences https://academic.oup.com/pcp/article/64/8/858/7148152, https://www.biorxiv.org/cont\nent/10.1101/2024.04.18.590023v2\nand https://www.biorxiv.org/content/10.1101/2025.01.26.634902v1) I mentioned talk about PSI\nas a quencher of PSII, not state transitions (as mentioned in the rebuttal letter). Although it is still\nnot well documented if this is a mechanism of importance in angiosperms including Nicotiana\n(and there is no useful mutant), what would it mean for the conclusions if spillover is there?\nResponse: We appreciate the reviewer highlighting this emerging line of research again. The\npapers provided propose that the formation of PSI-PSII supercomplexes, along with the\nassociated FRET-mediated excitation spillover from PSII to PSI, constitute a potential quenching\nmechanism, possibly involving both PsbS and zeaxanthin. In our model framework, such a\nspillover mechanism might potentially be captured by the PZ ↔ QZ interconversion or\nrepresented as part of qZ contribution, especially if PSI-PSII supercomplex formation correlates\nwith zeaxanthin accumulation. We agree that further mechanistic insights and mutant tools will\nbe valuable for evaluating the physiological significance of this process in different species. In\nresponse to this comment, we added the following sentence to the Introduction of the\nmanuscript:\n(Main text Page 4-5 Line 85-88) \"Recent studies have also proposed the formation\nof PSI-PSII supercomplexes and associated excitation spillover from PSII to PSI as\na potential quenching mechanism involving Zea and PsbS40-42, though its\nphysiological relevance in N. benthamiana remains to be fully established.\"\n3. Anyway, I am still somewhat doubtful about the value of this contribution. Maybe I do not\nunderstand enough mathematics/biophysics to appreciate why this is suitable for Nature\nCommunications, I feel that I do not really know more about NPQ after reading and thinking of\nthe ms than I did before. If the other reviewers and the editor think differently I will not\ncomplain if it gets accepted, but I am not convinced that it is important enough…\nResponse: We appreciate the reviewer’s honest feedback. Please also see the response to\nComment 3 & 5 of Reviewer 1.\nWe agree that discovering new NPQ mechanisms is of great interest. However, one of our main\nmotivations for studying NPQ is to ultimately improve crop yield. What is currently lacking in\nthe field is a comprehensive framework that can isolate the contributions of each quenching\ncomponent and construct a model with predictive power across different genotypes.\nTherefore, the aim of this particular work is not to uncover new molecular players in NPQ, but\nrather to provide a quantitative modeling framework that enables kinetic interpretation of\nchlorophyll fluorescence lifetime-measured NPQ behavior under dynamic light conditions. This\ncomplements existing PAM-based approaches (the advantages of fluorescence lifetime\nmeasurements are discussed extensively in our previous response and in the Methods section of\nthe manuscript) and helps bridge a long-standing methodological gap in NPQ kinetic analysis.\nhighlight the most important ones again:\n1. Quantification of the per molecule effectiveness of different xanthophylls in promoting\nquenching (Vio, Ant, Zea, and Lut)\nThese values, presented in Table 2, are not predictions but are directly obtained by fitting the\nmodel to experimental data. While debates exist regarding which carotenoid is the most\neffective (e.g., Lut vs Zea), our data provide a quantitative in vivo answer expressed in terms\nof effectiveness per molecule.\n2. Time-resolved contribution of each NPQ component\nAs shown in Fig. 4 and Movie S1, we quantify the dynamical contribution of individual\nquenching components in various genotypes over illumination time. We emphasize the\nnovelty of this dynamic perspective: the relative contributions of different NPQ components\nvary significantly across different time points and illumination phases. Again, these values\nare not merely predictions, but are extracted through robust fitting to high-quality\nexperimental data. Given the reliability of our data and fits, we are considerably confident in\nthe accuracy of these estimates and the conclusions drawn.\nUltimately, we believe the value of our work lies in offering a broadly applicable and extensible\ntool for dissecting NPQ contributions and dynamics in vivo. With further improvements in the\nfuture, we hope this model can become even more adaptable to natural illumination conditions\nand gain predictive capability regarding the expression levels of key proteins (as discussed in\nFig. 5) to improve overall light-harvesting efficiency. In this way, our study provides a\nfoundational step toward guiding future improvements in crop yield through more refined\nunderstanding and engineering of NPQ.\nAs is evident from this study, the work would not have been possible without a deep integration\nof expertise spanning plant biology, biochemistry, and genetics, as well as physical chemistry\n(spectroscopy and kinetic modeling). The highly interdisciplinary nature of this work bridging\nexperimental and theoretical approaches is precisely why we hope to publish in Nature\nCommunications. We believe that this platform offers an ideal venue for reaching a broad\nscientific audience, which can foster cross-disciplinary engagement. In that spirit, we have\ngreatly appreciated the diverse perspectives reflected in the reviewers’ comments, which align\nwith our goal of encouraging dialogue across fields.\nReviewer #4 (Remarks to the Author):\n1. The authors have engaged with my previous review in a constructive manner and addressed all\nthe points I raised. As noted by the authors and raised by other reviewers, NPQ is an area in\nwhich there has been a lot of disagreement and argument over details. It is refreshing to see the\napproach here that attempts to take a broader view. The changes made by the authors improve,\nfor me at least, the readability of the paper.\nbut assuming other referees are satisfied with this, I am happy to support publication.\nResponse: We thank the reviewer for recognizing our effort to adopt a broader and integrative\napproach to studying NPQ, especially in a field where mechanistic interpretations have often\ndiverged. We are especially encouraged that the revisions have improved the readability of the\nmanuscript from the reviewer’s perspective.\nReviewer #4 (Remarks on code availability):\nThis aspect is outside my area of expertise.\nReviewer #5 (Remarks to the Author):\n1. This is a revised manuscript that I evaluated already. Revisions took in due account criticisms\nand suggestions. Thus, the revised manuscript is presently suitable for publication and will\nconstitute a valid tool for screening mutants for improved light use efficiency in different\nconditions. This is a valuable contribution for both basic and applied science.\nResponse: We thank the reviewer for their encouraging comments. It is gratifying to know that\nthe revisions have addressed the earlier concerns and improved the manuscript’s clarity. We\nappreciate the recognition that this work may serve as a useful tool for screening mutants to\nimprove crop yield. We hope that our future development of this model will further enhance its\napplicability and ultimately contribute to achieving that goal.\n2. Having said so, Authors might have considered the results they have obtained with the (new)\ngenotype ZEP npq4 of minor importance with respect to the general importance of the ms which\ndeals with modelling. Nevertheless, the reduced lifetime in the dark in the ZEP npq4 points to an\nautonomous activity of Zea in quenching that could well be highlighted in the discussion.\nResponse: We thank the reviewer for their careful review of the data and agree with their\nassessment regarding npq4zep2. In response, we have incorporated the following content into the\nDiscussion.\n(Main text Page 17 Line 387-391) “The significant qZ contributions in our model\nare consistent with the existence of a light-independent Zea-mediated quenching\nmechanism16. This interpretation is further supported by the observed reduction in\nthe initial 𝜏 in npq4zep2 compared to npq4, suggesting a ΔpH-insensitive Zea-\n𝑑𝑎𝑟𝑘\ndependent quenching component that remains active in the dark-acclimated state in\nnpq4zep2.”\nReviewer #5 (Remarks on code availability):\nwill take some time for a more thoughtful evaluation.\n11"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors replied to my comment point-by-point:\n1. Choice of mutants\nThe authors argue that thylakoids are more suitable for transient absorption (TA) measurements, and I am prepared to accept that point in principle. However, the material has not been properly characterized with respect to electron transport and the resulting proton gradient (ΔpH) that drives NPQ. Both the kinetics and amplitude of these processes should be reported. Without such data, neither the measurements nor the resulting model can be considered reliable. Furthermore, reference 26 does not, in my view, provide higher-quality data than comparable work in Arabidopsis, nor does it present convincing evidence that zeaxanthin directly receives excitation energy from chlorophyll.\n2. Zeaxanthin as a quencher\nThe authors appear unwilling to engage with the full scope of research on the relationship between zeaxanthin and NPQ published over the past few decades. Instead, they continue to promote the long-standing and now outdated view originating with Demmig-Adams et al. some 35 years ago. This selective treatment of the literature risks perpetuating a simplified narrative that does not reflect current understanding.\n3. qI component\nThe model presented does not question the mechanistic origins of qI but rather addresses only its kinetic behavior. This raises the question: what is the real utility of such a model for physiologists? Without a mechanistic framework, a purely kinetic description of such a complex component offers limited biological insight.\n4. Kinetic aspects of NPQ\nEven within the narrow kinetic focus, the treatment of NPQ dynamics in this manuscript remains incomplete. The relevant kinetic aspects of NPQ have not been adequately reviewed, and the analysis feels partial at best.\n5. Suitability for Nature Communications\nFrom the discussion surrounding the modelling work of Snellenburg et al., it is evident that this project is highly specialized and riddled with unresolved controversies. As such, it seems more appropriate for a specialist journal in photosynthesis research, rather than a broad-audience outlet such as Nature Communications. The current scope and accessibility do not justify publication in a general high-impact venue.\n6. Authors’ own admissions\nThe authors themselves acknowledge that their approach requires further experimental validation and, in all likelihood, improved quality in the kinetic fitting of their data. This further reinforces my view that the manuscript, in its current form, is not ready for publication in its intended journal.", "reviewer_2": "Photoprotection mechanisms collectively referred to as non-photochemical quenching (NPQ) remain incompletely understood due to the large number of underlying components and their diversity among species. In this study, the authors established a convenient technique to monitor chlorophyll fluorescence lifetime changes accompanying the induction and relaxation of NPQ under fluctuating light conditions. Furthermore, they proposed a simplified NPQ model, composed of only the essential elements, that accurately reproduces these fluorescence lifetime dynamics. I recommend the prompt publication of this manuscript. Compared with the commonly used PAM-based parameter, (Fm-Fm')/Fm’, the newly proposed τF(t) has two distinct advantages: it is less influenced by residual NPQ present before the onset of measurements, and it exhibits higher linearity with respect to changes in quencher concentration. The authors applied model fitting to fluorescence lifetime data obtained from both wild-type plants and multiple NPQ mutants. Beginning with a double mutant containing the fewest NPQ components and progressively incorporating additional factors, they ultimately determined the parameter set representing all NPQ factors in wild-type plants. The validity of this NPQ model was confirmed by its ability to predict, with high accuracy, the fluorescence lifetime dynamics of independent NPQ mutants under fluctuating light conditions. The authors further suggest that this model could provide insights into improving photosynthetic responses and potentially contribute to crop yield enhancement. In constructing the model, only essential components were deliberately included, ensuring neither redundancy nor omission. Consequently, the model does not aim to fully replicate the complex behavior of NPQ in natural environments, nor does it present novel discoveries regarding NPQ mechanisms. The authors explicitly state that these were not the primary objectives of their work. Since the inception of NPQ research over half a century ago, non-invasive PAM measurements have greatly advanced the field. The approach proposed in this manuscript—combining fluorescence lifetime measurements with a streamlined NPQ model—offers the potential to detect subtle alterations in minor NPQ components that PAM-based analyses cannot resolve. This advance is expected to be valuable for future photosynthesis research and for practical applications such as crop breeding. The publication and subsequent broad validation of this method across diverse environments and species may eventually establish it as an alternative to conventional PAM-based techniques.", "reviewer_3": "The ms has been modified in several ways to satisfy the concerns of me and the other reviewers. I think that the authors have done a good job in improving the explanation of what they have done and why. In particular, I appreciate the list of assumptions made and not made in the Extended methods section. But as not everyone immediately reads the SI, there should a specific reference to this information in the main text, there may be others that like me need this description. Also, the references https://academic.oup.com/pcp/article/64/8/858/7148152, https://www.biorxiv.org/content/10.1101/2024.04.18.590023v2 and https://www.biorxiv.org/content/10.1101/2025.01.26.634902v1) I mentioned talk about PSI as a quencher of PSII, not state transitions (as mentioned in the rebuttal letter). Although it is still not well documented if this is a mechanism of importance in angiosperms including Nicotiana (and there is no useful mutant), what would it mean for the conclusions if spillover is there? Anyway, I am still somewhat doubtful about the value of this contribution. Maybe I do not understand enough mathematics/biophysics to appreciate why this is suitable for Nature Communications, I feel that I do not really know more about NPQ after reading and thinking of the ms than I did before. If the other reviewers and the editor think differently I will not complain if it gets accepted, but I am not convinced that it is important enough…", "reviewer_4": "The authors have engaged with my previous review in a constructive manner and addressed all the points I raised. As noted by the authors and raised by other reviewers, NPQ is an area in which there has been a lot of disagreement and argument over details. It is refreshing to see the approach here that attempts to take a broader view. The changes made by the authors improve, for me at least, the readability of the paper. As noted in the previous review, I have only low expertise in the modelling aspects of the work, but assuming other referees are satisfied with this, I am happy to support publication.", "reviewer_5": "This is a revided manuscript that I evaluated already. Revisions took in due account criticisms and suggestions. Thus, the revised manuscript is presently suitable for publication and will constitute a valid tool for screening mutants for improved light use efficiency in different conditions. This is a valuable contribution for both basic and applied science. Having said so, Authors might have considered the results they have obtained with the (new) genotype ZEP npq4 of minor importance with respect to the general importance of the ms which deals with modelling. Nevertheless, the reduced lifetime in the dark in the ZEP npq4 points to an autonomous activity of Zea in quenching that could well be highlighted in the discussion."}, "author_response": "RESPONSE TO REVIEWER COMMENTS\nFor clarity, we have color-coded this response letter as follows:\nBlack: Reviewer comments\nBlue: Our responses\nRed: Direct citations of the manuscript (Line numbers refer to the no tracked-changes version;\npage numbers are consistent across both versions.)\nReviewer #1 (Remarks to the Author):\nThe authors replied to my comment point-by-point:\n1. Choice of mutants\nThe authors argue that thylakoids are more suitable for transient absorption (TA) measurements,\nand I am prepared to accept that point in principle. However, the material has not been properly\ncharacterized with respect to electron transport and the resulting proton gradient (ΔpH) that\ndrives NPQ. Both the kinetics and amplitude of these processes should be reported. Without such\ndata, neither the measurements nor the resulting model can be considered reliable. Furthermore,\nreference 26 does not, in my view, provide higher-quality data than comparable work in\nArabidopsis, nor does it present convincing evidence that zeaxanthin directly receives excitation\nenergy from chlorophyll.\nResponse: It is well established that ΔpH generated through electron transport is a primary driver\nof NPQ induction. This relationship has been validated in numerous studies across model\nsystems, including both Arabidopsis and Tobacco species. For example, Cardol et al. (2010,\nBiochimica et Biophysica Acta, doi:10.1016/j.bbabio.2009.10.002) explored the influence of\nproton motive force across the thylakoid membrane on NPQ onset in tobacco; Miyake et al.\n(2005a, Plant Cell Physiology, doi:10.1093/pcp/pci067) examined the role of PSI/PSII electron\nfluxes in determining NPQ magnitude; and Miyake et al. (2005b, Plant Cell Physiology,\ndoi:10.1093/pcp/pci197) investigated the effects of light intensity on cyclic electron flow and its\nrelationship to NPQ. Therefore, our work builds upon this foundational understanding, rather\nthan re-demonstrating it specifically for N. benthamiana.\nAlso note that the kinetics of ΔpH activation are considered fast enough on the timescale of our\nexperiment to be effectively instantaneous, hence ΔpH is phenomenologically modeled as an\non/off switch of constant amplitude. Since all experiments are performed at the same light\nintensity, this is a reasonable simplifying assumption to make to avoid overparameterization of\nthe model.\nWe would also like to re-emphasize that our model does not presuppose a specific physical\nmechanism of Zea-mediated quenching. Instead, it captures the kinetics of NPQ components in a\nmanner consistent with the availability of individual quenchers. We explicitly state that Zea’s\nrole in our model should be interpreted functionally, based on the timing and extent of its\n1\ncontribution to quenching, rather than as evidence of a particular photophysical mechanism such\nas direct energy transfer from Chl to Zea.\nWith respect to the spectroscopic evidence, we note that high-quality data demonstrating direct\nZea-mediated quenching via excitation energy transfer through the Qy band are limited even in\nArabidopsis. If the reviewer does not find reference 26 sufficiently convincing, we respectfully\nsubmit that, to our knowledge, no higher-quality spectroscopic dataset currently exists in\nArabidopsis or in equivalent/more intact systems.\n2. Zeaxanthin as a quencher\nThe authors appear unwilling to engage with the full scope of research on the relationship\nbetween zeaxanthin and NPQ published over the past few decades. Instead, they continue to\npromote the long-standing and now outdated view originating with Demmig-Adams et al. some\n35 years ago. This selective treatment of the literature risks perpetuating a simplified narrative\nthat does not reflect current understanding.\nResponse: We thank the reviewer for pointing out the alternative theories of zeaxanthin’s\ncontribution to quenching. In response, we have added the following sentences to the\nIntroduction of the main text:\n(Main text Page 5 Line 81-85) “Other models propose, however, that carotenoids\nfunction not as direct quenchers, but as allosteric effectors that induce\nconformational changes in LHCs to facilitate energy dissipation36–38. Structural and\nspectroscopic studies support Zea-dependent but structurally mediated pathways\ncontributing to quenching, suggesting that Zea may enable or stabilize specific\nprotein conformations that facilitate NPQ without directly quenching Chl excited\nstates36,39.”\nWe would also like to clarify (as discussed in the first response letter and in Model Assumptions\nin the Extended Methods) that our model assumes the quenching rate constant depends linearly\non the concentration of QX for qE or the concentration of Zea for qZ. This framework is agnostic\nto the specific mechanism by which carotenoids contribute to quenching, whether through direct\nenergy transfer or through indirect mechanisms such as allosteric modulation. To make this\nclearer for readers, we have revised our wording throughout the entire manuscript. For example,\nwe changed “quenchers” to “contributors to quenching” or “effectors of quenching” and\n“quenching efficiency” to “quenching effectiveness,” to better reflect the agnostic nature of our\nmodel.\nAdditionally, in response to this comment and to Comment 5, we apologize for omitting prior\nwork on modeling NPQ. We found a number of relevant references (43–50; see the revised\nmanuscript’s Reference list) which are now included in a new paragraph in the Introduction as\nfollows:\n(Main text Page 5 Line 96-100) “There have been a number of previous modeling\nstudies of NPQ kinetics, and qE in particular43–50. These models have generally been\nbased on the VAZ cycle, with Zea and protonated PsbS acting as the sole quenching-\n2\nrelated components, most commonly represented by the 4-state 2-site quenching\nmodel proposed by Jahns and Holzwarth14, which takes into account both open and\nclosed RCs and quenched and unquenched PSII antennas.”\nWe discuss the connections between the current work and references 43-50 in a new paragraph\nin the Discussion Section:\n(Main text Page 15 Line 326-341) “In this work, our experimental protocol closes\nthe RCs. We allow each component of the VAZ cycle to act as an inducible\ncontributor to quenching, with differing activation rates and quenching\neffectiveness. We also include qI and qZ as well as the contributions of Lut, which\nis an important contributor to qE at short timescales and the major component for\nthe npq1 mutant which was also studied by Morales et al.45. As Stirbet et al. point\nout50, several existing models assume that the VAZ cycle (in particular, Zea) and\nPsbS protonation are the only components involved in qE quenching (the “4 state 2-\nsite” model), simplifying NPQ dynamics but reducing the ability to capture complex\nphotoprotective systems. These models also commonly make the simplifying\nassumption that the total pool of [Vio] + [Zea] is constant over short timescales.\nHowever, this limits the ability to compare kinetics across genotypes, which can\nexhibit large variations in the size of the total xanthophyll pool even after dark\nadaptation (Figure S1, S2). The memory effect explored by Matuszynska et al. is an\nintrinsic part of our model43, as it was in our earlier algal model51,52, and is unraveled\nin greater mechanistic detail here, demonstrating the importance of the Ant\nintermediate to NPQ memory during dark periods. Thus, our work here expands\nupon the existing body of qE modeling studies by providing a detailed molecular\npicture of photoprotection, connecting lifetime dynamics directly to specific\nmolecular contributors and kinetic rates, and capturing multiple NPQ components\n(qE, qI, qZ).”\nWe provide further comments comparing existing models with ours in our response to Comment\n5.\n3. qI component\nThe model presented does not question the mechanistic origins of qI but rather addresses only its\nkinetic behavior. This raises the question: what is the real utility of such a model for\nphysiologists? Without a mechanistic framework, a purely kinetic description of such a complex\ncomponent offers limited biological insight.\nResponse: Indeed, the mechanistic basis of qI involves a complex and not yet fully understood\nset of molecular processes, including photodamage, D1 turnover, and possibly sustained\npigment-protein aggregation. However, elucidating these mechanistic details lies outside the\nscope of our current study, which focuses on quantifying and separating the kinetics of\noverlapping NPQ components across genotypes.\nto this work, it has been challenging to disentangle qI contributions across different mutants or\nspecies, particularly due to its overlap with qE and qZ. Our model provides a framework to\nquantitatively resolve qI dynamics, as demonstrated in Figure 4 and Movie S1, which reveal\ndistinct time-dependent contributions of qI in wild-type and mutants.\nImportantly, isolating the kinetics of qI is foundational to the structure of our model. Without\naccounting for qI, it would not be possible to reliably separate the faster components, such as qE\nand qZ, especially since the qI contribution and onset vary significantly across genotypes and\nevolve over time. Assuming identical qI behavior in all mutants would introduce substantial\ninaccuracies. Thus, including qI in the model is essential to unlock more mechanistic insights\ndownstream, such as the per-molecule quenching effectiveness of each component, the time-\nresolved NPQ contributions, and the predictive capacity for NPQ phenotypes across mutants,\nwhich constitute the central findings of our study.\nIn addition, a kinetic approximation of qI is also essential because the principle behind NPQ is\nthat an increase in quenching will reduce photodamage (qI may not represent the entirety of\nphotodamage, but remains a useful proxy). Thus, mutants with higher NPQ capacities will have\nlower accumulations of qI, as we see demonstrated in our model (Fig 4). Modeling the quantity\nof qI is thus not only useful for reliably capturing different NPQ kinetics but also provides an\nexplanation for why mutants differ in NPQ recovery—some sustain more photodamage than\nothers—and allows direct observation of the impact on NPQ on photoinhibition levels. We have\nadded the following sentences to the manuscript to provide additional clarification.\n(Main text Page 18 Line 405-406) “Thus, although qI is not modeled\nmechanistically, we are still able to observe the effects of NPQ activity on the\naccumulation of photodamage.”\n(Main text Page 18 Line 414-416) “qI accumulates at an increased rate and acts as\nthe major quenching contributor, explaining the lack of NPQ recovery in darkness”\nFinally, we note that although the exact molecular mechanism of qI remains under investigation,\nour model provides a practical and extensible starting point for quantitatively analyzing qI using\naccessible chlorophyll fluorescence lifetime data. As new mechanistic details emerge, this\nmodeling framework can be readily adapted to incorporate such refinements and may be helpful\nfor testing various mechanistic hypotheses.\nRegarding biological insight, we note that our earlier modeling work (Zaks et al., PNAS 2012)\nhas been cited 183 times according to Google Scholar, and has been incorporated by others into\nphysiological crop growth models such as BioCro. BioCro is a model that predicts plant growth\nover time given crop-specific parameters and environmental data as input (BioCro.org). We\nbWe\nbelieve our current model will be even more useful, as it incorporates a more complete set of\nNPQ components, provides specific molecular contributions and biochemical rates from\nfluorescence lifetime-based modeling, predicts experimental results more accurately, and offers\nguidance for the design of overexpression mutants.\n4\n4. Kinetic aspects of NPQ\nEven within the narrow kinetic focus, the treatment of NPQ dynamics in this manuscript remains\nincomplete. The relevant kinetic aspects of NPQ have not been adequately reviewed, and the\nanalysis feels partial at best.\nResponse: See response to Comment 1 and 3. Without further specificity, it is difficult to\nrespond to this comment. There are indeed entrenched positions on the mechanisms of NPQ, but\nwithout putting detailed models out for discussion by the broader community it is difficult to see\na route to progress.\n5. Suitability for Nature Communications\nFrom the discussion surrounding the modelling work of Snellenburg et al., it is evident that this\nproject is highly specialized and riddled with unresolved controversies. As such, it seems more\nappropriate for a specialist journal in photosynthesis research, rather than a broad-audience outlet\nsuch as Nature Communications. The current scope and accessibility do not justify publication in\na general high-impact venue.\nResponse: Please see our response to Comment 2.\nTurning specifically to the Snellenburg et al. work mentioned by Reviewer 1, the current paper\nand Snellenburg et al. are not contradictory, but rather they approach the task of modeling NPQ\nfrom entirely different angles. The emphasis of the Snellenburg et al. model is on characterizing\nthe state of PSII using purely kinetic/mathematical parameters. They do not attempt to\ncharacterize biochemical pathways in any detail as that is not the goal of their study.\nSnellenburg et al. modeled the evolution of PAM fluorescence by fitting population fractions and\nquantum yields to four PSII states. Their framework describes how much of PSII becomes\nquenched under given conditions. In contrast, our model reveals the involvement of different\nxanthophylls in promoting quenching and their relative effectiveness at the molecular level. By\ndirectly connecting lifetime changes to specific quenchers and kinetic rates, our approach\nprovides a mechanistic picture of how photoprotection proceeds in photosynthetic systems.\nBy exciting Chl a with a saturating laser pulse we reduce the problem to one in which reaction\ncenters are closed. If we didn't do that, the reaction centers would be either open or closed (an\nadditional factor to model), in which case the work by Snellenburg et al. would become useful.\nOur assumption of reaction centers being closed is not an \"unsolved controversy\" but rather a\ndirect result of our experimental design.\nThe work by Snellenburg et al. also makes assumptions, namely that the pool of xanthophylls is\nconstant and importantly that the amount of violaxanthin and zeaxanthin are constant over the\ncourse of the experiment. This is a simplifying assumption, as the authors write: \"the fitting\nresults show that given the short duration of the experiments this extra complexity is not needed\nto adequately describe the data.\" The authors readily acknowledge that in reality, V to Z\ninterconversion is taking place.\nThus, there is no contradiction or unresolved controversy between the two works, as Reviewer 1\nseems to suggest.\ncommunity, the lifetime-based modeling framework itself is broadly applicable: it offers an\nintuitive and quantitative tool for disentangling dynamic quenching processes, even for a\ncomplex light-harvesting system. Moreover, by organizing these mechanisms into modular\ncomponents, our manuscript helps general audiences understand how the photosynthetic\napparatus functions as an integrated, dynamically regulated system.\nRegarding the manuscript’s suitability for Nature Communications, please also see our response\nto Comment 3 of Reviewer 3.\n6. Authors’ own admissions\nThe authors themselves acknowledge that their approach requires further experimental validation\nand, in all likelihood, improved quality in the kinetic fitting of their data. This further reinforces\nmy view that the manuscript, in its current form, is not ready for publication in its intended\njournal.\nResponse: Please see response to Comment 5.\nReviewer #1 (Remarks on code availability):\nn/a\nReviewer #2 (Remarks to the Author):\nPhotoprotection mechanisms collectively referred to as non-photochemical quenching (NPQ)\nremain incompletely understood due to the large number of underlying components and their\ndiversity among species. In this study, the authors established a convenient technique to monitor\nchlorophyll fluorescence lifetime changes accompanying the induction and relaxation of NPQ\nunder fluctuating light conditions. Furthermore, they proposed a simplified NPQ model,\ncomposed of only the essential elements, that accurately reproduces these fluorescence lifetime\ndynamics. I recommend the prompt publication of this manuscript.\nCompared with the commonly used PAM-based parameter, (Fm-Fm')/Fm’, the newly proposed\nτF(t) has two distinct advantages: it is less influenced by residual NPQ present before the onset\nof measurements, and it exhibits higher linearity with respect to changes in quencher\nconcentration. The authors applied model fitting to fluorescence lifetime data obtained from both\nwild-type plants and multiple NPQ mutants. Beginning with a double mutant containing the\nfewest NPQ components and progressively incorporating additional factors, they ultimately\ndetermined the parameter set representing all NPQ factors in wild-type plants. The validity of\nthis NPQ model was confirmed by its ability to predict, with high accuracy, the fluorescence\nlifetime dynamics of independent NPQ mutants under fluctuating light conditions. The authors\nfurther suggest that this model could provide insights into improving photosynthetic responses\nand potentially contribute to crop yield enhancement.\nneither redundancy nor omission. Consequently, the model does not aim to fully replicate the\ncomplex behavior of NPQ in natural environments, nor does it present novel discoveries\nregarding NPQ mechanisms. The authors explicitly state that these were not the primary\nobjectives of their work.\nSince the inception of NPQ research over half a century ago, non-invasive PAM measurements\nhave greatly advanced the field. The approach proposed in this manuscript—combining\nfluorescence lifetime measurements with a streamlined NPQ model—offers the potential to\ndetect subtle alterations in minor NPQ components that PAM-based analyses cannot resolve.\nThis advance is expected to be valuable for future photosynthesis research and for practical\napplications such as crop breeding. The publication and subsequent broad validation of this\nmethod across diverse environments and species may eventually establish it as an alternative to\nconventional PAM-based techniques.\nResponse: We thank the reviewer for their positive assessment of our work.\nAs the reviewer noted and we discussed in Sylak-Glassman et al. Photosynth Res 127, 69-76\n(2015)71, PAM measures the total amount of fluorescence and may not always be related to the\nrate of fluorescence quenching, because of chloroplast movement, damage etc. In contrast,\nfluorescence lifetime measurements directly reflect the rate of excited-state quenching and can\nthus be quantitatively modeled using a kinetic framework like the one we present here. This\nsame point has also been made by Papageorgiou and Govindjee, as quoted in our previous\nresponse (Barbara Demmig-Adams et al., Non-Photochemical Quenching and Energy\nDissipation in Plants, Algae and Cyanobacteria. (Springer Netherlands, Dordrecht, 2014), p21.).\nWe agree, however, that PAM measurements have been and will continue to be highly valuable\nfor NPQ studies. We hope that continued development of our modeling methodology will\nfacilitate greater interoperability and accessibility, helping to bridge the gap between these two\ntypes of measurements for broader applications.\nReviewer #3 (Remarks to the Author):\n1. The ms has been modified in several ways to satisfy the concerns of me and the other\nreviewers. I think that the authors have done a good job in improving the explanation of what\nthey have done and why. In particular, I appreciate the list of assumptions made and not made in\nthe Extended methods section. But as not everyone immediately reads the SI, there should a\nspecific reference to this information in the main text, there may be others that like me need this\ndescription.\nResponse: We appreciate the suggestion and have added a specific comment in the main text\npointing to the list of assumptions made and not made in the SI.\n(Main text Page 5 Line 93-95) “A complete list of assumptions underlying the\nconstruction of this model, including those that were deliberately excluded, is\nprovided in the Supplementary Information (Extended Methods).”\n7\n2. Also, the\nreferences https://academic.oup.com/pcp/article/64/8/858/7148152, https://www.biorxiv.org/cont\nent/10.1101/2024.04.18.590023v2\nand https://www.biorxiv.org/content/10.1101/2025.01.26.634902v1) I mentioned talk about PSI\nas a quencher of PSII, not state transitions (as mentioned in the rebuttal letter). Although it is still\nnot well documented if this is a mechanism of importance in angiosperms including Nicotiana\n(and there is no useful mutant), what would it mean for the conclusions if spillover is there?\nResponse: We appreciate the reviewer highlighting this emerging line of research again. The\npapers provided propose that the formation of PSI-PSII supercomplexes, along with the\nassociated FRET-mediated excitation spillover from PSII to PSI, constitute a potential quenching\nmechanism, possibly involving both PsbS and zeaxanthin. In our model framework, such a\nspillover mechanism might potentially be captured by the PZ ↔ QZ interconversion or\nrepresented as part of qZ contribution, especially if PSI-PSII supercomplex formation correlates\nwith zeaxanthin accumulation. We agree that further mechanistic insights and mutant tools will\nbe valuable for evaluating the physiological significance of this process in different species. In\nresponse to this comment, we added the following sentence to the Introduction of the\nmanuscript:\n(Main text Page 4-5 Line 85-88) \"Recent studies have also proposed the formation\nof PSI-PSII supercomplexes and associated excitation spillover from PSII to PSI as\na potential quenching mechanism involving Zea and PsbS40-42, though its\nphysiological relevance in N. benthamiana remains to be fully established.\"\n3. Anyway, I am still somewhat doubtful about the value of this contribution. Maybe I do not\nunderstand enough mathematics/biophysics to appreciate why this is suitable for Nature\nCommunications, I feel that I do not really know more about NPQ after reading and thinking of\nthe ms than I did before. If the other reviewers and the editor think differently I will not\ncomplain if it gets accepted, but I am not convinced that it is important enough…\nResponse: We appreciate the reviewer’s honest feedback. Please also see the response to\nComment 3 & 5 of Reviewer 1.\nWe agree that discovering new NPQ mechanisms is of great interest. However, one of our main\nmotivations for studying NPQ is to ultimately improve crop yield. What is currently lacking in\nthe field is a comprehensive framework that can isolate the contributions of each quenching\ncomponent and construct a model with predictive power across different genotypes.\nTherefore, the aim of this particular work is not to uncover new molecular players in NPQ, but\nrather to provide a quantitative modeling framework that enables kinetic interpretation of\nchlorophyll fluorescence lifetime-measured NPQ behavior under dynamic light conditions. This\ncomplements existing PAM-based approaches (the advantages of fluorescence lifetime\nmeasurements are discussed extensively in our previous response and in the Methods section of\nthe manuscript) and helps bridge a long-standing methodological gap in NPQ kinetic analysis.\nhighlight the most important ones again:\n1. Quantification of the per molecule effectiveness of different xanthophylls in promoting\nquenching (Vio, Ant, Zea, and Lut)\nThese values, presented in Table 2, are not predictions but are directly obtained by fitting the\nmodel to experimental data. While debates exist regarding which carotenoid is the most\neffective (e.g., Lut vs Zea), our data provide a quantitative in vivo answer expressed in terms\nof effectiveness per molecule.\n2. Time-resolved contribution of each NPQ component\nAs shown in Fig. 4 and Movie S1, we quantify the dynamical contribution of individual\nquenching components in various genotypes over illumination time. We emphasize the\nnovelty of this dynamic perspective: the relative contributions of different NPQ components\nvary significantly across different time points and illumination phases. Again, these values\nare not merely predictions, but are extracted through robust fitting to high-quality\nexperimental data. Given the reliability of our data and fits, we are considerably confident in\nthe accuracy of these estimates and the conclusions drawn.\nUltimately, we believe the value of our work lies in offering a broadly applicable and extensible\ntool for dissecting NPQ contributions and dynamics in vivo. With further improvements in the\nfuture, we hope this model can become even more adaptable to natural illumination conditions\nand gain predictive capability regarding the expression levels of key proteins (as discussed in\nFig. 5) to improve overall light-harvesting efficiency. In this way, our study provides a\nfoundational step toward guiding future improvements in crop yield through more refined\nunderstanding and engineering of NPQ.\nAs is evident from this study, the work would not have been possible without a deep integration\nof expertise spanning plant biology, biochemistry, and genetics, as well as physical chemistry\n(spectroscopy and kinetic modeling). The highly interdisciplinary nature of this work bridging\nexperimental and theoretical approaches is precisely why we hope to publish in Nature\nCommunications. We believe that this platform offers an ideal venue for reaching a broad\nscientific audience, which can foster cross-disciplinary engagement. In that spirit, we have\ngreatly appreciated the diverse perspectives reflected in the reviewers’ comments, which align\nwith our goal of encouraging dialogue across fields.\nReviewer #4 (Remarks to the Author):\n1. The authors have engaged with my previous review in a constructive manner and addressed all\nthe points I raised. As noted by the authors and raised by other reviewers, NPQ is an area in\nwhich there has been a lot of disagreement and argument over details. It is refreshing to see the\napproach here that attempts to take a broader view. The changes made by the authors improve,\nfor me at least, the readability of the paper.\nbut assuming other referees are satisfied with this, I am happy to support publication.\nResponse: We thank the reviewer for recognizing our effort to adopt a broader and integrative\napproach to studying NPQ, especially in a field where mechanistic interpretations have often\ndiverged. We are especially encouraged that the revisions have improved the readability of the\nmanuscript from the reviewer’s perspective.\nReviewer #4 (Remarks on code availability):\nThis aspect is outside my area of expertise.\nReviewer #5 (Remarks to the Author):\n1. This is a revised manuscript that I evaluated already. Revisions took in due account criticisms\nand suggestions. Thus, the revised manuscript is presently suitable for publication and will\nconstitute a valid tool for screening mutants for improved light use efficiency in different\nconditions. This is a valuable contribution for both basic and applied science.\nResponse: We thank the reviewer for their encouraging comments. It is gratifying to know that\nthe revisions have addressed the earlier concerns and improved the manuscript’s clarity. We\nappreciate the recognition that this work may serve as a useful tool for screening mutants to\nimprove crop yield. We hope that our future development of this model will further enhance its\napplicability and ultimately contribute to achieving that goal.\n2. Having said so, Authors might have considered the results they have obtained with the (new)\ngenotype ZEP npq4 of minor importance with respect to the general importance of the ms which\ndeals with modelling. Nevertheless, the reduced lifetime in the dark in the ZEP npq4 points to an\nautonomous activity of Zea in quenching that could well be highlighted in the discussion.\nResponse: We thank the reviewer for their careful review of the data and agree with their\nassessment regarding npq4zep2. In response, we have incorporated the following content into the\nDiscussion.\n(Main text Page 17 Line 387-391) “The significant qZ contributions in our model\nare consistent with the existence of a light-independent Zea-mediated quenching\nmechanism16. This interpretation is further supported by the observed reduction in\nthe initial 𝜏 in npq4zep2 compared to npq4, suggesting a ΔpH-insensitive Zea-\n𝑑𝑎𝑟𝑘\ndependent quenching component that remains active in the dark-acclimated state in\nnpq4zep2.”\nReviewer #5 (Remarks on code availability):\nwill take some time for a more thoughtful evaluation.\n11"}, {"round": 3, "reviewer_comments": {"reviewer_1": "I appreciate the authors’ detailed and thoughtful responses and acknowledge that the manuscript has been improved in several important respects, particularly with regard to clarification of modelling assumptions, expansion of the literature review, and more careful positioning of zeaxanthin’s role in NPQ. However, despite these improvements, my central concerns remain unresolved, and I do not feel able to recommend publication of the manuscript in Nature Communications. Most importantly, the study still lacks experimental characterisation of electron transport activity and ΔpH generation in the specific thylakoid preparations used for the transient absorption and lifetime measurements. The authors rely on general literature and modelling assumptions to justify treating ΔpH as an instantaneous, constant-amplitude switch, but no direct or proxy measurements (proton gradient indicators or electron transport rates) are provided. Without such information, it is not possible to assess whether the kinetic parameters extracted from the model are biologically meaningful or comparable across genotypes. This limitation undermines confidence in both the experimental interpretation and the quantitative modelling. While the authors correctly state that their model is intended to be mechanistically agnostic and primarily kinetic, this does not remove the need for adequate physiological constraints on the system being modelled. In my view, the absence of ΔpH and electron transport characterisation remains a fundamental weakness, rather than a secondary or stylistic issue. The authors have also clarified their position regarding zeaxanthin and expanded their discussion of alternative models of NPQ, which is welcome. Nevertheless, the work still engages with long-standing and unresolved controversies in photosynthesis research in a way that, in my opinion, limits its accessibility and general appeal to the broad readership of Nature Communications. The study will likely be of significant interest to specialists in NPQ modelling and ultrafast spectroscopy, but its current scope and experimental grounding do not, in my view, justify publication in a general high-impact journal. In summary, although I recognise the technical sophistication of the modelling approach and the effort invested in revision, I remain unconvinced that the manuscript has reached the level of experimental completeness, robustness, and general relevance required for Nature Communications. I therefore recommend decline, while noting that the work may be more suitable for a specialist journal in photosynthesis or biophysical modelling following further experimental validation.", "reviewer_3": "The ping-pong between authors and reviewers has improved the ms further, I have no specific comments on the presentation. I still think that this fits better in a specialized journal (and Reviewer 1 seems to agree) but if the decision will the accept, OK with me.", "reviewer_6": "In my opinion the assumptions of the model are appropriately justified. I think part of the problem lies in a misunderstanding between the reviewers and the authors. The reviewer writes: “Most importantly, the study still lacks experimental characterisation of electron transport activity and ΔpH generation in the specific thylakoid preparations used for the transient absorption and lifetime measurements.” It seems the reviewer assumes the measurements are done on isolated thylakoids and that transient absorption has been used. This confusion arises from the introduction where this is written: “To that end, we selected Nicotiana benthamiana as our model system due to its compatibility with transient absorption spectroscopic measurements, which facilitate investigation of potential qE quenching mechanisms and assessment of exciton diffusion length in relation to NPQ levels in isolated thylakoids”. However, in the manuscript only TCSPC measurements on whole leaves are reported, which is not mentioned in the introduction. The electron transport activity and ΔpH generation would be a matter of concern in isolated systems, but are well documented in intact leaves. The reviewer is right that it will take some time to build the pH gradient, but the time-resolution in this study is not very high, so it should not be a limiting factor. I have one point which I would like to have clarified. The authors write: “The memory effect explored by Matuszynska et al. is an intrinsic part of our model43, as it was in our earlier algal model51,52337 , and is unraveled in greater mechanistic detail 338 here, demonstrating the importance of the Ant intermediate to NPQ memory during dark periods.” This is about the observation that NPQ is induced faster upon a second illumination, which could be due to the presence of Ant or remaining Zea. However, in Figure S4 the faster NPQ induction is also observed for npq1, which cannot form Ant or Zea. The model still fits the data well, so there must be another explanation than remaining Ant/Zea."}, "author_response": "RESPONSE TO REVIEWER COMMENTS\nFor clarity, we have color-coded this response letter as follows:\nBlack: Reviewer comments\nBlue: Our responses\nRed: Direct citations of the manuscript (Line numbers refer to the pdf version of the final edits\nof the manuscript.)\nReviewer #1 (Remarks to the Author):\nI appreciate the authors’ detailed and thoughtful responses and acknowledge that the manuscript\nhas been improved in several important respects, particularly with regard to clarification of\nmodelling assumptions, expansion of the literature review, and more careful positioning of\nzeaxanthin’s role in NPQ. However, despite these improvements, my central concerns remain\nunresolved, and I do not feel able to recommend publication of the manuscript in Nature\nCommunications. Most importantly, the study still lacks experimental characterisation of\nelectron transport activity and ΔpH generation in the specific thylakoid preparations used for the\ntransient absorption and lifetime measurements. The authors rely on general literature and\nmodelling assumptions to justify treating ΔpH as an instantaneous, constant-amplitude switch,\nbut no direct or proxy measurements (proton gradient indicators or electron transport rates) are\nprovided. Without such information, it is not possible to assess whether the kinetic parameters\nextracted from the model are biologically meaningful or comparable across genotypes. This\nlimitation undermines confidence in both the experimental interpretation and the quantitative\nmodelling. While the authors correctly state that their model is intended to be mechanistically\nagnostic and primarily kinetic, this does not remove the need for adequate physiological\nconstraints on the system being modelled. In my view, the absence of ΔpH and electron transport\ncharacterisation remains a fundamental weakness, rather than a secondary or stylistic issue. The\nauthors have also clarified their position regarding zeaxanthin and expanded their discussion of\nalternative models of NPQ, which is welcome. Nevertheless, the work still engages with long-\nstanding and unresolved controversies in photosynthesis research in a way that, in my opinion,\nlimits its accessibility and general appeal to the broad readership of Nature Communications.\nThe study will likely be of significant interest to specialists in NPQ modelling and ultrafast\nspectroscopy, but its current scope and experimental grounding do not, in my view, justify\npublication in a general high-impact journal.\nIn summary, although I recognise the technical sophistication of the modelling approach and the\neffort invested in revision, I remain unconvinced that the manuscript has reached the level of\nexperimental completeness, robustness, and general relevance required for Nature\nCommunications. I therefore recommend decline, while noting that the work may be more\nsuitable for a specialist journal in photosynthesis or biophysical modelling following further\nexperimental validation.\nResponse: We are glad to hear that our manuscript has improved after the previous revision.\nRegarding the ΔpH concern, we now realize that there may be a misunderstanding that this work\nwas performed using isolated thylakoids. Instead, all experimental data used in this manuscript,\nincluding chlorophyll fluorescence lifetime measurements (TCSPC) and pigment profiling by\nHPLC, were conducted in intact, freshly harvested whole leaves of N. benthamiana after\nsufficient dark acclimation. This is stated in the Abstract and Results and described in more\ndetail in the Methods. To make this clearer, we have revised a few sentences in the Introduction,\nas follows.\n(Main text Page 7 Line 139-140) “This choice could enable both kinetic and\nmechanistic analysis of NPQ in land plants when combined with complementary\ntechniques in a series of studies. In the present study, however, to preserve\nphysiological function as much as possible, we utilized fresh, intact leaves of N.\nbenthamiana to systematically investigate the NPQ response using a comprehensive\nset of single and double NPQ mutants26 (Table 1) across three regular/irregular\nactinic light sequences.”\n(Main text Page 7 Line 146-147) “Model parameters are directly fitted to the\nexperimental Chl fluorescence lifetimes, guided by xanthophyll concentrations\nmeasured via high-performance liquid chromatography (HPLC), with all\nmeasurements performed in intact leaves as described in more detail in the\nMethods.”\nWe apologize for any confusion caused by our mention of previous work26,57 using\ntransient absorption spectroscopy as part of the reason we chose N. benthamiana as a model\norganism for a series of our NPQ studies, including this one. In the two previous studies we\nreferenced26,57, transient absorption spectroscopy required sending a laser pulse through the\nsample and detecting the transmitted signal, which makes whole-leaf measurements impractical\ndue to strong scattering. We therefore were constrained to use isolated thylakoids in those\ntransient absorption studies26,57. However, we totally agree that some physiological functions,\nincluding ΔpH, can be affected by the thylakoid extraction process, and that measuring a more\nintact system (such as whole leaves) is preferred whenever possible. Accordingly, for the present\nstudy, because all measurements were conducted in whole leaves, we believe it is reasonable to\ntreat ΔpH as a rapid switch compared to the timescale of the NPQ behaviors we quantify\n(minutes). We hope that, in future work, we can model more complicated ΔpH behavior and\nincorporate that into our model.\nWe respectfully maintain that the manuscript is appropriate for Nature Communications,\nas it presents a generalizable approach for disentangling photoprotective kinetics in intact leaves\nand demonstrates predictive power across genotypes and illumination regimes, with implications\nfor optimizing photoprotection under fluctuating light and improving light-use efficiency—an\narea of broad interest beyond NPQ or modelling specialists.\nReviewer #3 (Remarks to the Author):\ncomments on the presentation. I still think that this fits better in a specialized journal (and\nReviewer 1 seems to agree) but if the decision will the accept, OK with me.\nResponse: We thank the reviewer’s constructive feedback throughout the revision process, which\nhas helped us improve the manuscript further. With respect to whether the work is appropriate\nfor Nature Communications; please refer to our comments to Reviewer 1. We are grateful that,\nshould the editors judge the manuscript appropriate for Nature Communications, the reviewer\nwould be supportive.\nReviewer #6 (Remarks to the Author):\nIn my opinion the assumptions of the model are appropriately justified. I think part of the\nproblem lies in a misunderstanding between the reviewers and the authors. The reviewer writes:\n“Most importantly, the study still lacks experimental characterisation of electron transport\nactivity and ΔpH generation in the specific thylakoid preparations used for the transient\nabsorption and lifetime measurements.” It seems the reviewer assumes the measurements are\ndone on isolated thylakoids and that transient absorption has been used. This confusion arises\nfrom the introduction where this is written: “To that end, we selected Nicotiana benthamiana as\nour model system due to its compatibility with transient absorption spectroscopic measurements,\nwhich facilitate investigation of potential qE quenching mechanisms and assessment of exciton\ndiffusion length in relation to NPQ levels in isolated thylakoids”. However, in the manuscript\nonly TCSPC measurements on whole leaves are reported, which is not mentioned in the\nintroduction.\nThe electron transport activity and ΔpH generation would be a matter of concern in isolated\nsystems, but are well documented in intact leaves. The reviewer is right that it will take some\ntime to build the pH gradient, but the time-resolution in this study is not very high, so it should\nnot be a limiting factor.\nI have one point which I would like to have clarified.\nThe authors write: “The memory effect explored by Matuszynska et al. is an intrinsic part of our\nmodel43, as it was in our earlier algal model51,52337 , and is unraveled in greater mechanistic\ndetail 338 here, demonstrating the importance of the Ant intermediate to NPQ memory during\ndark periods.” This is about the observation that NPQ is induced faster upon a second\nillumination, which could be due to the presence of Ant or remaining Zea. However, in Figure\nS4 the faster NPQ induction is also observed for npq1, which cannot form Ant or Zea. The\nmodel still fits the data well, so there must be another explanation than remaining Ant/Zea.\nResponse: As the time series in Figure 4 and Supplementary Movie 1 show, Lut (qE ) is always\nL\nthe first responder when present. In genotypes with a functional VAZ cycle, as time of\nillumination progresses, the contributions from Zea (qE and qZ) grow and become dominant by\nZ\ntimes longer than 5 minutes. In the npq1 mutant, which lacks a VAZ cycle, the rapid initial\nresponse of Lut (qE ) becomes dominant (with a small contribution from Vio) during both the\nL\ninitial and subsequent illumination periods. This is presumably because Lut is constitutively\npresent in LHC complexes and NPQ can be rapidly activated upon illumination. We also note\nthat the relative contribution of Vio (qE ) is somewhat larger in npq1 than in WT (e.g., Figure\nV\n4D).\nThese results highlight the importance of a time-resolved, dynamic picture when\ninterpreting the contributions of different quenching species. To make our conclusions clearer,\nwe updated the presentation of Supplementary Movie 1 (20HL) by adding background shading\nto indicate HL/dark periods, and added Supplementary Movie 2, which visualizes the dynamical\ncontribution of each component under the 5HL-10D-5HL sequence. In particular, Movie S2\nshows the rapid relaxation of qE within ~1 min in darkness and its rapid re-initiation upon the\nL\nsecond HL period. We also revised the Results and Discussion to emphasize the rapid activation\nof NPQ in npq1 upon both the initial and re-illumination, and to provide additional clarification.\n(Main text Page 11 Line 250-251) “In npq1 (Fig. 2C), the NPQ components qI,\nqE ,and qE exhibited rapid activation, contributing to a sharp decrease in lifetime\nV L\nduring both the initial and second illumination periods, although the model slightly\nunderestimated Lut activation rates.”\n(Main text Page 18-19 Line 423-424) “In npq1, lack of the VDE-mediated VAZ\ncycle results in increased Vio contribution to quenching, and Lut engages rapidly in\nquenching at the onset of illumination, consistent with our observations in WT.”\n(Main text Page 19 Line 426-436) “Additionally, Supplementary Movie 2, which\nshows the same NPQ component decomposition under the 5HL-10D-5HL actinic\nlight sequence, visualizes how these components contribute to the NPQ relaxation\nin darkness and NPQ re-initiation upon the second HL period. Interestingly, NPQ\ncontributed by Lut (qE ) relaxes the fastest after the transition from HL to darkness,\nL\ndiminishing within ~1 min in WT, followed by qE . By contrast, qZ decays much\nZ\nmore slowly in darkness, aside from the slowest component, qI. Notably, npq1\nshows faster NPQ re-initiation upon the second HL period (see Results and\nSupplementary Figure 4) despite the absence of Ant or Zea, supporting the rapid\nswitching of qE discussed above. These results highlight the importance of a time-\nL\nresolved dynamic picture when interpreting the contributions of different quenching\ncomponents, since they are highly time-dependent and strongly shaped by\nillumination duration and history.”\n4"}]

Decision letter