Biological nitrogen fixation maintains carbon/nitrogen balance and photosynthesis at elevated CO2.

Understanding crop responses to elevated CO2 is necessary to meet increasing agricultural demands. Crops may not achieve maximum potential yields at high CO2 due to photosynthetic downregulation, often associated with nitrogen limitation. Legumes have been proposed to have an advantage at elevated CO2 due to their ability to exchange carbon for nitrogen. Here, the effects of biological nitrogen fixation (BNF) on the physiological and gene expression responses to elevated CO2 were examined at multiple nitrogen levels by comparing alfalfa mutants incapable of nitrogen fixation to wild-type. Elemental analysis revealed a role for BNF in maintaining shoot carbon/nitrogen (C/N) balance under all nitrogen treatments at elevated CO2, whereas the effect of BNF on biomass was only observed at elevated CO2 and the lowest nitrogen dose. Lower photosynthetic rates at were associated with the imbalance in shoot C/N. Genome-wide transcriptional responses were used to identify carbon and nitrogen metabolism genes underlying the traits. Transcription factors important to C/N signalling were identified from inferred regulatory networks. This work supports the hypothesis that maintenance of C/N homoeostasis at elevated CO2 can be achieved in plants capable of BNF and revealed important regulators in the underlying networks including an alfalfa (Golden2-like) GLK ortholog.

Nitrogen sensing and regulatory networks: it's about time and space.

A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its "hit-and-run" mode of target gene regulation, and temporal transcriptional cascade identified by "network walking." Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.

First report of severe tolpyralate sensitivity in corn (Zea mays) discovers a novel genetic factor conferring crop response to a herbicide.

BACKGROUND: Tolpyralate, a relatively new inhibitor of 4-hydroxyphenylpyruvate dioxygenase (HPPD), is registered for postemergence use in all types of corn (Zea mays L.) and has a record of excellent crop tolerance. A report of severe crop injury to sweet corn inbred (XSEN187) led to the following objectives: (i) determine whether sensitivity to tolpyralate in XSEN187 exists, and if confirmed, (ii) determine the genetic basis of tolpyralate sensitivity, and (iii) screen other corn germplasm for sensitivity to tolpyralate. RESULTS: Inbred XSEN187 was confirmed sensitive to tolpyralate. Inclusion of methylated seed oil or nonionic surfactant in the spray volume was necessary for severe crop injury. Tolpyralate sensitivity in XSEN187 is not conferred by alleles at Nsf1, a cytochrome P450-encoding gene (CYP81A9) conferring tolerance to many corn herbicides. Evidence suggests that tolpyralate sensitivity in XSEN187 is conferred by a single gene mapped to the Chr05: 283 240-1 222 909 bp interval. Moreover, tolpyralate sensitivity was observed in 48 other sweet corn and field corn inbreds. CONCLUSIONS: Severe sensitivity to tolpyralate exists in sweet corn and field corn germplasm when the herbicide is applied according to label directions. Whereas the corn response to several other herbicides, including HPPD-inhibitors, is conferred by the Nsf1 locus, corn sensitivity to tolpyralate is the result of a different locus. The use of tolpyralate should consider herbicide tolerance in inbred lines from which corn hybrids were derived, whereas alleles that render corn germplasm sensitive to tolpyralate should be eliminated from breeding populations, inbreds, and commercial cultivars. © 2023 Illinois Foundation Seeds, Inc and The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

DamID-seq: A Genome-Wide DNA Methylation Method that Captures Both Transient and Stable TF-DNA Interactions in Plant Cells.

Capturing the dynamic and transient interactions of a transcription factor (TF) with its genome-wide targets whose regulation leads to plants' adaptation to their changing environment is a major technical challenge. This is a widespread problem with biochemical methods such as chromatin immunoprecipitation-sequencing (ChIP-seq) which are biased towards capturing stable TF-target gene interactions. Herein, we describe how DNA adenine methyltransferase identification and sequencing (DamID-seq) can be used to capture both transient and stable TF-target interactions by DNA methylation. The DamID technique uses a TF protein fused to a DNA adenine methyltransferase (Dam) from E. coli. When expressed in a plant cell, the Dam-TF fusion protein will methylate adenine (A) bases near the sites of TF-DNA interactions. In this way, DamID results in a permanent, stable DNA methylation mark on TF-target gene promoters, even if the target gene is only transiently "touched" by the Dam-TF fusion protein. Here we provide a step-by-step protocol to perform DamID-seq experiments in isolated plant cells for any Dam-TF fusion protein of interest. We also provide information that will enable researchers to analyze DamID-seq data to identify TF-binding sites in the genome. Our protocol includes instructions for vector cloning of the Dam-TF fusion proteins, plant cell protoplast transfections, DamID preps, library preparation, and sequencing data analysis. The protocol outlined in this chapter is performed in Arabidopsis thaliana, however, the DamID-seq workflow developed in this guide is broadly applicable to other plants and organisms.

Building High-Confidence Gene Regulatory Networks by Integrating Validated TF-Target Gene Interactions Using ConnecTF.

Many methods are now available to identify or predict the target genes of transcription factors (TFs) in plants. These include experimental approaches such as in vivo or in vitro TF-target gene-binding assays and various methods for identifying regulated targets in mutants, transgenics, or isolated plant cells. In addition, computational approaches are used to infer TF-target gene interactions from the regulatory elements or gene expression changes across treatments. While each of these approaches has now been applied to a large number of TFs from many species, each method has its own limitations which necessitates that multiple data types are integrated to build the most accurate representation of the gene regulatory networks operating in plants. To make the analyses of TF-target interaction datasets available to the broader research community, we have developed the ConnecTF web platform ( https://connectf.org/ ). In this chapter, we describe how ConnecTF can be used to integrate validated and predicted TF-target gene interactions in order to dissect the regulatory role of TFs in developmental and stress response pathways. Using as our examples KN1 and RA1, two well-characterized maize TFs involved in developing floral tissue, we demonstrate how ConnecTF can be used to (1) compare the target genes between TFs, (2) identify direct vs. indirect targets by combining TF-binding and TF-regulation datasets, (3) chart and visualize network paths between TFs and their downstream targets, and (4) prune inferred user networks for high-confidence predicted interactions using validated TF-target gene data. Finally, we provide instructions for setting up a private version of ConnecTF that enables research groups to store and analyze their own TF-target gene interaction datasets.

The TARGET System: Rapid Identification of Direct Targets of Transcription Factors by Gene Regulation in Plant Cells.

The TARGET system allows for the rapid identification of direct regulated gene targets of transcription factors (TFs). It employs the transient transformation of plant protoplasts with inducible nuclear entry of the TF and subsequent transcriptomic and/or ChIP-seq analysis. The ability to separate direct TF-target gene regulatory interactions from indirect downstream responses and the significantly shorter amount of time required to perform the assay, compared to the generation of transgenics, make this plant cell-based approach a valuable tool for a higher throughput approach to identify the genome-wide targets of multiple TFs, to build validated transcriptional networks in plants. Here, we describe the use of the TARGET system in Arabidopsis seedling root protoplasts to map the gene regulatory network downstream of transcription factors-of-interest.

Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice.

Nitrogen (N) and Water (W) - two resources critical for crop productivity - are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated in planta. This analysis sets a precision threshold of 0.31, used to "prune" the GENIE3 network for high-confidence TF→target gene edges, comprising 88 TFs and 5,716 N-and/or-W response genes. Next, we ranked these 88 TFs based on their significant influence on NUEg target genes responsive to N and/or W signaling. This resulted in a list of 18 prioritized TFs that regulate 551 NUEg target genes responsive to N and/or W signals. We validated the direct regulated targets of two of these candidate NUEg TFs in a plant cell-based TF assay called TARGET, for which we also had in planta data for comparison. Gene ontology analysis revealed that 6/18 NUEg TFs - OsbZIP23 (LOC_Os02g52780), Oshox22 (LOC_Os04g45810), LOB39 (LOC_Os03g41330), Oshox13 (LOC_Os03g08960), LOC_Os11g38870, and LOC_Os06g14670 - regulate genes annotated for N and/or W signaling. Our results show that OsbZIP23 and Oshox22, known regulators of drought tolerance, also coordinate W-responses with NUEg. This validated network can aid in developing/breeding rice with improved yield on marginal, low N-input, drought-prone soils.

Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks.

All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets-at both the local and genome-wide levels-and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.

ConnecTF: A platform to integrate transcription factor-gene interactions and validate regulatory networks.

Deciphering gene regulatory networks (GRNs) is both a promise and challenge of systems biology. The promise lies in identifying key transcription factors (TFs) that enable an organism to react to changes in its environment. The challenge lies in validating GRNs that involve hundreds of TFs with hundreds of thousands of interactions with their genome-wide targets experimentally determined by high-throughput sequencing. To address this challenge, we developed ConnecTF, a species-independent, web-based platform that integrates genome-wide studies of TF-target binding, TF-target regulation, and other TF-centric omic datasets and uses these to build and refine validated or inferred GRNs. We demonstrate the functionality of ConnecTF by showing how integration within and across TF-target datasets uncovers biological insights. Case study 1 uses integration of TF-target gene regulation and binding datasets to uncover TF mode-of-action and identify potential TF partners for 14 TFs in abscisic acid signaling. Case study 2 demonstrates how genome-wide TF-target data and automated functions in ConnecTF are used in precision/recall analysis and pruning of an inferred GRN for nitrogen signaling. Case study 3 uses ConnecTF to chart a network path from NLP7, a master TF in nitrogen signaling, to direct secondary TF2s and to its indirect targets in a Network Walking approach. The public version of ConnecTF (https://ConnecTF.org) contains 3,738,278 TF-target interactions for 423 TFs in Arabidopsis, 839,210 TF-target interactions for 139 TFs in maize (Zea mays), and 293,094 TF-target interactions for 26 TFs in rice (Oryza sativa). The database and tools in ConnecTF will advance the exploration of GRNs in plant systems biology applications for model and crop species.

Author Correction: OutPredict: multiple datasets can improve prediction of expression and inference of causality.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

OutPredict: multiple datasets can improve prediction of expression and inference of causality.

The ability to accurately predict the causal relationships from transcription factors to genes would greatly enhance our understanding of transcriptional dynamics. This could lead to applications in which one or more transcription factors could be manipulated to effect a change in genes leading to the enhancement of some desired trait. Here we present a method called OutPredict that constructs a model for each gene based on time series (and other) data and that predicts gene's expression in a previously unseen subsequent time point. The model also infers causal relationships based on the most important transcription factors for each gene model, some of which have been validated from previous physical experiments. The method benefits from known network edges and steady-state data to enhance predictive accuracy. Our results across B. subtilis, Arabidopsis, E.coli, Drosophila and the DREAM4 simulated in silico dataset show improved predictive accuracy ranging from 40% to 60% over other state-of-the-art methods. We find that gene expression models can benefit from the addition of steady-state data to predict expression values of time series. Finally, we validate, based on limited available data, that the influential edges we infer correspond to known relationships significantly more than expected by chance or by state-of-the-art methods.

Transient genome-wide interactions of the master transcription factor NLP7 initiate a rapid nitrogen-response cascade.

Dynamic reprogramming of gene regulatory networks (GRNs) enables organisms to rapidly respond to environmental perturbation. However, the underlying transient interactions between transcription factors (TFs) and genome-wide targets typically elude biochemical detection. Here, we capture both stable and transient TF-target interactions genome-wide within minutes after controlled TF nuclear import using time-series chromatin immunoprecipitation (ChIP-seq) and/or DNA adenine methyltransferase identification (DamID-seq). The transient TF-target interactions captured uncover the early mode-of-action of NIN-LIKE PROTEIN 7 (NLP7), a master regulator of the nitrogen signaling pathway in plants. These transient NLP7 targets captured in root cells using temporal TF perturbation account for 50% of NLP7-regulated genes not detectably bound by NLP7 in planta. Rapid and transient NLP7 binding activates early nitrogen response TFs, which we validate to amplify the NLP7-initiated transcriptional cascade. Our approaches to capture transient TF-target interactions genome-wide can be applied to validate dynamic GRN models for any pathway or organism of interest.

Nitrate in 2020: Thirty Years from Transport to Signaling Networks.

Nitrogen (N) is an essential macronutrient for plants and a major limiting factor for plant growth and crop production. Nitrate is the main source of N available to plants in agricultural soils and in many natural environments. Sustaining agricultural productivity is of paramount importance in the current scenario of increasing world population, diversification of crop uses, and climate change. Plant productivity for major crops around the world, however, is still supported by excess application of N-rich fertilizers with detrimental economic and environmental impacts. Thus, understanding how plants regulate nitrate uptake and metabolism is key for developing new crops with enhanced N use efficiency and to cope with future world food demands. The study of plant responses to nitrate has gained considerable interest over the last 30 years. This review provides an overview of key findings in nitrate research, spanning biochemistry, molecular genetics, genomics, and systems biology. We discuss how we have reached our current view of nitrate transport, local and systemic nitrate sensing/signaling, and the regulatory networks underlying nitrate-controlled outputs in plants. We hope this summary will serve not only as a timeline and information repository but also as a baseline to define outstanding questions for future research.

Microbial Enrichment Culture Responsible for the Complete Oxidative Biodegradation of 3-Amino-1,2,4-triazol-5-one (ATO), the Reduced Daughter Product of the Insensitive Munitions Compound 3-Nitro-1,2,4-triazol-5-one (NTO).

3-Nitro-1,2,4-triazol-5-one (NTO) is one of the main ingredients of many insensitive munitions, which are being used as replacements for conventional explosives. As its use becomes widespread, more research is needed to assess its environmental fate. Previous studies have shown that NTO is biologically reduced to 3-amino-1,2,4-triazol-5-one (ATO). However, the final degradation products of ATO are still unknown. We have studied the aerobic degradation of ATO by enrichment cultures derived from the soil. After multiple transfers, ATO degradation was monitored in closed bottles through measurements of inorganic carbon and nitrogen species. The results indicate that the members of the enrichment culture utilize ATO as the sole source of carbon and nitrogen. As ATO was mineralized to CO2, N2, and NH4+, microbial growth was observed in the culture. Co-substrates addition did not increase the ATO degradation rate. Quantitative polymerase chain reaction analysis revealed that the organisms that enriched using ATO as carbon and nitrogen source were Terrimonas spp., Ramlibacter-related spp., Mesorhizobium spp., Hydrogenophaga spp., Ralstonia spp., Pseudomonas spp., Ectothiorhodospiraceae, and Sphingopyxis. This is the first study to report the complete mineralization of ATO by soil microorganisms, expanding our understanding of natural attenuation and bioremediation of the explosive NTO.

Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions.

Charting a temporal path in gene networks requires linking early transcription factor (TF)-triggered events to downstream effects. We scale-up a cell-based TF-perturbation assay to identify direct regulated targets of 33 nitrogen (N)-early response TFs encompassing 88% of N-responsive Arabidopsis genes. We uncover a duality where each TF is an inducer and repressor, and in vitro cis-motifs are typically specific to regulation directionality. Validated TF-targets (71,836) are used to refine precision of a time-inferred root network, connecting 145 N-responsive TFs and 311 targets. These data are used to chart network paths from direct TF1-regulated targets identified in cells to indirect targets responding only in planta via Network Walking. We uncover network paths from TGA1 and CRF4 to direct TF2 targets, which in turn regulate 76% and 87% of TF1 indirect targets in planta, respectively. These results have implications for N-use and the approach can reveal temporal networks for any biological system.

Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants.

This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our "just-in-time" analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to "prune" the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF "N-specificity" index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs-CRF4, SNZ, CDF1, HHO5/6, and PHL1-validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and 15NO3- uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal "transcriptional logic" for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine.

Engineering the lutein epoxide cycle into Arabidopsis thaliana.

Although sunlight provides the energy necessary for plants to survive and grow, light can also damage reaction centers of photosystem II (PSII) and reduce photochemical efficiency. To prevent damage, plants possess photoprotective mechanisms that dissipate excess excitation. A subset of these mechanisms is collectively referred to as NPQ, or nonphotochemical quenching of chlorophyll a fluorescence. The regulation of NPQ is intrinsically linked to the cycling of xanthophylls that affects the kinetics and extent of the photoprotective response. The violaxanthin cycle (VAZ cycle) and the lutein epoxide cycle (LxL cycle) are two xanthophyll cycles found in vascular plants. The VAZ cycle has been studied extensively, owing in large part to its presence in model plant species where mutants are available to aid in its characterization. In contrast, the LxL cycle is not found in model plants, and its role in photosynthetic processes has been more difficult to define. To address this challenge, we introduced the LxL cycle into Arabidopsis thaliana and functionally isolated it from the VAZ cycle. Using these plant lines, we showed an increase in dark-acclimated PSII efficiency associated with Lx accumulation and demonstrated that violaxanthin deepoxidase is responsible for the light-driven deepoxidation of Lx. Conversion of Lx to L was reversible during periods of low light and occurred considerably faster than rates previously described in nonmodel species. Finally, we present clear evidence of the LxL cycle's role in modulating a rapid component of NPQ that is necessary to prevent photoinhibition in excess light.

Distinct roles of the photosystem II protein PsbS and zeaxanthin in the regulation of light harvesting in plants revealed by fluorescence lifetime snapshots.

The photosystem II (PSII) protein PsbS and the enzyme violaxanthin deepoxidase (VDE) are known to influence the dynamics of energy-dependent quenching (qE), the component of nonphotochemical quenching (NPQ) that allows plants to respond to fast fluctuations in light intensity. Although the absence of PsbS and VDE has been shown to change the amount of quenching, there have not been any measurements that can detect whether the presence of these proteins alters the type of quenching that occurs. The chlorophyll fluorescence lifetime probes the excited-state chlorophyll relaxation dynamics and can be used to determine the amount of quenching as well as whether two different genotypes with the same amount of NPQ have similar dynamics of excited-state chlorophyll relaxation. We measured the fluorescence lifetimes on whole leaves of Arabidopsis thaliana throughout the induction and relaxation of NPQ for wild type and the qE mutants, npq4, which lacks PsbS; npq1, which lacks VDE and cannot convert violaxanthin to zeaxanthin; and npq1 npq4, which lacks both VDE and PsbS. These measurements show that although PsbS changes the amount of quenching and the rate at which quenching turns on, it does not affect the relaxation dynamics of excited chlorophyll during quenching. In addition, the data suggest that PsbS responds not only to ΔpH but also to the Δψ across the thylakoid membrane. In contrast, the presence of VDE, which is necessary for the accumulation of zeaxanthin, affects the excited-state chlorophyll relaxation dynamics.

Atomic force microscopy of photosystem II and its unit cell clustering quantitatively delineate the mesoscale variability in Arabidopsis thylakoids.

Photoautotrophic organisms efficiently regulate absorption of light energy to sustain photochemistry while promoting photoprotection. Photoprotection is achieved in part by triggering a series of dissipative processes termed non-photochemical quenching (NPQ), which depend on the re-organization of photosystem (PS) II supercomplexes in thylakoid membranes. Using atomic force microscopy, we characterized the structural attributes of grana thylakoids from Arabidopsis thaliana to correlate differences in PSII organization with the role of SOQ1, a recently discovered thylakoid protein that prevents formation of a slowly reversible NPQ state. We developed a statistical image analysis suite to discriminate disordered from crystalline particles and classify crystalline arrays according to their unit cell properties. Through detailed analysis of the local organization of PSII supercomplexes in ordered and disordered phases, we found evidence that interactions among light-harvesting antenna complexes are weakened in the absence of SOQ1, inducing protein rearrangements that favor larger separations between PSII complexes in the majority (disordered) phase and reshaping the PSII crystallization landscape. The features we observe are distinct from known protein rearrangements associated with NPQ, providing further support for a role of SOQ1 in a novel NPQ pathway. The particle clustering and unit cell methodology developed here is generalizable to multiple types of microscopy and will enable unbiased analysis and comparison of large data sets.

Dynamic mechanical responses of Arabidopsis thylakoid membranes during PSII-specific illumination.

Remodeling of thylakoid membranes in response to illumination is an important process for the regulation of photosynthesis. We investigated the thylakoid network from Arabidopsis thaliana using atomic force microscopy to capture dynamic changes in height, elasticity, and viscosity of isolated thylakoid membranes caused by changes in illumination. We also correlated the mechanical response of the thylakoid network with membrane ultrastructure using electron microscopy. We find that the elasticity of the thylakoid membranes increases immediately upon PSII-specific illumination, followed by a delayed height change. Direct visualization by electron microscopy confirms that there is a significant change in the packing repeat distance of the membrane stacks in response to illumination. Although experiments with Gramicidin show that the change in elasticity depends primarily on the transmembrane pH gradient, the height change requires both the pH gradient and STN7-kinase-dependent phosphorylation of LHCII. Our studies indicate that lumen expansion in response to illumination is not simply a result of the influx of water, and we propose a dynamic model in which protein interactions within the lumen drive these changes.