# Plasticity of the Arabidopsis leaf lipidome and proteome in response to pathogen infection and heat stress

Patricia Scholz, Nathan M Doner, Katharina Gutbrod, Cornelia Herrfurth, Philipp W Niemeyer, Magdiel S S Lim, Katharina F Blersch, Kerstin Schmitt, Oliver Valerius, John Shanklin, et al.

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Patricia Scholz, Nathan M Doner, Katharina Gutbrod, Cornelia Herrfurth, Philipp W Niemeyer, et al. Plasticity of the Arabidopsis leaf lipidome and proteome in response to pathogen infection and heat stress. Plant Physiology, 2024, 10.1093/plphys/kiae274. hal-04831186

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# Plasticity of the Arabidopsis leaf lipidome and proteome in response to pathogen infection and heat stress

Patricia Scholz,<fnref n="1" />,*,<fnref n="1" /> ID Nathan M. Doner,<fnref n="2" />,† ID Katharina Gutbrod,<fnref n="3" /> ID Cornelia Herrfurth,<fnref n="1" />,<fnref n="4" /> ID Philipp W. Niemeyer,<fnref n="1" /> ID

Magdiel S.S. Lim,<fnref n="5" /> ID Katharina F. Blersch,<fnref n="5" /> ID Kerstin Schmitt,<fnref n="6" /> ID Oliver Valerius,<fnref n="6" /> ID John Shanklin,<fnref n="7" /> ID Ivo Feussner,<fnref n="1" />,<fnref n="4" /> ID

Peter Dörmann,<fnref n="3" /> ID Gerhard H. Braus,<fnref n="4" />,<fnref n="6" /> ID Robert T. Mullen,<fnref n="2" /> ID and Till Ischebeck<fnref n="1" />,<fnref n="4" />,<fnref n="5" />,† ID

## Abstract

Plants must cope with a variety of stressors during their life cycle, and the adaptive responses to these environmental cues involve all cellular organelles. Among them, comparatively little is known about the contribution of cytosolic lipid droplets (LDs) and their core set of neutral lipids and associated surface proteins to the rewiring of cellular processes in response to stress. Here, we analyzed the changes that occur in the lipidome and proteome of Arabidopsis (Arabidopsis thaliana) leaves after pathogen infection with Botrytis cinerea or Pseudomonas syringae, or after heat stress. Analyses were carried out in wild-type plants and the oil-rich double mutant trigalactosyldiacylglycerol-1-1 sugar dependent 1-4 (tgd1-1 sdp1-4) that allowed for an allied study of the LD proteome in stressed leaves. Using liquid chromatography-tandem mass spectrometry-based methods, we showed that a hyperaccumulation of the primary LD core lipid TAG is a general response to stress and that acyl chain and sterol composition are remodeled during cellular adaptation. Likewise, comparative analysis of the LD protein composition in stress-treated leaves highlighted the plasticity of the LD proteome as part of the general stress response. We further identified at least two additional LD-associated proteins, whose localization to LDs in leaves was confirmed by confocal microscopy of fluorescent protein fusions. Taken together, these results highlight LDs as dynamic contributors to the cellular adaptation processes that underlie how plants respond to environmental stress.

## Introduction

Plants naturally encounter a plethora of abiotic and biotic threats during their life cycle. Consequently, plant cells have to be highly adaptive at the transcriptionic, proteomic, and metabolic level, which requires the interplay of different signaling pathways and organelles (Kumar et al. 2016; Zhu 2016; Crawford et al. 2018). One pertinent example of this cellular interplay is lipid remodeling, whereby fatty acid synthesis and lipid turnover in the plastids, and lipid turnover in the endomembrane system act together to modify cellular membrane composition in response to stress. More specifically, acyl chains removed from the membrane lipid pool appear to be transferred into triacylglycerols (TAGs) stored in cytosolic lipid droplets (LDs; Xu and Shanklin 2016). In heat-stressed Arabidopsis (Arabidopsis thaliana) seedlings for example, cytosolic TAGs with a high degree of unsaturation accumulate and the respective polyunsaturated acyl chains

originate, at least in part, from chloroplasts (Mueller et al. 2015, 2017). Similarly, in tobacco (Nicotiana tabacum) pollen tubes, heat stress leads to an increase in the proportion of saturated acyl chains in the membrane lipids phosphatidylcholine (PC) and phosphatidylethanolamine (PE), at the expense of mono- and polyunsaturated acyl chains, while total TAG levels increase (Krawczyk et al. 2022a). Similar effects have been observed in heat-stressed leaves of Arabidopsis (Higashi et al. 2015) and accumulation of TAG is linked to a number of stresses, e.g. freezing stress (Moellering et al. 2010), drought and cold treatment (Tarazona et al. 2015) or pathogen infection (Schieferle et al. 2021), which implies that LDs are important organelles in the plant stress response.

Cytosolic LDs consist of a hydrophobic core of neutral lipids, primarily TAGs and sterol esters (SEs), delimited by a monolayer of phospholipids (Guzha et al. 2023). Embedded into and/or

<footnote n="1">¹Albrecht-von-Haller-Institute for Plant Sciences, Department of Plant Biochemistry, University of Goettingen, Goettingen 37077, Germany</footnote>

<footnote n="2">²Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON N1G 2W1, Canada</footnote>

<footnote n="3">³Institute of Molecular Physiology and Biotechnology of Plants (IMBIO), University of Bonn, Bonn 53115, Germany</footnote>

<footnote n="4">⁴Goettingen Center for Molecular Biosciences (GZMB), University of Goettingen, Goettingen 37077, Germany</footnote>

<footnote n="5">⁵Green Biotechnology, Institute of Plant Biology and Biotechnology (IBBP), University of Münster, Münster 48143, Germany</footnote>

<footnote n="6">⁶Institute for Microbiology and Genetics, Service Unit LCMS Protein Analytics Department for Molecular Microbiology and Genetics, University of Goettingen, Goettingen 37077, Germany</footnote>

<footnote n="7">⁷Department of Biology, Brookhaven National Laboratory, Upton, NY 11973, USA</footnote>

<footnote>*Author for correspondence: patricia.scholz@ens-lyon.fr (P.S.), till.ischebeck@uni-munster.de (T.I.)</footnote>

<footnote>†Present address: Laboratoire Reproduction et Développement des Plantes (RDP), Université de Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRAE, Lyon, France.</footnote>

<footnote>‡Present address: Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA.</footnote>

<footnote>The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plphys/pages/General-Instructions) is Till Ischebeck (till.ischebeck@uni-munster.de).</footnote>
associated with the surface of the monolayer are various proteins that convey to the LDs different functions depending on the cellular context (Brocard et al. 2017; Ischebeck et al. 2020; Kretzschmar et al. 2020). Most studies on LDs have been carried out with tissues/organs where they are highly abundant, including seeds, seedlings, and pollen (Vance and Huang 1987; Tzen et al. 1993; Chen et al. 1999; Lin et al. 2002; Hsieh and Huang 2004; Kretzschmar et al. 2020). Indeed, the best described LD proteomes are those in oilseeds, where members of the oleosin (OLE), caleosin (CLO), and steroleosin (also called hydroxysteroid dehydrogenase [HSD]) protein families predominate (Jolivet et al. 2004, 2009; Katavic et al. 2006; Kretzschmar et al. 2018, 2020).

LDs function in seeds predominantly as lipid storage organelles, and oleosins are considered to function primarily in stabilizing LDs and preventing their coalescence (Cummins et al. 1993; Murphy 1993). However, during seedling establishment, the LD proteome undergoes a transformation as the seed LD proteins are degraded and other LD proteins confer additional functionalities to the LDs (Deruyffelaere et al. 2018; Kretzschmar et al. 2018, 2020). Among the latter, the LD-ASSOCIATED PROTEIN (LDAP) family, the LDAP-INTERACTING PROTEIN (LDIP), and SEIPIN proteins, which are endoplasmic reticulum (ER) membrane proteins that are situated at ER-LD junctions, are crucial for LD biogenesis (Cai et al. 2015; Taurino et al. 2018; Pyc et al. 2021). Additional LD proteins that function in seedlings have also been described, including those with enzyme activities involved in the synthesis or breakdown of primary and secondary metabolites (Corey et al. 1993; Diener et al. 2000; Shimada et al. 2014; Müller and Ischebeck 2018). Furthermore, a protein partially localizing to LDs, LIPID DROPLETS AND STOMATA 1 (LDS1)/RABC1 (At1g43890), influences LD dynamics during guard cell development (Ge et al. 2022).

Environmental changes also influence LD-related processes. LD abundance in Arabidopsis leaves, for instance, increases in response to drought, cold, or heat stress (Gidda et al. 2016; Doner et al. 2021). Correspondingly, Arabidopsis plants that over-accumulate LDs, such as transgenic lines overexpressing LDAPs (Gidda et al. 2016), are also more drought-tolerant (Kim et al. 2016), further suggesting that LDs play a role in stress tolerance. In addition, CLO3 is probably the best characterized LD protein in nonseed tissues and has been implicated in stress responses in leaves (Partridge and Murphy 2009; Aubert et al. 2010; Blée et al. 2014). For example, a role of CLO3 in the biotic stress response has been attributed to its peroxygenase function (Shimada et al. 2014). After infection of Arabidopsis leaves with the fungal pathogen *Colletotrichum higginsianum*, CLO3, and α-DIOXYGENASE 1 (α-DOX1) accumulate at LDs in the perilesional area of the infection. There, they are thought to serve in tandem to convert α-linolenic acid, stored in the neutral lipids of LDs, into 2-hydroxy-octadecatrienoic acid (2-HOT), which then counteracts fungal spread (Shimada et al. 2014).

Despite the growing knowledge on selected LDs proteins and their roles in vegetative plant organs, the relatively low abundance of LDs in these tissues has generally been a technical challenge for lipidomic and/or proteomic studies. Nevertheless, successful studies have been carried out on the leaf LD proteome of senescing and drought-stress leaves, as well as leaves infected with *Pseudomonas syringae* pv. *tomato* (Pto) DC3000 *avrRpm1* (Brocard et al. 2017; Fernández-Santos et al. 2020; Doner et al. 2021), which have resulted in the identification of several additional LD proteins. However, the dynamics of LDs, especially in terms of their proteome, during the plant-stress response remain unclear. Leaves are constantly exposed to a vast array of environmental conditions, so it seems likely that the composition of leaf

LDs is highly flexible in order to react to these external cues. Here, we assessed the changes that leaves and leaf LDs undergo when subjected to biotic and abiotic stresses. Arabidopsis plants were infected with one of two different pathogens or exposed to heat stress, and subsequently their leaf proteome and lipidome were analyzed. Comparisons with control treatments allowed us to observe the alterations induced by the three different treatments. In addition, proteomic analysis of LD-enriched fractions isolated from leaves subjected to the same treatments enabled us to survey specifically the dynamics of the LD proteome and, in doing so, identify two LD proteins, previously not described to be associated with LDs.

For the comparison, we required a mutant line with high TAG levels also in unstressed conditions and chose the double mutant *trigalactosyldiacylglycerol1-1 sugar dependent 1-4* (*tgd1-1 sdp1-4*) (Fan et al. 2014). The mutant *sdp1-1* was discovered in a forward genetic screen on impaired seedling establishment in absence of sugar and the *SDP1* gene was shown to encode for a lipase that hydrolyses TAG and DAG (Eastmond 2006); a later study also demonstrated the *sdp1-1* mutant to accumulate also TAG and LDs in vegetative tissues (Kelly et al. 2013). *tgd1-1* was originally described to be affected in a protein in the plastidial envelope that is involved in phosphatidic acid (PA) metabolism (Xu et al. 2005) and then to have a lipid transport function (Xu et al. 2010). While the mutant itself has already increased TAG levels (Xu et al. 2005), this increase was additive in the *sdp1-4* background (Fan et al. 2014).

## Results

### Heat stress and pathogen infection cause differential changes in the Arabidopsis leaf lipidome

Reports on TAG accumulation and elevated numbers of LDs induced by abiotic and biotic stresses were among the first indications for the involvement of LDs in the stress response of leaves (Gidda et al. 2016; Higashi and Saito 2019; Doner et al. 2021; Schieferle et al. 2021). To confirm these previous observations and determine if the overall lipidome is also affected by our treatments, we analyzed changes in the leaf lipidome in infected or heat-stressed plants (Supplementary Datasets S1 to S7). For this, we subjected 7-wk-old Arabidopsis wild-type plants to heat stress (consisting of 24 h at 37 °C) or spray-infected them with either *Botrytis cinerea* or *P. syringae* pv. *tomato* DC3000 *avrPto*/*avrPtoB* (hereafter: *Pseudomonas*), and subsequently carried out lipidomic analyses.

Overall, most of the analyzed lipid classes did not change significantly in abundance. However, TAG levels were significantly higher after *Pseudomonas* infection and heat treatment and were also increased, albeit not significantly, after infection with *B. cinerea* (Fig. 1). Interestingly, the amount of SEs decreased under heat stress. Furthermore, several lipid classes showed an altered distribution of molecular lipid species (Figs. 2 and 3; Supplementary Figs. S1 to S4), most prominently for heat-stressed leaves with increased proportions of acyl chains with a lower number of carbon atoms and fewer double bonds in membrane lipids like mono- and digitalactosyldiacylglycerol (monogalactosyldiacylglycerol (MGDG), digitalactosyldiacylglycerol (DGDG), Fig. 2) and PC and PE. In contrast, in TAGs of heat-stressed leaves, lipid species with a higher number of carbon atoms and more double bonds increased in proportion. For infection treatments, acyl chain adaptations occurred with a less pronounced pattern but interestingly with reverse effects (Fig. 3). For example, 36:6 increased in
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Changes in total abundance of membrane and hydrophobic lipids after different stress treatments. Arabidopsis plants were infected with *B. cinerea* or *P. syringae* pv. *tomato DC3000 AvrPto/AvrPtoB* (*Pseudomonas*), or kept for 24 h at 37 °C (heat stress) and compared to mock-infected or nonstressed plants. After stress treatment, leaves were harvested, lipids isolated, and their amounts determined by mass spectrometry. Values of all species in the indicated lipid classes were added up and this sum was normalized to the respective control. Statistical comparisons were calculated with Student's t-test, using Holm-Bonferroni correction for multiple comparisons. Values are shown as mean ± standard deviation. Significant differences are indicated with * and *** for P < 0.05 and P < 0.001, respectively. n ≥ 4 biological replicates. MGDG, monogalactosyldiacylglycerol; DGDG, digalactosyldiacylglycerol; SQDG, sulfoquinovosyldiacylglycerol; PG, phosphatidylglycerol; PC, phosphatidylcholine, PE, phosphatidylethanolamine; PI, phosphatidylinositol; PS, phosphatidylserine; FS, free sterols; SEs, sterol esters; TAG, triacylglycerol.</figcaption></figure>

PE and PC under both biotic stresses, while 34:2 decreased. Furthermore, both *Pseudomonas* and *B. cinerea* infections led to similar changes in the profile of free sterols, as the contribution of stigmasterol and isofucosterol increased at the expense of β-sitosterol (Fig. 3), while the effects of heat stress on free sterols were less prominent (Supplementary Fig. S1).

## The Arabidopsis leaf proteome changes in response to biotic and heat stress

To test if the proteome also undergoes changes, we extracted the total proteome from Arabidopsis leaves subject to the same stress treatments. Proteins from the mutant line *tgd1-1 sdp1-4*, that was stressed in parallel, were also extracted. Additionally, LD-enriched fractions were isolated. We used the *tgd1-1 sdp1-4* double mutant, as it has increased TAG levels stored in LDs in leaves (Fan et al. 2014) thereby making it easier to isolate LDs especially under unstressed conditions. At the same time, TAG accumulation in this mutant is not triggered by transcription factors that have a direct influence on gene expression like WRINKLED1 or LEAFY COTYLEDON 2 (Cernac and Benning 2004; Kim et al. 2015; Qiao et al. 2022). For all samples, peptides were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and MS raw data was processed with the MaxQuant software (Cox and Mann 2008) to identify and relatively quantify the original proteins. Two algorithms were used for quantification: the intensity-based absolute quantification (iBAQ) and the label-free quantification (LFQ) algorithm (Cox and Mann 2008; Schwanhäusser et al. 2011; Cox et al. 2014). Protein quantification values were then normalized as per thousand of the total combined intensity in each sample, resulting in relative iBAQ (riBAQ) and relative LFQ (rLFQ) values (Supplementary Data Set 8, all metadata can be found in Supplementary Table S1). rLFQ values

were used for comparisons of samples with similar protein compositions, i.e. total extract (TE) samples of different conditions or genotypes. riBAQ values were used for calculations of enrichment factors between LD-enriched fractions and TE fractions, as here sample composition differed strongly. Overall, this data set allowed us to compare (i) the effects of the different stresses on the total proteome in the wild type and double mutant line, (ii) differences between the wild type and the *tgd1-1 sdp1-4* double mutant, (iii) protein abundances in total cellular fractions and LD fractions to identify previously unknown LD-associated proteins, and (iv) changes especially in the LD proteome under stress in the double mutant.

Based on rLFQ values we compared changes of protein abundance in Col-0 leaves and calculated statistical significance of changes between the different treatments and their respective controls (Supplementary Data Set 9 to S11). The results were visualized as volcano plots, and proteins whose abundance was changed at least 1.5-fold between conditions with P < 0.05 were selected for further analysis (Fig. 4).

Among the selected proteins, we identified individual proteins that had been linked previously to the respective stress treatment. For instance, for *B. cinerea* infection, the phospholipase PLA2A was only detected in our proteomic dataset after infection (Fig. 4; Supplementary Data Set 9), which is consistent with a previous study that showed its accumulation after infection and dependent on jasmonic acid (JA) signaling (La Camera et al. 2005). Among the most highly increased proteins after *Pseudomonas* infection were the two pathogenesis-related (PR) proteins PR1 and PR2 (Supplementary Data Set 10), both of which are known to be upregulated as part of systemic acquired resistance to pathogen infection (Uknes et al. 1993; Fu and Dong 2013). A third PR-protein, PR5, also more than doubled in abundance. Another defense-related protein that increased upon *Pseudomonas*
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Heat-induced changes in membrane lipid and TAG composition. Arabidopsis Col-0 was heat-stressed (37 °C for 24 h) or kept at normal temperature regime (control). Subsequently, membrane and hydrophobic lipids were isolated and analyzed by mass spectrometry. For individual lipids classes, the relative species composition was calculated and is shown here for the glycoglycerolipids MGDG and DGDG, the phosphoglycerolipids PC and PE, and TAG. Lipid species are described by the combined number of all carbon atoms and double bonds of all fatty acids esterified to the glycerol backbone. Statistical comparisons were calculated with Student's t-test, using Holm-Bonferroni correction for multiple comparisons. Values are shown as mean ± standard deviation. Significant differences are indicated with *, **, and *** for P < 0.05, P < 0.01, and P < 0.001, respectively. n ≥ 4 biological replicates.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Alterations in the Arabidopsis lipid profile of PC, PE, and FS after infection. Arabidopsis Col-0 plants were infected with *B. cinerea* or *P. syringae* pv. *tomato DC3000 ΔavrPto/avrPtoB* (*Pseudomonas*). After the infection, lipids were isolated from leaves and analyzed by mass spectrometry. The relative composition of lipid species was determined and is displayed here for PC, PE, and FS. For the phosphoglycerolipids, lipid species are described by the combined number of all carbon atoms and double bonds of all fatty acids esterified to the glycerol backbone. Statistical comparisons were calculated with Student's t-test, using Holm-Bonferroni correction for multiple comparisons. Values are shown as mean ± standard deviation. Significant differences are indicated with *, **, and *** for *P* < 0.05, *P* < 0.01, and *P* < 0.001, respectively. *n* ≥ 4 biological replicates.</figcaption></figure>

infection was HYPERSENSITIVE INDUCED REACTION 2 (HIR2; Supplementary Data Set 7), which organizes immune receptors at the plasma membrane into nanoclusters (Qi et al. 2011; Qi and Katagiri 2012). Finally, in response to heat treatment, the

heat shock protein HSP70-4 showed the most pronounced increase in protein abundance (Fig. 4), consistent with its role in thermotolerance against long-term heat stress (Wang et al. 2021). Other heat shock proteins like HSP90.2 and HSP90.5
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Alterations of total cellular proteins of Arabidopsis leaves subjected to different stress treatments. Arabidopsis plants were infected with *B. cinerea* (*B. cinerea*), *P. syringae* pv. *tomato* DC3000 Δ*avrPto*/Δ*avrPtoB* (*Pseudomonas*) or heat stressed for 24 h at 37 °C. Protein abundances (rLFQ values) of individual proteins were normalized to the respective values of the control treatment and the resulting ratio was log₂-transformed. Statistical significance of the log₂-fold change was calculated by Student's t-test. The constructed volcano plots indicate proteins that are significantly enriched (upper right) or depleted (upper left) in reaction to the individual stress treatments. For each experiment, only proteins detected in all replicates of either mock-treated or infected plants were included in the analysis. For heat stress, proteins present in at least four replicates of either heat-stressed or control plants were analyzed. Vertical lines indicate 1.5-fold enrichment or depletion, while the horizontal line indicates a significance of P = 0.05. Proteins further mentioned in the text are labeled and highlighted in cyan. *n* = 5 (biological replicates) for *B. cinerea* and its mock control, *n* = 3 for *Pseudomonas* treatment and its control, *n* = 5 for heat stress treatment and *n* = 4 for its control.</figcaption></figure>

increased in abundance (Fig. 4, Supplementary Data Set 11) as well, confirming that the plants were able to sense and respond to the applied temperature conditions.

In order to determine if whole networks of interacting and/or functionally related proteins are changed under stress, we employed the web tool at the STRING v11.5 database (https://string-db.org; Szklarczyk et al. 2021) to assess the differentially abundant proteins for each treatment with at least a 1.5-fold change and P < 0.05. Proteins with decreased or increased abundance were evaluated separately and only interactions of high confidence were analyzed. In plants stressed with *B. cinerea* or heat, chloroplastic and photosynthetic proteins dominated amongst proteins with decreased abundance (Supplementary Fig. S5). Among the proteins with decreased abundance after *B. cinerea* infection were different members of photosystem I, II, and the light-harvesting complexes. Similarly, after heat treatment, proteins of photosystem II were decreased as well as proteins of the chloroplastic electron transport chain and subunits of Rubisco.

The upregulated proteins of the different treatments did not form as extensive networks as the downregulated ones (Supplementary Fig. S5). Among the most extensive interaction networks upon heat stress was the one formed by heat shock proteins and chaperone proteins with the functional role of assisting protein folding. Other protein networks mostly contained two or three proteins, nevertheless, these small networks pointed toward metabolic adjustments induced by the different stress

treatments. For example, heat treatment induced the accumulation of two catalases, CAT2 and CAT3 (Shimada et al. 2014; Gidda et al. 2016) while infection with *B. cinerea* or *Pseudomonas* both led to an accumulation of glutathione S-transferase proteins. More specific for *B. cinerea*, metabolic adaptation included the upregulation of proteins involved in the biosynthesis of tryptophan and the detoxification of cyanide as for example TRYPTOPHAN SYNTHASE ALPHA CHAIN, TRYPTOPHAN SYNTHASE BETA-SUBUNIT 1, β-CYANOALANINE SYNTHASE C1 and NITRILASE 4 (Yamaguchi et al. 2000; Piotrowski et al. 2001).

Among the 117 proteins with a known or putative function in lipid metabolism (Supplementary Data Set 12), only few were significantly changed, such as peroxisomal 3-ketoacyl-CoA thiolase, which is involved in β-oxidation (Germain et al. 2001) and was 2-fold upregulated under heat stress.

## The Arabidopsis *tgd1-1 sdp1-4* double mutant is altered in the leaf lipidome and proteome

As mentioned, LDs have been implicated with both biotic and abiotic stress responses (Shimada et al. 2014; Gidda et al. 2016; Kim et al. 2016; Fernández-Santos et al. 2020), and we also found strong increases of TAG under stress in our experiments. Given this, we investigated the LD proteome of Arabidopsis leaves under stress making use of the *tgd1-1 sdp1-4* double mutant. TAG levels in this mutant were reported to reach approximately 8% of
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Differences in the proteome of Arabidopsis Col-0 compared to the double mutant *tgd1-1 sdp1-4*. The proteome of Col-0 and *tgd1-1 sdp1-4* total protein fractions of nonstressed plants was analyzed. Changes in protein abundance are visualized in a volcano plot, displaying proteins accumulated (upper right) or depleted (upper left) in the double mutant (A). Proteins were only included in the analysis if they were present in all replicate samples of at least one line. Vertical lines indicate 1.5-fold enrichment or depletion, and the horizontal line indicates a *P*-value of 0.05. Proteins further discussed in the text are marked: the glutathione S-transferase GSTF7 and the PR proteins PR1, PR2 and PR5 accumulate in the mutant (cyan dots), while the allene oxide cyclases AOC2 and AOC4 are depleted (orange dots). PR gene expression was further analyzed in leaves of Col-0 or *tgd1-1 sdp1-4* and expression levels were calculated relative to the reference gene PTB1 (AT3G01150) (B). Leaves of both plant lines were also analyzed by UPLC-nanoESI-MS/MS for their SA and SAG content (C). *P*-values in (A) were calculated by Student's *t*-test. Values are shown as mean ± standard deviation in (B) and (C). Statistical analysis in (B) and (C) was carried out with the Wilcoxon-Mann-Whitney test, using Holm-Bonferroni correction for multiple comparisons. Statistical differences with *P* < 0.05 are indicated by different letters. *n* ≥ 3 biological replicates in (A), *n* = 6 biological replicates in (B) and (C). For (B) two independent technical replicates of each biological replicate were measured.</figcaption></figure>

leaf dry weight with a concomitant increase in number and size of leaf LDs (Fan et al. 2014). We could confirm a 65-fold increase of TAG levels in leaves of the *tgd1-1 sdp1-4* double mutant (Supplementary Fig. S6). The acyl chain composition of leaf TAG in the mutant was also strongly altered, favoring TAGs with 54 carbon atoms, whose proportion increased from ca. 50% to ca. 70%. Interestingly though, the relative proportion of the most desaturated TAG species 54:9 decreased (Supplementary Fig. S6). For SEs, a similar decrease of SE species with the acyl chain 18:3 could be observed, and the overall amounts of SEs decreased by ca. 25% (Supplementary Figs. S6 and S7). Among membrane lipids, the mutant leaves contained significantly less of the plastidial lipids MGDG and DGDG while PC and phosphatidylinositol (PI) were significantly increased. Further, the acyl chain composition followed the trend already observed for TAGs and SEs, with generally decreased percentages of more highly desaturated lipid species (Supplementary Figs. S6 and S7). For example, in MGDG, DGDG, PC, and PE, the proportions of 36:6 species were strongly decreased. In contrast, the phytosterol composition was not affected (Supplementary Fig. S7).

When we examined the changes in the proteome of *tgd1-1 sdp1-4* mutant plant leaves under stress, we observed some common trends with the wild type (Supplementary Datasets S13 to S15). This included for example a decrease in plastidial and photosynthetic proteins in reaction to stress and also the treatment-dependent accumulation of individual proteins described in the previous section (Supplementary Datasets S9 to S11). However, when stress-responsive proteins were selected with the same criteria as for the wild type, less than 50% of proteins were shared

amongst the up- or downregulated proteins of each treatment. As these differences pointed to underlying changes in the proteome of the double mutant, we decided to compare the proteome of *tgd1-1 sdp1-4* to the wild type under nonstressed conditions (Fig. 5A, Supplementary Data Set 16). Using the same selection criteria as for the analysis of treatment-induced changes (enrichment or depletion of at least 1.5-fold, *P* < 0.05), 253 affected proteins were selected for further analysis.

When analyzed with the STRING web tool, two protein-interaction networks among the downregulated proteins were most striking (Supplementary Fig. S8). That is, several components of the light harvesting complexes (among others LHCA3, LHCB3) and the photosynthetic electron transfer chain (e.g. PSBA, PSBE, PETA, PETD, PSAE-2) were decreased in the double mutant. The same was true for several ribosomal proteins of both cytosolic and plastidial ribosomes. Among the upregulated proteins in the double mutant, enzymes of various metabolic pathways could be found, including four glutamine synthetases (GLN1;1, GLN1;2, GLN1;3, GS2) and three proteins of the glycine cleavage system (GLDT, GLDP1, GDC-H1) that plays a role in photorespiration (Bauwe et al. 2010).

Among the less abundant proteins in the *tgd1-1 sdp1-4* mutant proteome, we observed two allene oxide cyclase proteins, AOC2 and AOC4, which catalyze the cyclization step in the biosynthesis of JA (Fig. 5A). In contrast, the salicylic acid (SA)-related proteins PR2, PR3, and PR5 were upregulated and this upregulation in comparison to the wild type was also observed under stress conditions (Supplementary Fig. S9). Following up on this possible change in SA-related signaling, we tested the gene expression of PR1, PR2,
<figure><img src="image_7.png" /><figcaption>**Figure 6.** Enrichment analysis of proteins in the LD-enriched fractions prepared from Arabidopsis leaves. LDs were enriched from leaves of Arabidopsis *tgd1-1 sdp1-4* plants that were either untreated or subjected to different stresses. Subsequently, the proteome of the LD-enriched fractions and the corresponding total leaf protein extract was measured. Of the detected proteins, a volcano plot was created, plotting the enrichment of each protein in the LD-fraction against its respectively calculated *P*-value (**A**). Treatments were combined, however, proteins were only included in the analysis if they were identified by at least two peptides and were present in at least three replicates in one of the sample types. Proteins significantly enriched in the LD-fraction cluster in the upper right corner and this section of the volcano plot is depicted enlarged in (**B**). Previously known LD proteins are marked with larger dot size and in orange; proteins investigated in this study that did or did not localize to LDs are highlighted in red and cyan, respectively. Known and LD proteins identified in this study are labeled, in addition the protein SEC61γ is indicated. *P*-values were calculated by Student's t-test. Vertical lines indicate 1.5-fold enrichment or depletion, while the horizontal line indicates a significance of *P* < 0.05. LD, lipid droplet; TE, total protein extract; CAS1, CYCLOARTENOL SYNTHASE 1; CB5-D/E, CYTOCHROME B5 ISOFORM D/E; CLO3, CALEOSIN 3; ERD7/14, EARLY-RESPONSIVE TO DEHYDRATION 7/14; GPAT4, GLYCEROL-3-PHOSPHATE ACYLTRANSFERASE 4; LDAP1/3, LD-ASSOCIATED PROTEIN 1/3; LDIP, LDAP INTERACTING PROTEIN; LDNP, LD-LOCALISED NTF2 FAMILY PROTEIN; LDS1, LIPID DROPLETS AND STOMATA 1; LIME, LD-associated methyltransferase; OBL3, OIL BODY LIPASE 3; PUX10, PLANT UBX DOMAIN CONTAINING PROTEIN 10; SEC61γ, SEC61 GAMMA; SMT1, STEROL METHYLTRANSFERASE 1.</figcaption></figure>

and PR5 in nonstressed leaves of Col-0 and *tgd1-1 sdp1-4* by RT-PCR and observed increased transcript levels in the mutant (Fig. 5B). Finally, we also measured phytohormone levels in leaves. In line with the observed changes of PR gene expression and protein abundance, the base level of SA in nonstressed plants is increased in *tgd1-1 sdp1-4*, as is the amount of its glycosylated derivative, salicylic acid glucoside (SAG; Fig. 5C). Due to the low amount of JA and JA-derivatives in nonstressed plants, we were not able to determine if their basal concentrations were also affected, as the lower protein amounts of AOC2 and AOC4 suggested.

## Survey of proteins enriched at Arabidopsis leaf LDs reveals LD localization of LDNP and CB5-E

An important aim of this work was to identify proteins so far unknown to localize to LDs in leaves, since our understanding of LD biology hinges on the understanding of its associated proteins and their functions. We, therefore, obtained an enriched LD fraction from leaves of *tgd1-1 sdp1-4* mutant Arabidopsis plants and investigated the proteome using quantitative label-free proteomics. First, to evaluate the success of LD enrichment, we combined the riBAQ values of all known detected LD proteins (Gidda et al. 2016; Brocard et al. 2017; Pyc et al. 2017; Kretzschmar et al. 2018, 2020; Fernández-Santos et al. 2020; Doner et al. 2021; Ge et al. 2022; Li et al. 2022) revealing a strong enrichment of this combined abundance by a factor of 65 to 775 in the LD fractions

of the different treatment conditions (Supplementary Fig. S10). We then tested if other organelles co-enrich with LDs. Using the plant proteome database (PPDB; http://ppdb.tc.cornell.edu/; Sun et al. 2009), the abundance of all proteins with an assigned unique subcellular localization was combined. The ER and plastoglobule proteomes were most prominently co-enriched by factors of 4.8 to 45 and 4.9 to 33, respectively, which might be a reflection of LD biogenesis at the ER and the similarities in the density of cytosolic LDs and chloroplastic plastoglobuli. All other subcellular structures were either not strongly enriched or significantly depleted in the LD-enriched fractions (Supplementary Fig. S10).

In order to identify potential LD proteins, previously not described to be LD associated, we then calculated the enrichment and its statistical significance for individual proteins (Supplementary Data Set 17). By combining all datasets from the different treatments, we were able to identify proteins that consistently showed a higher accumulation in the LD-enriched fractions (Fig. 6). In total, 553 proteins significantly accumulated in the LD-enriched fractions, 102 of which had an enrichment factor of more than 16 (Fig. 6B). Among these were several known LD proteins, most prominently CLO3, LDAP3, and LDIP (Shimada et al. 2014; Gidda et al. 2016; Pyc et al. 2017). Another protein with a high enrichment value was SEC61γ, a subunit of the SEC61 translocon previously described at ER-LD contact sites (Kretzschmar et al. 2020). A high LD enrichment was also observed for a protein (AT5G04830) that is annotated at The Arabidopsis
<figure><img src="image_8.png" /><figcaption>**Figure 7.** LDNP and CB5-E localize to LDs in *N. benthamiana* leaf cells. Subcellular localization studies of N- and/or C-terminal mCherry-tagged LDNP and CB5-E were carried out by transient expression in *N. benthamiana* leaves. Proteins were fused to an mCherry-tag (magenta channel) and LDs were stained with BODIPY 493/503 (cyan channel). Shown are also the corresponding merged images; boxes in (A and D) highlight regions of the cell shown with higher magnification in the insets. LDNP-mCherry localization to BODIPY-stained LDs was observed when the protein was expressed alone (A) or when co-expressed with MmDGAT2 (B), which causes a proliferation of LDs in plant cells (Cai et al. 2019). CB5-E appended at its C-terminus to mCherry did not localize to LDs (C), however, N-terminal mCherry-tagged CB5-E localized to LDs (D). Bars = 10 µm (2 µm in insets).</figcaption></figure>

Information Resource (TAIR; Berardini et al. 2015) as NUCLEAR TRANSPORT FACTOR 2 (NTF2) family member but has not been functionally characterized. Additional proteins that were studied further were selected based on their enrichment value, predicted transmembrane (TMD) regions, and/or possible LD-related functions. Among these were two cytochrome $b_5$ proteins, CB5-D (AT5G48810) and CB5-E (AT5G53560), that were both highly enriched in the LD fraction (Fig. 6B) and might serve in electron transfer during possible lipid oxidation reactions at LDs. We also examined the intrinsically disordered protein EARLY RESPONSIVE TO DEHYDRATION 14 (AT1G76180), which is proposed to act as chaperone in stressed plants (Kovacs et al. 2008; Szalainé Ágoston et al. 2011), as well as six other so far uncharacterized proteins, i.e. AT1G65270, AT1G72170, AT3G18430, AT4G12590, AT4G16450, and AT5G01750, some of which are annotated (at TAIR) to contain domains of unknown function (AT1G72170, AT4G12590, AT5G01750) and some are annotated to be part of membrane protein complexes (AT1G65270, AT1G72170, AT4G12590). Further, we focused on two other proteins, AT3G18430 and AT4G16450, that are annotated as a calcium-binding EF-hand family protein and NADH-ubiquinone oxidoreductase, respectively, and therefore drew our attention as being potentially involved in signaling or redox processes at the LD.

We analyzed the subcellular localization of all the above-mentioned candidate LD proteins by transient expression in *Nicotiana benthamiana* leaves, which is a well-established model plant system for protein localization (Sparkes et al. 2006), including LD proteins (Kretzschmar et al. 2020; Doner et al. 2021; Pyc et al. 2021; Krawczyk et al. 2022b). Candidates were expressed with an N- or C-terminal mCherry fluorescent tag and subcellular localization was analyzed by confocal laser-scanning microscopy (CLSM). To assess possible LD co-localization, LDs were stained with the neutral lipid-specific stain BODIPY 493/503 (Listenberger and Brown 2007). Furthermore, selected candidate LD proteins were co-expressed with the mouse (*Mus musculus*) enzyme DIACYLGLYCEROL ACYLTRANSFERASE 2 (MmDGAT2), which enhances LD proliferation in plant cells (Cai et al. 2019). As shown in Fig. 7 the NTF2 protein family member

often localized to the surface of BODIPY-stained LDs, particularly in cells co-expressing MmDGAT2 (Fig. 7, A and B). We therefore termed this protein *LD-LOCALIZED NTF2 FAMILY PROTEIN* (LDNP). For CB5-E, we observed two distinct subcellular localizations depending on the position of the appended mCherry: the C-terminal mCherry-tagged CB5-E (CB5-E-mCherry) localized to reticular structures, consistent with the ER (Fig. 7C), while N-terminal mCherry-tagged CB5-E (mCherry-CB5-E) displayed mostly LD localization (Fig. 7D). Given that bioinformatic tools predict a single TMD near the C-terminus of CB5-E (Supplementary Fig. S11), this region might be less accessible with a C-terminal-appended mCherry and therefore resulting in the observed differences in CB5-E localization. Overall, the other eight candidate proteins appended to mCherry at their N- and/or C-termini did not localize to BODIPY-stained LDs, although CB5-D appeared to partially localize to LDs and some proteins (e.g. AT5G01750 and AT1G76180 [ERD14]) displayed a distinct reticular, ER-like fluorescence patterns that were often closely associated with LDs (Supplementary Fig. S12), which may be notable given the role of the ER in LD biogenesis (Guzha et al. 2023). Indeed, the apparent association of ERD14 in reticulum-like structures associated with LDs was more pronounced upon co-expression with MmDGAT2 (Supplementary Fig. S12), suggesting a possible role for ERD14 at the ER during LD proliferation.

## The Arabidopsis leaf LD proteome responds to environmental stresses

Based on an examination of the LD proteome in leaves of *tgd1-1 sdp1-4* mutant plants without stress treatment, *CLO3* and *LDAP3* were the most abundant proteins (Table 1) as they combined abundance amounts to around 80% of total LD protein. Additional proteins detected in the LDs of leaves without treatment included *LDAP1* and *LDAP2*, their interacting protein LDIP, and the protein ERD7 (Gidda et al. 2016; Pyc et al. 2017; Doner et al. 2021). Several other proteins connected to metabolism (i.e. *GPAT4*, *STEROL METHYLTRANSFERASE 1*, *CYCLOARTENOL SYNTHASE 1*, *OIL BODY LIPASE 3*, *LD-ASSOCIATED LIPASE 2*, *α-DOX1*, *LD-ASSOCIATED METHYLTRANSFERASE 1* or 2) and
<table><caption>**Table 1.** Composition of LD proteome in LD-enriched fractions of leaves from Arabidopsis *tgd1-1 sdp1-4*</caption><thead><tr><th rowspan="2">Gene symbol</th><th rowspan="2">AGI code</th><th rowspan="2">Protein name</th><th colspan="7">% of LD proteins</th></tr><tr><th>No treatment</th><th>Mock (B. cinerea)</th><th>B. cinerea</th><th>Mock (Pseudo-monas)</th><th>Pseudo-monas</th><th>Control</th><th>Heat stress</th></tr></thead><tbody><tr><td>α-DOX1</td><td>AT3G01420</td><td>α-DIOXYGENASE 1</td><td>0.044</td><td>0</td><td>1.864</td><td>0</td><td>0.296</td><td>0.003</td><td>0</td></tr><tr><td>CAS1</td><td>AT2G07050</td><td>CYCLOARTENOL SYNTHASE 1</td><td>0.455</td><td>0</td><td>0.071</td><td>0.654</td><td>0.153</td><td>1.540</td><td>0.587</td></tr><tr><td>CB5-E</td><td>AT5G53560</td><td>CYTOCHROME B5 ISOFORM E</td><td>2.216</td><td>3.109</td><td>3.022</td><td>1.340</td><td>5.322</td><td>1.148</td><td>0.389</td></tr><tr><td>CLO3</td><td>AT2G33380</td><td>CALEOSIN 3</td><td>41.633</td><td>33.859</td><td>53.205</td><td>40.516</td><td>73.881</td><td>9.051</td><td>18.235</td></tr><tr><td>ERD7</td><td>AT2G17840</td><td>EARLY RESPONSIVE TO DEHYDRATION 7</td><td>1.463</td><td>0</td><td>0.490</td><td>0.030</td><td>0.058</td><td>5.378</td><td>5.226</td></tr><tr><td>GPAT4</td><td>AT1G01610</td><td>GLYCEROL-3-PHOSPHATE ACYLTRANSFERASE 4</td><td>0.218</td><td>0</td><td>0.063</td><td>0</td><td>0</td><td>2.003</td><td>0.756</td></tr><tr><td>GPAT8</td><td>AT4G00400</td><td>GLYCEROL-3-PHOSPHATE ACYLTRANSFERASE 8</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.109</td><td>0</td></tr><tr><td>LDAP1</td><td>AT1G67360</td><td>LD-ASSOCIATED PROTEIN 1</td><td>1.071</td><td>0.889</td><td>1.924</td><td>0.402</td><td>0</td><td>4.171</td><td>4.232</td></tr><tr><td>LDAP2</td><td>AT2G47780</td><td>LD-ASSOCIATED PROTEIN 2</td><td>0.166</td><td>0</td><td>0.077</td><td>0</td><td>0</td><td>0.154</td><td>0.064</td></tr><tr><td>LDAP3</td><td>AT3G05500</td><td>LD-ASSOCIATED PROTEIN 3</td><td>41.577</td><td>50.700</td><td>30.210</td><td>43.158</td><td>15.446</td><td>53.114</td><td>51.045</td></tr><tr><td>LDIP</td><td>AT5G16550</td><td>LDAP-INTERACTING PROTEIN</td><td>4.056</td><td>2.635</td><td>2.154</td><td>2.613</td><td>0.994</td><td>6.115</td><td>7.684</td></tr><tr><td>LDNP</td><td>AT5G04830</td><td>LD-LOCALIZED NTF2 FAMILY PROTEIN</td><td>3.135</td><td>2.641</td><td>2.634</td><td>6.021</td><td>2.456</td><td>3.332</td><td>3.723</td></tr><tr><td>LDS1</td><td>AT1G43890</td><td>LIPID DROPLETS AND STOMATA 1</td><td>1.803</td><td>2.555</td><td>1.253</td><td>2.356</td><td>1.061</td><td>3.536</td><td>3.208</td></tr><tr><td>LIDL2</td><td>AT1G73920</td><td>LD-ASSOCIATED LIPASE 2</td><td>0.109</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.061</td><td>0.036</td></tr><tr><td>LIME1/2<sup>a</sup></td><td>AT4G33110/AT4G33120<sup>a</sup></td><td>LD-ASSOCIATED METHYLTRANSFERASE 1/2</td><td>0.432</td><td>0.473</td><td>0.699</td><td>0.609</td><td>0</td><td>1.963</td><td>1.350</td></tr><tr><td>OBL3</td><td>AT1G45201</td><td>OIL BODY LIPASE 3</td><td>0.618</td><td>0.592</td><td>0.338</td><td>0.922</td><td>0.250</td><td>3.025</td><td>1.918</td></tr><tr><td>PUX10</td><td>AT4G10790</td><td>PLANT UBX DOMAIN CONTAINING PROTEIN 10</td><td>0.479</td><td>0.728</td><td>0.297</td><td>0.307</td><td>0</td><td>0.580</td><td>0.259</td></tr><tr><td>SMT1</td><td>AT5G13710</td><td>STEROL METHYLTRANSFERASE 1</td><td>0.525</td><td>1.818</td><td>1.698</td><td>1.073</td><td>0.084</td><td>4.716</td><td>1.286</td></tr></tbody></table>

LDs were isolated from Arabidopsis leaves of *tgd1-1 sdp1-4* in nonstressed conditions and after different stresses. Stress treatments of leaves included infection with *B. cinerea* (*B. cinerea*) or *P. syringae* pv. *tomato* DC3000 *avrPto* *avrPtoB* (*Pseudomonas*), or heat stress for 24 h at 37 °C. For each stress, a mock or control treatment was performed. The relative contribution of individual LD proteins to the total LD protein abundance (riBAQ values) was calculated for each treatment separately.

protein degradation (PLANT UBX DOMAIN-CONTAINING PROTEIN) were also found in smaller amounts. In addition, LDNP and CB5-E described above were detected (Table 1).

We did not observe any additional LD proteins specific to the different stress treatments. However, we were able to follow the changes in the LD proteome composition in response to the individual stresses (Fig. 8). Overall, the combined abundance of LD proteins tended to increase after stress, especially after infections (Fig. 8A). However, increased abundance was not evenly distributed across all LD proteins, so that the relative proportions of detected LD proteins in the known LD proteome changed (Fig. 8B, Table 1). For example, the proportion of CLO3 increased after all three treatments in contrast to LDAP3, whose relative contribution either decreased or did not change (Fig. 8B). Among the low-abundant leaf LD proteins, α-DOX1 strongly accumulated at LDs after both pathogen treatments, which fits with the previously described functional interaction between CLO3 and α-DOX1 (Shimada et al. 2014). Interestingly, α-DOX1 did not increase in response to heat treatment (Fig. 8C), pointing to a more specific role in defense against pathogens. For CB5-E, we also observed its accumulation specifically after pathogen infection, although on a smaller scale than for α-DOX1 (Fig. 8C). Furthermore, LD-ASSOCIATED METHYLTRANSFERASE 1 or 2 (the distinct isoform could not be resolved based on the analysis) increased under *B. cinerea* but not *Pseudomonas* infection. Most other proteins fluctuated in abundance but showed no clear trend with the possible exception of PUX10, which was depleted after all the treatments (Fig. 8C).

## Discussion

### Biotic and abiotic stresses alter lipid homeostasis in Arabidopsis leaves

Higher temperatures lead to increased membrane fluidity so that membrane lipids are usually remodeled and acyl chains with three double bonds are replaced by more saturated acyl chains (Falcone et al. 2004; Higashi and Saito 2019; Yu et al. 2021). Such a remodeling was also observed in our study with presumably 16:0 and 18:0-containing lipid species increasing especially in PC, and 18:3 in MGDG and DGDG being replaced by 18:2 and 18:1 (Fig. 2). TAG acts as sink for the released acyl chains (Mueller et al. 2017), which is reflected in the increase of 54:8 and 54:9-TAG species after heat stress that we and others observed (Higashi et al. 2015; Mueller et al. 2015 2017). Interestingly, the amount of SEs decreased (Fig. 1), corroborating previous results (Shiva et al. 2020). Furthermore, other sterol derivatives like sterol glycosides and acylated sterol glycosides have been reported to increase in response to heat stress in Arabidopsis leaves and tobacco pollen tubes (Shiva et al. 2020; Krawczyk et al. 2022a). This indicates that LDs cannot only serve as a sink for acyl chains stored as TAG, but at the same time might be a source for free sterols or sterol derivatives derived from LD-stored SEs. These sterols could help to stabilize membranes under heat stress (Dufourc 2008). Regarding the potential dual role of LDs as a metabolic sink and source, it is unclear if SEs and TAGs in plant cells are both present in mixed-compound LDs or form distinct LD subpopulations, as has been reported for LDs in some animal cells (Khor et al. 2014).

<footnote><sup>a</sup>Denotes proteins that could not be identified unequivocally.</footnote>
<figure><img src="image_9.png" /><figcaption>**Figure 8.** Changes in the known LD proteome of leaves after stress treatments. The protein abundance (riBAQ values) of known LD proteins in the LD-enriched fraction isolated from Arabidopsis leaves was followed in reaction to infection with *B. cinerea*, or *Pto DC3000 ΔavrPto/ ΔavrPtoB* (*Pseudomonas*), or heat stress for 24 h at 37 °C. The total LD protein abundance was calculated by summing up riBAQ values of all LD proteins for each treatment (A). In addition, the relative contribution of all detected LD proteins to the total LD protein abundance was calculated (B). Stacked bar plots show the relative proportion of individual LD proteins in the order displayed in the legend, i.e. percentage of GPAT8 at the top and percentage of CLO3 at the bottom. The changes in abundance of individual proteins was followed by calculating the ratio of their riBAQ values in LD fractions of stressed plants relative to their riBAQ values in respective control treatments (C). Values above and below 1 indicate enrichment or depletion upon individual stresses, respectively. The horizontal line highlights ratios of 1. Proteins were only included if they were identified by at least two peptides and were detected in at least three replicates of at least one sample type. n ≥ 3 replicates per treatment. α-DOX1, α-DIOXYGENASE 1; CAS1, CYCLOARTENOL SYNTHASE 1; CB5-E, CYTOCHROME B5 ISOFORM E; CLO3, CALEOSIN 3; ERD7, EARLY-RESPONSIVE TO DEHYDRATION 7; GPAT4/8, GLYCEROL-3-PHOSPHATE ACYLTRANSFERASE 4/8; LDAP1/2/3, LD-ASSOCIATED PROTEIN 1/2/3; LDIP, LDAP INTERACTING PROTEIN; LDNP, LD-LOCALISED NTF2 FAMILY PROTEIN; LDS1, LIPID DROPLETS AND STOMATA 1; LIDL2, LIDL2, LD-ASSOCIATED LIPASE 2; LIME, LD-ASSOCIATED METHYLTRANSFERASE 1/2 (ambiguously identified); OBL3, OIL BODY LIPASE 3; PUX10, PLANT UBX DOMAIN CONTAINING PROTEIN; SEC61γ, SEC61 GAMMA; SMT1, STEROL METHYLTRANSFERASE 1.</figcaption></figure>
Unlike lipidomic changes after heat stress, less is known about the impact of pathogen infection. Infection of leaves with the avirulent *Pseudomonas* strain P. syringae pv. tomato DC3000 *avrRpm1* causes an increase in TAG levels (Schieferle et al. 2021) and leads to increased numbers of leaf LDs (Fernández-Santos et al. 2020) within one day after infection. Using another avirulent *Pseudomonas* strain (P. syringae pv. tomato DC3000 Δ*avrPto*/Δ*avrPtoB*) and measuring neutral lipids after symptom development, we observed a similar trend to increased TAG levels (Fig. 1), which was mainly driven by the TAG species 54:8 and 54:9 (Supplementary Datasets S4S6). Fungal infection of Arabidopsis with B. cinerea in leaves (Fig. 1) and *Verticillium longisporum* in roots (Schieferle et al. 2021) are also accompanied by a trend toward increased TAG levels. Interestingly, in case of V. longisporum, TAG accumulation in leaves was proposed as a systemic effect initiated by the infected roots (Schieferle et al. 2021). It thus seems that increased TAG levels are a general plant response to infection. However, the reasons for that are not clear yet. With regard to sterol lipids, the most striking change after infection is the increase of stigmasterol at the expense of β-sitosterol (Fig. 3). This effect on free sterols has been described previously (Griebel and Zeier 2010; Wang et al. 2012), however, descriptions of its role in the plant-pathogen interaction are conflicting. In Arabidopsis, the conversion of β-sitosterol to stigmasterol is catalyzed by the cytochrome P450 enzyme AtCYP710A1 (Morikawa et al. 2006), and T-DNA mutants of CYP710A1 with decreased stigmasterol levels were reported to be more resistant to infection with P. syringae pv. maculicola (Griebel and Zeier 2010). Later reports on another cyp710a1 mutant described the opposite effect though, as the mutant was more susceptible to a variety of P. syringae strains (Wang et al. 2012). We were unable to detect AtCYP710A1 in our proteomic dataset so it is unclear if the increase in stigmasterol is linked to an increase in its producing enzyme or if pathogen infection might alter enzyme activity of AtCYO710A1.

In general, the substantial changes observed in the lipidome especially under heat stress are not reflected in the abundance of proteins involved in lipid metabolism, at least regarding the proteins that were detected. This could be due to low coverage of such proteins, as only around 20% of the proteins were detected in our data (Supplementary Data Set 12). Previous reports on heat-stressed leaves indicated regulation of lipid-related genes on the transcriptional level (Higashi et al. 2015). However, in a study on the transcript changes in pollen tubes under heat stress (Krawczyk et al. 2022a), only few changes were observed. As of this it can be speculated that lipid remodeling is also controlled by post-translational modifications or other factors, whereas on the proteome level other adaptations take place.

## The Arabidopsis cellular proteome is readjusted under stress

One common theme under stress was the reduction in the levels of photosynthesis-related proteins. This could be a protection mechanism to reduce photosynthesis rates given that chlorophyll fluorescence studies have shown reduced quantum efficiency in response to infection (Bonfig et al. 2006; Pavicic et al. 2021) or heat stress (Kim and Portis 2005; Salvucci 2007). In addition, heat stress is especially linked to decreases in Rubisco activity (Kobza and Edwards 1987; Kim and Portis 2005; Salvucci 2007), consistent with our observation of Rubisco subunits among the depleted proteins (Supplementary Fig. S5). Furthermore, for infection, several transcriptomics studies have shown that reduced gene expression of photosynthetic genes is a general response to a plethora of pathogens, possibly to allow for the upregulation

of defense response pathways (Bilgin et al. 2010; Jiang et al. 2017). On the other hand, reduced photosynthesis efficiency and oxidative stress in the plastids could also lead to an increased damage and degradation of photosynthesis-related proteins. Interestingly, especially in plants subjected to infection stress there was a concurrent trend toward decreased plastidial membrane lipids (Fig. 1), which could be channeled into TAG as has been proposed for heat-stressed seedling (Mueller et al. 2017). While not directly linked, the consequences of plastidial stress might thus lead to thylakoid membrane adaptations that are reflected in both proteome and lipidome of the affected plants.

Proteins that were upregulated were more diverse between the treatments, conferring specificity to the plant's reaction. In the proteome of heat-stressed plants this resulted in an accumulation of heat shock proteins (HSPs; Fig. 4), as expected. HSPs prevent the formation of protein aggregates and assist protein folding and protein transport across membranes (Lin et al. 2001; Rosenzweig et al. 2019). Additionally, several chloroplast chaperonin subunits were upregulated in response to heat stress, including CPN60A1 and CPN60B1 of chaperonin 60, which interacts in the assembly of Rubisco (Hemmingsen et al. 1988; Ishikawa et al. 2003).

In the proteome of infected leaves, several metabolic proteins were upregulated, including glutathione S-transferases (GSTs) after both infections (Supplementary Fig. S5). Different GSTs were previously reported to change in abundance in a 2D-proteomics study of Arabidopsis infected with *Alternaria brassicicola* (Mukherjee et al. 2010). GSTs thus may form part of a general defense responses, although the functional role of various GST proteins are likely different. GSTF6, for example, has been implicated in the biosynthesis of camalexin (Su et al. 2011), a phytoalexin with antifungal properties against some B. cinerea strains (Kliebenstein et al. 2005). GSTF2, on the other hand, was suggested to bind small molecules, including antimicrobial compounds like camalexin, and to transport them within the cell to their site of action (Dixon et al. 2010; Ahmad et al. 2017). Further metabolic pathways upregulated after B. cinerea infection were tryptophan biosynthesis and cyanide detoxification. Both might be connected to synthesis of camalexin, as it derives from tryptophan, and in camalexin biosynthesis, cyanide is released (Böttcher et al. 2009). Interestingly, the phospholipase PLA2A, which was upregulated after B. cinerea infection, has been described to negatively influence plant resistance to B. cinerea (La Camera et al. 2005), indicating that metabolic adjustments are influenced by both the plant and the pathogen. Altogether, the rewiring of metabolism appears as a common feature after pathogen infection, and the selectivity of altered metabolic pathways conveys the distinct responses toward specific pathogens. At the same time, none of the observed proteomic changes are directly connected to the concurrent lipid adaptations that were observed after the three stress treatments. To study the connections between proteins and lipids in stress responses it therefore seems necessary to focus on individual lipid-related enzymes or lipid-dominated organisms like LDs.

## Effects of the *tgd1-1 sdp1-4* mutations in *Arabidopsis* are not limited to lipid metabolism

We used the Arabidopsis double mutant *tgd1-1 sdp1-4* as a tool for LD isolation, and we also included it in our lipidomic and proteomic analyses. The original characterization of the double mutant had highlighted its strong increase in TAG levels in leaves (Fan et al. 2014) and we confirmed this increase in our lipidomics measurements (Supplementary Fig. S6). While the increase of TAG is
caused by both the mutations in SDP1 and TGD1, the changes in the membrane lipidome are likely mostly caused by the mutation of TGD1 alone. TGD1 is involved in the import of membrane lipid precursors into plastids, and its loss in the *tgd1-1* mutant leads to a moderate reduction of thylakoid galactolipids and an altered FA profile of galactolipids, PC, PE, and PA (Xu et al. 2003; Fan et al. 2014). We found similar alterations not only in PG that predominantly occurs in plastids (Joyard et al. 2004) but also in PI and phosphatidylserine, indicating that also nonplastidial lipid metabolism is strongly affected.

Regarding the proteome of the mutant, it is notable that we detected LDAP3 as the most abundant protein of the LDAP family (Fig. 8B, Table 1). In previous proteomic studies of senescent or drought-stressed leaves of Arabidopsis Col-0, LDAP1 was the major LDAP protein (Brocard et al. 2017; Doner et al. 2021). A possible explanation could be that expression of LDAP1 in contrast to LDAP3 is upregulated in response to drought stress (Wilkins et al. 2010) and during leaf senescence (Schmid et al. 2005). Apart from the differences between LDAP1 and LDAP3, the composition of the LD proteome is overall quite similar to previously published LD proteomes in senescent and drought-stressed Col-0 leaves (Brocard et al. 2017; Doner et al. 2021). As well as in these other proteomic studies, CLO3 was a dominant LD protein and, along with the LDAP proteins, comprised up to more than 90% of the LD proteome. Other less abundant LD proteins were also found, including LDIP and α-DOX1, with relative contributions to the total LD proteome that did not exceed 2% (Brocard et al. 2017; Doner et al. 2021) and thus similar to our observations in *tgd1-1 sdp1-4* (Table 1).

Besides the proteome of LD-enriched fractions, we also analyzed total protein fractions from leaves of *tgd1-1 sdp1-4*, which we compared to total protein fractions from Arabidopsis wild type. This comparison revealed the upregulation of the defense-related proteins PR1, PR2, and PR5 and the concomitant increase in SA levels in *tgd1-1 sdp1-4* (Fig. 5). Neither TGD1 nor SDP1 have been linked to SA synthesis in Arabidopsis (Rekhter et al. 2019). However, previous studies reported that interference with plastid lipid metabolism impacts endogenous levels of jasmonates (Lin et al. 2016). Knockout of a gene encoding a chloroplast outer envelope protein that synthesizes DGDG caused the concomitant increase of JA and JA-Ile (Lin et al. 2016). Assuming that the *tgd1* mutation might also affect phytohormones that derive from plastids, the observed differing levels of SA in *tgd1-1 sdp1-4* could then be a secondary effect on the crosstalk between the phytohormones SA and jasmonates (Spoel et al. 2003; Pieterse et al. 2012). In this context, it is interesting to note that reduced growth is a phenotype of both *tgd1-1* and *tgd1-1 sdp1-4* (Xu et al. 2005; Fan et al. 2014). Given the differences in TAG accumulation between the single and the double mutant (Fan et al. 2014), reduced growth is probably not caused by the hyperaccumulation of TAGs, instead, autoimmune reactions might contribute to this growth difference. If that is the case, connections between autoimmunity and mutations designed to increase TAG levels should be considered in any future biotechnological approaches aimed at the hyperaccumulation of TAG in vegetative tissues.

## Observed LD-localization of Arabidopsis LDNP and CB5-E indicates additional LD functions

While the number of known LD-associated plant protein families has steadily increased in recent years (Fernández-Santos et al. 2020; Kretzschmar et al. 2020; Doner et al. 2021; Ge et al. 2022; Krawczyk et al. 2022b; Li et al. 2022), the number is still only

roughly two dozen (Guzha et al. 2023). These studies were often followed up by the functional characterizations of these proteins, greatly increasing our understanding of LDs (Shimada et al. 2014; Gidda et al. 2016; Deruyffelaere et al. 2018; Kretzschmar et al. 2018; Pyc et al. 2021; Krawczyk et al. 2022b). Here, we identified two additional proteins with the ability to localize to LDs in plant leaves, CB5-E and LDNP (Fig. 7). Of these, the LD localization of LDNP became more distinct upon LD proliferation due to the co-expression with MmDGAT2, which could indicate that LDNP requires a certain threshold number of LDs to localize to LDs. CB5-E and LDNP were also shown previously to be enriched in LD-fractions from Arabidopsis seedlings and drought-stressed leaves, but neither protein was investigated in regard to LD targeting (Kretzschmar et al. 2018, 2020; Doner et al. 2021). CB5-E was originally described as cytochrome *b*₅ protein able to accept electrons from the NADH-dependent cytochrome *b*₅ reductase CBR (Fukuchi-Mizutani et al. 1999). CB5-E is part of a five-member protein family in Arabidopsis, and its homolog CB5-D was also enriched in our LD fractions (Supplementary Data Set 17). Arabidopsis CBR and CB5 proteins are involved in desaturation reactions (Kumar et al. 2006, 2012). In microsomal fractions of castor bean, desaturation and hydroxylation reactions in the synthesis of ricinoleic acid depended on cytochrome *b*₅ proteins (Smith et al. 1992). CB5-D has also been previously localized to the ER (Maggio et al. 2007). We could indeed observe ER localization for CB5-D and CB5-E. However, CB5-E also localized predominantly to LDs in cells co-expressing MmDGAT2 (Fig. 7). This raises the question if particularly CB5-E could be involved in additional redox reactions at the LD surface during LD proliferation.

In comparison to CB5-E, much less is known about the function(s) of LDNP. Expression of LDNP did not show specific tissue or developmental preferences (Klepikova et al. 2016) and the protein is also found in LD fractions of 24 to 60 h old seedlings and drought-stressed leaves of Arabidopsis (Kretzschmar et al. 2020; Doner et al. 2021). LDNP was classified as a NTF2 family protein, probably based on a predicted protein fold, which is thought to form a cone-like shape with internal cavity (Eberhardt et al. 2013). This protein structure is also found in several other enzymatic and nonenzymatic proteins (Eberhardt et al. 2013), making LDNP an interesting target for further functional characterization.

## Changes in the Arabidopsis LD proteome establish LDs as an additional player in stress responses

We analyzed the LD proteome in leaves of the *tgd1-1 sdp1-4* double mutant after different stresses and observed that it was altered dynamically (Fig. 8B). When analyzing these changes, it has to be considered that there are significant differences in the overall proteome of leaf total protein fractions between the double mutant and the wild type. The observed alterations of the LD proteome of *tgd1-1 sdp1-4* therefore might not be completely the same at leaf LDs in Col-0. Nevertheless, shared upregulated proteins between wild type and the double mutant demonstrate that our treatments induced similar pathways in both Arabidopsis lines. Hence it seems likely that our stress treatments induce analogous reactions in the LD proteome of both the double mutant and the wild type. One example is the increased protein abundances of CLO3 and α-DOX1 that fit well with their previously described roles in pathogen defense in wild-type Arabidopsis plants (Shimada et al. 2014).
CLO3 and α-DOX1 were among the most strongly responding LD proteins to pathogen treatment, and the protein abundance of CLO3 additionally increased after heat stress (Fig. 8). This is in line with reported transcriptome changes of leaves infected with *B. cinerea* and seedlings subjected to heat, salinity, and osmotic stress, i.e. CLO3 expression was induced by all treatments, whereas α-DOX1 expression was reported as increased during infection and osmotic but not heat treatment (Sham et al. 2015).

How LDs are integrated in plant stress response remains an interesting question. Environmental cues might alter the interaction of LDs with other organelles, e.g. prompting the formation of membrane contact sites for lipid remodeling. A first such contact site of LDs with the plasma membrane has been recently described (Krawczyk et al. 2022b) and although the physiological relevance is unclear as of yet, the interaction of LDs in plant membrane contact sites especially in reaction to stress are an exciting avenue of future LD research.

# Materials and methods

## Plant lines and growth conditions

Lipidomic and proteomic experiments were carried out with Arabidopsis (A. thaliana) Col-0 and the oil-rich *tgd1-1 sdp1-4* double mutant line (Fan et al. 2014).

Seeds of Arabidopsis lines were surface-sterilized with 6% (w/v) sodium hypochlorite and 0.1% (v/v) Triton X-100 and germinated on half-strength Murashige and Skoog (MS; Duchefa Biochemie, Haarlem, The Netherlands) medium (Murashige and Skoog 1962) containing 0.8% (w/v) agar. After 10 d, seedlings were transferred to soil (Einheitserde SPECIAL Vermehrung, Patzer Erden, Sinntal-Altengronau, Germany) and grown under short-day condition (8 h light/16 h darkness) at 22 °C in the light and 18 °C in the dark as described previously (Guzha et al. 2022). For lipidomic or proteomic analyses, plants were grown for 7 wk before stress treatment was applied and samples were prepared as described below.

## P. Syringae and B. cinerea strains

For plant infections and further proteomic and lipidomic analysis of LDs, the *Pseudomonas* strain P. syringae pv. tomato (Pto) DC3000 *AvrPto AvrPtoB* and the *B. cinerea* strain B05.10 were used.

Spores of *B. cinerea* were cultured on potato dextrose agar (PDA; Merck KGaA, Darmstadt, Germany) for 10 d before conidiospores were harvested by washing them off with 1/4 potato dextrose broth (PDB; Merck KGaA, Darmstadt, Germany) and filtering through Miracloth (Merck KGaA, Darmstadt, Germany). Spores were counted with a counting chamber (Fuchs-Rosenthal) and stocks in 15% (v/v) glycerol were stored at -80 °C.

## Pathogen treatment for lipidomic and proteomic analysis and subsequent LD-enrichment

For omics-samples of infection treatments, both pathogens were used in spray infections. Infection with Pto DC3000 *AvrPto AvrPtoB* was adapted from Yao et al. (2013). Briefly, Pto DC3000 *AvrPto AvrPtoB* was cultured in NYG-medium (0.5% [w/v] pepstone, 0.3% [w/v] yeast extract, 2% [v/v] glycerol; Merck KGaA, Darmstadt, Germany) with appropriate antibiotics (50 µg/mL rifampicin, 50 µg/mL kanamycin; Duchefa Biochemie, Haarlem, The Netherlands) over night and harvested by centrifugation (1,500×g 20 min, room temperature) at the day of infection. Bacteria were washed once with 10 mM MgCl₂ and then resuspended in 10 mM MgCl₂ and 0.02% (v/v) Silwet. Bacterial density

was adjusted to an OD₆₀₀ of 1.0 and the bacterial suspension sprayed onto well-watered plants until all leaves were evenly wet. Control plants were sprayed with 10 mM MgCl₂ and 0.02% (v/v) Silwet. Plants were then covered with plastic hoods to keep them at high humidity.

*B. cinerea* spores from glycerol stocks were diluted to a concentration of 5 × 10⁴ spores/mL in 1/4 PDB (Merck KGaA, Darmstadt, Germany) and pregerminated for 4 h at room temperature. Plants were sprayed until sprayed droplets ran off the leaves and subsequently kept at humid conditions. Mock-treated plants were treated analogously with 1/4 PDB.

Plants were observed until they developed first symptoms to ensure infections were effective and plant proteome alterations had occurred. Sampling was thus carried out 4 to 5 d after infection by leaf homogenization and LD isolation by ultracentrifugation adapting previous protocols (Kretzschmar et al. 2018). In addition to the pathogen- and mock-treated plants, one proteomic dataset was obtained from plants that were not treated at all.

For LD enrichment used for proteomic analysis, leaves were ground in grinding buffer (50 mM Tris-HCl pH 7.5, 10 mM KCl, 0.4 mM sucrose 200 µM proteinase inhibitor PMSF; Carl Roth GmbH +Co. KG, Karlsruhe, Germany) with sea sand as abrasive agent. Grinding buffer, mortar and pestle were precooled to 0 °C and samples were kept between 0 and 4 °C during processing. To remove sand and cellular debris, samples were centrifuged for 1 min at 100×g. An aliquot was taken as total protein extract sample, and proteins were precipitated in 96% ethanol [v/v] at -20 °C. Subsequently, samples were centrifuged at 100,000×g for 35 min in a swing-out rotor. LDs were collected as fat pad from the top of the sample and washed in grinding buffer. After washing, the fat pad was collected and proteins were precipitated with 96% (v/v) ethanol at -20 °C.

## Heat stress treatment for lipidomic and proteomic analysis and subsequent LD-enrichment

For heat stress, plants were kept at 37 °C for 24 h, starting with the beginning of the light cycle, while control plants were kept at normal temperatures. For proteomic analysis, LDs were enriched directly after heat stress. To that end, leaves were ground with grinding buffer (10 mM sodium phosphate buffer pH 7.5, 150 mM NaCl, 0.6 mM sucrose, 25 mM Lomant's reagent, 10 mM N-ethylmaleimide, 200 µM proteinase inhibitor PMSF; Carl Roth GmbH+Co. KG, Karlsruhe, Germany and Merck KGaA, Darmstadt, Germany). Sea sand was used as abrasive agent and together with cellular debris removed after grinding by centrifugation for 1 min at 100×g. Total protein extract samples were precipitated from the supernatant at -20 °C with 96% [v/v] ethanol in 10× excess. LDs were enriched by two ultracentrifugation steps at 100,000×g for 60 min in a swing-out rotor. After the first ultracentrifugation, LDs were collected as fat pad and resuspended in washing buffer (10 mM sodium phosphate buffer pH 7.5, 150 mM NaCl, 0.4 mM sucrose, 200 µM proteinase inhibitor PMSF, 0.1% [v/v] Tween 20; Carl Roth GmbH+Co. KG, Karlsruhe, Germany). The LD suspension was overlaid with overlay buffer (10 mM sodium phosphate buffer pH 7.5, 150 mM NaCl, 0.2 mM sucrose, 200 µM proteinase inhibitor PMSF, 0.1% [v/v] Tween 20; Carl Roth GmbH+Co. KG, Karlsruhe, Germany). After the second ultracentrifugation, LDs were collected as floating fat pad and proteins precipitated at -20 °C with 96% [v/v] ethanol. Before protein precipitation, samples were kept at 0 to 4 °C throughout LD enrichment; buffers, mortar, and pestle were precooled to the same temperature.
## Lipidomic sample preparation and measurements

Infection with Pto DC3000 *AavrPto*AavrPtoB and B. cinerea, and heat-stress treatment were carried out as for proteomic samples. Samples were also harvested after the same incubation times and flash-frozen in liquid nitrogen.

Extraction and analysis of TAG and SEs was adapted from Herrfurth et al. (2021). In brief, frozen leaf material was homogenized with a ball mill, and 500 mg of each sample were extracted by monophasic extraction with 6 mL propan-2-ol:hexane:water (60:26:14, v/v/v) at 60 °C. Tripentadecanoin was added as internal standard. After extraction, samples were centrifuged (2,500 × g, 10 min) and the supernatant evaporated to dryness under a nitrogen stream. Extracted lipids were reconstituted in 400 µL tetrahydrofuran:methanol:water (7:2:1, v/v/v). UPLC-nanoESI-MS/MS analysis was carried out as described (Herrfurth et al. 2021) with the parameters listed in Supplementary Table S2. Peak integration was performed with the MultiQuant software (AB Sciex, Framingham, MA, USA). Quantitative analysis of integrated peak values was done in RStudio 4.0.1.

For the analysis of the free sterols, the samples were extracted in the same manner and heptadecanoic acid was added as an internal standard. Aliquots of the samples (1/8 of the volume) were dried under nitrogen stream, dissolved in 15 µL pyridine and derivatized with 30 µL N-methyl-N-(trimethylsilyl)trifluoracetamide (MSTFA) before being analyzed by GC-MS (Agilent 7890B GC-Agilent 5977N-MSD) as previously described (Berghoff et al. 2021).

Phospho- and galactolipids were also extracted from frozen and homogenized leaf material. To inactivate phospholipase activity, samples were initially incubated in boiling propan-2-ol for 20 min. After evaporating the leaf material to dryness, lipids were sequentially extracted with 1 mL of chloroform:methanol (1:2, v/v), 1 mL of chloroform:methanol (2:1, v/v), and 1 mL of chloroform. For each extraction step, samples were vortexed thoroughly, centrifuged for 10 min at 1,500 × g and the supernatants collected in a new tube. To the combined supernatant, 0.75 mL of 300 mM NH₄CH₃CO₂ was added, samples were vortexed thoroughly, centrifuged for 5 min at 1,500 × g, and the lower phase transferred to a new tube. Extracts were evaporated to dryness and dissolved in chloroform:methanol:300 mM ammonium acetate (300:665:35, v/v/v). The dry weight of the sample was determined and the amount of the internal standard adjusted accordingly. Samples were analyzed via direct infusion nanospray MS on an Agilent 6530 Accurate-Mass Q-TOF LC/MS instrument equipped with a ChipCube interface as previously described (Welti et al. 2002; Gasulla et al. 2013; Gutbrod et al. 2021). The parameters for the MS/MS experiments are listed in Supplementary Data Set 1.

## Proteomic sample preparation and LC-MS/MS analysis of peptides

Proteins were defatted and washed with 80% [v/v] ethanol and then dissolved in 6 M urea and 5% (w/v) SDS. Determination of protein concentration, in-gel tryptic-digest and peptide desalting was carried out as described previously (Shevchenko et al. 2006; Rappsilber et al. 2007; Kretzschmar et al. 2018). Dried peptides were reconstituted in 20 µL sample buffer (2% acetonitrile, 0.1% formic acid, all [v/v]) and subjected to LC-MS/MS analysis. To that end, 1 to 3 µL of each sample were subjected to reverse phase LC for peptide separation using an RSLCnano Ultimate 3000 system (Thermo Fisher Scientific). Peptides were loaded on an

Acclaim PepMap 100 pre-column (100 µm × 2 cm, C18, 5 µm, 100 Å; Thermo Fisher Scientific) with 0.07% [v/v] trifluoroacetic acid at a flow rate of 20 µL/min for 3 min. Analytical separation of peptides was accomplished on an Acclaim PepMap RSLC column (75 µm × 50 cm, C18, 2 µm, 100 Å; Thermo Fisher Scientific) at a flow rate of 300 nL/min. The solvent composition followed a gradual change within 94 min: from 96% [v/v] solvent A (0.1% [v/v] formic acid) and 4% [v/v] solvent B (80% [v/v] acetonitrile, 0.1% [v/v] formic acid) to 10% [v/v] solvent B within 2 min, to 30% [v/v] solvent B within the next 58 min, to 45% [v/v] solvent B within the following 22 min, and to 90% [v/v] solvent B within the last 12 min of the gradient. All solvents and acids had Optima grade for LC-MS (Thermo Fisher Scientific). Eluting peptides were on-line ionized by nano-electrospray (nESI) using the Nanospray Flex Ion Source (Thermo Fisher Scientific) at 1.5 kV (liquid junction) and transferred to the mass spectrometer. For mass spectrometry, either an Orbitrap Velos Pro hybrid mass spectrometer or a Q Exactive HF mass spectrometer (both Thermo Fisher Scientific) were used. On the Orbitrap Velos Pro hybrid mass spectrometer, full scans were recorded within a mass range of 300 to 1,850 m/z at a resolution of 30,000 with the Orbitrap-FT analyzer. Full scans were followed by data-dependent top 10 CID fragmentation (dynamic exclusion enabled) within the ion trap Velos Pro analyzer. For the Q Exactive HF mass spectrometer, full scans were recorded in a mass range of 300 to 1,650 m/z at a resolution of 30,000 and followed by data-dependent top 10 HCD fragmentation at a resolution of 15,000 (dynamic exclusion enabled). LC-MS method programming and data acquisition was performed with the XCalibur 4.0 software (Thermo Fisher Scientific).

## Computational processing of MS/MS data

MS/MS raw data were processed in the MaxQuant software (version 1.6.2.17) for feature detection, peptide identification, and protein group assembly (Cox and Mann 2008). Mostly, default settings were used with additional settings as specified in Supplementary Table S1. The TAIR10 protein database was used as reference for identification. For quantification, label-free quantification was calculated according to the iBAQ and LFQ algorithms (Cox and Mann 2008; Schwanhäusser et al. 2011; Cox et al. 2014). Further data analysis was done in Perseus 1.6.2.2 (Tyanova et al. 2016), Excel (Microsoft Office), and RStudio 4.0.1 (RStudio Team 2021). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/). All relevant data can be found within the manuscript and its supporting materials. Proteomic raw data can be found in the PRIDE database (Vizcaíno et al. 2014) under the identifier PXD045596 (https://www.ebi.ac.uk/ pride/). All metadata can be found in Supplementary Table S1.

Protein localization was annotated based on the Plant Proteome Database (Sun et al. 2009) as of June 14 2022. LD localization was assigned based on previous studies (Kretzschmar et al. 2018, 2020; Fernández-Santos et al. 2020; Doner et al. 2021; Ge et al. 2022; Li et al. 2022). Volcano plots were calculated in Perseus. rLFQ and riBAQ values of proteins were log2-transformed and missing values were imputed from normal distribution (parameters: width 0.3, down shift 2.5, separately for each sample). For the comparison of different treatments, plant lines or isolated fraction, P-values were calculated by two-sided t-tests. For plotting, transformed and imputed rLFQ or riBAQ values, and calculated P-values were exported and plotted in RStudio 4.0.1.

Proteins with relevance in lipid metabolism were selected based on a compiled list of Arabidopsis lipid genes using KEGG pathway (https://www.genome.jp/kegg/pathway.html, latest
update March 10 2020), Aralip (http://aralip.plantbiology.msu.edu/pathways/pathways; (Li-Beisson et al. 2013) and genes from (Kelly and Feussner 2016; Kretzschmar et al. 2020); and from Plant Sphingolipid Metabolism and Function, Chapter 11 (Luttgeharm et al. 2016). n.d., not detected in control and treatment, n.d. in control, only detected under treatment. P-values are based on Student's t-test.

Identification of candidate proteins for LD localization was done using web tools. Potential membrane-spanning regions were predicted using the TMHMM-2.0 server, which predicts transmembrane helices based on hidden Markov models (Krogh et al. 2001).

## PR gene expression analysis via real-time qPCR

Leaves of wild-type and *tgd1-1 sdp1-4* plants were frozen in liquid nitrogen and ground to fine powder with a ball mill (Retsch GmbH, Haan, Germany). RNA was extracted from 100 mg of ground leaf material per biological replicate and line. RNA isolation was done using the Spectrum Plant Total RNA Kit (Sigma-Aldrich) and subsequently treated with DNasel (Thermo Fisher Scientific, Waltham, MA, USA) according to manufacturer's instructions. For cDNA synthesis, 0.5 µg RNA was reverse transcribed (Maxima Reverse Transcriptase; Thermo Fisher Scientific). The reaction product was diluted 1:10 in double-distilled water before real-time qPCR. For each sample, 4 µL of the diluted cDNA was used together with Takyon No Rox SYBR MasterMix dTTP Blue (Eurogentec, Seraing, Belgium). AT3G01150 was chosen as reference gene (Czechowski et al. 2005), used primers for all genes of interest and the reference gene are listed in Supplementary Table S3. The following PCR program was used for amplification: 95 °C for 1 min 20 s (95 °C for 20 s, 58 °C for 20 s, 72 °C for 40 s) × 39, 72 °C 4 min. Amplicons were tested by melt curve analysis. PCR amplification and melt curve analysis were carried out in an iQ5 qPCR cycler (BioRad Laboratories, Hercules, CA, USA).

## Analysis of phytohormone levels

Phytohormones were extracted by biphasic extraction with methyl-tert-butyl ether based on Matyash et al. (2008). After extraction, phytohormones were reversed phase-separated using an ACQUITY UPLC system (Waters Corp., Milford, MA, USA) and analyzed by nanoelectrospray ionization (nanoESI) (TriVersa Nanomate, Advion BioSciences, Ithaca, NY, USA) coupled with an AB Sciex 4000 QTRAP tandem mass spectrometer (AB Sciex, Framingham, MA, USA) employed in scheduled multiple reaction monitoring modes (Herrfurth and Feussner 2020) with the following modifications. For quantification, 10 ng D₄-SA (C/D/N Isotopes Inc., Pointe-Claire, Canada) were added at the beginning of the extraction procedure. For SA and SAG analysis, the following mass transitions were included: 137/93 [declustering potential (DP) –25 V, entrance potential (EP) –6 V, collision energy (CE) –20 V] for SA, 141/97 (DP –25 V, EP –6 V, CE –22 V) for D₄-SA, and 299/137 (DP –30 V, EP –4 V, CE –18 V) for SAG.

## Molecular cloning and candidate localization studies in Nicotiana benthamiana leaves

Open reading frames of selected candidate genes were amplified from cDNA prepared with Maxima Reverse Transcriptase (Thermo Fisher Scientific) according to manufacturer's instruction using leaf RNA that had been extracted using the Spectrum Plant Total RNA Kit (Merck KGaA, Darmstadt, Germany). Constructs were amplified with the Phusion High-Fidelity DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA)

following manufacturer's instructions and using primers listed in Supplementary Table S4. Gateway cloning into the plant binary vectors pMDC32-ChC and pMDC32-ChN was carried out by traditional or fast Gateway cloning as described in (Müller et al. 2017). Vector construction of pMDC32-ChC and pMDC32-ChN has been described previously in Kretzschmar et al. (2020) and Doner et al. (2021), respectively. The construct of MmDGAT2 in pMDC32, which was used for co-expression experiments, has been described in Cai et al. (2019). Localization of candidates was analyzed in leaves of *N. benthamiana* that were transiently transformed by infiltration with *Agrobacterium tumefaciens* harboring candidate expression vectors. *N. benthamiana* plant growth, leaf infiltration, BODIPY 493/503 staining, and CLSM imaging was performed as previously described (Gidda et al. 2016; Kretzschmar et al. 2020). Micrographs of leaves were acquired using a Leica SP5 CLSM (Leica Microsystems). Excitations and emission signals for fluorescent proteins and BODIPY were collected sequentially as single optical sections in double-labeling experiments like those described in Gidda et al. (2016). BODIPY was excited with an Argon laser at 488 nm and detected at a bandwidth of 500 to 530 nm, and mCherry was excited with an HeNe laser at 543 nm and detected at a bandwidth of 590 to 646 nm.

## Accession numbers

AT3G01420 (α-DOX1); AT1G73680 (α-DOX2); AT2G07050 (CAS); AT5G53560 (CB5-E); AT2G33380 (CLO3); AT2G17840 (ERD7); AT1G01610 (GPAT4); AT4G00400 (GPAT8); AT1G67360 (LDAP1); AT2G47780 (LDAP2); AT3G05500 (LDAP3); AT5G16550 (LDIP); AT5G04830 (LDNP); AT1G43890 (LDS1); AT1G73920 (LIDL2); AT4G33110 (LIME1); AT4G33120 (LIME2); AT1G45201 (OBL3); AT4G10790 (PUX10); AT5G13710 (SMT1); NP_080660.1 (MmDGAT2).

## Acknowledgments

We are grateful to Marcel Wiermer for his support with pathogen infections and we thank him and George Haughn for valuable advice. We thank Changcheng Xu (Brookhaven National Laboratory) for generously providing the *tgd1-1 sdp1-4* seeds. Technical assistance was provided by Helga Peisker, Jannis Anstatt, Denis Krone, and Annabel Maisl.

## Author contributions

P.S., J.S., R.T.M., and T.I. designed the work, P.S., N.M.D., K.G., C.H., P.N., M.S.S.L., K.B., K.S., and O.V. performed research, P.S., N.M.D., K.G., C.H., K.S., I.F., P.D., G.H.B., R.T.M., and T.I. analyzed data, and P.S., R.T.M., and T.I. wrote the manuscript with contributions by the other authors. All authors critically read and revised the manuscript and approved the final version.

## Supplementary data

The following materials are available in the online version of this article.

**Supplementary Figure S1.** Membrane and sterol lipid composition in heat-stressed leaves.

**Supplementary Figure S2.** Membrane lipid composition after infection by *B. cinerea*.

**Supplementary Figure S3.** Composition of membrane lipids after infection with *P. syringae*.
**Supplementary Figure S4.** Relative composition of TAGs and SEs species after different infection treatments.

**Supplementary Figure S5.** STRING networks of differentially accumulating proteins of Arabidopsis Col-0 leaves after different stresses.

**Supplementary Figure S6.** Alterations in total abundance and species composition of membrane and hydrophobic lipids of the Arabidopsis double mutant *tgd1-1 sdp1-4*.

**Supplementary Figure S7.** Alterations in species composition of membrane and sterol lipids of the Arabidopsis double mutant *tgd1-1 sdp1-4*.

**Supplementary Figure S8.** STRING networks of differentially accumulating proteins in leaf total protein fractions of Arabidopsis Col-0 and *tgd1-1 sdp1-4*.

**Supplementary Figure S9.** Protein abundance of three PR proteins in Arabidopsis leaves of Col-0 and *tgd1-1 sdp1-4* after different treatments.

**Supplementary Figure S10.** Enrichment of different organelle proteomes in the LD-enriched fraction.

**Supplementary Figure S11.** Prediction of the membrane-interacting region in CB5-E.

**Supplementary Figure S12.** Additional subcellular localization studies of candidate proteins in *N. benthamiana* leaves.

**Supplementary Table S1.** Metadata file for LC-MS/MS data processing with MaxQuant.

**Supplementary Table S2.** Parameters for lipid analysis by UPLC-nanoESI-MS/MS.

**Supplementary Table S3.** Primers used for gene expression analysis via qPCR.

**Supplementary Table S4.** Primers used for Gateway cloning and sequencing.

**Supplementary Data Set 1.** Arabidopsis leaf membrane lipids—absolute amounts.

**Supplementary Data Set 2.** Arabidopsis leaf membrane lipids—relative composition of species per class.

**Supplementary Data Set 3.** Arabidopsis leaf neutral lipids—absolute peak areas.

**Supplementary Data Set 4.** Arabidopsis leaf neutral lipids—normalized icf-corrected peak areas.

**Supplementary Data Set 5.** Relative contribution of SEs with a common sterol moiety to total SEs signal intensity.

**Supplementary Data Set 6.** Relative proportions of individual TAG species.

**Supplementary Data Set 7.** Free sterols.

**Supplementary Data Set 8.** Proteins found in Arabidopsis leaves—normalized rLFQ and riBAQ values.

**Supplementary Data Set 9.** Comparison of protein abundance in Arabidopsis Col-0 leaves after infection with *B. cinerea* to mock-treated plants.

**Supplementary Data Set 10.** Comparison of protein abundance in Arabidopsis Col-0 leaves in reaction to infection with *P. syringae* pv. *tomato DC3000 AvrPto AvrPtoB*.

**Supplementary Data Set 11.** Comparison of protein abundance in Arabidopsis Col-0 leaves after heat stress.

**Supplementary Data Set 12.** Proteins with relevance in lipid metabolism found in Arabidopsis leaves—normalized rLFQ and riBAQ values.

**Supplementary Data Set 13.** Comparison of protein abundance in Arabidopsis *tgd1-1 sdp1-4* leaves after infection with *B. cinerea* to mock-treated plants.

**Supplementary Data Set 14.** Comparison of protein abundance in Arabidopsis *tgd1-1 sdp1-4* leaves in reaction to infection with *P. syringae* pv. *tomato DC3000 AvrPto AvrPtoB*.

**Supplementary Data Set 15.** Comparison of protein abundance in Arabidopsis *tgd1-1 sdp1-4* leaves after heat stress.

**Supplementary Data Set 16.** Comparison of proteins in non-stressed leaves of Arabidopsis Col-0 and *tgd1-1 sdp1-4*.

**Supplementary Data Set 17.** Comparison of proteins in LD-enriched fractions to total protein fractions of Arabidopsis *tgd1-1 sdp1-4* leaves.

## Funding

This work was supported by funding from the German Research Foundation (DFG) (IS 273/10-1 and IRTG 2172 PRoTECT to T.I., INST 186/1167-1 to I.F.) and the Studienstiftung des deutschen Volkes (to P.S.), the U.S. Department of Energy, Office of Science, BES-Physical Biosciences Program (DE-SC0016536, in part to R.T.M. to support N. *benthamiana* experiments, and KC0304000 to J.S.), the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-04629 to R.T.M.), and an Ontario Graduate Scholarship provided by the Ontario Ministry of Colleges and Universities to N.M.D. Proteomics measurements performed at the Service Unit LCMS Protein Analytics of the Göttingen Center for Molecular Biosciences (GZMB) was supported by DFG funding (INST 186/1230-1 FUGG to Stefanie Pöggeler).

*Conflict of interest statement.* None declared.

## Data availability

The data underlying this article are available in the article and in its online supplementary material or are available in PRIDE database at https://www.ebi.ac.uk/pride/, and can be accessed under the identifier PXD045596.

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