HR3/RORα-mediated cholesterol sensing regulates TOR signaling

Domain: biology
Published: 2026-03-30
Editorial outcome: minor_revision

Abstract
Cells and organisms adjust their growth based on the availability of cholesterol, which is essential for cellular functions. However, the mechanisms by which cells sense cholesterol levels and translate these into growth signals are not fully understood. We report that cholesterol rapidly activates the master growth-regulatory TOR pathway in Drosophila tissues. We identify the nuclear receptor HR3, an ortholog of mammalian RORα, as an essential factor in cholesterol-induced TOR activation. We demonstrate that HR3 binds cholesterol and promotes TOR-pathway activation through a non-genomic mechanism acting upstream of the Rag GTPases while also restraining longer-term responses through genomic regulation. We also find that RORα is necessary for cholesterol-mediated TOR activation in human cells, suggesting that HR3/RORα-mediated signaling represents a conserved mechanism for cholesterol sensing that couples cholesterol availability to TOR-pathway activity. These findings advance our understanding of how cholesterol influences cell growth, with implications for cholesterol-related diseases and cancer.

Introduction
Cholesterol is widely recognized as a fundamental component of cellular membranes. Beyond its structural role as a lipid, it functions as a critical signaling molecule, affecting signaling pathways and physiological processes. Cholesterol can interact with proteins through specific cholesterol-binding domains, thereby influencing various cellular processes such as signal transduction1-4. Furthermore, cholesterol is the precursor to other signaling molecules, including steroid hormones, bile acids, and oxysterols, that can activate specific receptors and modulate gene expression5, 6. Dysregulated cholesterol levels have been implicated in many health disorders ranging from cardiovascular diseases to promoting cancerous growth7, 8. The deep involvement of cholesterol in physiology highlights the importance of cellular mechanisms that sense cholesterol availability and in response modulate cellular growth.

In mammalian cells, which can synthesize sterols de novo, cholesterol homeostasis is governed by sterol-sensing mechanisms within the endoplasmic reticulum (ER). Through regulation of the transcription factor Sterol Response Element Binding Protein (SREBP), cholesterol biosynthesis and cellular uptake are modulated to maintain lipid homeostasis9. The demand for cholesterol increases during rapid cell proliferation – for example, during development and in cancer cells. To meet this demand, upregulation of pathways such as phosphatidylinositol 3-kinase (PI3K)/Protein Kinase B (PKB/AKT) enhances the uptake of exogenous cholesterol10. Although cholesterol is necessary for cell-membrane synthesis, increased cholesterol availability also directly drives cell growth and fuels rapid proliferation in cancers. Unlike the well-characterized SREBP pathway, the cellular mechanisms by which cholesterol abundance or deficiency is linked to growth-regulatory pathways are poorly defined.

We recently demonstrated that cholesterol stimulates systemic growth during development through the activation of insulin signaling in Drosophila11. While our genetic evidence implicated the target of rapamycin (TOR) pathway in this process, the cholesterol-induced TOR responses and the molecular mechanisms linking cholesterol to TOR activation – particularly identifying the components that directly interact with cholesterol to register its availability – remained elusive. Thus, the mechanisms by which cellular cholesterol levels are conveyed to TOR for translation into growth signals are poorly understood.

The TOR pathway integrates environmental cues including nutrients, oxygen availability, growth stimuli, and cellular stressors into a single signal that regulates cellular growth12, 13. Activated TOR promotes cellular growth and protein synthesis by promoting ribosomal biogenesis and enhancing translation while simultaneously inhibiting autophagy and regulating metabolism. In mammalian cell culture, cholesterol has emerged as a key factor that activates the TOR pathway3, 14. Extracellular cholesterol is taken up through endocytosis and trafficked to the late endosome/lysosome compartment. When cholesterol accumulates within these organelles, the TOR complex is recruited by a large protein assembly to the lysosomal membrane, where it interacts with the small GTPase Rheb, which in turn activates TOR kinase activity. In conditions of nutrient scarcity, including low cholesterol, TOR remains localized to the cytosol. In this compartment, TOR has little access to Rheb and therefore cannot be activated.

A large network of proteins orchestrates the nutrient-responsive localization of TOR to the lysosome (Fig. 1a). The lysosomal recruitment of TOR is ultimately governed by small GTPases of the Rag family12, 15. These proteins associate with the LAMTOR1 subunit of the Ragulator complex, which anchors them to the lysosomal membrane. In response to nutrient abundance, a diverse array of mechanisms configures the GTP/GDP loading of Rag heterodimers in such way that they bind to RAPTOR, a component of the TOR complex, which maintains TOR in association with the lysosomal surface, where it can be activated by Rheb. Rag heterodimers are active and can interact with RAPTOR when RagA/B is guanosine triphosphate (GTP)-bound and RagC/D is guanosine diphosphate (GDP)-bound12. This balance is maintained by GTPase-activating protein (GAP) complexes such as GATOR1 and guanine nucleotide exchange factor (GEF) complexes including Ragulator. In nutrient-depleted states, GATOR1 induces GTP hydrolysis on RagA, leading to loss of TOR activation. This mechanism is influenced by amino acids, which affect the GAP activity of GATOR1. Low amino-acid levels also negatively regulate Ragulator, preventing it from activating the Rag GTPases through its GEF functionality.

In contrast to the level of detail with which amino acid-mediated TOR activation is understood, the cholesterol-sensing mechanisms that are capable of directly binding to or detecting intracellular cholesterol and interacting with the TOR pathway are poorly defined. Multiple intracellular cholesterol pools are present, including those in lysosomes, deposited across the plasma membrane and ER, as well as at membrane contact sites16. Given the variability in cholesterol concentration across these distinct cellular compartments, it is likely that several distinct cholesterol-sensing mechanisms exist, each specifically designed to detect different cholesterol pools and independently modulate the TOR pathway. Recent reports suggest that a mechanism by which lysosomal cholesterol activates the TOR complex in mammalian cells involves LYCHOS, a G protein–coupled receptor (GPCR)-like protein integral to the lysosomal membrane that modulates the Rag nucleotide-binding state in a cholesterol-dependent manner via interaction with GATOR114. Additionally, the mammalian lysosomal transmembrane protein SLC38A9 is important for the activation of TOR in response to both cholesterol and amino acids3. In Drosophila, dietary cholesterol activates TOR, with increasing levels driving rapid growth and development11. Intracellular cholesterol accumulation in lysosomal compartments caused by deficiency in the cholesterol transporter Niemann-Pick Type C1 (NPC1) drives strong activation of the TOR pathway in both Drosophila and mammals3, 11. This common feature suggests that the mechanisms coupling cholesterol levels to the activation of the TOR pathway are evolutionarily ancient and conserved. Drosophila possesses an ortholog of LYCHOS, named Anchor, but an SLC38A9-like protein does not appear to exist in flies, implying that other ancient and possibly conserved pathways exist that detect cholesterol and link its availability to the activation of TOR signaling.

Nuclear receptors bind lipophilic molecules and exert their regulatory influence through both direct effects on transcription (genomic effects) and more immediate signaling mechanisms that do not involve changes in gene transcription (non-genomic effects), orchestrating a diverse range of cellular processes17, 18. DHR96, a Drosophila nuclear receptor involved in cholesterol homeostasis, binds cholesterol and regulates the expression of genes involved in cholesterol uptake, metabolism, and transport1, 4. In mammals, the nuclear receptor Retinoic acid receptor (RAR)-related Orphan Receptor alpha (RORα) influences cholesterol homeostasis by modulating the transcription of genes important for cholesterol synthesis, uptake, and efflux19. However, whether nuclear receptors bind cholesterol and transduce levels of this lipid to modulate the TOR pathway is not known.

Here, we demonstrate that dietary cholesterol intake leads to rapid and dynamic activation of the TOR pathway in tissues of Drosophila. This response is modulated by the Drosophila RORα ortholog, HR3, which – like RORα – binds cholesterol and is activated by this ligand. Although HR3 is known to be transcriptionally upregulated by the ecdysone receptor, EcR, our results reveal that HR3 regulates growth through the TOR pathway in response to cholesterol independently of ecdysone-mediated effects. This regulation involves rapid cholesterol-induced TOR activation that in part is independent of the transcriptional functions of HR3, through an isoform of HR3 that lacks a DNA-binding domain (DBD). Reducing HR3 levels in cells attenuates the overactivation of TOR caused by the intralysosomal accumulation of cholesterol resulting from depletion of NPC1 (Npc1a in Drosophila). This indicates that HR3 is necessary for TOR activation by lysosomal cholesterol. Our findings suggest that HR3 activates the TOR pathway upstream of the Rag proteins. Furthermore, our findings indicate that RORα is involved in cholesterol-induced TOR activation in human cells, suggesting that a conserved function of HR3/RORα is to couple cholesterol levels to TOR-pathway activation.

Results
Cholesterol induces TOR-pathway activity
Dietary intake of cholesterol and its subsequent cellular absorption from the bloodstream are key for cellular cholesterol acquisition in both Drosophila and mammals. Cholesterol esters carried by lipoprotein particles are internalized from circulating lipoproteins through endocytosis and are transported via the endosomal pathway to lysosomes, where they are hydrolyzed so that free (unesterified) cholesterol can be trafficked between hydrophobic compartments of the cell9, 20. Unlike mammals, which can also synthesize cholesterol de novo, Drosophila (and other insects) lack a functional sterol-biosynthesis pathway. Consequently, cholesterol availability in Drosophila can be precisely manipulated via dietary modifications21. We used a synthetic diet22 that allows cholesterol supplementation at a range of doses, which promotes larval growth and development11, 23. We specifically examined the role of dietary cholesterol during the last larval instar, a period characterized by rapid growth driven by nutrient intake. To investigate the dose-dependent effects on TOR-pathway activity, we supplemented the diet with cholesterol at concentrations ranging from 1.2 µg/mL to 80 µg/mL, reflecting ecologically relevant sterol levels24, 25. As illustrated in Supplementary Fig. 1a, larvae were initially cholesterol-depleted by transferring them to synthetic food containing 0 µg/mL cholesterol for 10 hours at 84 hours after egg laying (AEL), then re-fed with synthetic food containing different doses of cholesterol for 6 hours (until 100 h AEL). Dietary cholesterol dose-dependently activated TOR signaling in fat-body tissue, as indicated by increased phosphorylation of Ribosomal protein S6 (pS6) and of its activator S6 kinase (pS6K), a direct target of TOR, reflecting TOR pathway activity26 (Fig. 1b,c). Higher cholesterol doses correlated with elevated whole-body protein levels, suggesting that cholesterol promotes organismal protein synthesis and growth (Fig. 1d), consistent with TOR activation.

When larvae were transferred from a cholesterol-free synthetic medium (0 µg/mL) to a cholesterol-replete one (80 µg/mL), we observed a time-dependent activation of the TOR pathway in the fat body (Fig. 1e). Activation occurred within 30 minutes, peaking at 1 hour and subsequently exhibiting a dynamic oscillatory pattern. Taking into account the time required for food consumption and gut-mediated cholesterol distribution, cholesterol effects on TOR are rapid. Considering that natural food sources typically exhibit a range of sterol concentrations, rather than being absolutely sterol free, we investigated whether a transition from a low non-zero concentration to a cholesterol-replete diet would elicit a similar TOR-pathway activation pattern. Larvae were transferred to low-cholesterol food containing 1.2 µg/mL cholesterol for 10 hours at 84 hours AEL (Supplementary Fig. 1a). In this condition, the transition to high (80 µg/mL) cholesterol at 94 h also resulted in increased fat-body pS6, but with levels peaking later at 6 hours post-shift (Fig. 1e,f). Notably, the kinetics of the pS6 response depended on the pre-feeding cholesterol level. While transfer from cholesterol-free medium (0 µg/mL) to 80 µg/mL elicited an early maximal response (Fig. 1e), refeeding to 80 µg/mL after feeding low-cholesterol medium (1.2 µg/mL) induced a delayed increase in pS6 levels that peaked later (Fig. 1f).

Developmental growth of larval tissues is modulated by the antagonistic actions of the growth-promoting, nutrient-responsive TOR and insulin pathways and the opposing effect of ecdysone signaling, which negatively regulates systemic growth via the receptor EcR27. Despite cholesterol’s being the precursor for steroid biosynthesis, we have previously demonstrated that cholesterol promotes growth in an ecdysone-independent manner by stimulating insulin secretion, a process dependent on the TOR pathway in the cells of the fat body and the blood-brain barrier11. To substantiate this further, we compared the TOR-pathway response in the fat tissue of animals fed cholesterol to the response in those fed ecdysone, using concentrations of ecdysone sufficient to drive developmental progression28. Larvae were switched at 84 hours AEL from standard diet to a low (1.2 µg/mL)-cholesterol synthetic diet for 10 hours, after which they were placed on synthetic diet supplemented with either high cholesterol (80 µg/mL) or ecdysone (Supplementary Fig. 1a). Dietary cholesterol induced a marked increase in fat-body pS6 levels compared to effects of dietary ecdysone, suggesting that cholesterol activates the TOR pathway through a mechanism that does not involve ecdysone (Supplementary Fig. 1b).

To confirm that the observed pS6 elevation in response to cholesterol availability was indeed mediated by the TOR pathway, we performed RNAi-mediated knockdown of TOR or the gene encoding its kinase substrate S6K, a key pathway component. Knockdown of TOR or S6K in the fat body (driven by Cg-GAL4, Cg>) abrogated the pS6 increase induced by dietary cholesterol (Fig. 1g and Supplementary Fig. 1c). We also examined the impact of dietary cholesterol on TOR activity in the prothoracic gland (PG), an endocrine tissue that requires substantial cholesterol flux for ecdysone synthesis and thus is particularly sensitive to cholesterol23. Cholesterol feeding increased pS6 levels in the PG, an effect that was dependent on TOR signaling, since it was abolished by PG-specific TOR knockdown. This was achieved by inducing TOR knockdown at 120 hours AEL [using temperature-sensitive (TS) Tub-GAL80TS with phm-GAL4 – together, phmTS>], when larvae were transferred to low-cholesterol food containing 1.2 µg/mL cholesterol for 10 hours before refeeding with cholesterol-replete medium (40 µg/mL; Fig. 1h).

In this system, animals were raised at lower temperatures (18 °C), at which GAL80TS represses GAL4 activity. When larvae are shifted to 29 °C, GAL80TS becomes inactive, thereby allowing GAL4 to drive UAS-TOR-RNAi expression only after the temperature shift (Supplementary Fig. 1d). This makes it possible to restrict Tor knockdown to a defined time window in late larval development. Although the temporal profiles differ modestly between the fat body and the PG, cholesterol increases pS6 in both tissues within the examined time window. Additionally, employing a commercially available intermediate-sterol, cornmeal-based diet (NutriFly, NF)11, we demonstrated that larvae reared chronically on this diet supplemented with 40 µg/mL cholesterol throughout development exhibited elevated pS6 levels in the PG, indicative of enhanced TOR activity (Supplementary Fig. 1e). This effect was confirmed to be TOR-dependent, as the attenuation of TOR activity via knockdown eliminated the supplementation-induced increase in pS6 levels. To further exclude an ecdysone-mediated mechanism, we used phmTS> to knock down torso, the gene encoding the PTTH receptor, which stimulates ecdysone synthesis in the PG, and the biosynthetic enzymes phantom (phm) and disembodied (dib), which mediate essential steps in the ecdysone biosynthetic pathway. RNAi induction was performed at 120 h AEL to avoid perturbing the transition from the second to the third larval instar; at 18 °C, larvae had already reached the early third-instar stage by this time. Larvae were then placed on low-cholesterol medium (1.2 µg/mL) for 10 h. Switching these animals to a high-cholesterol diet (80 µg/mL) for 6 h increased fat-body pS6 staining to control-like levels despite impaired ecdysone production (Supplementary Fig. 1f). This suggests that the cholesterol-induced elevation of pS6 occurs independently of ecdysone production, consistent with direct cholesterol-induced activation of TOR (Fig. 1f and Supplementary Fig. 1c,e).

To further confirm the dynamic response of the TOR pathway to cholesterol, we used a transcriptional reporter of TOR function. In this system, expression of Luciferase from regulatory elements of the TOR-inhibited gene unkempt (unk) is inversely correlated with TOR activity29. When animals were transferred from a cholesterol-depleted synthetic medium (0 µg/mL) to a medium containing 80 µg/mL cholesterol, we observed a reduction of Luciferase activity in whole-larval lysates within 30 minutes, suggesting TOR-pathway activation (Fig. 2a). This pattern mirrored the dynamics observed for the pS6 response (Fig. 1e), and it was different from the systemic response to dietary protein refeeding following complete protein deprivation, which exhibited a gradual decline in reporter activity and thus indicates increasing TOR activity (Fig. 2b).

We next employed an unbiased quantitative mass-spectrometry-based phosphoproteomics approach to further elucidate the signaling response to cholesterol (Fig. 2c). These results show the same rapid and dynamic increase in S6 phosphorylation in response to cholesterol feeding when animals were transferred from a medium devoid of cholesterol (0 µg/mL) to one containing 80 µg/mL of this nutrient (Fig. 2d, left panel, and Supplementary Data 1). In contrast to this dynamic pattern, protein refeeding induced a sustained upregulation of TOR-pathway activity as reflected in pS6 levels (Fig. 2d, right panel, and Supplementary Data 2), recapitulating the results of the luciferase-reporter assay. A comparative analysis of proteins demonstrating significant phosphorylation changes (fold change upon transfer from cholesterol- or protein-starvation condition to a complete medium of greater than 30 percent, with a p-value less than 0.05) at various refeeding intervals (15 minutes, 30 minutes, and 1, 2, 4, 6, and 10 hours) revealed 996 proteins that were (de)phosphorylated in response to dietary cholesterol, whereas 1,665 proteins showed altered phosphorylation in response to dietary protein (Fig. 2e,f). Ribosomal protein S6 and eukaryotic Initiation Factor 4G1 (eIF4G1), another target of TOR that connects nutrient sensing to translation30, were among the proteins that were modified within 30 minutes in both cholesterol and amino-acid response (Supplementary Fig. 2a). This overlap indicates that TOR activity is rapidly promoted by both dietary cholesterol and amino acids.

The data also show dynamic changes in protein phosphorylation patterns over a 10-hour period when animals are refed cholesterol, a response phenomenon not exhibited after protein feeding. To resolve the temporal organization of nutrient-induced signaling, we performed fuzzy c-means clustering of phosphorylation dynamics following cholesterol or protein refeeding across the 10-hour time course (Fig. 2g,h and Supplementary Figs. 2b-d and 3a,b). This analysis revealed marked qualitative differences in how signaling networks were engaged by the two nutrients. Cholesterol refeeding elicited a highly dynamic and temporally structured phosphorylation program, with sites partitioning into multiple distinct clusters exhibiting different temporal response profiles (Supplementary Fig. 2b-c). Reactome enrichment analysis (Supplementary Fig. 2d) showed that these clusters were differentially associated with functional modules, including RNA metabolism and processing.

In contrast, protein refeeding produced more sustained and comparatively monotonic phosphorylation responses, with fewer pronounced temporal transitions within each cluster (Supplementary Fig. 3a,b). Enrichment analysis also revealed involvement of RNA-processing pathways. Collectively, temporal clustering indicates that cholesterol and protein refeeding differ in the temporal dynamics of signaling network activation, with cholesterol inducing dynamic, phase-specific responses and protein promoting more sustained signaling. Taken together, our data show that dietary cholesterol feeding induces dynamic physiological responses, including TOR activation, in fat-body and PG cells and in whole animals. Furthermore, these changes are distinct in some ways from those that occur in response to amino-acid replenishment.

HR3 is activated by cholesterol binding
Previous research suggests that HR3 interacts genetically with S6K31, and the closest human orthologs of HR3, RORα and -β, have been found to bind cholesterol32. However, the functional significance of this interaction remains unclear. The sequence similarity between the ligand-binding domains (LBDs) of HR3 and RORα17 suggests that cholesterol may also be a natural ligand of HR3. To test this hypothesis in silico, we used the molecular modeling package Boltz-2 to generate a structural model of the HR3 ligand-binding domain (LBD) and to assess whether cholesterol would be placed within the predicted HR3 pocket in a pose similar to that observed in the experimentally determined RORα LBD structure (Fig. 3a,b). Here, without intervention, the Boltz-2 docking process placed cholesterol into the HR3 ligand-binding pocket in the same orientation as the cholesterol ligand in RORα (Fig. 3a,b and Supplementary Fig. 4a,b). Consistent with the strong similarity of their primary sequences, we also found close alignment of structural elements between HR3 and RORα (Fig. 3c). As observed in the RORα-cholesterol structure, the 3β-hydroxyl group of cholesterol in the HR3 LBD is aligned with the side chains of two conserved arginine residues (R328 and R331; Supplementary Fig. 4c,d). In RORα, R367 and R370 (helix 5), along with a hydrogen-bonded water molecule in the pocket, were identified as contributing to interactions with the 3β-hydroxyl group of cholesterol33. In the structural alignment of RORα and the predicted HR3 structure, the HR3 residues R328 and R331 align closely with their RORα counterparts R367 and R370 (Supplementary Fig. 4c).

Following this structural prediction, we asked whether the cholesterol-binding pocket is also conserved at the sequence level. A sequence alignment of Drosophila HR3 with human RORα showed that many residues implicated in ligand binding are conserved in HR3 (Supplementary Fig. 4d). Several residues experimentally shown to be critical for cholesterol binding due to their close proximity to the ligand (≤4 Å) are strictly conserved between RORα and HR3 or replaced by residues with similar hydrophobic character32, 33, consistent with tight hydrophobic packing around the cholesterol ligand. Furthermore, hydrogen bonding between H484 and Y507 has been suggested as critical for cholesterol binding in RORα, perhaps by maintaining H484 in an amenable position. Both of these residues are conserved in HR3, underlining the conservation of key elements of the cholesterol-binding pocket32. Together, these sequence features complement the docking and structural alignment (Fig. 3a-c and Supplementary Fig. 4a-c) and are consistent with the HR3 LBD’s accommodating cholesterol.

To determine whether HR3 indeed binds cholesterol, we expressed the HR3 LBD in Hi5 insect cells and conducted affinity purification of this recombinant protein, followed by electrospray ionization mass-spectrometry (ESI-MS) under non-denaturing (native) conditions to identify any co-purified ligands. In non-denaturing conditions, full-mass-range scans detected peaks suggesting a receptor:ligand complex with an added mass approximately equal to that of cholesterol (386.6 Daltons) (Fig. 3d and Supplementary Fig. 4e). Collision-induced dissociation (CID) of the same native-MS preparation dissociated the ligand and yielded the corresponding apo/modified HR3 species (Fig. 3e). To confirm the identity of the ligand, purified HR3 LBD was extracted with acidified chloroform/methanol to isolate co-purifying small molecules, which were derivatized and analyzed by electron ionization gas chromatography-mass spectrometry (EI-GC/MS). Chromatography of the extract yielded a single major peak at 19 minutes (Fig. 3f) which, when ionized, produced a spectrum characteristic of derivatized cholesterol (Fig. 3g). A cholesterol standard processed through the same protocol yielded a close match to the spectrum of the putative ligand (Fig. 3h,i). Taken together, evidence from the combination of protein ESI-MS and GC/MS demonstrates that the LBD of HR3 binds cholesterol with a 1:1 stoichiometry.

To assess the binding of cholesterol to HR3 in vivo, along with functional outcomes of this binding, we tested the ability of cholesterol to activate a transgenic HR3 ligand-sensor construct (GAL4::HR3). In this system, the LBD of HR3 is fused with the DNA-binding domain (DBD) of GAL4. Ligand binding to the LBD of the fusion protein activates expression of a UAS-GFP reporter gene34 (Fig. 3j). Cholesterol addition to permeabilized living embryos carrying this system induced widespread GFP expression, indicating that exogenous cholesterol can bind and activate the LBD of HR3 (Fig. 3k). We next used this ligand sensor to examine whether the LBD of HR3 can be activated by cholesterol derived from the diet. Cholesterol refeeding of larvae after a deprivation period on synthetic diet lacking cholesterol led to increased GFP staining in the fat body, consistent with binding of cholesterol (or a derivative) obtained through the diet (Fig. 3l). We also confirmed that this cholesterol treatment produces the characteristically dynamic fat-body TOR activation pattern as measured by pS6 staining (Supplementary Fig. 5a). These findings collectively indicate that cholesterol binds to the LBD of HR3 and that this protein functions as a cholesterol-responsive receptor.

HR3 modulates TOR activity and regulates systemic growth
Given the activation of HR3 by dietary cholesterol, we investigated whether HR3 might influence systemic growth via modulation of the TOR signaling pathway on standard diet. Knockdown of HR3 in the fat body or PG using an RNAi construct that targets all of the annotated transcript variants of HR3 led to reductions in pS6 staining in these tissues (Fig. 4a,b and Supplementary Fig. 1a for fat-body conditions and Supplementary Fig. 1d for PG-specific GAL80TS induction conditions). This attenuation indicates a regulatory interaction whereby HR3 promotes S6 phosphorylation, perhaps mediated by TOR and S6K. To more directly show that phosphorylation of S6K and S6 occurs downstream of HR3, we simultaneously expressed a constitutively active S6K allele in the fat body, which suppressed the pS6 reduction induced by HR3 knockdown (Fig. 4c), implying that HR3 modulates S6 phosphorylation via the TOR-S6K signaling pathway. It is well established that reduced fat-body TOR activity attenuates systemic growth35. In line with this, we found that HR3 knockdown in the fat body reduced systemic growth, as reflected in a decrease in larval mass, validated using two distinct fat-body-specific drivers (Cg> and pumpless-GAL4, ppl>) and three independent RNAi lines (Fig. 4d,e and Supplementary Fig. 5b).

To distinguish whether the reduced larval mass resulted from slower growth rather than a developmental delay or arrest, we next induced HR3-RNAi later in development, at the time when larvae transition into the third and final instar. This approach avoids perturbing earlier developmental transitions and allows growth rate to be assessed independently of stage-specific timing effects. Knockdown of HR3 in the fat body (CgTS>) or in the PG (phmTS>) was induced 120 h AEL by transfer from 18 °C to 29 °C (Supplementary Fig. 1d), and we monitored both larval mass and the timing of pupariation (Supplementary Fig. 5c,d). Although fat-body-targeted HR3 knockdown during the third instar significantly reduced larval body size, the timing of pupariation was unchanged (Supplementary Fig. 5c). Because developmental progression remained normal while final size decreased, we conclude that HR3 depletion in the fat body slowed the rate of growth, consistent with earlier reports showing that reduced fat-body TOR activity diminishes systemic growth in Drosophila13, 35. In contrast, the consequences of TOR-pathway manipulation in the PG are more complex, because TOR activity in this endocrine tissue regulates ecdysone production, which in turn sets developmental timing11, 36-38. Consistent with this established role, HR3 knockdown induced in the third instar in the PG altered the timing of pupariation (Supplementary Fig. 5d), reflecting the PG’s role in controlling the timing of developmental transitions rather than the growth rate.

HR3 regulates TOR pathway activity in response to cholesterol
Given the role of HR3 in binding cholesterol and modulating TOR pathway activity, we investigated whether HR3 is necessary for the cholesterol-induced activation of the TOR pathway. Larvae, at 84 hours AEL, were first placed on a 1.2-µg/mL-cholesterol diet for 10 hours, followed by a shift to a high-cholesterol diet (80 µg/mL). Fat-body-specific knockdown of all HR3 variants through RNAi resulted in diminished cholesterol-induced activation of the TOR pathway over 6 hours, as indicated by reduced pS6 levels (Fig. 5a). Consistent with our previous findings (Fig. 1f), we found that elevated dietary cholesterol rapidly activated TOR signaling in fat tissue within 1 hour, with the activation progressively intensifying up to 6 hours post-transfer. By contrast, cholesterol replenishment failed to elevate pS6 levels within an hour in animals with fat body-specific HR3 knockdown, and the long-term activation was attenuated in these animals. These findings were further corroborated using two further independent RNAi constructs also targeting all HR3 isoforms, showing that fat-body-specific knockdown of HR3 largely abolished cholesterol-stimulated TOR pathway activation (Fig. 5b). We next explored whether HR3 selectively activates TOR in response to cholesterol. Contrary to the diminished TOR activation by cholesterol, the response of TOR to dietary amino acids remained unperturbed by HR3 knockdown (Fig. 5c). This indicates that HR3 does not influence the activation of TOR by dietary amino acids, distinguishing its role in cholesterol-mediated TOR activation.

To further examine any potential genetic interaction between HR3 and EcR, we knocked down EcR in the fat body and assessed the TOR pathway's response to cholesterol. Given that HR3 is an ecdysone-inducible gene17, loss of EcR would be expected to reduce HR3 expression, which would in turn dampen the TOR response to cholesterol, similar to the effects of HR3 knockdown itself. Contrary to this expectation, EcR knockdown in the fat body enhanced the response to cholesterol (Supplementary Fig. 5e). EcR suppresses cellular cholesterol uptake39, so this result is consistent with increased cholesterol uptake that activates TOR. The opposite effects of HR3 and EcR knockdowns suggest that HR3 contributes to cholesterol-induced TOR activation in an EcR-independent manner.

To gain deeper insight into the HR3-mediated mechanisms underlying cholesterol-induced TOR activation, we profiled the genomic and phosphoproteomic responses to cholesterol in animals ubiquitously expressing knockdown of either HR3 or TOR. Using Tub-GAL80TS in conjunction with the Tub-GAL4 driver (TubTS>), RNAi was induced in larvae at 120 hours AEL by switching animals from 18 oC to 29 oC (as outlined in Supplementary Fig. 1d). Nine hours later, these animals were switched from a standard diet to a low-cholesterol diet (1.2 µg/mL) for 10 hours. Subsequently, larvae were transferred to a high-cholesterol diet (80 µg/mL) for varying lengths of time to assess their response. In our RNA-seq profiling efforts, we identified 2073 genes exhibiting significant regulatory changes (both upregulation and downregulation) in response to a high-cholesterol diet (80 µg/mL) in control larvae, compared against age-matched larvae maintained on a low cholesterol diet (1.2 µg/mL), which served as reference point for all comparisons (Fig. 5d and Supplementary Data 3). The transcriptional response to cholesterol was strongly dependent on both HR3 and TOR, as evidenced by a loss of cholesterol response in 980 (47%) of these genes in animals lacking either HR3 or TOR. This underscores the critical roles of HR3 and TOR in orchestrating the transcriptional machinery responsive to cholesterol. Additionally, other sets of genes became aberrantly responsive to cholesterol upon the knockdown of either HR3 or TOR, with an overlap of 125 genes between these sets, suggesting that these factors normally act to repress such responses. Collectively, these data imply that HR3 and TOR are required to initiate a specific transcriptional response to cholesterol through regulatory mechanisms that activate certain genes while suppressing the response of others to cholesterol.

Further investigation was carried out through Reactome pathway analysis applied to the 980 genes that require both HR3 and TOR for proper cholesterol response. This analysis revealed a significant enrichment of genes involved in pathways that include the TSC2 complex and insulin signaling, specifically PI3K/AKT/FOXO-related pathways (Fig. 5e). Given that TSC2 is a critical inhibitor of the TOR signaling pathway, and considering the interplay between TOR and insulin signaling, these findings further support our model in which cholesterol modulates TOR signaling via a mechanism that requires HR3.

We next conducted phosphoproteomic analysis for deeper insights into the HR3- and TOR-dependent pathway responses to cholesterol. All comparisons were made against the baseline established at the zero-hour-refeeding time point of control larvae maintained on a 1.2-µg/mL cholesterol diet for the preceding 10 hours, before transfer to cholesterol-replete medium. Our results indicated a rapid (within 1 hour) alteration of phosphorylation of 81 proteins (those showing a change of over 30 percent with a p-value of less than 0.05) that are HR3- and TOR-dependent (they do not occur in the absence of either protein; Fig. 5f and Supplementary Data 4). Notably, a substantial number of proteins (544) changed phosphorylation in response to cholesterol in HR3 knockdown animals but not in controls, suggesting that HR3 normally suppresses these changes. Mirroring the pattern seen in fat-body pS6 levels upon transferring animals from 1.2-µg/mL to 80-µg/mL cholesterol (Fig. 1e and 5a), the cholesterol response was greater at 6 hours relative to 1 hour, with 405 proteins exhibiting HR3-and-TOR-dependent phosphorylation changes (Fig. 5g and Supplementary Data 4). Pathway analysis of this HR3-and-TOR-dependent phosphoproteomic response highlighted the TOR pathway, along with ribosome- and translation-related processes, which are regulated by TOR activation (Fig 5h and Supplementary Fig. 5f). The HR3-dependent phosphorylations included S6 itself (Fig. 5i), consistent with the HR3-dependent cholesterol-mediated phosphorylation of S6 in the fat body (Fig. 5a,b), as well as LAMTOR and RAPTOR (Fig. 5j,k), two key regulatory components acting upstream of TOR that regulate its activity via interactions with the Rags. This implies HR3 operates at the level of, or upstream of, the LAMTOR (Ragulator) complex and RAPTOR-dependent TOR activation, consistent with insights from other recent studies in mammalian systems indicating that cholesterol activates the TOR pathway via these components3, 14. At the 6-hour time point, only 239 cholesterol-induced changes in protein phosphorylation were observed in HR3 knockdown animals (Fig. 5g), in contrast to the 544 alterations observed at the one-hour time point (Fig. 5f). This pattern suggests that while HR3 normally activates the TOR pathway within 1 hour in response to cholesterol, it concurrently modulates a broad spectrum of proteins and blocks their phosphorylation, presumably to maintain a balanced cellular response. This dual role of HR3 suggests a complex regulatory mechanism through which it both stimulates and restrains the phosphorylation of proteins in a time-dependent manner in response to cholesterol levels, ensuring a precisely controlled cellular adaptation.

Cholesterol-induced TOR activation via HR3 is independent of its DNA-binding function
To delineate how HR3 conveys cholesterol availability to the TOR signaling cascade, we investigated the hierarchical relationship between HR3 and TOR. Given the previously documented genetic interaction between S6K and an HR3 isoform lacking the DNA-binding domain (DBD)31, we explored whether this HR3(LBD) isoform participates in HR3’s mediation of cholesterol’s physiological effects (Fig. 6a). Overexpression of this shorter HR3 isoform containing only the LBD in the fat body led to increased systemic growth, as evidenced by increased larval weight (Fig. 6b) without altering the timing of pupariation (Supplementary Fig. 6a). To ascertain whether the systemic growth effects of HR3(LBD) expression were mediated by the TOR pathway, we simultaneously silenced TOR expression in the fat body, which in turn abrogated the growth-promoting effects of the overexpressed HR3(LBD) variant (Fig. 6c). This finding indicates that the HR3(LBD) isoform requires TOR function for its growth-promoting effects and suggests that HR3 can modulate TOR-pathway activity through a mechanism that does not require its DBD function. To pursue this notion further, we assessed whether HR3(LBD) is a mediator of cholesterol-stimulated TOR pathway activation. Chronic exposure to a cholesterol-enriched diet of NF supplemented with 40 µg/mL cholesterol combined with overexpression of HR3(LBD) in the fat body significantly enhanced pS6 levels, suggesting that HR3(LBD) promotes cholesterol-induced TOR-pathway activation (Fig. 6d). Aligning with phosphoproteomics findings that potentially position HR3 upstream of the Rag proteins, the augmented pS6 levels caused by HR3(LBD) overexpression were entirely negated by concurrent TOR knockdown, demonstrating that HR3(LBD) acts at or above the level of TOR.

Next, we asked whether HR3(LBD) is involved in the acute activation of the TOR pathway in response to cholesterol. Larvae, 84 hours AEL, were initially fed a diet containing 1.2 µg/mL cholesterol for 10 hours, then transferred to a diet with the higher cholesterol concentration of 80 µg/mL to examine their response. In animals overexpressing HR3(LBD) in the fat body and fed 80 µg/mL cholesterol for 1 hour, pS6 levels were significantly elevated compared to controls, an effect that was blocked by TOR knockdown (Supplementary Fig. 6b). The increase in pS6 was even more pronounced at the 6-hour time point, indicating that the HR3(LBD) isoform is a key mediator of cholesterol-induced TOR-pathway activation (Fig. 6e). This effect was not observed when the diet was supplemented with ecdysone instead of cholesterol, demonstrating that HR3 mediates a cholesterol-specific response (Supplementary Fig. 6c). Notably, this HR3(LBD) overexpression also induced significant intercellular variability in pS6 levels after 6 hours of cholesterol stimulation (Fig. 6f and Supplementary Fig. 6d,e), suggesting that overexpressing HR3(LBD) leads to a progressive destabilization or desynchronization of TOR-pathway dynamics. These findings support the hypothesis that HR3 plays a cell-autonomous role in cholesterol sensing, acting upstream of TOR.

To further dissect how different HR3 domains contribute to TOR regulation, we compared the effects of overexpressing full-length HR3 or a truncated form that retains only the DNA-binding domain, HR3K243X, with those of HR3(LBD). HR3K243X carries a premature stop codon at residue 243 and encodes a protein that preserves the DNA-binding domain but lacks the ligand-binding and activation domains and therefore cannot respond to cholesterol or other ligands. Whereas HR3(LBD) overexpression enhanced cholesterol-induced pS6 staining in the fat body, both full-length HR3 and HR3K243X blunted the pS6 response, with full-length HR3 causing the stronger attenuation (Supplementary Fig. 6f). Because these constructs maintain DNA-binding capacity, but only full-length HR3 retains an intact LBD, these observations suggest that HR3’s DBD-dependent occupancy of target loci generally restrains TOR signaling, and that ligand-bound full-length HR3 can further engage transcriptional programs that dampen cholesterol-stimulated TOR activity, in contrast to the rapid and possibly non-genomic, LBD-dependent activation mediated by HR3(LBD). This dual behavior aligns with our genome-wide transcriptional and phosphoproteomic analyses (Fig. 5d,f,g), which show that HR3 not only engages a large set of cholesterol-responsive genes and phosphoproteins but also suppresses inappropriate or excessive cholesterol responses, consistent with a model in which HR3 provides both activating and feedback-limiting functions to maintain balanced TOR-pathway output.

HR3 mediates cholesterol-responsive TOR signaling via genomic and non-genomic mechanisms upstream of the Rag GTPases
Nuclear receptors are ligand-regulated transcription factors that canonically exert their effects through transcriptional regulation17. However, rapid effects are often mediated by non-genomic actions in which the receptor is localized in membranes or the cytosol and directly influences signal-transduction pathways40, 41. Given that HR3(LBD) mediates rapid non-genomic effects31, we asked whether cholesterol-mediated TOR activation might entail both genomic and non-genomic HR3 functions. To determine the mechanism by which cholesterol activates TOR, we inhibited translation using cycloheximide to suppress any regulation of the TOR pathway by newly expressed proteins. We cultured ex-vivo fat-body tissues from larvae at 96 AEL in Schneider's medium supplemented with lipid-depleted serum. Tissues were depleted of sterols through a 2-hour incubation with methyl-beta-cyclodextrin (MCD, 0.75%), after which they were stimulated with cholesterol complexed with MCD (0.1%). This ex-vivo cholesterol stimulation increased fat-body pS6 levels in tissues dissected from control animals (Fig. 6g and Supplementary Fig. 6g). Cholesterol stimulation also increased pS6 levels in fat-body tissues treated with cycloheximide, although not as robustly as in controls without cycloheximide. This suggests that a significant portion of the rapid response does not require new protein synthesis. Together this implies that cholesterol activates the TOR pathway through mechanisms independent of translation, and potentially transcription, as well as through processes that require new protein synthesis and could involve a transcriptional response.

We then assessed whether HR3 is integral to both processes by examining the pS6 response to cholesterol in fat bodies in which HR3 expression had been silenced. Fat bodies lacking HR3 exhibited no upregulation of pS6 in response to ex-vivo cholesterol stimulation (Fig. 6g), indicating that HR3 is essential for TOR-pathway activation by cholesterol. This demonstrates that cholesterol activates the TOR pathway through HR3 via tissue-autonomous mechanisms that may involve transcriptional regulation as well as non-genomic actions, such as interacting with signaling proteins upstream of TOR, such as the Rag GTPases. We therefore assessed the intracellular localization of HR3 and discovered that this nuclear receptor is not restricted to the nucleus, but also exhibits a cytosolic or membrane localization pattern (Fig. 6h and Supplementary Fig. 6h). This observation supports the possibility that HR3 may interact with components of the TOR pathway in the cytosol or at the surface of lysosomes. To further this, we explored whether inhibiting the Ragulator-Rag complex would negate the effects of HR3(LBD) overexpression. The TOR activity increase due to HR3(LBD) overexpression was completely abolished by RagA-B knockdown, whereas RagC-D knockdown only diminished the response, indicating that HR3-induced TOR activation is mainly mediated by RagA-B (Fig. 6e). We then induced lysosomal cholesterol accumulation by depleting Npc1a, which resulted in TOR activation. By simultaneously reducing HR3, we observed a decrease in the hyperactivation of TOR caused by Npc1a knockdown (Fig. 6i,j and Supplementary Fig. 6i). This indicates that HR3 is required for TOR activation in response to cholesterol accumulation within lysosomes. Notably, loss of NPC1 has also been shown to activate TOR in mammalian cells, indicating that lysosomal cholesterol accumulation represents a conserved trigger for TOR activation across species.

To further refine how HR3 interacts with this conserved lysosomal cholesterol-TOR axis, we tested whether the highly conserved protein LYCHOS (known as Anchor in Drosophila) contributes to cholesterol-induced TOR activation. In mammalian cells, LYCHOS is thought to be a lysosomal transmembrane protein and has been implicated in promoting cholesterol-dependent TOR activation at lysosomes. LYCHOS is proposed to bind cholesterol at an intramembrane site between its permease-like and GPCR-like domains and, via this interaction, to engage GATOR and relieve its inhibition of the Rag GTPases14, 42. While transmembrane proteins will make nonspecific hydrophobic contacts with membrane cholesterol, nuclear receptors such as HR3 instead bind ligands with high specificity in buried ligand-binding pockets within their LBDs, and ligand occupancy directly modulates their activity43. Despite the strong sequence conservation and one-to-one orthology between Drosophila Anchor and mammalian LYCHOS, we were surprised to find that fat-body knockdown of LYCHOS did not reduce cholesterol-induced TOR activation (Supplementary Fig. 7a,b). A second independent RNAi line targeting LYCHOS produced comparable cholesterol-induced pS6 responses (Supplementary Fig. 7b,c), further indicating that LYCHOS is dispensable for cholesterol-stimulated TOR activation in the fat body. These findings indicate that, in Drosophila, cholesterol activates TOR independently of LYCHOS/Anchor.

We next asked whether GATOR itself is required for cholesterol-induced TOR activation in the fat body. As expected for a negative regulator of the Rag GTPases, knockdown of the GATOR component Iml1 increased basal pS6 levels in the fat body of cholesterol-depleted animals (Supplementary Fig. 7d). However, cholesterol refeeding elicited similar relative increases in pS6 in control and Iml1-RNAi animals, indicating that the cholesterol response does not depend on GATOR activity. Strikingly, simultaneous knockdown of HR3 strongly attenuated both the elevated basal TOR activity and the cholesterol-induced TOR activation caused by Iml1 depletion (Supplementary Fig. 7d), showing that HR3 is required for TOR activation even when GATOR1-mediated inhibition is relieved. Although lysosomal cholesterol accumulation associated with NPC1 loss activates TOR in both Drosophila and mammals, the LYCHOS–GATOR module is dispensable for this process in flies. Together, our data identify HR3 as a key cholesterol-responsive component of this conserved axis, linking cholesterol availability to TOR-pathway activation by providing input upstream of the Rag GTPases.

Human RORα activates the TOR pathway in response to exogenous cholesterol
Considering the functional and sequence conservation between human RORα and its Drosophila counterpart, HR3, we investigated the potential role of RORα in modulating mammalian TOR-pathway activity in response to cholesterol. We used human Karpas-707H cells, a multiple myeloma cancer cell line strongly expressing RORα, to assess TOR signaling in response to exogenous cholesterol. In-vitro cholesterol depletion (0.75% MCD for 2 hours) of these cells reduced TOR signaling, reflected in decreased pS6 abundance, whereas cholesterol repletion (complexed with 0.1% MCD) enhanced pS6 levels dose-dependently (Fig. 7a,b and Supplementary Fig. 8a,b), establishing a direct correlation between extracellular cholesterol levels and TOR-pathway activation. RORα knockdown using synthetic siRNA attenuated the pS6 response to cholesterol (Fig. 7c and Supplementary Fig. 8c), mirroring the HR3-knockdown effect observed in Drosophila (Fig. 5a,b).

For more comprehensive insights into RORα-mediated TOR-pathway activation by cholesterol, we performed phosphoproteomic analysis of Karpas-707H cells. Comparisons were made against cholesterol-depleted cells, considering a 30% change in phosphorylation with a p-value<0.05 to be significant. Within 1 hour of cholesterol replenishment, 100 significant RORα-dependent protein phosphorylation changes occurred, increasing to 145 changes after 6 hours (Fig. 7d,e and Supplementary Data 5). Pathway analysis showed a pronounced enrichment in components of the TOR and PI3K/AKT pathways among the RORα-dependent cholesterol-induced phosphorylation changes (Fig. 7f,g). Proteins that were (de)phosphorylated in response to cholesterol in a RORα-dependent manner included S6 and LAMTOR1 (Fig. 7h), aligning with our findings in the fly (Fig. 5i,j), along with other key components of the TOR and insulin-signaling pathways including the TOR inhibitor TSC2, proteins of the KICSTOR complex required for proper Rag modulation by nutrients, SIK3, the direct TOR kinase target 4EBP1, and the insulin/PI3K/PIP3 mediator PKB/AKT. These in-vitro findings also reinforce our in-vivo observations, suggesting cell-autonomous modulation of TOR activity by RORα/HR3.

In results similar to those seen with HR3 knockdown in the fly, 61 proteins were aberrantly responsive in terms of phosphorylation after 1 hour when RORα was silenced, indicating that RORα normally restrains these responses (Fig. 7d,e). Following 6 hours of cholesterol stimulation, a larger array of proteins (318) showed cholesterol-induced phosphorylation changes only in RORα-deficient cells. This indicates the necessity of RORα/HR3 in maintaining homeostatic cellular responses to cholesterol over time. Collectively, our findings suggest that RORα and HR3 are essential for translating cholesterol abundance into TOR-pathway activation that governs cellular growth and metabolism (Fig. 7i).

Methods
Drosophila media and husbandry
Flies were kept at 25 °C and 60% humidity with a 12-hour/12-hour daily light cycle. Fly stocks were maintained on standard fly food (STD-FF) medium containing 6% sucrose, 0.8% agar, 3.4% yeast, 8.2% cornmeal, 0.16% Tegosept antifungal agent, and 0.48% propionic acid preservative. Unless a different diet is indicated, this medium was also used for experiments. The fly lines used are listed in Supplementary Table 1, along with other salient reagents.

Timed egg collections
Virgin females from the driver stocks were mated with males of the RNAi lines in bottles containing STD-FF and additional dry-yeast pellets approximately two days prior to timed egg collections. The day before egg collection began, animals were transferred to egg-laying chambers with apple-juice/agar egg-collection plates supplemented with yeast paste. Egg-collection plates were changed on the following day for egg collection during a 4-hour period (ZT1 to ZT5) at 25 °C at 60% humidity. For genotypes not carrying GAL80TS, hatched first-instar larvae were collected after 24 hours at 25 °C and transferred to vials containing STD-FF or NutriFly (NF) at 30 larvae per vial containing 10 mL of medium. Offspring carrying Tub-GAL80TS were collected after a 48-hour hatching period at 18 °C to prevent GAL4 activity during early larval development.

Chronic high-cholesterol diet
Bloomington NutriFly (NF) medium (Genesee Scientific, “Bloomington formula”, #66-113), which contains a relatively low yeast/sterol content, was cooked according to product guidelines with added 0.16% Tegosept antifungal agent. The medium was supplemented while liquid with cholesterol (from a 8-mg/mL stock of cholesterol in ethanol) to a final added concentration of 40 μg/mL (NF+40) or 80 μg/mL (NF+80). Control media were prepared by adding the same amount of 100% ethanol. First-instar larvae were picked from egg collections and developed in these media until experiments were conducted during the third instar.

Synthetic-food re-feeding experiments: Cholesterol, protein, and ecdysone
The synthetic food (SF) diet is based on the one described in Reis, 201622, adjusted to increase its nutritional content and solidity. Casein was used at double the previous concentration, agar was increased to 1% concentration, sucrose was increased to 2.7-fold, and choline chloride was doubled. The final components of 1 L of food are 10 g lipid-depleted agar, 146.6 g lipid-depleted casein, 35.5 g sucrose, 0.64 g choline chloride, 0.85 g inosine, 0.76 g uridine, 133 mL of 10 g/L NaHCO stock, 133 mL of a 37.3 g/L KH PO stock), 133 mL of 7.1 g/L K HPO stock, 133 mL of 6.2-g/L MgSO ۰7H O stock, 133 mL of Vitamin A stock, 13.3 mL of Vitamin B stock, 333 mL deionized water, 13.3 mL of 10% Tegosept in ethanol. Vitamin A stock contained 20 mg thiamine, 100 mg riboflavin, 120 mg nicotinic acid, 160 mg D-pantothenic acid hemicalcium salt, 25 mg pyridoxine, 2 mg biotin in 1 L deionized water. Vitamin B stock was made of 500 mg folic acid dissolved in 167 mL 20% ethanol. Cholesterol content was adjusted by adding either pure ethanol or 8 mg/mL cholesterol in ethanol to reach final cholesterol concentrations of 0, 1.2, 6, 15, 25, 40, or 80 µg/mL Casein and agar were lipid-depleted through six chloroform extractions of at least 6 hours each, with stirring, at room temperature, at a 1:5 ratio of solids to chloroform. After each extraction, solid material was collected by filtration (using Whatman paper in a ceramic funnel), and lipid-free material was dried after the last collection by allowing chloroform to evaporate under a fume hood.

Larvae from timed egg collections were developed on STD-FF till early third instar: genotypes without GAL80TS, 25 °C until 84 hours AEL; those with GAL80TS, 18 °C until 120 h AEL. Larvae were then collected by floating in 20% sucrose solution, washed using DI water, and transferred using a paint brush or entomological forceps to synthetic media in a 24-well dish, each well stoppered with a foam plug. For cholesterol- and ecdysone-feeding experiments, animals were transferred at this time to media containing either 0 µg/mL or 1.2 µg/mL cholesterol (with other ingredients constant); for protein-feeding experiments, this medium lacked casein but did contain 80 µg/mL cholesterol. After 10-12 hours on this medium, the larvae were collected using DI water and transferred either to fresh nutrient-dropout medium (0 or 1.2 µg/mL cholesterol or 0 mg/mL protein) or to a re-feeding medium as indicated in each figure (containing 40 or 80 µg/mL cholesterol, 250 µg/mL 20-hydroxyecdysone + 1.2 µg/mL cholesterol, or 14.7 mg/mL casein as appropriate) in new 24-well plates. After the indicated time intervals (15 minutes, 30 minutes, or 1, 2, 4, 6, or 10 hours), the larvae were collected using water washes.

Larval-mass measurements
First-instar larvae were collected from timed egg-lays, transferred to the indicated diets, and maintained at 25 °C. At 96 h AEL, larvae were collected and rinsed with deionized water. Batches of larvae were chilled in DI water on ice, and individual animals were blotted dry on a KimWipe and weighed using a Sartorius SE2 Micro Balance.

Transgene construction
To create the UAS-HR3 (full-length) construct, the coding sequence of the HR3-PA isoform (FlyBase) was codon-optimized, synthesized as an EcoRI/XbaI fragment, cloned into the vector pUAST-attB (obtained from the University of Indiana Drosophila Genomics Resource Center, plasmid #1419, donated by J. Bischof and K. Basler61), and sequence-verified by GeneArt Services (ThermoFisher). A similar procedure was used to create the UAS-HR3K243X DBD-only construct, in which a stop codon was substituted for lysine 243, mimicking the homozygous-lethal HR3K243X allele31. The constructs were shipped to BestGene, Inc. (Chino Hills, California) for integration into the genome at the VK00037 attP site on chromosome arm 2L. These constructs’ DNA sequences are given in Supplementary Table 2.

Transcript analysis by quantitative PCR
Total RNA was isolated using the NucleoSpin RNA kit (Macherey-Nagel, #740955) according to the manufacturer’s instructions. For each genotype or condition, six independent biological replicates were prepared, and each replicate consisted of five whole animals. Samples were homogenized in 2-mL tubes containing RA1 lysis buffer supplemented with 1% β-mercaptoethanol using a TissueLyser LT bead mill (Qiagen) with 5-mm stainless-steel beads (Qiagen #69989). Complementary DNA was generated from purified RNA using the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems, #4368814). Quantitative PCR was performed using RealQ Plus 2× SYBR Green Master Mix (Ampliqon, #A324402) on a QuantStudio 5 Real-Time PCR System (Applied Biosystems). Gene expression was normalized to the Drosophila reference gene Rp49. Primer sequences are provided in Supplementary Table 1.

Promega Steady-Glo Luciferase assay
Three feeding late-third-instar unkempt-Luciferase larvae for each sample were collected into Glo Lysis Buffer (Promega, E2661) in 2-mL Eppendorf tubes. For normal assays, 200 µL of buffer was used per sample; for large animals or if protein quantification was performed in parallel, 400 µL was used. The samples were homogenized with a 5-mm steel bead in a TissueLyser bead mill (Qiagen; operated at 50 Hz for 30 seconds). The homogenized samples were incubated at room temperature for 10 minutes for complete cell lysis and then centrifuged at 13,000 x g for 5 minutes to pellet insoluble material. An aliquot (150 µL) of supernatant was removed to new Eppendorf tubes for protein quantification and placed on ice. For luciferase measurement, 20 µL of each sample was transferred at room temperature to an opaque white 96-well plate (Costar), and 20 µL of Steady-Glo Luciferase Reagent (Promega, E2510) was added to each well, with gentle mixing by pipetting. The plate was centrifuged at 1000 x g to settle the liquids and incubated at room temperature in the dark for 10 minutes to allow the luciferase reaction to reach a steady state, after which the luminescence was measured using an EnSight multi-mode plate reader (PerkinElmer).

Protein Quantification (Bicinchoninic Acid Method)
The protein concentrations in the aliquots set aside during the luciferase assay and for human western-blot protein-loading calculations were measured using a bicinchoninic acid (BCA)-based method. BCA working reagent was prepared by combining bicinchoninic acid solution (Sigma-Aldrich, #B9643) and 4% cupric sulfate solution (Sigma-Aldrich, #C2284) at a 50:1 ratio. Sample supernatant was diluted by adding of 3 volumes of PBS, and four microliters of the diluted material were pipetted into a 384-well plate. Thirty microliters of BCA working reagent was added to each well, with gentle mixing by pipette, and the plate was centrifuged at 1000 x g to settle liquids and incubated for 30 minutes at 37 °C. The absorbance of each sample at 540 nm was measured using an EnSight multi-mode plate reader (PerkinElmer).

Western blotting of Drosophila samples
Three to eight feeding (pre-wandering) late-third-instar larvae for each sample were lysed in ice-cold SDS sample buffer (Bio-Rad, 2x Laemmli Sample Buffer, #1610737), containing 5% beta-mercaptoethanol and protease and phosphatase inhibitors (Roche Complete Mini protease inhibitor, Sigma-Aldrich #11836153001, and Roche Complete Ultra phosphatase inhibitor, Sigma-Aldrich #05892970001), 60 µL per larva, using a TissueLyser bead mill (Qiagen). Samples were denatured at 95 °C for 5 minutes, and insoluble material was pelleted by centrifugation at 13,000 x g for 5 minutes. Samples were loaded into precast 4%-20% gradient polyacrylamide gels (Bio-Rad, #4561094) and electrophoresed at 150 V for approximately 35 minutes. Proteins were transferred to 0.2-µm nitrocellulose membrane using a Trans-Blot Turbo Transfer Pack (Bio-Rad, #1704159) and the Bio-Rad dry-transfer apparatus. Membranes were then blocked in Intercept Blocking Buffer (LI-COR, #927-70001) for 1 hour at room temperature with gentle agitation. Phospho-S6K (pS6K) and histone H3 were detected by incubating with rabbit anti-pS6K (Cell Signaling #9209S, diluted 1:1000) and rabbit anti-histone-H3 (Abcam #1791, diluted 1:1000) in Odyssey blocking buffer (LI-COR) + 0.2% Tween-20 (Sigma-Aldrich, #P9416) overnight at 4 °C with gentle agitation. Membranes were washed three times with PBS+0.1% Tween-20, and secondary staining was performed with IRDye 680RD-labeled goat anti-mouse and IRDye 800CW conjugated goat anti-rabbit (LI-COR, #925-68070 and #925-32210, each diluted 1:10,000 in Intercept Blocking Buffer + 0.2% Tween-20) for 45 minutes at room temperature with gentle agitation. Membranes were washed three times with PBS+0.1% Tween-20 followed by a single wash with PBS. Bands were visualized using an Odyssey Fc gel reader (LI-COR) and quantified using the LI-COR Image Studio Gel Reader program.

Immunostaining, microscopy, and quantification
Larval tissue was dissected in PBS and fixed in freshly prepared 4% paraformaldehyde (EM grade) in PBS at room temperature: for fat-body tissue, 10-15 larvae were inverted and fixed at room temperature for 45 minutes, whereas for prothoracic-gland (PG) samples, tissues were accumulated in ice-cold 4% PFA during dissection and fixed at room temperature for 70 minutes. Samples were quickly rinsed in PBST (PBS + 0.1% Triton X-100) and washed three times for 15 minutes in PBST. The samples were then blocked in PBST+3% normal goat serum (Sigma) for at least 30 minutes at room temperature with gentle agitation. Tissues were incubated with primary antibodies diluted in PBST + 3% normal goat serum overnight at 4 °C with gentle agitation. Tissue was then washed three times in PBST. Samples were incubated with secondary antibodies diluted in PBST at 4 °C, in the dark, overnight with gentle agitation. For actin staining, samples were incubated with phalloidin (Alexa Fluor 647 conjugate, ThermoFisher #A22287, or Alexa Fluor 488 conjugate, #A12379, diluted 1:100 in PBST for 1 hour), and for nuclear staining, samples were incubated in DAPI (1:500 in PBS, ThermoFisher, #62248) for 0.5-1 hour. Samples were washed twice in PBST and once in PBS and mounted on glass slides coated twice with poly-L-lysine (Sigma-Aldrich, #P8920-100ML). The mounted tissue was imaged using a Zeiss LSM 900 confocal microscope using a 20x objective (NA 0.8). All samples compared within a given figure panel were processed similarly (fixation, staining preparations and imaging hardware settings). In all cases, each experimental sample was prepared simultaneously with its respective control using the same reagent preparations. Data sets from multiple preparations are normalized to controls before comparison.

For quantification of PG cell size and pS6 intensity, a composite of channels was created, and individual cells and their nuclei (based on actin and DAPI staining) were traced at the center of the cell thickness in the Z-stack, saving this to the region-of-interest (ROI) manager in the image-analysis package FIJI62. The Raw Integrated Density (RID) of staining in each channel was quantified within each ROI. The pS6 intensity in the cytoplasm was calculated as (Cell_RID - Nuclear_RID) / (Cell_Area - Nuclear_Area). For quantification of fat-body pS6 staining, a large number of tissues were mounted on each slide. Using a 5x overview scan in the DAPI channel on the Zeiss LSM 900 confocal microscope, areas of flat, single-cell-thick tissue were selected for each large piece of fat body and were imaged with the 20x objective with a 1.5-μm Z spacing. Z-stacks were pre-processed in FIJI using a macro for creating a composite image and Z-projected using “sum”. Areas of folded or overlaid tissues were removed by manual segmentation; the channels were split, and a binary mask was created from the DAPI channel. This mask was used to remove the nucleus from the pS6 channel, since nuclear pS6 staining is believed to be spurious26. Quantification was automated using CellProfiler, version 3.1.963: each cell’s pS6 signal was quantified by segmentation of the tissue into individual cells using the actin (phalloidin staining) channel and the binary mask created using DAPI in order to produce a cytoplasmic pS6 signal. The pS6 signal was measured as the average intensity across each area (“integrated intensity” divided by the cytoplasmic area).

Primary antibodies used were rabbit anti-pS626 against the epitope RRR(phospho-S)A(phospho-S)IRE(phospho-S)K, used at 1:500; mouse anti-GFP (clone 3E6, ThermoFisher #A11120, RRID AB:221568, at 1:500); guinea-pig anti-Shroud64 (a kind gift from R. Niwa, University of Tsukuba; 1:200); and rabbit anti-HR331, a generous gift from J. Montagne (Friedrich Miescher Institute for Biomedical Research, Switzerland, 1:250). Secondary antibodies (ThermoFisher, 1:500) were all raised in goats and were cross-adsorbed by the manufacturer to reduce off-target binding. These included anti-rabbit, Alexa Fluor 488 conjugate (#A32731, RRID AB_2633280); anti-rabbit, Alexa Fluor 555 conjugate (#A32732, RRID AB_2633281); anti-rabbit, Alexa Fluor 647 conjugate (#A32733, RRID AB_2633282); anti-guinea-pig, Alexa Fluor 647 conjugate (#A21450, RRID AB_2735091); and anti-mouse, Alexa Fluor 488 conjugate (#A32723, RRID AB_2633275).

Ex-vivo culture of Drosophila fat body
Larvae from a four-hour egg collection were raised on STD-FF at 25 °C. At 96 h AEL, fat-body tissue was dissected in Schneider’s insect medium (Gibco, #21720). Tissue was cholesterol-depleted using Schneider’s medium containing 10% lipid-depleted-serum (LDS, Sigma-Aldrich, #S5394-50mL) and 0.75% methyl-β-cyclodextrin (MCD, Sigma-Aldrich, #C4555), for 2 hours at room temperature with gentle rocking in a glass staining dish with glass cover (Assistant, #42020010). For experiments including cycloheximide, 15 minutes prior to cholesterol stimulation, cholesterol-depletion medium was replaced with fresh cholesterol-depletion medium containing 100 μg/mL cycloheximide (Sigma-Aldrich, #239765) from a 100-mg/mL stock solution in DMSO. Tissue was washed twice with room temperature Schneider’s medium and then incubated in cholesterol-depletion or cholesterol-stimulation medium for 1 hour. Conditions including cycloheximide also contained it during the stimulation period. Cholesterol-stimulation medium consisted of Schneider’s medium containing 10% LDS, 0.2% MCD, and 100 µM (39 µg/mL) cholesterol from a 20-mg/mL cholesterol stock in ethanol. After incubations, media were replaced by 4% PFA for 45-minute tissue fixation at room temperature. Fixed tissues were then processed for immunostaining as usual.

Human cell culture
The Karpas 707H cell line was established from the bone marrow of a 53 year old male with multiple myeloma65. Cells were obtained from Cambridge University (Cambridge Enterprise Limited) and cultured in StableCell RPMI-1640 culture medium (Sigma-Aldrich, #R2405-500ML) containing 15% fetal bovine serum (Gibco, #10500-064) and Pen-Strep antibiotic mixture (100 u/mL Penicillin, 100 µg/mL Streptomycin, Gibco, #15140-122). The cultures were maintained at 37 °C and 5% CO . Karpas cells partly attach to plasticware after 3-4 days of culturing, while some cells remain in suspension. To prevent selective pressure during passaging (cells can be partly released with tapping on cultureware), Karpas cells were passaged by collecting cells in suspension and using Trypsin-EDTA (Gibco, #25200056) to fully float attached cells.

Cell-culture cholesterol starvation/re-stimulation
The methods used are similar to those described in Shin et al.14 and Castellano et al.3 Cells were cholesterol-depleted using cell-culture media supplemented with methyl-β-cyclodextrin (MCD, Sigma-Aldrich, #C4555) and lipid-depleted serum (LDS; lipoprotein-deficient serum from fetal calf, Sigma-Aldrich, #S5394-50mL). Cells were re-stimulated with cholesterol using cell-culture medium supplemented with MCD:cholesterol complex and LDS.

Cells were plated in standard growth medium, RPMI-1640 medium containing 15% fetal bovine serum and Pen-Strep, in 6-well plates (Greiner bio-one, #657160) and allowed to attach for 3-4 days. Cells were then rinsed with serum-free RPMI-1640 medium and then incubated with 0.75% MCD with 0.5% LDS in RMPI-1640 (without antibiotics) for 2 hours in order to cholesterol-deplete the cells. Cells were re-stimulated with cholesterol in RPMI-1640 using complexed MCD:cholesterol (1x: 0.1% MCD and 50 µM/~20 µg/mL cholesterol: 2x dose: 0.2% MCD and 100 µM cholesterol) plus 0.5% LDS.

MCD:cholesterol complex was prepared by diluting a 20-mg/mL cholesterol stock in ethanol into a 15-mL Falcon tube containing RMPI-1640, 0.1% MCD, and 0.5% LDS, resulting in a 50-uM final concentration of both cholesterol and MCD. The tube was then vortexed and incubated in a 37 °C water bath for 2 hours before being applied to the cells.

Knockdown of RORα using siRNA
Knockdown of RORα in human cells was performed using the siGenome SMARTpool Human RORA (#6095) siRNA from the Horizon Discovery “Dharmacon” portfolio (#M-003440-01-005) and Lipofectamine 2000 (Invitrogen, #11668-030). The siRNA stock was diluted to 20 µM in RNase-free water following the product protocols. A siRNA duplex targeting Renilla Luciferase (a gene not present in these cells) was used as a control. siRNA and Lipofectamine solutions were prepared according to product specifications. Cells were rinsed with serum-free RPMI-1640 medium before being transfected with 5 µL of siRNA pool (20 µM) and 5 µL Lipofectamine 2000 in a 6-well plate in 1 mL of antibiotic-free Opti-MEM (Gibco, #31985-062) medium plus 2 mL antibiotic-free cell-culture medium. For experiments other than phosphoproteomics, the medium was changed 24 hours after treatment to new RPMI-1640 with 15% fetal bovine serum (without antibiotics). Seventy-two hours after knock-down treatment, cells were processed for further experiments (cholesterol treatments or collection of samples). Samples for phosphoproteomics were exposed to the siRNA treatment for the entire 72 h incubation, with additional siRNA and culture medium being added after 24 h for a stronger knock-down.

Human-cell lysis and immunoblotting
Cells in suspension were collected using RPMI-1640 and centrifuged for 4 minutes at 500 x g, and the supernatant was removed. Lysis buffer was added to still-attached cells in the culture flask and to the pelleted suspension quickly after medium was removed. Cells were lysed in ice-cold RIPA buffer (50 mM Tris-HCl pH 7.5, 150 mM sodium chloride, 0.5% sodium deoxycholate, 1% Nonidet P-40, 0.1% SDS) supplemented with Roche Complete Protease Inhibitor Cocktail with EDTA (Roche, #11836153001), PhosStop (used at 2x, Roche, #PHOSS-RO), 11 mg/mL β-glycerophosphate (from a 110-mg/mL 10x stock solution), ~100 mM sodium fluoride (a saturated solution in RIPA buffer, roughly 1 M, was made as a 10x stock solution), and Benzonase endonuclease (Sigma-Aldrich, #E9422). Cells were transferred to pre-cooled Eppendorf tubes on ice. The samples were incubated on ice for 10 minutes after which they were spun down to pellet nuclei and membrane at maximum speed (14,000 rpm) at 4 °C for 15 minutes. An aliquot of supernatant was reserved for BCA protein measurement. For Western-blot samples, 230 μL of supernatant was mixed with 70 μL 5x Laemmli buffer (4% SDS, 10% β-mercaptoethanol, 20% glycerol, 0.004% bromophenol blue, 0.125 M Tris-HCl, pH 6.8) or 230 μL 2x Laemmli Sample Buffer (Bio-Rad, #1610737), and samples were immediately denatured for 5 minutes at 95 °C.

Loaded sample sizes were adjusted to equalize total protein based on BCA protein measurements. Protein separation was done using electrophoresis in polyacrylamide gels using 1x Running Buffer (10x running buffer: Tris base 30.2 g, glycine 188 g, 10% SDS solution 100 mL, in deionized water to 1 L) using a BioRad Mini-PROTEAN Tetra Vertical Electrophoresis Cell system. Protein mass ladder PageRuler Plus Prestained Ladder (Thermo Scientific, #26615) or Chameleon Duo Pre-stained Protein Ladder (LI-COR, #928-60000) was used. The gel in Fig. 7b was run on a 12% Mini-Protean TGX precast protein gel (Bio-Rad, #4561034), whereas the one in Fig. 7c was run using hand-cast gels. Gels were run at 20 mA per gel at max voltage (300 V) for approximately 1 hour. The blot in Fig. 7b was transferred using semi-dry transfer as described in the Drosophila western-blot section, whereas Fig. 7c was transferred to 0.2-µm nitrocellulose membrane (Amersham Proteon Nitrocellulose blotting membrane) using wet transfer: the blotting was performed using 1x Running Buffer with 20% methanol, kept cold with ice packs, run at 100 V for 1 hour. Ponceau stain was used to visualize the transferred proteins.

Membranes were blocked for 1 hour at room temperature using PBS + 5% nonfat milk powder + 0.1% Tween-20 and stained overnight at 4 °C in primary-antibody mix (diluted in PBS + 0.1% Tween-20 + 5% bovine serum albumin). Rabbit anti-pS6 (Cell Signaling, #4857) was used at 1:1000, mouse anti-S6 (Cell Signaling, #2317) was used at 1:1000, and mouse anti-α-Tubulin (University of Iowa Developmental Studies Hybridoma Bank clone #AA4.3) was diluted 1:5000, rabbit anti-RORα (human) (Cell Signaling Technology, clone E6G51, #34639S) was used at 1:1000, and rabbit anti-human-GAPDH (Cell Signaling Technology, clone 14C10, #2118L) was used at 1:2500. Membranes were washed three times for 15 minutes in PBS+0.1% Tween-20 at room temperature before secondary staining. Secondaries (goat anti-rabbit IgG H+L, HRP conjugate (ThermoFisher, #31466, or Jackson ImmunoResearch, #111-035-003) and goat anti-mouse IgG H+L, HRP conjugate (Jackson ImmunoResearch, #115-035-003) were diluted 1:10,000 in PBS + +5% nonfat milk powder + 0.1% Tween-20 at 1:10,000 and incubated on the blot for 1-2 hours at room temperature. Stain was detected using SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Scientific, #34094) or ECL Prime Western Blotting Detection Reagents (Amersham, #RPN2232) chemiluminescence using a Bio-Rad ChemiDoc Touch Imaging System or an Odyssey Fc gel reader (LI-COR).

RNA sequencing
TOR or HR3 were knocked down in Drosophila larvae using the temperature-inducible ubiquitous driver Tub-GAL80TS; Tub-GAL4 (TubTS>). An 8-hour egg collection was made at 18 °C, and egg-laying plates were maintained at 18 °C (to prevent activation of the GAL4 system) for an additional 44 hours before first-instar larvae were collected using a metal probe into vials containing standard food (30 larvae per vial). The collected larvae were maintained at 18 °C for a further 72 hours before being transferred to 29 °C to induce RNAi expression (at 120 hours AEL). After nine hours of RNAi induction on standard food (at 129 hours AEL), larvae were transferred to low-cholesterol (1.2 µg/mL) synthetic medium and incubated at 29 °C for 11 hours more (until 140 hours AEL). Larvae were then transferred to synthetic food containing either 80 µg/mL cholesterol (for replenishment) or 1.2 µg/mL (control) and incubated at 29 °C for 10 hours. At this time (150 hours AEL), five feeding late-third-instar larvae were collected for each of five replicates into ice-cold RLT buffer (Qiagen RNeasy Mini kit, #74104) containing 1% β-mercaptoethanol and homogenized using a bead mill (Qiagen TissueLyser LT). RNA was purified using the Qiagen RNeasy Mini kit with DNase (Macherey-Nagel, #740955.250) treatment. Samples were shipped on dry ice to Novogene Europe (UK), where RNA sequencing and bioinformatic analyses were performed. Differentially expressed genes were identified using DESeq2. P-values were calculated using Wald tests (two-sided) and adjusted for multiple testing using the Benjamini-Hochberg procedure.

Phosphoproteomics
Drosophila: Cholesterol vs. protein refeeding: Refeeding experiments were conducted as described above in the Drosophila refeeding method section. Fifteen (cholesterol assays) or twenty (protein assays) feeding late-third-instar larvae for each of two replicates were collected by washing with water, blotted dry on a KimWipe, and snap-frozen in Eppendorf tubes on dry ice. A sample was collected from the nutrient-deprivation condition at the start of refeeding as time-point 0, and further samples were collected at 15 and 30 minutes and at 1, 2, 4, 6, and 10 hours after the start of refeeding. Samples were stored at -80 °C until they were processed. HR3 and TOR knock-down: TOR and HR3 were knocked down in Drosophila larvae using the temperature-inducible ubiquitous driver Tub-GAL80TS; Tub-GAL4. After an 8-hour egg collection at 18 °C, the plates were maintained at 18 °C for an additional 40 hours before first-instar larvae were transferred to vials of STD-FF. These were maintained at 18 °C for an additional 72 hours before being transferred to 29 °C to induce RNAi at 120 h AEL. Nine hours later, at 129 h AEL, the animals were transferred to low-cholesterol synthetic medium (1.2 µg/mL CH) and incubated for 11 further hours at 29 °C before being transferred (at 150 h AEL) to fresh synthetic food containing either 1.2 (continued low-CH exposure) or 80 (replenishment) µg/mL cholesterol, still at 29 °C. After one hour and six hours, two samples of 15 larvae each for each treatment were collected, washed with DI water, blotted dry, and snap-frozen on dry ice before storage at -80 °C.

Karpas-707H: Cells treated for knockdown of RORα or Luciferase (mock knockdown control) were grown in 6-well plates till 80-90% confluence. Cells were cholesterol-depleted with MCD for 2 hours and washed twice with RPMI. Cholesterol-depletion or cholesterol-stimulation medium was added, and both treatments were sampled in triplicate after one hour; an additional triplicate sample of cholesterol-stimulated cells was taken at 6 hours post-stimulation. All samples were washed once in RPMI. Cells in suspension were collected in a Falcon tube, and cells attached to the culture ware were gently collected in RPMI medium using a cell scraper and added to the suspended cells. Cells were pelleted at 900 RCF for 4 minutes, supernatant was removed, and cells were frozen on dry ice and stored at -80 °C.

Proteins from larval samples were extracted in 300 µL 5% sodium deoxycholate (SDC) in 50 mM HEPES (pH 8.5) containing Roche cOmplete protease inhibitor and PhosSTOP phosphatase inhibitors (Sigma) using a FastPrep-24 bead beater (MP Biomedicals) using 25-30 1.4 mm ceramic beads (OMNI international, US). Each tube was subjected to 3x45-second bead beating. After treatment the solution was diluted with an additional 300 µL of 50 mM HEPES (pH 8.5) and subjected to a second round of 3x45-second bead beating. The samples were centrifuged at 20,000 x g, and the supernatant was transferred to a low-binding 1.5-mL Eppendorf tube and subjected to probe sonication for 2x20 seconds at 60% amplitude. After sonication, the sample was denatured at 110 degrees for 5 minutes and centrifuged for 20 min at 20,000 x g to pellet insoluble material. The supernatant was transferred to another tube and the protein concentration was measured using a Nanodrop spectrophotometer. For the Karpas 707H cells, the proteins were extracted from the cell pellets in 300 µL 3% SDC in 50 mM HEPES, pH 8.5, containing cOmplete protease inhibitor and PhosSTOP phosphatase inhibitors (Sigma), using probe sonication for 2x20 seconds at 60% amplitude. After sonication, the sample was denatured at 110 degrees for 5 minutes and subsequently centrifuged for 20 minutes at 20,000 x g to pellet insoluble material. The supernatant was transferred to another tube and the protein concentration was measured using a Nanodrop N60 spectrophotometer.

A total of 100 µg of protein was taken from each sample and subjected to reduction and alkylation using 10 mM DTT for 20 minutes followed by 20 mM iodoacetamide for 20 minutes. Trypsin (5%) was added and the solutions were incubated at 37 C overnight. After incubation, a further 1% bolus of trypsin was added and the samples were incubated for one further hour at 37C. After incubation, the cleaved peptide solutions were labeled with tandem mass tags (TMTpro) 18-plex according to the manufacturer’s protocols. After labeling, the 18 samples were combined into one sample containing all the larval peptides and one containing the Karpas 707H material. The SDC was removed by acidification and subsequent centrifugation for 20 minutes at 20,000 x g. The supernatant was transferred to a low-binding Eppendorf tube and dried until 150 µL was left. The enrichment of phosphopeptides using titanium dioxide and subsequent high-pH reversed-phase (RP) fractionation were performed as described66. In brief, the TMT-labeled peptide mix was incubated with TiO beads (0.6 mg of beads per 0.1 mg of protein) in the presence of 5% TFA, 80% acetonitrile, and 1 M glycolic acid. The TiO beads were removed by centrifugation, and the supernatant was incubated again with half the initial amount of beads. After incubation, the TiO beads were pooled and washed with 200 μL of 1% TFA in 80% acetonitrile, followed by a wash with 100 μL of 0.2% TFA in 10% acetonitrile. The beads were dried, and phosphopeptides were eluted with 1% ammonium hydroxide (pH 11). Finally, the eluted phosphopeptides were dried down and resuspended in 20 mM ammonium formate (pH 9.4) prior to high-pH reversed-phase fractionation.

The phosphopeptide-enriched fraction from the larvae and Karpas 707H cells were analyzed by tandem mass spectrometry using an EASY nanoLC system coupled with a Fusion Lumos Tribrid or an Orbitrap Eclipse Tribrid. Lyophilized peptides from the high-pH RP fractionation (12-20 concatenated fractions) were re-solubilized in 3-5 µL of 0.1% formic acid (FA) and loaded onto a 20-cm analytical column (100-μm inner diameter) packed with ReproSil – Pur C18 AQ 1.9 μm RP material. The peptides were eluted with an organic solvent gradient from 100% phase A (0.1% FA) to 25% phase B (95% ACN, 0.1% FA) for 80-100 minutes (depending on fraction), then from 25% B to 40% B for 10-20 minutes before the column was washed with 95% B. The flow rate was set to 300 nL/minute during elution. For the two instruments the automatic gain-control target value of 1.5x106 ions in MS and a maximum fill time of 50 ms were used. Each MS scan was acquired at high resolution (120,000 full width half maximum (FWHM)) at m/z 200 in the Orbitrap with a mass range of 350-1400/1500 Da. The instruments were set to select as many precursor ions as possible in 3 seconds between the MS analyses. Peptide ions were selected from the MS for higher-energy collision-induced dissociation (HCD) fragmentation (collision energy: 34%). Fragment ions were detected in the Orbitrap at high resolution (50,000 FWHM) for a target value of 1.5x105 ions and a maximum injection time of 200 ms using an isolation window of 0.7 Da and a dynamic exclusion of 20-45 seconds. All raw data were viewed in Xcalibur v3.0 (ThermoFisher Scientific).

Peptide/protein identification and quantitation. All LC-MS/MS raw data files from Drosophila-larva experiments were searched in Proteome Discoverer (PD) version 2.5.0.400 (ThermoFisher Scientific). The raw data were searched in PD using the SEQUEST HT search algorithm against the Fly Database protein FASTA file. The searches had the following criteria: enzyme, trypsin; maximum missed cleavages, 2; fixed modifications, TMTpro (N-terminal), TMTpro (K) and Carbamidomethyl (C). Variable modification for the phosphopeptides was Phospho (S/T/Y) and Deamidation (N) whereas no variable modifications were used for the “non-modified peptides”. For the results from the Karpas 707H cells the raw data files were searched in PD using first an in-house Mascot server (Version 2.2.04, Matrix Science Ltd., London, UK) against the Swissprot human protein database using the following criteria: enzyme, trypsin; maximum missed cleavages, 2; fixed modifications, TMTpro (N-terminal), TMTpro (K) and Carbamidomethyl (C). Variable modification for the phosphopeptides was Phospho (S/T/Y). The peptide fragment ion spectra that were not identified with a peptide in Mascot with below 1% False Discovery Rate (FDR) was further subjected to database searching using SEQUEST HT in PD against the Human UniProt FASTA database, using the same criteria as above. The TMTpro reporter ion signals were quantified using S/N and they were normalized to the total peptide S/N in the PD program. The two-sided ANOVA test with Benjamini-Hochberg FDR procedure in PD was used to generate p-values for all the phosphopeptides and proteins identified in the database searches.

Data filtering and bioinformatics analysis of phosphoproteomics and RNAseq
Phosphoproteomics data for pathway analysis was filtered for differences >30% in magnitude and p<0.05 between cholesterol starvation and replenishment within each genotype. An Excel macro was used to identify populations of interest. For the RNAseq experiments, gene-expression levels are reported as FPKM (fragments per kilobase of transcript per million bases sequenced), which takes into account both sequencing depth and gene length. Differential expression analysis was conducted by Novogene through read-count normalization, model-dependent p-value estimation using a Wald test, and false-discovery-rate estimation using the Benjamini-Hochberg method. Differential expression was further analyzed by comparing genotype-based differences between sets of genes exhibiting cholesterol-induced expression change, resulting in seven categories of genotype-specific cholesterol-induced regulation (with set sizes from Fig. 5d for illustration): (1) genes requiring both HR3 and TOR to exhibit a cholesterol-induced change (occurring only in the control genotype) – 980; (2) those cholesterol-induced changes that require HR3 but are independent of TOR (occurring in controls and TOR knockdowns) – 452; (3) cholesterol-induced changes requiring TOR but independent of HR3 (occurring in controls and HR3 knockdowns) – 374; (4) those changes requiring neither HR3 nor TOR (occurring in all samples) – 267; (5) cholesterol-induced changes repressed by HR3 but not by TOR – novel changes occurring only in HR3-RNAi samples – 1029; (6) cholesterol-induced changes repressed by TOR but not by HR3 – novel changes occurring only in TOR-RNAi – 644; (7) cholesterol-induced changes repressible by either HR3 or TOR – novel changes that occur in both knockdowns but not in controls – 125. Pathway analysis was carried out on both the phosphoproteomics and RNAseq data using the Panther Classification system (Protein Analysis Through Evolutionary Relationships), with the statistical overrepresentation test using Reactome database (version 85)67. In addition to the statistical analysis already conducted during data filtering, a Fisher’s t-test was applied, and only pathways with a p<0.05 for enrichment were considered. For RNA-seq data, model-dependent p-values were used, and for all phosphopeptides, p-values were determined using the two-sided ANOVA test with Benjamini-Hochberg FDR procedure in Proteome Discoverer.

Temporal patterns of phosphorylation were analyzed using Fuzzy C-Means (FCM) clustering in the R environment (4.5.0). The analysis employed the cluster package for the FCM algorithm, factoextra for optimization, pheatmap for visualization, and ggplot2 for plotting. Before clustering, only phosphosites exhibiting significant change at least at one time point were selected. To reduce redundancy, duplicate entries mapping to the same phosphosite were consolidated by averaging log-2 fold-change values. These unique profiles were then analyzed using FCM clustering (function fanny). The optimal number of clusters (k=5) was determined using the Elbow method (package factoextra). Temporal profiles were Z-score-standardized before clustering. Heatmaps were generated using the pheatmap package, with visualization performed on Z-score standardized values. To visualize the identified temporal patterns, rows were annotated by their fuzzy cluster assignment and ordered using hierarchical clustering. Column order was fixed to maintain the chronological sequence of experimental time points. The heatmap was then rotated so that columns represented phosphosites and rows represented time points. To characterize the biological functions associated with distinct temporal patterns, functional enrichment analysis was performed for each cluster using the clusterProfiler and ReactomePA packages. Gene symbols were extracted from the phosphosite identifiers and mapped to Entrez IDs using the org.Dm.eg.db annotation database. To ensure a gene-centric analysis, duplicate gene entries resulting from multiple phosphosites on the same protein were removed within each cluster prior to statistical testing. Over-representation analysis (ORA) was conducted against the Reactome database. Statistical significance was assessed using a hypergeometric test. To control for multiple testing, p-values were adjusted using the Benjamini-Hochberg (BH) procedure. Pathways were considered significantly enriched when the adjusted p-value (q-value) was less than 0.15.

Recombinant HR3 protein expression and purification
For baculovirus-mediated expression in insect cell culture, the HR3 LBD sequence (I230-T487; GenBank accession NP_788303) was subcloned into a pFASTBAC DUAL vector (Invitrogen) that adds a 6xHis tag and a thrombin cleavage site68. The hexahistidine-tagged protein was expressed in Hi5 insect cells (Trichoplusia ni) grown in 1 liter of M3 medium (Sigma) supplemented with 10% fetal bovine serum (Wisent). Cells were grown at 26 °C for two days in baffled flasks in a shaking incubator. The cells were harvested by centrifugation at 2,000 x g, washed with PBS (20 mL), and centrifuged again before the pellets were frozen in liquid nitrogen and stored at -80 °C. Prior to purification, the cell paste was thawed, resuspended in lysis buffer, and sonicated on ice 3 times for 1 minute each. Lysates were clarified by centrifugation at 13,000 x g, and tagged protein was bound using Ni-NTA affinity chromatography (column volume 1 mL). Column-bound protein was washed with 300 mL of wash buffer (30 mM imidazole, 500 mM NaCl, 5% glycerol, 0.5 mM TCEP, and 10 mM Tris; pH 8.2). Protein was eluted from the column using a similar buffer containing 250 mM imidazole. The eluate was concentrated to a volume of 5 mL and loaded onto a size-exclusion-chromatography column (Superdex 16/60, GE Healthcare); HR3 LBD was eluted in a buffer of 150 mM NaCl, 0.5 mM TCEP, and 10 mM Tris (pH 8.2) using an AKTA FPLC apparatus (GE Healthcare). After using SDS-PAGE to confirm the purity of eluted fractions, a 10-mg sample of the receptor protein was diluted in buffer Q (30 mM NaCl, 5% propanediol, 5 mM DTT, 20 mM Tris, pH 8.5) and loaded onto a Source 30Q anion-exchange column ( GE Healthcare). Using a linear NaCl gradient (30-mM to 2-M NaCl in Q), the protein was eluted using an AKTA FPLC (GE healthcare) and again checked for purity using SDS-PAGE.

Non-denaturing mass spectrometry
ES-MS was carried out as previously described68. Briefly, mass analysis was done using a quadrupole time-of-flight mass spectrometer (Q-TOF, Micromass) equipped with a nano electrospray (ES) source. Purified HR3 LBD was desalted in four dilution/concentration steps using centrifugal concentration (Millipore) into 20-mM ammonium acetate (pH 6.2) and adjusted to a protein concentration of 1 mg/ml. Protein was loaded in a gold-coated capillary (Protona) with the tip opened to produce an orifice of approximately 10 m. Positive ESI-MS was performed at a capillary voltage of 1.8 to 2 kV and a cone voltage of 10–50 V. Collision-induced dissociation experiments were carried out with a collision energy of 80–150 V using argon as the collision gas. Average molecular masses were calculated using Maxnet1 in Mass Lynx 4.0 (Micromass).

Extraction of HR3 and GC/MS analysis
Using solvent-washed glassware, endogenous ligand was extracted from 1 mg of purified HR3 LBD by combining 3 mL of purified receptor with 9 mL of acidified chloroform:methanol (2:1 v/v) and mixing vigorously for 1 minute. Organic and aqueous phases were separated by centrifugation (13,000 x g), and the organic phase was transferred to a clean tube. The aqueous phase was extracted twice more, and the organic fractions were pooled. The acidified chloroform:methanol extract was repeatedly washed with 0.2 volumes of distilled water and reseparated until the aqueous phase became neutral (pH 7). The organic extract was evaporated to dryness under nitrogen, and residue was dissolved in 50 μL pyridine. A 20-μL aliquot of this sample was combined with 10 μL of N O-bis-(trimethylsilyl)trifluroacetamide (N O-BSTFA) and trimethylsilyl-derivatized at 60 °C for 30 minutes. The same procedure was followed for derivatization of the cholesterol reference69 (Steraloids).

Gas chromatography mass spectrometry
The derivatized samples were analyzed using a gas chromatograph (3900 GC, Varian) coupled to an ion-trap mass spectrometer (Saturn 2100T MS/MS, Varian) equipped with an electron-impact ion source. Samples were chromatogaphically separated using helium as a carrier gas (1.5 mL/min) on a 30-m x 0.25-mm x 0.25-μm fused-silica column (CP8944, Varian) using a two-segment temperature gradient of 80-150 °C at 20 °C/minute and 150-345 °C at 10 °C/minute. Eluted analyte from chromatography was ionized in the positive-ion mode with a scan range of 40–650 m/z. Data were analyzed using MS Data Review in MS Workstation (Varian).

Structure prediction and cholesterol placement
The Drosophila HR3 ligand-binding domain (LBD) sequence was obtained from UniProt (A1Z858) and trimmed to residues Ile437–Thr694, corresponding to the region most similar to the human RORα LBD. Boltz-2 (refs. 70 and 71) was used to predict the HR3 LBD structure in isolation and in complex with cholesterol. Structure prediction was performed using Boltz-2 default settings, except that the number of recycles was increased to 300 and the number of diffusion samples was increased to 100. Cholesterol was provided to Boltz-2 as a ligand in SMILES format, and no constraints were applied and no binding pocket or docking location was specified. A multiple sequence alignment (MSA) for HR3 was generated using the ColabFold MSA server and provided as input for Boltz-2 predictions. For the HR3:cholesterol complex, Boltz-2 generated 100 independent predictions; models were ranked by the Boltz-2 confidence metric, and the top-ranked model was selected for downstream analysis. The predicted HR3 LBD model was aligned to the experimentally determined RORα LBD structure bound to cholesterol (PDB 1N83) in PyMOL, and ligand poses and selected pocket residues were visualized. The relative orientation of the cholesterol 3β-hydroxyl group with respect to conserved pocket residues was inspected in the aligned structures.

HR3 Ligand-sensor embryo treatment
Transgenic HR3 ligand-sensor (GAL4_DBD::HR3_LBD; UAS-GFP) embryos were permeabilized and treated for 15 minutes at 25 °C with cholesterol in MBIM culture media as previously described in Palanker et al.34 The MBIM was then removed, and the embryos covered with halocarbon oil and allowed to develop for a minimum of 2 hours prior to observation.

Statistical analysis
Statistical analysis was computed using the Prism software package (GraphPad, version 10). Fat-body pS6-staining data was tested for outliers using the ROUT method and a Q of 10 due to the large variation within the tissue. All data sets were assessed for normality prior to statistical analysis, and appropriate parametric or non-parametric tests were selected. Graphs were generally created using the Prism software, showing all data points and mean ± standard error of the mean. Detailed statistical information can be found in each figure’s legend. Graphical illustrations of RNAseq and phosphoproteomics data were made using SRplot72.

Data availability
All data generated in this study are available within this manuscript, its figures or its supplementary files or on public databases. Source data are provided with this paper. The transcriptomic data generated for this study have been deposited in the NCBI Gene Expression Omnibus (GEO) repository under the accession code GSE270221 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE270221]. The mass-spectrometry proteomics data generated for this work have been deposited to the ProteomeXchange Consortium73 via the PRIDE74 repository under the dataset identifier PXD074014 [https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD074014]. Request for other data, such as raw imagery, should be directed to the Lead Author.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In the current manuscript, Lassen and others demonstrate that HR3, an evolutionarily conserved nuclear receptor, mediates\nstimulatory effects of cholesterol on target of rapamycin (TOR) signaling through a non-genomic pathway in the fruit fly\nDrosophila melanogaster. Moreover, they also show that RORalpha, a mammalian ortholog of HR3, plays a similar role in\nhuman cells, suggesting that this HR3/RORalpha-mediated TOR signaling activation is an evolutionarily conserved\nsignaling pathway.\nResearchers including the authors’ group have recently revealed the stimulatory effects of cholesterol on TOR signaling in\ndifferent model organisms. There was also an old study reporting a non-genomic, growth-promoting effect of HR3 in\nDrosophila. But nobody has ever connected these dots to integrate them into a nice story. The current study thus fills a\ncritical knowledge gap in the field and is therefore expected to appeal to many researchers in the relevant field. I have\nseveral points for the authors to consider as listed below, hoping that they would help them further improve their manuscript.\nMajor points:\n1) To exclude the possibility that the steroid hormone ecdysone is mediating the S6K-stimulating effect of cholesterol\nfeeding (Figure 1), the authors performed ecdysone (20E) feeding experiment in Figure S1 and concluded that cholesterol\nactivates the TOR pathway independently of ecdysone (p. 5, lines 185-187). Although the authors’ conclusion is most likely\ncorrect, this particular experiment is not ideal, as we can never assume that feeding a certain amount of 20E properly reflects\nthe fluctuation of ecdysone titer in the hemolymph upon cholesterol feeding. As the authors have a tool to transiently\nsuppress gene expression in the prothoracic gland (phmTS>), I would recommend the authors to investigate this possibility\nfurther by transiently shutting down ecdysone production in the prothoracic gland (by knocking down a Halloween gene, for\nexample), feeding them with cholesterol, and checking the pS6 levels in the fat body.\n2) Figure 6C lacks one important experimental group, with Tor-RNAi and no HR3(LBD) OE. This group is critical for\nevaluating if the growth-stimulating effect of HR3(LBD) OE is indeed canceled by Tor-RNAi.\n3) Current interpretation of the data presented in Figure 6E is vague and confusing. Although the authors mention that “the\nTOR activity increase due to HR3(LBD) overexpression was largely dependent on both RagA-B and RagC-D” (p.9, lines\n423-424), RagA-B-RNAi and RagC-D-RNAi actually showed the opposite results when combined with HR3(LBD) OE. More\nspecifically, RagA-B-RNAi completely suppressed the effects of HR3(LBD) OE, whereas RagC-D-RNAi did not. It therefore\nseems to me that the TOR-stimulating effect of HR3 is mainly mediated by RagA-B. These results need to be interpreted\nmore carefully.\n4) Figure 7B and 7C needs to be repeated multiple times and quantified with appropriate statistical analyses.\nMinor points:\n5) p.4, line 131, “ecdysone steroid hormone EcR”: I guess the authors meant to say “ecdysone receptor, EcR.”\n6) In Figure 2, panels A-B and G-H are aligned in the order of cholesterol-protein (purple-green), whereas this order is\nflipped in panels D, E, and F. It makes it easier for readers to interpret the data if they are aligned the same way throughout\nthe entire figure.\n7) Figure 3F: It seems that the peak at 330 m/z is mislabeled as 300?\n8) p.8, lines 348-350, “This implies …, consistent with insights from other recent studies in mammalian systems.”: This\nsentence reads as if RORalpha has already been shown to modulate TOR activities in mammals. Please rephrase.\n9) It has been shown in Drosophila that sterols other than cholesterol can be converted into functional ecdysteroids that are\nequivalent to ecdysone/20E (e.g., Lavrynenko et al., 2015, PMID: 26395481). It is therefore interesting to discuss whether\nHR3 is a specific sensor of cholesterol, or it can sense a broader range of sterols that can be converted into functional\necdysteroids.", "reviewer_2": "The authors describe several exciting and noteworthy results:\nThey demonstrate that Drosophila DH3 binds cholesterol, identifying for the first time a ligand for the well-described\nreceptor. They further show that DH3 mediates the effect of cholesterol on protein synthesis via Tor and phosphorylation of\nSK6. It also directs an increase in body mass in larvae in a cholesterol and Tor dependent fashion.\nDH3 is a well-characterized transcriptional regulator, but here the authors present data showing that its cholesterol\ndependent role requires only the ligand binding domain (LBD), and that cholesterol binds to the LBD. This finding\nsignificantly extends the role of DH3 beyond its well-described transcriptional role in mediating Ecdysone function and\ndemonstrates a cholesterol-dependent non-genomic function of DH3.\nThe authors further show that the mammalian DH3 homologue RORα has a similar function in human Karpas 707H cells,\ndemonstrating a conserved function for these proteins in growth control.\nThe fly system is uniquely suited for these studies since flies do not produce cholesterol and need to take it up through food.\nCholesterol levels in both isolated tissues and whole animals can therefore easily be manipulated in experiments.\nFurthermore, the available GAL4DH3-LBD reporter is well suited to monitor ligand dependent activation.\nThis work significantly expands previous findings that DH3LBD form mediates Tor function and links it mechanistically to\ncholesterol.\nThe conclusions are well supported through genetics, proteomics, and genomic approaches.\nFollowing are a few questions and suggestions:\n1. The potential role of Ecdysone in this process is complex and remains unclear. It is well known that Ecdysone induces\nDH3, which in turn transcriptionally regulates further mediators of Ecdysone action. To what degree Ecdysone may play a\nrole in the newly described role of DH3 is therefore a complex question. The authors show that addition of Ecdysone does\nnot evoke the same response as cholesterol. As the authors point out, one would not expect the non-protein synthesis\ndependent role of DH3 to be directly Ecdysone receptor dependent. One of the unanswered questions is whether the LBD-\nonly transcript is Ecdysone dependent. Given the complexity of the Ecdysone/DH3 interaction, placement of the results in\nthe supplemental section is a good idea. Perhaps the discussion of these complexities and the reference to the\ncorresponding figures would be well placed in the discussion?\n2. The GAL4 DH3-LBD reporter is a nice and well- characterized in developmental context, and this is the first instance\nwhere a ligand for this “orphan receptor” has been identified. The authors may want to expand on the discussion that there\nmight be several ligands for the protein. This might include a notion that the described experiments are done in larvae,\nwhere growth is a major aspect, in contrast to adults. This leaves open the question whether similar findings might be\nobserved in adult flies (and tissues other than the fat body).\n3. Details in the Methods section are well described. While somewhat redundant, adding some relevant methodical details\nto figure legends would help readers understand some of the complex figures. For example, in figure 5 I-K where HR3-RNAi is conditionally expressed using Gal80ts, addition of the induction details in the figure legend would be helpful (at what age, for how long). A sentence or two in the text explaining the principle of Gal80ts induction would also help readers who don’t know the system. Similarly, a short description of the GAL4DH3-LBD sensor would be helpful.\n4. For the majority of experiments, the authors do not use inducible expression. This is not an issue for reporter assays\n(Observation pS6E, induction of growth in response to cholesterol exposure), but it raises interesting questions when RNAi\nconstructs or the LBD-overexpression construct are assayed. While the assays are often done in larvae by changing\ncholesterol levels, it is noteworthy that RNAi knockdown or LBD overexpression in these cases have been happening all\nthrough development. Have the authors observed changes in developmental speed/ overall growth caused by these\nmanipulations? Have they compared these animals with control animals? If no changes were observed, how do the authors\ninterpret this? A limited overall role of the fat body or PG in these processes? How about compensatory changes of the\nsystem (which might be relevant for the interpretation of the cholesterol assays).\n5. Have the authors studied the effect of overexpression of the entire protein? A comparison to the effect of LBD-only\nexpression on gene expression and phospho-proteins might identify the contribution of genomic effects.\n6. What is known about proposed structures of DH3 or RORα? Are there similarities to known cholesterol-binding proteins?\nAre there DH3 mutants that might have impaired cholesterol binding function?", "reviewer_3": "Lassen et al. investigated the role of HR3 in cholesterol sensing and the cholesterol-induced activation of the TOR\npathway in Drosophila. The authors first demonstrated the role of cholesterol in activating the TOR pathway. Moreover, they found\nthat cholesterol can directly bind to HR3, and cholesterol-induced activation of the TOR pathway is HR3-dependent but\nindependent of HR3’s DNA-binding function. At the end, the authors showed that human RORα can also activate the TOR\npathway in response to exogenous cholesterol. The overall experimental design is reasonable, and the data are clearly\npresented, highlighting some very interesting phenomena. However, the mechanistic insights need to be further explored.\nThe authors should address the following questions and make the necessary revisions.\nHR3 primarily functions as a transcription factor. It is intriguing to explore which genes are regulated by cholesterol binding\nto HR3. However, the authors claim that cholesterol-induced TOR activation via HR3 is independent of its DNA-binding\nfunction. Thus, it is crucial to address the key mechanistic question of how cholesterol-HR3 regulates the TOR pathway.\nIn Figure 1 F and G, the TOR pathway responds differently to cholesterol in the fat body and PG. In the fat body, TOR is\nrapidly activated within 0.5 hours and then decreases at 2 hours. In contrast, TOR activation in the PG continues to increase\nover 2 hours. What causes these differences?\nIn Figure 1D, the activation of the TOR pathway peaks at 1 hour, whereas in Figures 2A and 2D (right), it peaks at 0.5 hours,\nand in Figure 3I, it peaks at 2 hours. How can these variations be explained?\nFigure 2C shows that each time point has only two replicates (n=2), which is insufficient. Although the authors may want to\ninclude all samples in a single TMT experiment, this design does not allow for statistical analysis. It is unclear how the\nstatistical p-values were calculated in Figures 2GH. The number of replicates for other omics experiments should also be\nspecified.\nFigure 2C shows that the proteomics data were obtained from the flow-through of TiO2 enrichment, which introduces some\nvariations. Why didn't the authors take a small portion of the sample before TiO2 enrichment for bulk proteomics?\nAdditionally, it should be clarified whether the quantification of phosphorylation was normalized based on protein\nabundance.\nFigures 2C-H: The authors have designed a complex omics experiment, but the analysis is too superficial, lacking\ninformation on temporal changes. Although the experiment included eight time points, the data in Figures F-H represent\nchanges at a single time point. The authors should provide a comprehensive analysis of phosphorylation and proteome\nchanges over time during cholesterol treatment, distinguishing changes between fast and slow responses.\nOther omics experiments have similar issues and need revisions. For example, Figures 5D-G show three different omics\ndatasets at three different time points, making it difficult to correlate the data.\nFigures 3A and 3B: Please provide deconvoluted MS data. The legend for Figure 3B describes the spectrum as collision-\ninduced dissociation, while the main text describes it as denatured samples, which are two completely different experiments.\nThe authors need to clarify this discrepancy.\nThe interaction between cholesterol and the HR3 LBD requires more functional validations. For example, does the deletion\nof LBD abolish cholesterol-induced TOR activation?\nMinor Points:\nThe authors primarily use pS6 as a marker for TOR pathway activation. Are there other markers, such as phosphorylation of\nTOR itself, that can be used to validate changes in TOR pathway activity?", "reviewer_4": "The manuscript of Lassen and coauthors investigates the molecular mechanisms that link dietary intake of cholesterol with\nthe growth regulatory TOR pathway. By using Drosophila melanogaster as a model system, they first demonstrate that\ndietary cholesterol feeding induces TOR activation in fat bodies and PG cells as well as in whole animals. They show that\nthe nuclear receptor HR3, the Drosophila ortholog of human ROR , is activated by cholesterol binding and modulates TOR\nactivity independently of ecdysone-mediated effects They further demonstrate that HR3 can mediate Cholesterol-induced\nTOR activation through an isoform that lacks DNA-binding domain. Reducing HR3 in cells can mitigate the hyperactivation\nof TOR and intralysosomal accumulation of cholesterol caused by depleting Npc1a. These results indicates that HR3 is\nrequired for TOR activation by lysosomal cholesterol. Finally, they use KARPAS-707H cells, which strongly express human\nROR , to explore the potential role of human ROR  in modulating the TOR-pathway in response to exogenous cholesterol.\nCollectively their findings suggest that HR3/ ROR  represents a conserved mechanism for coupling cholesterol levels to\nTOR pathway activation.\nOverall, data in the manuscript are solid, providing a significant advance in our understanding of how\ncholesterol activates cell growth via TOR signaling with potential implications in cancer and cholesterol-related pathologies.\nThe work and data analysis are well conducted and support the conclusions and the research methodologies are sound and innovative\nincluding phosphoproteomic analysis and mass spectrometry, RNA-seq. Thus, the manuscript is suitable for publication in\nthis journal. I only have minor points:\n1- Figure 7I illustrates the model for the role of HR3 in cholesterol-mediated TOR activation. However, the model seems\nrelatively simple with respect to the large amounts of data reported in the paper. I would suggest to add a figure (figure 8)\nreporting the main conclusions of the paper.\n2- A diagram illustrating the TOR pathway in Drosophila, the subcellular localization of the TOR pathway proteins (for\nexample RagA and RagB) could facilitate the reader. This diagram could be shown in Figure 1 and cited in the introduction.\n3- The subcellular localization of HR3 (and human ROR  as well) is a key point for the conclusions in the paper. However,\nHR3 localization is shown in a supplemental figure S4F.\nI would suggest to incorporate these data into one of the main figures.\n4- On page 7, lines 270-271: “Knockdown of HR3 in the fat body or PG using an RNAi construct that targets all of the\nannotated transcript variants of HR3 led to reductions in pS6 staining in these tissues”. The authors should clarify how they\ndemonstrated (or it was previously shown), or where they show in the manuscript, that the RNAi construct targets all of the\nannotated transcript variants of HR3."}, "author_response": "Response to reviewers’ comments\nReviewer #1 (Remarks to the Author):\nIn the current manuscript, Lassen and others demonstrate that HR3, an evolutionarily conserved nuclear\nreceptor, mediates stimulatory effects of cholesterol on target of rapamycin (TOR) signaling through a non-\ngenomic pathway in the fruit fly Drosophila melanogaster. Moreover, they also show that RORalpha, a\nmammalian ortholog of HR3, plays a similar role in human cells, suggesting that this HR3/RORalpha-\nmediated TOR signaling activation is an evolutionarily conserved signaling pathway.\nResearchers including the authors’ group have recently revealed the stimulatory effects of cholesterol on\nTOR signaling in different model organisms. There was also an old study reporting a non-genomic, growth-\npromoting effect of HR3 in Drosophila. But nobody has ever connected these dots to integrate them into a\nnice story. The current study thus fills a critical knowledge gap in the field and is therefore expected to\nappeal to many researchers in the relevant field. I have several points for the authors to consider as listed\nbelow, hoping that they would help them further improve their manuscript.\nAuthor response: We thank the reviewer for the positive and thoughtful assessment of our work and for\nrecognizing the conceptual advance it represents. We appreciate the constructive suggestions and have\nrevised the manuscript accordingly, as detailed in our responses to the individual points below.\nMajor points:\nReviewer point 1) To exclude the possibility that the steroid hormone ecdysone is mediating the S6K-\nstimulating effect of cholesterol feeding (Figure 1), the authors performed ecdysone (20E) feeding\nexperiment in Figure S1 and concluded that cholesterol activates the TOR pathway independently of\necdysone (p. 5, lines 185-187). Although the authors’ conclusion is most likely correct, this particular\nexperiment is not ideal, as we can never assume that feeding a certain amount of 20E properly reflects\nthe fluctuation of ecdysone titer in the hemolymph upon cholesterol feeding. As the authors have a tool to\ntransiently suppress gene expression in the prothoracic gland (phmTS>), I would recommend the authors\nto investigate this possibility further by transiently shutting down ecdysone production in the prothoracic\ngland (by knocking down a Halloween gene, for example), feeding them with cholesterol, and checking the\npS6 levels in the fat body.\nAuthor response: Thank you for the insightful suggestion. We agree that acute 20E feeding is an imperfect\nproxy for endogenous ecdysone dynamics, and we have solidified that finding along the suggested line. In\na new experiment for the revision, we used phmTS> to transiently suppress ecdysone production in the\nprothoracic gland (PG) by knocking down torso (the PTTH receptor), phantom (phm), or disembodied (dib),\nwhich are all individually essential for ecdysone synthesis. Larvae were unperturbed through development\nuntil 120 hours after egg-laying (AEL), at which they were placed on low (1.2 µg/mL)-cholesterol medium\nand gene knockdown was induced by raising the temperature. After ten hours of knockdown and cholesterol\ndepletion, the animals were then re-fed for 6 h with either 1.2 µg/mL (low)- or 80 µg/mL (high)-cholesterol\ndiet. Despite reduced or absent ecdysone synthesis due to PG-targeted gene knockdown, cholesterol\nrefeeding increased fat-body pS6 to control-like levels, indicating that the response is independent of\necdysone production and consistent with direct TOR activation. These data and quantification are now\nincluded in new Supplementary Figure 1f in the revised manuscript, and the Results and Methods have\nbeen updated accordingly.\nReviewer point 2) Figure 6C lacks one important experimental group, with Tor-RNAi and no HR3(LBD)\nOE. This group is critical for evaluating if the growth-stimulating effect of HR3(LBD) OE is indeed\ncanceled by Tor-RNAi.\nAuthor response: We agree that it is appropriate to add Tor-RNAi alone and have included a new figure\nwith Tor-RNAi without HR3(LBD) overexpression. The revised Figure 6c shows that the growth-promoting\neffect of HR3(LBD) overexpression is abolished when Tor is knocked down, indicating that TOR activity\nis required for HR3(LBD)-dependent growth.\nReviewer point 3) Current interpretation of the data presented in Figure 6E is vague and confusing.\nAlthough the authors mention that “the TOR activity increase due to HR3(LBD) overexpression was largely\ndependent on both RagA-B and RagC-D” (p.9, lines 423-424), RagA-B-RNAi and RagC-D-RNAi actually\nshowed the opposite results when combined with HR3(LBD) OE. More specifically, RagA-B-RNAi\ncompletely suppressed the effects of HR3(LBD) OE, whereas RagC-D-RNAi did not. It therefore seems to\nme that the TOR-stimulating effect of HR3 is mainly mediated by RagA-B. These results need to be\ninterpreted more carefully.\nAuthor response: We thank the reviewer for highlighting this and agree that the results indicate the\nresponse is mainly mediated by RagA-B. To make that result clear, we have rephrased our original wording\nin the Results section (previously: “largely dependent on both RagA-B and RagC-D”). The revised\nmanuscript text now states that the increase in TOR activity due to HR3(LBD) overexpression was\ncompletely abolished by RagA-B knockdown, whereas RagC-D knockdown only diminished the response,\nindicating that HR3-induced TOR activation is mainly mediated by RagA-B. We believe this revision\nimproves the interpretation of the results, and we thank the reviewer for raising this point.\nReviewer point 4) Figure 7B and 7C needs to be repeated multiple times and quantified with appropriate\nstatistical analyses.\nAuthor response: We thank the reviewer for this comment. We have now added quantified data based on\nindependent biological samples for the experiments shown in Figure 7b and 7c in a completely new panel,\nSupplementary Figure 8a-c. The quantification and statistical analysis of normalized pS6 and RORα levels\nconfirms the data described in Figure 7b and 7c and supports our conclusion that cholesterol activates TOR\nsignaling through RORα.\nMinor points:\nReviewer point 5) p.4, line 131, “ecdysone steroid hormone EcR”: I guess the authors meant to say\n“ecdysone receptor, EcR.”\nAuthor response: Thank you for catching this. We have corrected “ecdysone steroid hormone EcR” to\n“ecdysone receptor, EcR” in the revised manuscript.\nReviewer point 6) In Figure 2, panels A-B and G-H are aligned in the order of cholesterol-protein (purple-\ngreen), whereas this order is flipped in panels D, E, and F. It makes it easier for readers to interpret the data\nif they are aligned the same way throughout the entire figure.\nAuthor response: Thank you for the suggestion. We have flipped panels d-f so the order now matches A-\nB and G-H. This alignment should make the figure easier to interpret.\nReviewer point 7) Figure 3F: It seems that the peak at 330 m/z is mislabeled as 300?\nAuthor response: Thanks for catching this. We have corrected the label to m/z 330 in the revised Figure\n3F.\nReviewer point 8) p.8, lines 348-350, “This implies …, consistent with insights from other recent studies\nin mammalian systems.”: This sentence reads as if RORalpha has already been shown to modulate TOR\nactivities in mammals. Please rephrase.\nAuthor response: Thank you for the suggestion. We have clarified the sentence to avoid implying prior\nevidence that RORα modulates TOR in mammals. The revised text now reads: “This implies HR3 operates\nat the level of, or upstream of, the LAMTOR (Ragulator) complex and RAPTOR-dependent TOR activation,\nconsistent with insights from other recent studies in mammalian systems indicating that cholesterol\nactivates the TOR pathway via these components.” This wording specifies pathway components\n(LAMTOR/Ragulator and RAPTOR-dependent recruitment) without asserting a direct role for RORα in\nmammalian TOR regulation.\nReviewer point 9) It has been shown in Drosophila that sterols other than cholesterol can be converted into\nfunctional ecdysteroids that are equivalent to ecdysone/20E (e.g., Lavrynenko et al., 2015, PMID:\n26395481). It is therefore interesting to discuss whether HR3 is a specific sensor of cholesterol, or it can\nsense a broader range of sterols that can be converted into functional ecdysteroids.\nAuthor response: To test whether sterols other than cholesterol can activate HR3 is an interesting idea.\nHowever, this activation test requires the transgenic HR3 ligand-sensor (HR3::GAL4; UAS-GFP).\nUnfortunately, we had found during the revision that our in-house stock of the HR3::GAL4 (II); UAS-GFP\n(III) began showing variability of the mini-white marker (multiple eye colors), which can arise in\nhomozygous transgenic lines due to, e.g., selection against the presence of the inserted transgene. To\neliminate any ambiguity and ensure reproducibility, we therefore tried to re-obtain the line again from the\nBloomington Drosophila Stock Center. Unfortunately, over several months the stock had to be shipped\ntwice, and both shipments arrived nonviable, which delayed our work and prevented us from performing\nadditional experiments to test other sterol ligands.\nWe agree that it is very plausible that sterols closely related to cholesterol can ligate and activate HR3.\nAlthough our current data specifically show cholesterol-dependent activation of HR3, we have now\nincluded an additional paragraph in the Discussion of the revised manuscript that directly addresses whether\nHR3 might sense sterols beyond cholesterol that can be converted into functional ecdysteroids, and we cite\nLavrynenko et al., 2015, as well as an additional study showing that cholesterol derivatives can activate\nRORα 1. This includes discussion that RORα can be activated by 7-dehydrocholesterol, which is a key\nintermediate in the ecdysone-biosynthetic pathway, raising the possibility that HR3 could also be activated\nby 7-dehydrocholesterol that is converted into ecdysone in the prothoracic gland. We believe that while this\nremains an important question raised by our study, the new discussion appropriately highlights it as an\ninteresting avenue for future work.\nThe new discussion paragraph is: “In insects, several dietary sterols besides cholesterol can be converted\ninto functional ecdysteroids equivalent to ecdysone and the more active form, 20-hydroxyecdysone (20E)\n51. Our data argue that HR3-mediated TOR activation reflects a direct sterol-sensing route rather than an\necdysone-driven effect, because (i) the cholesterol response is rapid and partly cycloheximide-insensitive,\nand (ii) loss of ecdysone signaling by EcR knockdown or blockade of ecdysone biosynthesis did not reduce\ncholesterol-mediated TOR-pathway activation. However, we cannot exclude that HR3 binds a broader set\nof dietary sterols that can be converted into ecdysteroids. Mammalian receptor RORα activity can be\nmodulated by multiple sterols, including 7-dehydrocholesterol and cholesterol sulfate 33. 7-\ndehydrocholesterol is a key sterol intermediate in the ecdysone biosynthetic pathway, raising the possibility\nthat HR3 might also sense this precursor in the PG. Determining the ligand specificity of HR3, including\nsterols beyond cholesterol, will clarify whether HR3 is cholesterol-specific or a more general sterol sensor,\nwhich remains an interesting question for future studies. Because our current experiments were performed\nin larvae, where growth and biosynthetic activity are high, it will be important to determine whether similar\nHR3-sterol interactions occur in adults, where metabolic priorities differ. Likewise, whether HR3 responds\nto sterols in tissues other than the fat body, such as the gut or gonads, remains an open and intriguing\nquestion.”\nIf the reviewer believes that this matter should be explored further, we propose that we might pursue\ncomputational modeling of the binding of diverse sterols in the predicted HR3 binding pocket, along the\nlines of what we have done for cholesterol in the revised Fig. 3b,c and Supplemental Fig. 4a-c.\nReviewer #2 (Remarks to the Author):\nThe authors describe several exciting and noteworthy results:\nThey demonstrate that Drosophila DH3 binds cholesterol, identifying for the first time a ligand for the well-\ndescribed receptor. They further show that DH3 mediates the effect of cholesterol on protein synthesis via\nTor and phosphorylation of SK6. It also directs an increase in body mass in larvae in a cholesterol and Tor\ndependent fashion.\nDH3 is a well-characterized transcriptional regulator, but here the authors present data showing that its\ncholesterol dependent role requires only the ligand binding domain (LBD), and that cholesterol binds to the\nLBD. This finding significantly extends the role of DH3 beyond its well-described transcriptional role in\nmediating Ecdysone function and demonstrates a cholesterol-dependent non-genomic function of DH3.\nThe authors further show that the mammalian DH3 homologue RORα has a similar function in human\nKarpas 707H cells, demonstrating a conserved function for these proteins in growth control.\nThe fly system is uniquely suited for these studies since flies do not produce cholesterol and need to take it\nup through food. Cholesterol levels in both isolated tissues and whole animals can therefore easily be\nmanipulated in experiments. Furthermore, the available GAL4DH3-LBD reporter is well suited to monitor\nligand dependent activation.\nThis work significantly expands previous findings that DH3LBD form mediates Tor function and links it\nmechanistically to cholesterol.\nThe conclusions are well supported through genetics, proteomics, and genomic approaches.\nFollowing are a few questions and suggestions:\nAuthor response: We are grateful for the reviewer’s supportive assessment, and we appreciate the\nrecognition of our key findings identifying cholesterol as an HR3 ligand and delineating its non-genomic\nrole in TOR/S6K-mediated growth control, as well as the results indicating that this is a conserved role for\nRORα in human cells. The reviewer also makes specific helpful comments, which we address in our\nresponses below.\nReviewer point 1) The potential role of Ecdysone in this process is complex and remains unclear. It is well\nknown that Ecdysone induces DH3, which in turn transcriptionally regulates further mediators of Ecdysone\naction. To what degree Ecdysone may play a role in the newly described role of DH3 is therefore a complex\nquestion. The authors show that addition of Ecdysone does not evoke the same response as cholesterol. As\nthe authors point out, one would not expect the non-protein synthesis dependent role of DH3 to be directly\nEcdysone receptor dependent. One of the unanswered questions is whether the LBD-only transcript is\nEcdysone dependent. Given the complexity of the Ecdysone/DH3 interaction, placement of the results in\nthe supplemental section is a good idea. Perhaps the discussion of these complexities and the reference to\nthe corresponding figures would be well placed in the discussion?\nAuthor response: We agree that the ecdysone-HR3 relationship is complex, and the reviewer brings up a\ngood point. However, to what extent the \"long\" transcripts that encode the DBD::LBD protein and the\n\"short\" variant that encodes only the LBD-only transcript are regulated independently is difficult to answer.\nAccording to 5ʹ-RACE experiments performed by Montagne et al. (2010)2 to identify the beginning of the\nnovel short \"RS\" transcript variant, this mRNA is initiated at a genomic site that is transcribed within one\nof the exons of the longer transcript forms, and further transcription and splicing mimic those of the longer\nforms. Translation from these shorter messages makes use of a more distal initiator codon, giving rise to\nthe LBD-only protein. Therefore, there is no sequence that is specific to the short transcript, a circumstance\nthat prevents our measuring its abundance by, for example, qPCR. We therefore addressed the point\nfunctionally by adding further experiments showing that cholesterol-mediated TOR activation is\nindependent of ecdysone. In the revised manuscript, we show that cholesterol still drives TOR signaling\n(pS6) when the ecdysone pathway is impaired by knockdown of the PTTH-receptor gene torso in the\nprothoracic gland (PG) or by silencing the ecdysone-biosynthetic genes phantom and disembodied in the\nPG, each of which is required for ecdysone production (new Supplementary Fig. 1f). These manipulations\ndid not diminish the TOR response to cholesterol. Together with our finding that Ecdysone Receptor (EcR)\nknockdown does not reduce the cholesterol-induced TOR response (Supplementary Fig. 5e) and our\nevidence that HR3 promotes TOR in part through non-genomic mechanisms (Fig. 6g), these results support\nthe ecdysone-independence of cholesterol:HR3-mediated TOR activation. In the Discussion, we further\nnote that ecdysone induces HR3 expression, suggesting a temporal interplay in which ecdysone sets HR3\nexpression levels, while HR3, in turn, modulates TOR-dependent growth according to cholesterol\navailability.\nReviewer point 2) The GAL4 DH3-LBD reporter is a nice and well- characterized in developmental\ncontext, and this is the first instance where a ligand for this “orphan receptor” has been identified. The\nauthors may want to expand on the discussion that there might be several ligands for the protein. This might\ninclude a notion that the described experiments are done in larvae, where growth is a major aspect, in\ncontrast to adults. This leaves open the question whether similar findings might be observed in adult flies\n(and tissues other than the fat body).\nAuthor response: We appreciate this insightful suggestion and agree that HR3 may respond to multiple\nsterols depending on developmental stage and tissue context. In the revised manuscript, we have expanded\nthe Discussion to address this point. We now note that while our data indicate a direct sterol-sensing\nfunction of HR3, we cannot exclude the possibility that HR3 binds a broader set of dietary sterols that can\nbe converted into ecdysteroids. We further discuss that our experiments were performed in larvae, in which\ngrowth is paramount, and that it will be important to determine whether similar HR3-sterol interactions\noccur in adults or in other tissues such as the discs, gut, or gonads. The new discussion paragraph is: “In\ninsects, several dietary sterols besides cholesterol can be converted into functional ecdysteroids equivalent\nto ecdysone and the more active form, 20-hydroxyecdysone (20E) 51. Our data argue that HR3-mediated\nTOR activation reflects a direct sterol-sensing route rather than an ecdysone-driven effect, because (i) the\ncholesterol response is rapid and partly cycloheximide-insensitive, and (ii) loss of ecdysone signaling by\nEcR knockdown or blockade of ecdysone biosynthesis did not reduce cholesterol-mediated TOR-pathway\nactivation. However, we cannot exclude that HR3 binds a broader set of dietary sterols that can be\nconverted into ecdysteroids. Mammalian receptor RORα activity can be modulated by multiple sterols,\nincluding 7-dehydrocholesterol and cholesterol sulfate 33. 7-dehydrocholesterol is a key sterol intermediate\nin the ecdysone biosynthetic pathway, raising the possibility that HR3 might also sense this precursor in\nthe PG. Determining the ligand specificity of HR3, including sterols beyond cholesterol, will clarify whether\nHR3 is cholesterol-specific or a more general sterol sensor, which remains an interesting question for future\nstudies. Because our current experiments were performed in larvae, where growth and biosynthetic activity\nare high, it will be important to determine whether similar HR3-sterol interactions occur in adults, where\nmetabolic priorities differ. Likewise, whether HR3 responds to sterols in tissues other than the fat body,\nsuch as the gut or gonads, remains an open and intriguing question.”\nReviewer point 3) Details in the Methods section are well described. While somewhat redundant, adding\nsome relevant methodical details to figure legends would help readers understand some of the complex\nfigures. For example, in figure 5 I-K where HR3-RNAi is conditionally expressed using Gal80ts, addition\nof the induction details in the figure legend would be helpful (at what age, for how long). A sentence or two\nin the text explaining the principle of Gal80ts induction would also help readers who don’t know the system.\nSimilarly, a short description of the GAL4DH3-LBD sensor would be helpful.\nAuthor response: We thank the reviewer for this helpful suggestion. In the revised manuscript, we have\nincorporated all of the requested clarifications directly into the relevant figure legends, added brief\nexplanatory passages in the appropriate places in the Results section, and included three new schematic\noverview panels that clearly explain the feeding and induction conditions as well as the HR3 ligand-sensor\nsystem. These additions improve the accessibility of the material for readers who may be less familiar with\nDrosophila genetic tools and the logic of our experimental setups.\nTo clarify how GAL80TS-mediated temporal control was used in the conditional RNAi experiments, we\nadded a short explanation to the Results text describing the principle of the system - specifically, that\nGAL80TS blocks GAL4 at low temperature and becomes inactive upon shifting larvae to 29 °C, thereby\npermitting induction of RNAi expression within a defined developmental window. We have also added\nexplicit induction details in the legend and two new schematic overview panels in Supplementary Fig. 1a\nand Supplementary Fig. 1d, which visually summarize the full feeding and induction paradigms used,\nincluding the developmental stage at which animals were shifted to 29 °C, how long they remained at this\ntemperature, and when cholesterol manipulations were initiated. These schematics also anchor the timing\nand rationale of the GAL80TS induction relative to larval age and cholesterol treatments. We refer to these\nfigures again later when HR3-RNAi is conditionally expressed, as the reviewer suggested, to clarify the\nexact conditions relevant to Fig 5i-k.\nIn addition, to address the reviewer’s request regarding the GAL4-HR3-LBD cholesterol sensor, we\nexpanded the legend of Fig. 3 to include a concise description of how the ligand sensor functions, and we\nadded a new panel (Fig. 3j) with an illustration of the ligand-sensor design. This schematic explains that\nthe HR3 ligand-binding domain fused to the GAL4 DNA-binding domain drives UAS-GFP expression only\nwhen cholesterol binds to HR3, thereby providing a readout of ligand-dependent activation in embryos and\nlarval tissues.\nTogether, these updates ensure that readers can easily follow the experimental logic, understand the function\nof the genetic tools, and interpret the timing and conditions used in the key cholesterol- and HR3-dependent\nassays. We believe that these additions substantially improve the clarity and accessibility of the manuscript.\nReviewer point 4) For the majority of experiments, the authors do not use inducible expression. This is not\nan issue for reporter assays (Observation pS6E, induction of growth in response to cholesterol exposure),\nbut it raises interesting questions when RNAi constructs or the LBD-overexpression construct are assayed.\nWhile the assays are often done in larvae by changing cholesterol levels, it is noteworthy that RNAi\nknockdown or LBD overexpression in these cases have been happening all through development. Have the\nauthors observed changes in developmental speed/ overall growth caused by these manipulations? Have\nthey compared these animals with control animals? If no changes were observed, how do the authors\ninterpret this? A limited overall role of the fat body or PG in these processes? How about compensatory changes of the\nsystem (which might be relevant for the interpretation of the cholesterol assays).\nAuthor response: The reviewer raises the important question regarding potential developmental effects of\nconstitutive RNAi and HR3(LBD) overexpression. In the revised manuscript, we have now directly\naddressed this point by generating new data sets for temporally induced manipulations using GAL80TS\ndrivers and by adding new data showing developmental timing for the HR3(LBD) overexpression line.\nFirst, to separate effects on growth rate from potential changes in developmental timing, we\nperformed new experiments in which HR3-RNAi was induced at the transition to the third instar. Using\ntemperature-sensitive fat-body (CgTS>) and prothoracic-gland (phmTS>) drivers, RNAi was activated at the\nL2-L3 transition (120 h AEL at 18 °C), which avoids perturbing earlier developmental transitions and\nallows us to examine effects on growth and timing during the third (L3) instar independently of earlier\nstages. As shown in the new Supplementary Fig. 5c, fat-body-specific HR3 knockdown using this approach\nreduced larval size without affecting the timing of pupariation, demonstrating that the decreased mass\nresults from a slower growth rate without impacting developmental progression. Since HR3 affects TOR\nsignaling, this finding is consistent with established roles of fat-body TOR activity in regulating systemic\ngrowth rate. In contrast, because TOR signaling in the prothoracic gland directly influences ecdysone\nproduction and thereby developmental timing, PG-specific HR3 knockdown induced at the same stage\naltered pupariation timing, as shown in a new Supplementary Fig. 5d. This tissue-specific difference\nsupports the conclusion that the fat body and PG contribute differently to systemic growth and timing, and\nthat constitutive manipulations may have distinct developmental consequences depending on the tissue\ninvolved.\nSecond, as suggested by the reviewer, we also examined whether overexpression of the HR3(LBD)\nisoform, which enhances cholesterol-induced TOR activation, affects overall developmental timing. The\nnewly added data (shown in Supplementary Fig. 6a) demonstrate that HR3(LBD) overexpression in the fat\nbody, which increases larval body mass, does not alter the timing of pupariation, indicating that this\nmanipulation enhances growth rate without disturbing developmental timing. These results argue against\nwidespread compensatory or developmental buffering effects and instead support a model in which\nHR3(LBD) primarily affects TOR-mediated growth responses. Together, these new temporally controlled\nexperiments show that constitutive knockdown or overexpression does not generally cause confounding\nalterations in developmental timing in the fat body, whereas manipulations in the PG behave as expected\nfor a tissue that controls systemic timing through ecdysone. We believe these additions and new experiments\naddress the reviewer’s point.\nReviewer point 5) Have the authors studied the effect of overexpression of the entire protein? A comparison\nto the effect of LBD-only expression on gene expression and phospho-proteins might identify the\ncontribution of genomic effects.\nAuthor response: We thank the reviewer for this insightful suggestion. In response, we generated new\ntransgenic UAS lines expressing either full-length HR3 or a truncated allele (HR3K243X) that produces only\nthe DNA-binding domain due to a premature “stop” mutation. We expressed these constructs in the fat\nbody, mirroring our previous experiments with the “short” HR3(LBD) isoform, which contains only the\nligand-binding domain and enhances cholesterol-induced TOR activation.\nThese new experiments indicate clear functional differences between HR3 isoforms. Whereas\noverexpression of HR3(LBD), which lacks a DBD, potentiates cholesterol-induced pS6 activation, full-\nlength HR3, including the DBD, strongly suppresses this response. The HR3K243X DBD-only variant\ndisplays an intermediate phenotype, producing detectable but weaker repression of cholesterol responses\nthan the full-length HR3. This effect is consistent with a model in which the DNA-binding domain mediates\ntranscriptional repression of TOR signaling, with the presence of the ligand-binding domain in the full-\nlength receptor enhancing this feedback-limiting genomic activity.\nThese findings integrate well with our genome-wide RNA-seq and phosphoproteomic datasets. In animals\nexpressing HR3 knockdown, numerous genes and phosphosites become aberrantly responsive to\ncholesterol (Fig, 5d,f,g), indicating that HR3 normally buffers or restrains excessive transcriptional and\nsignaling outputs. The strong repression of cholesterol response observed with full-length HR3 overexpression is\ntherefore consistent with HR3’s acting through its genomic functions to shape and restrict the longer-term\nTOR response to cholesterol.\nTogether, these additions indicate that HR3 possesses dual mechanisms of regulatory action. HR3(LBD)\nmediates rapid, non-genomic activation of TOR, whereas full-length HR3 engages ligand-enhanced\ntranscriptional programs that provide negative feedback and dampen TOR activation. Collectively, the new\nexperiments strengthen the mechanistic distinction between genomic and non-genomic HR3 functions and\naddress the reviewer’s request. We are grateful to the reviewer for encouraging us to pursue this line of\ninvestigation, which we believe has added an important additional layer of mechanistic insight into how\nHR3 regulates TOR-pathway activation. We have now added the resulting data as a new panel\n(Supplementary Fig. 6f) and incorporated the corresponding description into the Results and Discussion\nsections of the revised manuscript.\nReviewer point 6) What is known about proposed structures of DH3 or RORα? Are there similarities to\nknown cholesterol-binding proteins? Are there DH3 mutants that might have impaired cholesterol binding\nfunction?\nAuthor response: This is an interesting question. High-resolution crystal structures of the human HR3\northolog RORα have been reported, in which cholesterol is bound in the canonical nuclear-receptor ligand-\nbinding pocket, providing a structural framework for cholesterol recognition by this receptor family. In\ncontrast, an experimental structure of Drosophila HR3 has not been reported. To address this gap and the\nquestion raised by the reviewer, we added a structure-based analysis in the revised manuscript produced\nwith the Boltz-2 package to model the HR3 ligand-binding domain (LBD) structure and to assess\ncholesterol placement in the pocket. Strikingly, the unsupervised Boltz-2 docking algorithm placed\ncholesterol within the predicted HR3 LBD pocket in an orientation very similar to that observed for\ncholesterol in RORα, and the overall protein fold and key helices aligned well between HR3 and RORα\n(new Fig. 3a-c and supplementary Fig. 4a-c). These results support the idea that RORα and HR3 share a\nconserved, receptor-type cholesterol-binding architecture, rather than interacting with choleserol through\nbroadly hydrophobic patches in the manner of membrane-integral proteins such as those reported previously\n3.\nThis structural analysis also suggests specific candidate residues that may contribute to cholesterol\nrecognition by HR3. In the predicted HR3 pocket, the cholesterol 3β-hydroxyl is positioned near two\nconserved arginine side chains (HR3 R XXR ), orthologous with residues R XXR of RORα that\n328 331 367 370\nhave been implicated in stabilizing the 3β-hydroxyl via polar interactions (Supplementary Fig. 4c). In\naddition, we now provide sequence-level support for conservation of the ligand-binding pocket. A sequence\nalignment of Drosophila HR3 and human RORα shows that multiple residues implicated in sterol binding\nare conserved in HR3, including residues experimentally reported to contact cholesterol in RORα (within\n4 Å) or substituted with similar hydrophobic residues, consistent with conserved hydrophobic packing in\nthe pocket (new Supplementary Fig. 4d). We also note the conservation of residues proposed to support\ncholesterol binding in RORα through intrapocket interactions (H484 and Y507), both of which are\nconserved in HR3 (H451 and Y471: Supplementary Fig. 4d).\nWe believe these residues are strong candidates for structure-guided functional tests, and targeted\nsubstitutions at these positions would be expected to interfere with cholesterol binding and provide a direct\nway to genetically separate ligand binding from downstream signaling outputs. While we believe generating\nand validating such binding-pocket mutants is beyond the scope of the current revision, we consider this a\nclear next step enabled by our combined structural predictions and biochemical evidence for HR3-\ncholesterol binding.\nReviewer #3 (Remarks to the Author):\nLassen et al. investigated the role of HR3 in cholesterol sensing and the cholesterol-induced activation of\nthe TOR pathway in Drosophila. The authors first demonstrated the role of cholesterol in activating the\nTOR pathway. Moreover, they found that cholesterol can directly bind to HR3, and cholesterol-induced\nactivation of the TOR pathway is HR3-dependent but independent of HR3’s DNA-binding function. At the\nend, the authors showed that human RORα can also activate the TOR pathway in response to exogenous\ncholesterol. The overall experimental design is reasonable, and the data are clearly presented, highlighting\nsome very interesting phenomena. However, the mechanistic insights need to be further explored. The\nauthors should address the following questions and make the necessary revisions.\nAuthor response: We thank the reviewer for taking time to provide thoughtful and positive evaluation of\nour work. We are pleased that the reviewer finds the overall design reasonable, the data clearly presented,\nand the phenomena highlighted by our study interesting.\nWe would like to briefly clarify the scope of our project in relation to the reviewer’s request for additional\nmechanistic exploration beyond what is already presented in the manuscript. When the manuscript was\noriginally submitted to Nature Cell Biology, we were offered the choice of either performing additional\nwork to pinpoint the exact molecular node at which HR3 intersects with the TOR pathway through, for\nexample, proximity labeling or pull-down assays, or transferring the manuscript to Nature Communications,\nwhere the scope could remain more focused and would not require fully defining this precise mechanistic\nintersection. This is without doubt an interesting question, and the additional mechanistic detail obtained\nwill be useful,, but addressing the matter would require a substantial body of additional work, better suited\nfor future independent studies that go well beyond the scope of the present manuscript. We believe that our\nwork, which comprises an extensive set of experiments spanning quantitative phosphoproteomic profiling\nto genetic, biochemical, and functional analyses in both Drosophila and human cells, firmly establishes the\nrole of HR3 in the TOR system for the first time and thus establishes the foundation on which that future\nwork can be built.\nThus, we fully appreciate the reviewer’s request for deeper mechanistic insight and agree that this is an\nimportant point for future studies. Despite the understanding, outlined above, that the transfer to Nature\nCommunications would not require us to define the precise mechanistic intersection, we have nonetheless\nmade a substantial effort in the revised manuscript to further narrow down the mechanism and provide more\ndetailed mechanistic understanding. We have added experimental results that characterize in greater detail\nhow HR3 regulates TOR signaling. For this purpose, we generated new transgenic lines to enable tissue-\nspecific overexpression of full-length HR3 (containing both the ligand-binding domain, LBD, and the\nDNA-binding domain, DBD) as well as a construct containing only the DBD, allowing us to dissect the\ncontribution of the ligand-binding versus DNA-binding functions to TOR activation (new Supplementary\nFig. 6f). In addition, we performed further epistasis analysis between HR3 and TOR-pathway components,\nwhich indicate that HR3 acts downstream of GATOR1 and upstream of the Rags (RagA/B in particular) in\nthe TOR-activation pathway (new Supplementary Fig. 7d). We believe these additions, as well as other\nrevisions made in response to the points raised by the reviewer, address the reviewer’s comments, as\ndetailed in our point-by-point responses below.\nReviewer point 1) HR3 primarily functions as a transcription factor. It is intriguing to explore which genes\nare regulated by cholesterol binding to HR3. However, the authors claim that cholesterol-induced TOR\nactivation via HR3 is independent of its DNA-binding function. Thus, it is crucial to address the key\nmechanistic question of how cholesterol-HR3 regulates the TOR pathway.\nAuthor response: The reviewer raises the question about how HR3 mechanistically regulates the TOR\npathway. We agree that understanding how cholesterol-bound HR3 regulates TOR is interesting, given the\nproviso above, and we have provided additional data in the revised manuscript to address this.\nTo dissect the contribution of HR3’s DNA-binding versus non-genomic functions mentioned by the\nreviewer, we generated new UAS-driven transgenic lines expressing either full-length HR3 or a truncated\nHR3K243X allele that produces a version of HR3 containing only the DNA-binding domain (DBD) due to a\npremature stop codon. We expressed these constructs in the fat body to assess their effects on TOR-pathway\nactivity. Viewed together with data from experiments using the HR3(LBD) isoform, which contains only\nthe ligand-binding domain (LBD), the new data (now included as new Supplementary Fig. 6f and described\nin the Results and Discussion) clearly indicate functional division between the transcriptional effects\nmediated by HR3 and the non-genomic (protein-interaction) effects mediated by the HR3(LBD) isoform\nthat cannot bind DNA. These data show that whereas overexpression of HR3(LBD) potentiates cholesterol-\ninduced pS6 activation, full-length HR3 strongly suppresses this response. The DBD-only HR3K243X variant\nproduces an intermediate phenotype, causing detectable but weaker repression of cholesterol-induced TOR\nresponses than full-length HR3. Because both the full-length and DBD-only HR3K243X variants maintain\nDNA-binding capacity, but only full-length HR3 retains an intact LBD, these observations suggest that\nHR3’s DBD-dependent occupancy of target loci generally restrains TOR signaling, and that ligand-bound\nfull-length HR3 can further engage transcriptional programs that dampen cholesterol-stimulated TOR\nactivity. Together these findings support a model in which HR3 has dual variant-specific roles. The LBD-\nonly isoform mediates rapid, non-genomic activation of TOR in response to cholesterol, consistent with our\nconclusion that the acute cholesterol-HR3-TOR link does not require DNA binding. In contrast, the DBD\n(and hence full-length HR3) engages genomic programs that restrain TOR signaling over longer timescales.\nThis interpretation is reinforced by our genome-wide RNA-seq and phosphoproteomic analyses, which\nshow that loss of HR3 allows numerous genes and phosphosites to become aberrantly responsive to\ncholesterol (Fig. 5d,f,g), indicating that HR3 normally buffers excessive transcriptional and signaling\noutputs.\nIn addition, we added new genetic data that further pinpoint the level of the lysosomal TOR-activation\npathway at which cholesterol-bound HR3 acts. In mammals, lysosomal cholesterol has been proposed to\nactivate TOR through multiple routes. One of these passes through a LYCHOS-GATOR module that\nrelieves inhibition of the Rag GTPases3. We tested whether the corresponding pathway operates in flies.\nFat-body knockdown of the single, highly conserved Drosophila LYCHOS ortholog Anchor using two\nindependent RNAi lines did not reduce cholesterol-stimulated Ribosomal protein S6 phosphorylation, our\ndownstream indicator of TOR activity (Supplementary Fig. 7a,b), even though the knockdown efficiency\nwas up to ~80% (Supp. Fig. 7c), indicating that cholesterol-induced TOR activation in this tissue is\nLYCHOS/Anchor-independent.\nWe next asked whether the GATOR1 complex itself is required. GATOR1 acts as a GTPase-activating\nprotein (GAP) for RagA/B and thus has the effect of inhibiting TOR. As expected, knockdown of the\nGATOR1 component Iml1 increased basal TOR activity (pS6) by relieving this inhibition. However,\ncholesterol refeeding produced a similar relative increase in pS6 in control and Iml1-RNAi animals,\nindicating that the cholesterol response does not require GATOR1 (new Supplementary Fig. 7d).\nImportantly, simultaneous HR3 knockdown strongly attenuated both the elevated basal TOR activity and\nthe cholesterol-induced TOR activation caused by Iml1 depletion (new Supplementary Fig. 7d). Thus, HR3\nis required for normal cholesterol-induced TOR activation even when GATOR-mediated inhibition is\nrelieved. Together, our findings suggest that, although NPC1-dependent lysosomal cholesterol\naccumulation activates TOR in both Drosophila and mammals, the LYCHOS-GATOR module is\ndispensable for this process in flies, and HR3 provides a critical cholesterol-responsive input to TOR\nsignaling upstream of the Rag GTPases. Because we observe a similar requirement for the conserved human\nHR3 ortholog RORα in cholesterol-induced TOR activation, our data support the idea that an HR3/RORα-\nbased cholesterol-sensing module represents an evolutionarily conserved route linking sterol availability to\nTOR signaling. Moreover, unlike transmembrane proteins such as LYCHOS, which may interact with\nmembrane cholesterol through broadly hydrophobic contacts, HR3 and RORα are receptors with defined\nligand-binding pockets in which ligand occupancy can directly modulate receptor activity, providing a\nmechanistically specific basis for cholesterol sensing.\nWe have revised the Results and Discussion sections to accommodate these new findings, making this dual\ngenomic/non-genomic regulatory architecture explicit and considering how it explains the apparent DNA-\nbinding independence of acute TOR activation while integrating HR3’s canonical function as a transcription\nfactor. In the revised manuscript, we also include and discuss new data supporting the positioning of HR3\nwithin the lysosomal cholesterol-dependent TOR pathway as an essential input that operates independently\nof LYCHOS-GATOR and acts upstream of the Rag GTPases. We believe these additions provide\nsubstantially deeper mechanistic insight into how cholesterol-HR3 signaling regulates the TOR pathway\nand thus address the reviewer’s concern.\nReviewer point 2) In Figure 1 F and G, the TOR pathway responds differently to cholesterol in the fat body\nand PG. In the fat body, TOR is rapidly activated within 0.5 hours and then decreases at 2 hours. In contrast,\nTOR activation in the PG continues to increase over 2 hours. What causes these differences?\nAuthor response: It is indeed interesting that the kinetics of the pS6 response are not identical in the fat\nbody and the prothoracic gland (PG) in Fig. 1f,g (Fig. 1g,h in the revised manuscript). The differing\nphysiological roles of these tissues require distinct baseline signaling states and place different demands on\nintracellular trafficking systems. These differences may be especially strong with regard to lipid handling,\nsince the PG requires large amounts of cholesterol for cell-autonomous use in ecdysone synthesis but does\nnot otherwise store unusual amounts of lipid, whereas the fat body stores large amounts of lipids, including\ncholesterol, specifically for use by other tissues. Therefore the intracellular disposition of cholesterol stores,\nand the appropriate cellular response to the level of these stores, likely differs between tissues – perhaps in\nthe fat tissue, cholesterol is more rapidly shuttled into inert storage compartments inaccessible to the TOR\nsystem.\nWhatever the tissue-specific nuances may be, in both tissues cholesterol rapidly increases TOR-pathway\nactivity within 0.5 hours, and this activity remains elevated through the 2-hour time point, and in both cases\nthe response is abolished by tissue-specific TOR knockdown, indicating that the effect is TOR-dependent.\nWe therefore interpret the modest differences in the time courses as reflecting tissue-specific kinetics rather\nthan fundamentally different mechanisms. We have clarified this point in the revised Results section to\navoid overinterpreting small kinetic differences between tissues.\nReviewer point 3) In Figure 1D, the activation of the TOR pathway peaks at 1 hour, whereas in Figures 2A\nand 2D (right), it peaks at 0.5 hours, and in Figure 3I, it peaks at 2 hours. How can these variations be\nexplained?\nAuthor response: We agree that the apparent timing of peak activation varies across experiments. A clear\nmajor factor in this difference is the starting cholesterol condition. In Fig. 1d (Fig. 1e in the revised\nmanuscript) and in the cholesterol-induced phosphoproteomics time course in Fig. 2d,e, larvae were shifted\nfrom a fully cholesterol-free diet (0 µg/mL) to cholesterol-replete food (80 µg/mL), which elicited an early\nmaximal response that then changed dynamically over time. By contrast, when larvae were shifted from\nlow but non-zero cholesterol (1.2 µg/mL) to 80 µg/mL (Fig. 1e, and 3i, now Fig. 1f and supplementary Fig.\n5a in the revised manuscript), the increase in pS6 was delayed and peaked later. In addition, the different\nreadouts (pS6 immunostaining, unkempt-Luciferase, and phosphoproteomics) capture TOR activity with\ndistinct temporal integration and sampling times, which can further influence the apparent timing of the\nmaximum. We have clarified in the revised Results which time courses begin from 0 versus 1.2 µg/mL\ncholesterol and noted that these differing starting conditions alter the timing of the pS6 response, and we\navoid overinterpreting small differences in peak timing across assay modalities and experimental contexts.\nReviewer point 4) Figure 2C shows that each time point has only two replicates (n=2), which is insufficient.\nAlthough the authors may want to include all samples in a single TMT experiment, this design does not\nallow for statistical analysis. It is unclear how the statistical p-values were calculated in Figures 2GH. The\nnumber of replicates for other omics experiments should also be specified.\nAuthor response: We agree with the reviewer that two biological replicates per time point (n = 2) provides\nlimited statistical power, and we have made sure the manuscript to (i) clearly state replicate numbers for all\nomics experiments and (ii) clarify that p-values were calculated in Fig. 2g,h and throughout the proteomics\nanalyses using the ANOVA test built into the Proteome Discoverer software package. For the cholesterol-\nrepletion time course in Fig. 2, we were constrained by the capacity of the TMTpro 16-plex format, which\nlimited the number of biological replicates that could be included while still covering the full set of time\npoints and conditions within a single multiplexed experiment.\nDespite this limitation, statistical inference is still possible for TMT-based phosphoproteomics with n = 2\nby using the ANOVA-based models implemented in Proteome Discoverer, which estimate variance across\nreporter-ion intensities and conditions within the multiplexed sample and test for condition-dependent\nchanges. Phosphorylation changes were considered significant using the predefined criteria of P<0.05 and\na minimum fold-change threshold of 30%, as stated in the figure legends and the Methods section. These\nstatistical procedures are standard for multiplexed TMT-based phosphoproteomics and allow inference\nacross conditions despite the limited number of biological replicates per time point. In addition, it is worth\nnoting that standard statistical tests (including ANOVA) are mathematically valid for n = 2 (although less\nwell-powered), as also described by GraphPad Prism’s guidance on statistics with low n (see text below\nfrom GraphPad Prism, source: https://www.graphpad.com/support/faqid/591/)\nImportantly, our conclusions from Fig. 2 do not rely solely on this n = 2 time course. The key biological\ninferences, namely that dietary cholesterol rapidly activates TOR signaling and drives a distinctive dynamic\nphosphorylation response, are supported by multiple orthogonal datasets and independent experiments.\nThese include pS6 immunostaining time courses in the fat body (Fig. 1e,f), whole-animal TOR-reporter\nmeasurements using the unkempt-Luciferase assay (Fig. 2a), and independent genetic and mechanistic\nexperiments demonstrating the dependence of the cholesterol response on HR3 and TOR (Figs. 5-6 and\nassociated Supplementary Figures). In addition, later phosphoproteomic experiments in the manuscript that\ntest HR3 or TOR dependence were performed with increased replication (n = 3 independent biological\nreplicates per condition), and these experiments recapitulate the central conclusions from the initial time-\nc initial time-\ncourse analysis (Fig. 5f,g and associated Supplementary Tables), further strengthening the evidence base.\nFinally, genetic experiments (Fig. 5a-c; Fig. 6e,i,j) provide functional validation of the signaling\nrelationships inferred from the omics data.\nIn the revised manuscript, we have ensured that the n numbers and the statistical analyses are presented\nclearly and consistently across the Results, Figures, and Methods.\nReviewer point 5) Figure 2C shows that the proteomics data were obtained from the flow-through of TiO2\nenrichment, which introduces some variations. Why didn't the authors take a small portion of the sample\nbefore TiO2 enrichment for bulk proteomics? Additionally, it should be clarified whether the quantification\nof phosphorylation was normalized based on protein abundance.\nAuthor response: The reviewer is raising a point regarding normalization and the use of the TiO flow-\n2\nthrough. In large-scale phosphoproteomics experiments, phosphorylation changes are most commonly\nquantified without normalization to total protein abundance. There are two main reasons for this. First, after\nTiO enrichment, the corresponding non-phosphorylated peptide or protein is not consistently detected for\n2\nall phosphosites in the flow-through. Normalizing phosphosite intensities to total protein abundance would\ntherefore require exclusion of a substantial fraction of regulated phosphosites, reducing coverage and\npotentially biasing the analysis toward highly abundant proteins. Second, normalization to total protein\nintroduces additional statistical noise, as both the phosphopeptide and the corresponding non-\nphosphopeptide carry their own variance. Propagating these independent sources of variability can reduce\nstatistical power rather than improve it, which is why many phosphoproteomics studies report\nphosphorylation changes without protein-level normalization when global protein abundance changes are\nnot the primary focus. For these reasons, we did not routinely normalize phosphosite abundances to total\nprotein levels in the global analyses shown in Fig. 2 and related datasets. Instead, phosphosite quantification\nwas based on TMT reporter ion intensities normalized to total peptide signal, which corrects for sample\nloading and labeling efficiency and represents standard practice for time-resolved phosphoproteomics.\nHowever, to address the reviewer’s concern, we also analyzed the TiO flow-through to detect non-\n2\nphosphorylated peptides, and we normalized selected phosphosites in key TOR-pathway proteins to the\nmatched total (non-phosphopeptide) abundance when this information was available. Specifically, this\nprotein-level normalization was applied to TOR-related proteins in Fig. 5i–k and Fig. 7h, where\ncorresponding non-phosphorylated peptides were reliably detected. This has now been explicitly clarified\nin the Methods and Figure legends. We believe this added clarification in the revised manuscript and\ntargeted normalization address the reviewer’s concerns.\nReviewer point 6) Figures 2C-H: The authors have designed a complex omics experiment, but the analysis\nis too superficial, lacking information on temporal changes. Although the experiment included eight time\npoints, the data in Figures F-H represent changes at a single time point. The authors should provide a\ncomprehensive analysis of phosphorylation and proteome changes over time during cholesterol treatment,\ndistinguishing changes between fast and slow responses.\nAuthor response: We have now explicitly addressed this by performing and presenting a time-resolved\nanalysis of the phosphoproteomic data. To capture temporal dynamics rather than single time-point effects,\nwe applied fuzzy c-means clustering to the phosphorylation profiles measured across all eight time points\n(15 minutes to 10 hours) following cholesterol or protein refeeding and included these data in new Fig. 2g,h\nand Supplementary Figs. 2b-d and 3a,b. This approach groups phosphosites based on shared temporal\ntrajectories and allows individual sites to partially belong to multiple clusters, which is well suited for\nnutrient-responsive signaling programs.\nThis analysis revealed marked temproal differences in how signaling networks are engaged by the two\nnutrients. Cholesterol refeeding elicited a highly dynamic and temporally structured phosphorylation\nprogram, with sites partitioning into multiple distinct clusters exhibiting different temporal response\nprofiles (Supplementary Fig. 2b-d). In contrast, protein refeeding produced a more sustained and\ncomparatively monotonic phosphorylation response, with fewer pronounced temporal transitions within the\nclusters (Supplementary Fig. 3a,b). Thus, the dataset captures distinct temporal architectures rather than\neffects at a single time point. We further performed Reactome enrichment (ORA) for each temporal cluster,\ndemonstrating that clusters are differentially associated with functional modules. In particular, cholesterol-\nresponsive clusters showed differential association with pathways including RNA metabolism and\nprocessing, whereas enrichment analysis of protein-responsive clusters also revealed involvement of RNA\nprocessing pathways (Supplementary Figs. 2 and 3). Collectively, this temporal clustering analysis shows\nthat cholesterol induces dynamic, phase-specific responses, whereas protein promotes sustained signaling,\ndirectly addressing the reviewer’s request for a comprehensive time-resolved analysis distinguishing fast\nand slow responses. We have revised the Results text to emphasize these observations.\nReviewer point 7) Other omics experiments have similar issues and need revisions. For example, Figures\n5D-G show three different omics datasets at three different time points, making it difficult to correlate the\ndata.\nAuthor response: We agree that correlating multi-omics datasets measured at a limited number of time\npoints is inherently challenging. To address this, we explored whether a similar temporal clustering\napproach to that used for the phosphoproteomic dataset in Fig. 2 could be applied to the datasets shown in\nFig. 5. Specifically, we performed fuzzy c-means clustering on the Fig. 5 phosphoproteomic data (three\ntime points: 0, 1, and 6 hours). However, with only three time points, the analysis did not produce distinct\ntemporal clusters that yielded robust or interpretable pathway enrichment, and the resulting patterns were\nnot informative beyond what is already captured by the direct comparisons shown in the figure. For this\nreason, we decided not to include this analysis in the manuscript. For the same reason, we did also not apply\ntemporal clustering to the phosphoproteomic data shown in Fig. 7, which is also limited to three time points.\nIn contrast, the dataset in Fig. 2 includes eight time points across a 10-hour window, providing sufficient\ntemporal resolution to distinguish fast, delayed, and sustained responses and enabling meaningful clustering\nand pathway-enrichment analyses. We believe that applying identical temporal analyses to datasets with\nfundamentally different temporal resolutions would not be informative. Nevertheless, we are of course\nhappy to include clustering analyses for Fig. 5 or Fig. 7 in the manuscript if the reviewer feels this would\nbe valuable.\nReviewer point 8) Figures 3A and 3B: Please provide deconvoluted MS data. The legend for Figure 3B\ndescribes the spectrum as collision-induced dissociation, while the main text describes it as denatured\nsamples, which are two completely different experiments. The authors need to clarify this discrepancy.\nAuthor response: We thank the reviewer for bringing this discrepancy to our attention. We agree that the\nwording in the original text and figure legend could be interpreted as describing two different MS\nexperiments. We have corrected this for clarity in the revised manuscript.\nFigure 3a (3d in the revised manuscript) shows native (non-denaturing) ESI-MS of purified HR3 LBD,\nrevealing apo HR3 and a liganded HR3:cholesterol complex. Figure 3b (3e in the revised manuscript) shows\nthe same native MS preparation subjected to collision-induced dissociation (CID), which promotes loss of\nthe bound ligand and yields the corresponding apo HR3 species. Thus, panel e is not a separate denaturing-\nMS experiment, but a CID spectrum acquired from the native complex to demonstrate ligand dissociation.\nWe have revised the Results text to reflect this accurately.\nRegarding the request for deconvoluted MS data, these data were obtained some time ago, and we no longer\nhave access to the original files or deconvolution outputs beyond the spectra shown in the figure. To address\nthis transparently, we have added a supplementary Fig. 4e reporting the dominant charge state and\ncalculated neutral masses corresponding to the labeled m/z peaks in Fig. 3d,e. These calculations confirm\nthat the mass difference between apo HR3 and the liganded species is consistent with cholesterol.\nIn addition, to further strengthen the interpretation of cholesterol binding, we have included new structure-\nbased analyses in the revised manuscript. Using the modeling package Boltz-2, we generated a predicted\nHR3 ligand-binding domain structure and found that cholesterol is placed into the predicted HR3 pocket in\na configuration closely resembling that observed in the crystal structure of cholesterol-bound RORα (new\nFig. 3a-c; Supplementary Fig. 4a-c). We have also added a new HR3–RORα sequence alignment\nhighlighting conservation of residues implicated in ligand recognition, including key arginines near the 3β-\nhydroxyl group and multiple pocket residues reported to contact cholesterol in RORα, supporting\nconservation of the cholesterol-binding pocket in HR3 (Supplementary Fig. 4d). Together, these additions\nclarify the MS methodology and provide complementary structural support for cholesterol binding by HR3.\nReviewer point 9) The interaction between cholesterol and the HR3 LBD requires more functional\nvalidations. For example, does the deletion of LBD abolish cholesterol-induced TOR activation?\nAuthor response: We agree that validation of the HR3-cholesterol interaction is important. In the revised\nmanuscript, we therefore provide structural, biochemical, and functional evidence supporting that\ncholesterol acts through the HR3 ligand-binding domain (LBD) to regulate TOR signaling.\nFirst, we added in silico structural modeling showing that Boltz-2 places cholesterol into the predicted HR3\nLBD pocket without manual constraints, in a pose highly similar to the cholesterol ligand observed in the\nexperimentally solved RORα LBD structure (Fig. 3a,b). The predicted HR3 LBD also aligns well with\nRORα (Fig. 3c), and the cholesterol 3β-hydroxyl group is positioned near a pair of conserved arginine\nresidues that correspond to the pocket residues implicated in sterol coordination in RORα (Supplementary\nFig. 4a-d).\nSecond, we previously demonstrated direct cholesterol binding to the purified HR3 LBD by native MS and\nGC/MS, and we showed cholesterol-dependent activation of an HR3 LBD–based in vivo ligand sensor (Fig.\n3d–l).\nThird, to address the reviewer’s requested functional test of LBD necessity, we generated new domain-\nspecific HR3 transgenes that allowed us to directly compare an HR3 variant that retains the DNA-binding\ndomain (DBD) but lacks the LBD versus the variant that contains only the LBD (versus the isoform with\nboth domains). Specifically, we produced a full-length HR3 construct and an LBD-deleted truncation\n(HR3K243X), which retains the DNA-binding domain (DBD) but lacks the ligand-binding domain (LBD),\nand we compared these to the HR3(LBD) isoform that lacks the DBD and promotes cholesterol-induced\nTOR activation (new Supplementary Fig. 6f). Using these constructs, we find that HR3(LBD)\noverexpression enhances cholesterol-induced TOR activation (pS6) (Fig. 6d,e), whereas HR3K243X (DBD-\nonly; lacking the LBD) does not promote cholesterol-induced TOR activation and instead blunts the\nresponse, similar to full-length HR3, with full-length HR3 producing the strongest attenuation\n(Supplementary Fig. 6f). These results provide the requested functional validation and support that the LBD\nis required for cholesterol-dependent TOR activation, while the isolated LBD is sufficient to drive the\ncholesterol-induced TOR response.\nBecause full-length HR3 and HR3K243X both maintain DNA-binding capacity, but only full-length HR3\nretains an intact LBD, these observations suggest that DBD-dependent occupancy of target loci generally\nrestrains TOR signaling, and that ligand-bound full-length HR3 can further engage transcriptional programs\nthat dampen cholesterol-stimulated TOR activity, in contrast to the rapid, potentially non-genomic, LBD-\ndependent activation mediated by HR3(LBD). This dual behavior aligns with our genome-wide\ntranscriptional and phosphoproteomic analyses (Fig. 5d,f,g), which show that HR3 not only enables a large\nset of cholesterol-responsive genes and phosphoproteins, but also suppresses inappropriate or excessive\ncholesterol responses, consistent with a model in which HR3 provides both activating and feedback-limiting\nfunctions to maintain balanced TOR-pathway output. We have included text in the Results section of the\nrevised manuscript that explains these new domain-specific transgene data and highlights how they support\na model in which the HR3 LBD is the key determinant of cholesterol-dependent TOR activation, while the\nDBD-dependent functions primarily impose transcriptional restraint and feedback control over TOR output.\nTogether, these additions provide further mechanistic support that cholesterol engages HR3 via its LBD and\nthat this interaction is functionally important for cholesterol-induced TOR signaling.\nMinor Points:\nReviewer point 10) The authors primarily use pS6 as a marker for TOR pathway activation. Are there other\nmarkers, such as phosphorylation of TOR itself, that can be used to validate changes in TOR pathway\nactivity?\nAuthor response: We agree that it is best practice to support TOR activity changes with more than one\nreadout. In the manuscript, we already include multiple independent TOR-pathway activity measures\nbeyond pS6: (i) pS6K (a direct TOR target) in whole-larval immunoblotting (Fig. 1c), (ii) the unkempt-\nLuciferase TOR reporter, which provides an orthogonal functional readout of TOR activity in vivo (Fig.\n2a,b), and (iii) our phosphoproteomics datasets, which capture coordinated TOR-pathway-associated\nphosphorylation responses, including S6 and upstream TOR-pathway regulatory components (for example\nRAPTOR and LAMTOR/Ragulator-associated factors; Fig. 5i–k) and, in human cells, additional\nestablished TOR-pathway nodes including 4EBP1 and AKT/TSC2 axis components (Fig. 7h).\nRegarding phosphorylation of TOR itself, we did not use this as a primary validation readout because TOR\nphosphorylation sites are not always a reliable proxy for TOR kinase output across contexts, and in\nDrosophila in particular the availability and performance of phospho-TOR antibodies suitable for\nquantitative tissue assays can be limiting.\nReviewer #4 (Remarks to the Author):\nThe manuscript of Lassen and coauthors investigates the molecular mechanisms that link dietary intake of\ncholesterol with the growth regulatory TOR pathway. By using Drosophila melanogaster as a model system,\nthey first demonstrate that dietary cholesterol feeding induces TOR activation in fat bodies and PG cells as\nwell as in whole animals. They show that the nuclear receptor HR3, the Drosophila ortholog of human\nRORa, is activated by cholesterol binding and modulates TOR activity independently of ecdysone-\nmediated effects They further demonstrate that HR3 can mediate Cholesterol-induced TOR activation\nthrough an isoform that lacks DNA-binding domain. Reducing HR3 in cells can mitigate the\nhyperactivation of TOR and intralysosomal accumulation of cholesterol caused by depleting Npc1a. These\nresults indicates that HR3 is required for TOR activation by lysosomal cholesterol. Finally, they use\nKARPAS-707H cells, which strongly express human RORa, to explore the potential role of human RORa\nin modulating the TOR-pathway in response to exogenous cholesterol. Collectively their findings suggest\nthat HR3/ RORa represents a conserved mechanism for coupling cholesterol levels to TOR pathway\nactivation.\nOverall, data in the manuscript are solid, providing a significant advance in our understanding of how\ncholesterol activates cell growth via TOR signaling with potential implications in cancer and cholesterol-\nrelated pathologies. The work and data analysis are well conducted and support the conclusions and the\nresearch methodologies are sound and innovative including phosphoproteomic analysis and mass\nspectrometry, RNA-seq. Thus, the manuscript is suitable for publication in this journal. I only have minor\npoints:\nAuthor response: We appreciate the reviewer’s careful reading of our manuscript and the constructive\nfeedback. We are grateful for the positive evaluation of the significance, rigor, and methodological\ninnovation of our work. We have revised the manuscript to address all the minor points raised, which are\ndiscussed below.\nReviewer point 1) Figure 7I illustrates the model for the role of HR3 in cholesterol-mediated TOR\nactivation. However, the model seems relatively simple with respect to the large amounts of data reported\nin the paper. I would suggest to add a figure (figure 8) reporting the main conclusions of the paper.\nAuthor response: We agree that the original model schematic in Fig. 7i was too simplified relative to the\nbreadth of data presented in the manuscript. To address this, we have added a new summary figure (Fig. 7i)\nthat consolidates the main conclusions of the study into a single working model. The new figure provides\nmore mechanistic detail on where HR3 intersects with the TOR pathway, based on our genetic and\nphosphoproteomic data. In particular, the schematic places HR3 upstream of the RagA/B module and\nincorporates the evidence that cholesterol-induced TOR activation in the fat body is independent of the\nLYCHOS/Anchor and GATOR modules while still requiring HR3. The figure also emphasizes the dual role\nof HR3 in TOR regulation: the protein induces a rapid, non-genomic, LBD-dependent activation of TOR\nsignaling, alongside a DBD-dependent genomic program that restrains and shapes the longer-term response.\nReviewer point 2) A diagram illustrating the TOR pathway in Drosophila, the subcellular localization of\nthe TOR pathway proteins (for example RagA and RagB) could facilitate the reader. This diagram could be\nshown in Figure 1 and cited in the introduction.\nAuthor response: We agree that a schematic overview of the TOR pathway and the subcellular localization\nof key components would facilitate the reader, and we appreciate this helpful suggestion. We have therefore\nadded a new panel (Fig. 1a) as suggested illustrating the TOR pathway, including the localization of the\nRag module and associated regulators in the context of lysosomal TOR activation.\nReviewer point 3) The subcellular localization of HR3 (and human RORa as well) is a key point for the\nconclusions in the paper. However, HR3 localization is shown in a supplemental figure S4F.\nI would suggest to incorporate these data into one of the main figures.\nAuthor response: We agree that the subcellular localization of HR3 is an important aspect of our\nconclusions. To address this point, we have now incorporated representative HR3 immunostaining images\ninto the main figure as suggested by the reviewer (Fig. 6h), showing HR3 localization in the larval fat body\nunder cholesterol-deprived conditions and after cholesterol re-feeding.\nReviewer point 4) On page 7, lines 270-271: “Knockdown of HR3 in the fat body or PG using an RNAi construct that targets all of the\nannotated transcript variants of HR3 led to reductions in pS6 staining in these tissues”. The authors should clarify how they\ndemonstrated (or it was previously shown), or where they show in the manuscript, that the RNAi construct targets all of the\nannotated transcript variants of HR3.\nAuthor response: We appreciate the reviewer highlighting this point. In the revised manuscript, we have\nincluded new data to clarify how each of the three independent HR3-RNAi lines used in this study targets\nHR3. As shown in the new panel in Supplementary Figure 6f, we have provided schematic representations\nof the HR3-RNAi constructs used, indicating that each RNAi line targets distinct regions of the HR3 gene.\nThis revision clarifies how the RNAi approach targets all annotated HR3 transcript variants. Thank you for\nhelping us improve the clarity of the manuscript.\nReferences\n1. Kallen, J.A. et al. X-ray structure of the hRORalpha LBD at 1.63 A: structural and\nfunctional data that cholesterol or a cholesterol derivative is the natural ligand of\nRORalpha. Structure 10, 1697–1707 (2002).\n2. Montagne, J. et al. The nuclear receptor DHR3 modulates dS6 kinase-dependent\ngrowth in Drosophila. PLoS Genet 6, e1000937 (2010).\n3. Shin, H.R. et al. Lysosomal GPCR-like protein LYCHOS signals cholesterol suYiciency\nto mTORC1. Science 377, 1290–1298 (2022)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have thoroughly addressed all the concerns I raised in the first round. I do not have any further comments.\nCongratulations on this beautiful work!", "reviewer_2": "I am satisfied with the changes the authors have made in response to my comments and beyond. I commend the authors for their careful extended work and for improvements that have made it easier for readers to fully understand figures. The additional experiments the authors performed add significant new insight into the complexity of this receptor and are appreciated.", "reviewer_3": "I thank the authors for addressing my previous concerns and for the revisions made accordingly. These changes have clearly improved the overall quality, clarity, and rigor of the manuscript.\nI have one remaining technical point regarding the workflow illustrated in Figure 2C, which I believe requires clarification.\nRelated to my previous question: “Why didn't the authors take a small portion of the sample before TiO₂ enrichment for bulk proteomics?” It is not clear whether the bulk (global) proteome analysis was performed on the TiO₂ flow-through fraction or on the input sample prior to TiO₂ enrichment.\nAs currently shown, Figure 2C suggests that the bulk proteome was analyzed from the TiO₂ flow-through. This approach could potentially introduce substantial bias, as the flow-through is depleted of phosphopeptides and may not accurately represent the original proteome composition.\nIn addition, the workflow indicates HILIC (hydrophilic interaction liquid chromatography). Since HILIC, similar to TiO₂, is also commonly used for phosphopeptide enrichment, it is unclear whether two different enrichment strategies were applied.\nHowever, HILIC is not described in the Methods section. I therefore wonder whether this is a labeling error and whether the authors actually intended to indicate high-pH reversed-phase HPLC (high-pH RP-HPLC) instead.", "reviewer_4": "The manuscript of Lassen and coauthors investigates the molecular mechanisms that link dietary intake of cholesterol with the growth regulatory TOR pathway. By using Drosophila melanogaster as a model system, they first demonstrate that dietary cholesterol feeding induces TOR activation in fat bodies and PG cells as well as in whole animals. They show that the nuclear receptor HR3, the Drosophila ortholog of human RORα, is activated by cholesterol binding and modulates TOR activity independently of ecdysone-mediated effects They further demonstrate that HR3 can mediate Cholesterol-induced TOR activation through an isoform that lacks DNA-binding domain. Reducing HR3 in cells can mitigate the hyperactivation of TOR and intralysosomal accumulation of cholesterol caused by depleting Npc1a. These results indicates that HR3 is required for TOR activation by lysosomal cholesterol. Finally, they use KARPAS-707H cells, which strongly express human RORα, to explore the potential role of human RORα in modulating the TOR-pathway in response to exogenous cholesterol. Collectively their findings suggest that HR3/ RORα represents a conserved mechanism for coupling cholesterol levels to TOR pathway activation.\nThe revised version of the manuscript has addressed all the concerns that I raised in my previous review and is now suitable for publication in this journal.\nSpecifically they have added a new panel to figure 7 (Fig. 7i) showing the main conclusions of their study into a single working model based on their genetic and phosphoproteomic data.\nAs I suggested , because the subcellular localization of HR3 is an important aspect of the paper, they incorporated HR3 immunostaining images into the main figure (Fig. 6h) showing HR3 localization in the larval fat body under cholesterol-deprived conditions and after cholesterol re-feeding.\nFinally they included a new panel in Supplementary Figure 6f, providing schematic representations of the HR3-RNAi constructs that were used in the study and showing that each RNAi line targets distinct regions of the HR3 gene."}, "author_response": "February 10, 2026.\nReviewer #1\nReviewer: The authors have thoroughly addressed all the concerns I raised in the first round. I do not have any\nfurther comments. Congratulations on this beautiful work!\nAuthor response: We thank the reviewer for his or her very kind words and comments that helped us\nimprove our work.\nReviewer #2\nReviewer: I am satisfied with the changes the authors have made in response to my comments and beyond. I\ncommend the authors for their careful extended work and for improvements that have made it easier for readers\nto fully understand figures. The additional experiments the authors performed add significant new insight into the\ncomplexity of this receptor and are appreciated.\nAuthor response: We are happy to hear that our revisions have improved our manuscript to meet the\nreviewer’s expectations, and we thank the reviewer for his or her kind words. The reviewer’s\nsuggestions have certainly helped us to improve our work, and we have enjoyed working together with\nthe reviewer along the way to our finished product. Thanks!\nReviewer #3\nReviewer: I thank the authors for addressing my previous concerns and for the revisions made accordingly.\nThese changes have clearly improved the overall quality, clarity, and rigor of the manuscript.\nAuthor response: We are glad to hear that our revised manuscript meets the reviewer’s expectations,\nand we appreciate his or her kind words. We thank the reviewer for working with us to improve our\nmanuscript.\nReviewer; I have one remaining technical point regarding the workflow illustrated in Figure 2C, which I believe\nrequires clarification. Related to my previous question: “Why didn't the authors take a small portion of the sample\nbefore TiO₂ enrichment for bulk proteomics?” It is not clear whether the bulk (global) proteome analysis was\nperformed on the TiO₂ flow-through fraction or on the input sample prior to TiO₂ enrichment. As currently shown,\nFigure 2C suggests that the bulk proteome was analyzed from the TiO₂ flow-through. This approach could\npotentially introduce substantial bias, as the flow-through is depleted of phosphopeptides and may not accurately\nrepresent the original proteome composition. In addition, the workflow indicates HILIC (hydrophilic interaction\nliquid chromatography). Since HILIC, similar to TiO₂, is also commonly used for phosphopeptide enrichment, it\nis unclear whether two different enrichment strategies were applied.\nHowever, HILIC is not described in the Methods section. I therefore wonder\nwhether this is a labeling error and whether the authors actually intended to\nindicate high-pH reversed-phase HPLC (high-pH RP-HPLC) instead.\nAuthor response: We thank the reviewer for raising this important\ntechnical point regarding the workflow illustrated in Figure 2C. In\nthis study, global proteome analysis was not performed, as our\nexperimental design and downstream analyses were focused\nexclusively on phosphoproteomic signaling changes. Therefore, no\nbulk proteomics dataset derived from either the TiO flow-through\n2\nor the pre-enrichment input sample was generated or used in this\nmanuscript.\nWe also appreciate the reviewer’s astute observation regarding the\nfractionation step. The previous reference to HILIC was made in\nerror, and we have corrected the figure and its accompanying\ndescription to accurately indicate that high-pH reverse-phase HPLC,\nrather than HILIC, was used for fractionation (see updated figure).\nWe hope these revisions address the reviewer’s concerns\nsatisfactorily.\nReviewer #4\nReviewer: The revised version of the manuscript has addressed all the concerns that I raised in my previous\nreview and is now suitable for publication in this journal.\nAuthor response: We are happy to hear that our revised work meets all the reviewer’s expectations.\nWe truly appreciate his or her taking the time to help us improve the strength of our work, and we feel\nthat each reviewer’s input has indeed helped us to create a more solid contribution.\nNotes to the Editors\nIn this final stage of the revision process, we have made the changes detailed above in our reply to\nReviewer #3’s comments, and we have also made several small changes to our manuscript to respond\nto points raised in the Editorial Checklist. These amendments include shortening over-long figure\nlegends and adding missing details concerning statistical tests. We have also corrected a small number\nof typographical errors that we noticed during the process. Changed text in the manuscript is set in blue.\nIn addition to the meaningful corrections made to Fig. 2c in response to the reviewer’s comment and\nthe addition of a scale bar to Fig. 3k as requested in the Checklist, we have also made minor purely\ncosmetic changes to figures to improve their clarity (standardizing capitalization and italics, clarifying\naxis labels, adding descriptions of color scales). None of these changes affects the data or their\ninterpretation in any way.\nWe are excited to submit our final manuscript for publication in your fine journal. Once again, thank\nyou for all your assistance, and if you have any further questions or comments, please do not hesitate\nto contact me.\nSincerely,\nKim Rewitz\n2"}]

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