Homeostatic synaptic scaling allows for bi-directional adjustment of the strength of synaptic connections in response to changes in their input. Protein phosphorylation modulates many neuronal processes, but it has not been studied on a global scale during synaptic scaling. Here, we use liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses to measure changes in the phosphoproteome in response to up- or down-scaling in cultured cortical neurons over minutes to 24 h. Of ~45,000 phosphorylation events, ~3,300  are regulated by homeostatic scaling. Activity-sensitive phosphoproteins are predominantly located at synapses and involved in cytoskeletal reorganization. We identify many early phosphorylation events that could serve as sensors for the activity offset as well as late and/or persistent phosphoregulation that could represent effector mechanisms driving the homeostatic response. Much of the persistent phosphorylation is reciprocally regulated by up- or down-scaling, suggesting that mechanisms underlying these two poles of synaptic regulation make use of a common signaling axis.  •Global proteome and phosphoproteome dynamics following homeostatic synaptic scaling•Approximately 3,300 activity-sensitive, synapse-associated phospho-events•Persistent signaling of ~25% of initial phospho-events (min to 24 h)•Persistent and reciprocal phosphoregulation links synaptic up- and down-scaling Using LC-MS/MS, Desch et al. investigate protein phosphorylation as an additional layer of regulation shaping homeostatic synaptic scaling. Characterizing the temporal dynamics of activity-sensitive phosphorylation, they detect potential sensor and/or effector events for up- and down-scaling. They find sustained and reciprocal phosphorylation patterns, which mechanistically couple the opposing scaling phenotypes. Adjustments to neuronal network properties can be achieved by activity-dependent plasticity. Homeostatic synaptic scaling is one such form of plasticity that involves the compensatory and global up- or downregulation of synaptic strength in response to changes in the level of synaptic input. For example, homeostatic up-scaling occurs when network activity is reduced by the cessation of action potentials. In contrast, homeostatic down-scaling can be elicited when network activity is elevated by blocking inhibitory synaptic transmission , 2012. M2+ levels may serve as an activity-regulated early signal during scaling  is a well-studied kinase that is important for many different forms of plasticity. During long-term potentiation (LTP), Camk2a undergoes increases in auto-phosphorylation on Thr286, resulting in Ca2+-independent kinase activity  and then implement the scaling response (effectors). Both modeling and experimental studies suggest that intracellular Ca scaling . Phosphotability . Ca2+-seactivity . While pactivity .876) during the late phase of homeostatic up-scaling using phospho-deficient knock-in mice. Other recent studies have examined phosphorylation during sleep -based proteomics pipeline that enabled us to identify and quantify unmodified and phosphorylated proteins in a global and unbiased manner. We monitored the activity-sensitive phosphorylation events at the timescale of minutes or 1 day following scaling, establishing a dataset of 3,382 differentially regulated phosphorylation events of 1,285 unique proteins associated with different phases and types of homeostatic scaling. We found distinct regulatory signatures converging on proteins of the synaptic compartment that exhibited persistent, reciprocal, and time-sensitive phosphorylation. A quarter of the initial phosphorylation events detected were persistently regulated in a reciprocal manner, reflecting the opposite polarity of up- and down-scaling.To investigate the dynamics of activity-dependent protein phosphorylation in cortical neurons, we conducted MS-based phosphoproteomics during homeostatic plasticity A. HomeosAcross all experimental groups, our analysis detected 4,520 phosphorylated proteins identified in depth with 45,691 phosphopeptide species. These phosphorylation events were filtered for reliable phosphorylation site assignment with a location probability greater than 75% (class I), allowing us to map the modification site to a specific amino acid residue B. In dowThe induction of homeostatic up- or down-scaling led to a large and global regulation of protein phosphorylation see . TreatmeThe above dataset of 3,382 different activity-regulated phosphorylation events was associated with 1,285 unique proteins . DiffereAs the experiments were performed using mixed cultures obtained from the cerebral cortex, we assessed regulatory contribution of cell-type-specific proteins using the top 50 markers of a previously published cell-type-resolved proteomic dataset  or an exHow is protein phosphorylation regulated over time during homeostatic scaling? For both up- and down-scaling, the extent of phosphoregulation clearly increased with the duration of the manipulation C and 1D.2-transformed fold changes of all the persistently regulated phosphorylation events to hierarchical clustering  exhibited significant regulation at all three tested time points B. We subustering C.286 on Camk2a, which leads to Ca2+-independent enzyme activity (421 of Methyl-CpG-binding protein 2 (Mecp2), the Rett syndrome protein implicated in synaptic plasticity and an established Camk2a substrate (556 on Synapsin 1 (Syn1), another Camk2a-regulated site  , the cell-signaling mechanisms are not well understood. To examine whether the same proteins were regulated by up- and down-scaling, we expanded the clustering analysis to phosphorylation events that were regulated in at least four of six possible conditions across both experimental groups. The phosphorylation events that were regulated in both scaling groups showed a remarkable reciprocity in the sign of regulation A: upreguuctance) .Figure 3286), ERK1 (Thr203/Tyr205), or ERK2 (Thr183/Tyr185) showed significant reciprocal regulation. Subunits of Ca2+-sensitive phosphatases (Pppc3a-c) were also reciprocally phosphorylated .To understand how this bi-directional phosphorylation pattern is achieved, we investigated kinases and phosphatases that were differentially phosphorylated by scaling. We detected many regulated phospho-events on kinases and, to a lesser extent, also on phosphatases A–S6D. Prorylated D, thoughZ score;  was calcZ score; E. There,286 phosphorylation at both 5 min and 24 h, whereas up-scaling resulted in a trend for enhanced phosphorylation at 24 h  assigned to all three temporal classes . During TTX-induced up-scaling, significant changes were limited to three different phospho-sites that were either continuously regulated (Ser422: downregulated) or regulated during the late phase of stimulation . These modifications were mostly reciprocally regulated  and located within or close to the PDZ domains of Dlg4-regions that have already been associated with fine-tuning Dlg4’s molecular associations (592 phosphorylation of Rims1 (Ser413 in mice) was observed, which is associated with LTP and the recruitment of 14-3-3 adaptor proteins  and Cplx2 of the presynaptic active zone showed a significant change only during the early phase of Bic-induced down-scaling. In addition, Bic-induced phosphorylation of two postsynaptic proteins— synaptic adhesion-like molecule 1/2 (Lrfn2 and Lrfn1), known to interact with central proteins of the postsynaptic density .We detected 282 and 1,039 regulated phosphorylation events unique to the early phase of up- or down-scaling, respectively; these modifications could be responsible for sensing the global change in activity. A subset of 110 events was shared between up- and down-scaling. These common sensor events could detect a sign-dependent or sign-independent activity set point offset. Indeed, we noted that 47% of these common early phosphoregulatory events were reciprocal in nature, manifest as either a decrease or an increase in phosphorylation, depending on the type of scaling. We noted that in early time points, down-scaling was associated with many more differential phosphorylation events than up-scaling. This could be due to rapid, Bic-evoked change in intracellular CaSer1511; . Also, tchanisms , exhibit876, after 48 h up-scaling; Phosphorylation events that were present during the late phase of synaptic up- or down-scaling represent potential effectors—modifications that are required to express the homeostatic response. We detected 912 and 681 potential effector events unique to the late phase of up- and down-scaling, respectively. The number of common effector events shared between the opposite scaling groups was 149, with the majority (74%) exhibiting reciprocal, polarity-dependent regulation. Since synaptic scaling converges on changes of synaptic weight by changing the number of AMPA receptors in the postsynaptic membrane , obvious2+-sensitive kinases. For example, we detected the persistent phosphoregulation of Ser421 of the transcription factor Mecp2, which has been reported as a critical factor during synaptic scaling  of initially regulated phospho-events were continuously switched either on or off  at the tested time points. They could function as set point offset sensors or scaling effectors, but whether there is a true persistence or a re-appearing/cyclic behavior in the regulation of these events cannot be determined. This set of phospho-events was associated with proteins of the postsynapse involved in organization of the cytoskeleton and, during down-scaling, in modulation of synaptic transmission as well. The observed persistent phosphorylation differs from the proteomic regulation throughout homeostatic scaling, where little overlap in the identity of the newly synthesized proteins of the early (2 h) and late phases (24 h) was observed . Among t scaling . Ser421 cription . We fountraining . In modetraining . Moreovetraining , and pertraining .2+-sensitive phosphatase candidates matching the bi-directional profile we detected [Ppp3ca-c]). Reciprocal phosphorylation might also represent a polarity-specific tagging component allowing capture and/or retention of plasticity-related mRNAs or proteins driving downstream mechanisms, leading to scaling of corresponding polarity. Examples of phosphorylation sites that could serve as bi-directional switches are Ser422 or Ser516 on Dlg4 or Arhgef7, postsynaptic proteins involved in receptor-trafficking and signaling, respectively. In the late phase of scaling, phosphorylation of Ser418 of Dlg4 in the same reciprocal nature as Ser422 was also observed. With respect to protein structure, both sites are directly localized between the PDZ-3 and SH3 domain of Dlg4. These domains are known to enable the interaction with a broad spectrum of proteins .Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Erin M. Schuman  were prepared for each experiment. Three dishes each were treated with either 20 μM Bicuculline, 1 μM Tetrodotoxin or no drug (control) for 5 min, 15 min or 24 hr. Afterward, the cells were harvested by briefly washing with ice-cold DPBS (Invitrogen) supplemented with protease inhibitor cocktail  and phosphatase inhibitors , followed by scraping and pelleting by centrifugation.Cell pellets were lysed using lysis buffer , and then disrupted with a pipette and four sonication cycles for 30 s. Lysates were incubated with Benzonase  for 10 min at room temperature. To clear debris from the samples, they were centrifuged for 8 min at 13,000 x g. Protein concentration was determined by a BCA assay . The samples were diluted 1:5 prior to the assay to minimize interference of the high detergent concentration.For bottom-up MS analysis, protein digestion was performed according to an adapted version of the suspension trapping protocol as described by the manufacturer . In brief, 350 μg of protein in lysis buffer was reduced by DTT addition in a final concentration of 20 mM for 10 min. Then proteins were alkylated using iodoacetamide at a final concentration of 40 mM and incubated for 30 min at room temperature in the dark. Afterward, the sample was acidified by addition of phosphoric acid to a final concentration of 1.2%. Binding buffer  was added in a 1:7 lysate to buffer ratio. The mixture was loaded onto the S-Trap filter (mini) by centrifugation for 30 s at 4,000 x g in 450 μl-steps and washed with 400 μl binding buffer for four times. Sequencing-grade trypsin  was added in 150 μl digestion buffer (40 mM ammonium bicarbonate) in an enzyme-to-protein ratio of 1:50. The protease buffer was briefly (1-3 s) spun into the trap; solution passing the filter was re-added on top. Digestion was carried out overnight at room temperature under gentle agitation and in a humidified chamber to prevent filters from drying out. To elute peptides, the filter was rinsed in three consecutive steps by centrifugation at 1,000 x g for 60 s starting with 80 μl digestion buffer and two 80 μl washes with 0.2% formic acid (FA) in MS grade water.18-SepPak columns  as previously described  at room temperature and stored at −20°C until LC- MS analysis or further use. Enrichment for phosphorylated peptides was performed using Titanium dioxide beads  as described in the manufacturer’s protocol. Eluted peptides were dried in vacuo at room temperature and stored at −20°C until LC-MS analysis.After digestion, the peptides were desalted using Cescribed . DesalteEach experiment was carried out in four independent biological replicates.18-PepMap 100 trapping column  and separated on a C18-EasySpray analytical column  using a nano-HPLC (Dionex U3000 RSLCnano). Temperature of the column oven was maintained at 55°C.Dried peptides or phosphorylated peptides were reconstituted in 5% acetonitrile (ACN) with 0.1% FA or 2% ACN with 0.1% FA, respectively. Peptides were loaded onto a C2O with 0.05% triflouroacetic acid). Peptides were separated by a gradient of water (buffer A: 100% H2O and 0.1% FA) and acetonitrile  with a constant flow rate of 300 nL/min. The gradient for unmodified peptides went from 4% to 48% buffer B in 180 min, the gradient for phosphorylated peptides from 4% to 30% buffer B in 110 min and to 45% buffer B in 10 min. All solvents were LC-MS grade and purchased from Riedel-de Häen/Honeywell .Trapping was carried out for 6 min with a flow rate of 6 μl/min using loading buffer  coupled to the nano-HPLC (Dionex U3000 RSLCnano) by an EASY Spray ESI source. MS1 survey scans were acquired over a scan-range of 350 to 1400 mass-to-charge ratio (m/z) in the Orbitrap detector (resolution (R) = 120k, automatic gain control (AGC) = 2e5 and maximum injection time: 50 ms,). Sequence information was acquired by a “top speed” MS2 method with a fixed cycle time of 2 s for the survey and after MS/MS scans. MS2 scans were generated from the most abundant precursors with a minimum intensity of 5e3 and charge states from two to five. Selected precursors were isolated in the quadrupole using a 1.4 Da window and fragmented using higher-energy C-trap dissociation (HCD) at 30% normalized collision energy. For MS2, an AGC of 1e4 and a maximum injection time of 300 ms were used. Resulting fragments were detected in the ion trap using the rapid scan rate . Dynamic exclusion was set to 30 s with a mass tolerance of 10 parts per million (ppm). All LC- and MS-parameters are listed in the 286 , total Camk2a , Mapk1/ Mapk3 phosphorylated at pT183-pY185/ pT203-pY205  or total Mapk1/ Mapk3 . Secondary antibodies, anti-mouse  and anti-rabbit  were purchased from LI-COR . Densiometric quantification was conducted using LI-COR Image Studio Lite (RRID:SCR_013715). All experiments were performed in three independent biological replicates.Primary cortical cultures were prepared and maintained as described above. After 19 DIV, cells were incubated with either 20 μM Bicuculline, 1 μM Tetrodotoxin or no drug (control) for 5 minutes or 24 hr. Cell lysates were prepared as described and equal protein amounts were loaded onto 4% to 12% Bis-Tris NuPAGE gels (ThermoFisher Scientific). After electrophoreses, proteins were transferred to a PVDF membrane . Immunoblotting was performed with primary antibodies against Camk2a phosphorylated at pTSCR_014485; Rattus norvegicus database downloaded from the UniProtKB , a contaminant and decoy database. Tryptic peptides with a minimum length of seven amino acids and a maximum of two missed cleavage sites were included. Precursor mass tolerance was set to 4.5 ppm and fragment ion tolerance to 0.5 Da. Carboxyamidomethylation of cysteine residues was set as a static modification. Acetylation (Protein-N-term.) and oxidation of methionine residues were assigned as variable modifications. Analysis of the phosphoproteome included the assignment of phosphorylation of serine, threonine and tyrosine residues as variable modification. With the use of a decoy strategy, a false discovery rate (FDR) below 1% at protein, peptide and modification level was applied. The “match between runs” option was enabled . Only proteins identified by at least one unique peptide were considered for further analysis. Label-free quantification of proteins was performed by pairwise ratio determination using at least two common peptides in at least three consecutive full scans , only treatment-control pairs of proteins or phosphorylation events, which could be quantified in all four biological replicates in at least one technical replicate, were considered for further statistical analysis. Additional valid value filtering required 50% valid entries across each pairwise data matrix. We used the lme4 package in the statistical computing software R to perform a linear mixed effect analysis  and matched to the regulated phosphoproteome using their gene name. Furthermore, a systematic approach to identify excitatory neuron-enriched proteins was performed by matching a previously reported neuron-enriched classifier (To assess contribution of different cell-types known to occur in mixed cultures of primary brain areas, we first compared the regulated phosphoproteome to established marker proteins for neurons, microglia, oligodendrocytes and astrocytes . For thiassifier  to the rProteins of the synaptic phosphoproteome  were selTo compare phosphoregulation associated with synaptic plasticity , we extr


The finding explained how immune recognition receptors expressed by many immune cells, which lack intrinsic catalytic activity, can transduce activation signals via non-covalent association with non-receptor tyrosine kinases. The discovery also established the concept that a protein tyrosine phosphorylation cascade operated in T-cells. In this vein, we and others then showed that the CD4- and CD8-p56lck complexes phosphorylate the TCR complexes which led to the identification of other protein-tyrosine kinases such as ZAP-70 and an array of substrates that are now central to studies in T-cell immunity. Other receptors such as B-cell receptor, Fc receptors and others were also subsequently found to use src kinases to control cell growth. In T-cells, p56lck driven phosphorylation targets include co-receptors such as CD28 and CTLA-4 and immune cell-specific adaptor proteins such as LAT and SLP-76 which act to integrate signals proximal to surface receptors. CD4/CD8-p56lck regulated events in T-cells include intracellular calcium mobilization, integrin activation and the induction of transcription factors for gene expression. Lastly, the identification of the targets of p56lck in the TCR and CD28 provided the framework for the development of chimeric antigen receptor (CAR) therapy in the treatment of cancer. In this review, I outline a history of the development of events that led to the development of the “TCR signaling paradigm” and its implications to immunology and immunotherapy.The past 25 years have seen enormous progress in uncovering the receptors and signaling mechanisms on T-cells that activate their various effecter functions. Until the late 1980s, most studies on T-cells had focused on the influx of calcium and the levels of cAMP/GMP in T-cells. My laboratory then uncovered the interaction of CD4 and CD8 co-receptors with the protein-tyrosine kinase p56 The main evidence came from studies on LSTRA cells, T-cell lymphoma transformed by the Moloney Murine Leukemia Virus that showed elevated tyrosine phosphorylation of intracellular proteins  , or when a kinase provides the active site of an adjacent kinase (trans autophosphorylation). It did not escape our notice that the band at 55–65 kd was of a similar size as pp60c−src, although src was poorly expressed in T-cells. Perhaps a related kinase might be phosphorylating itself in precipitates, and perhaps it was immune cell-specific mirroring the cell-specific nature of receptors on the surface of immune cells. It may seem self-evident now, with the available information, but at the time this was a rather grand conceptional jump. In this context, a protein at 56 Kd, originally termed LSTRA protein-tyrosine kinase had been seen in LSTRA lymphoma T-cells by the labs of Bart Sefton and Edwin Krebs . I also began to discuss our unpublished findings openly with colleagues at the Dana-Farber Cancer Institute which led to a contact from Andre Veillette in the lab of Joseph Bolen at the National Institutes of Health. After some discussion, they agreed to collaborate showing the presence of the CD4 and CD8-p56lck complexes became models for how other immune receptors employ SFKs in immune cell activation. Lyn and Fyn were subsequently found to associate with the Igα/Igβ heterodimer subunits of the B cell receptor in B-cells   were eventually also shown to bind and generate signals via SFKs  and the human immunodeficiency virus (HIV-1), being the first example of a mediator to associate with the HIV-1 receptor, CD4  receptor and the PTK within the cell would introduce a specific pathway by which T-cells become activated. The T4 (CD4)-associated kinase could act to phosphorylate various intracellular candidates. An obvious and important candidate would be the subunits of the T3-Ti antigen receptor complex.” We envisioned this to occur during antigen-presentation by dendritic cells due to CD4 or CD8 and the TCR coordinate binding to MHC antigens. This event would bring p56lck into close physical proximity where trans-phosphorylation could occur. In agreement with this model, using substrate phosphorylation assays, we showed that CD4 and CD8-p56lck could trans-phosphorylate the TCR-ζ and the CD3γ, δ, ε chains on tyrosine residues xxYxx(I/L)x6–8Yxx(I/L) in the TCR associated chains Reth, , motifs lck? Is it a case of evolutionary redundancy, dosage compensation or do different ITAM send unique signals? Several groups heroically attempted to define a precise order of phosphorylation of the CD3ζ tyrosine residues  first followed by 3N >3C >2N >1C >2C  or rafts is needed is an open question , it is unclear whether this is mimics the close proximity of receptors induced by antibody crosslinking. Further, microdomains include the aggregation of numerous other immunoglobulin family members that could complete, or sterically interfere with potential CD4 and CD8 inter-molecular receptor interactions. Although enhanced p56lck activities has been seen in membranes expressing CD4 or CD8  that is myristoylated at Gly2 and palmitoylated at Cys3 and Cys5  and the related CSK-homologous kinase (Chk) phosphorylate the C-terminal tyrosine, thereby inhibiting p56lck is the transmembrane phosphatase (PTPase) CD45 . We originally found that CD4-lck precipitated two other bands that were labeled on tyrosine residues at 38–40 Kd and 70–80 Kd in in vitro kinase assays  and SLP-76 (SH2-domain-containing leukocyte protein of 76 kD). This fits with the notion that the p56lck is responsible for the main wave of tyrosine phosphorylation cascade of numerous substrates that includes ZAP-70 with a more specialized function in phosphorylating a limited additional number of key substrates needed for specific functions such as calcium mobilization.Importantly, in the context of the tyrosine phosphorylation cascade, the range of substrates of p56Part of the overall cascade includes immune cell-specific adaptors, proteins that lack enzymatic activities, and instead are made up of domains or sites that mediate complex formation Rudd, . They arLat−/− mice showed defects in thymic differentiation with a block at the double negative 3 stage  (Y-132) and the small adaptors, Growth factor receptor-bound protein 2 (GRB-2) , and GRB2-related adapter protein 2 (GADs) (Y-171 and 191)  in the endoplasmic reticulum (ER) which releases the ion into the cytoplasm. The ER also detects intracellular Ca2+ through stromal interaction molecule (STIM). Intracellular Ca2+ depletion triggers an influx from outside the cells as mediated by calcium-release activated calcium (CRAC) channel. Increased intracellular Ca2+ activates the phosphatase, calcineurin, which in turn dephosphorylates the nuclear factor of activated T cells (NFAT) for entry into the nucleus  , VAV-1 and another adaptor NCK    at the center of the interface of T-cells activated with antigen-presenting cells   and resting lymphocyte kinase (RLK). ITK modulates the development, function and differentiation of conventional T-cells and non-conventional NKT-cells  and PYK2 (proline-rich tyrosine kinase-2). FAKs are comprised of an N-terminal FERM  domain, a linker region, a kinase domain, a large proline-rich region, and a C-terminal focal adhesion targeting domain  (Abate-Daga and Davila, lck; however, T-cells expressing first-generation CARs with only ITAMs proved to be short-lived. Instead, additional CD28 “co-signals” were needed to enhance cell survival and in anti-tumor killing (June et al., in vivo antitumor activity of the gene-disrupted CAR T cells (Ren et al., Aside from T-cell activation, the discovery of CD4/CD8-p56lck to phosphorylate ITAMs and the tyrosine-based motifs within the CD28 co-receptor cytoplasmic tails. However, others have found that the optimal antigen response is dependent upon the incorporation of the receptor in endogenous TCR/CD3 complexes (Bridgeman et al., lck in addition to free p56lck to promote CAR-T efficacy. Overall, the CAR field developed as a result of fundamental studies that led to the discovery of the TCR complex and the signaling motifs activated by p56lck and which are needed to activate T-cells.Since CARs do not recognize MHC molecules, their reactivity of CAR-Ts is depends on active p56lck complexes opened a window in understanding the nature of signals that control the immune response against antigens. This fundamental mechanism controls the T-cell response in the areas of vaccines, transplantation, autoimmunity, and cancer. They were the first examples of a receptor binding to protein-tyrosine kinase and showed how immune recognition receptors which lack intrinsic catalytic activity can transduce activation signals via non-covalent association with non-receptor tyrosine kinases. Sometimes called the TCR signaling paradigm, the discovery established that the concept that a protein tyrosine phosphorylation cascade operated in T-cells and opened the door to the identification of other protein-tyrosine kinases such as ZAP-70 and an array of substrates such as immune cell adaptors that are now central to studies in T-cell immunity. Other receptors such as B-cell receptor, Fc receptors and others were also subsequently found to use src kinases to control cell growth. Moreover, the discovery of CD4/CD8-p56lck and its targets ITAMs and CD28 has led to the application of this knowledge in the design on CARs presently in use in cancer immunotherapy.The discovery of the CD4 and CD8-p56The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Resveratrol, a plant polyphenol, exhibits antiaging, antitumor, and vascular protection effects by activating Sirt1. However, the molecular mechanism of Sirt1 activation as induced by resveratrol remains unclear. By knockdown/rescue experiments, fluorometric Sirt1 activity assay, immunoprecipitation, and pull-down assays, we identify here that the tumor suppressor LKB1 (liver kinase B1) as a direct activator of Sirt1 elicited by resveratrol. Resveratrol promotes the binding between LKB1 and Sirt1, which we first reported, and this binding leads to LKB1-mediated phosphorylation of Sirt1 at three different serine residues in the C terminus of Sirt1. Mechanistically, LKB1-mediated phosphorylation increases intramolecular interactions in Sirt1, such as the binding of the C terminus to the deacetylase core domain, thereby eliminating DBC1  inhibition and promoting Sirt1–substrate interaction. Functionally, LKB1-dependent Sirt1 activation increases mitochondrial biogenesis and respiration through deacetylation and activation of the transcriptional coactivator PGC-1α. These results identify Sirt1 as a context-dependent target of LKB1 and suggest that a resveratrol-stimulated LKB1-Sirt1 pathway plays a vital role in mitochondrial metabolism, a key physiological process that contributes to numerous age-related diseases.The NAD It has cofactor . Therefo+-dependent deacetylase, Sirt1 activity is coupled to dynamic NAD+/NADH ratio in response to cellular metabolic status  is an antiviral toxin secreted by plants in response to environmental stress and began to enter into public horizon when it is associated with cardiovascular benefits found in red wine . As a caSeveral kinases have been reported to phosphorylate Sirt1 and regulate its deacetylase activity , 22, 23.Here, we show that resveratrol treatment promotes LKB1 to interact with Sirt1 and subsequent phosphorylation of the C terminus of Sirt1. LKB1-mediated phosphorylation increases the binding capacity between the C terminus and deacetylase core of Sirt1, releases the DBC1-induced intermolecular inhibition, and thus promotes the deacetylase core–substrate interaction. Thus, Sirt1 is a novel context-dependent substrate of LKB1 and the LKB1-Sirt1 pathway orchestrates resveratrol-elicited mitochondrial function.A, resveratrol activated Sirt1 and exhibited the highest activity among the six drugs tested, including four natural polyphenols that stimulate Sirt1 activity . To confirm the effect of resveratrol treatment on Sirt1 phosphorylation, aliquots of 293T and C2C12 cells were infected with lentivirus-based particles expressing FLAG-Sirt1 followed by resveratrol treatment and then subjected to immunoprecipitation at different time points. The isolated FLAG-Sirt1 proteins were probed for pan-phosphorylated Ser/Thr. As shown in Figure 1A, treatment of cells with resveratrol time-dependently increased the phosphorylation levels of Sirt1, suggesting that phosphorylation of Sirt1 is a functional readout of enzymatic activity.To delineate the potential mechanism underlying resveratrol-elicited Sirt1 activation, we first treated cells with resveratrol and measured Sirt1 activity by a fluorophore-conjugated acetylated p53 peptide (substrate of Sirt1) . As showFig. S1activity  and two in vitro Sirt1 activity assay by incubating FLAG-Sirt1 isolated from 293T cells with recombinant acetylated p53-GST (p53-K382ac) purified from Escherichia coli using a recently reported genetically encoded method with unnatural amino acids . As shown in Figure 1B, in the presence of NAD+, Ac-p53-GST was deacetylated by phosphorylated FLAG-Sirt1 from resveratrol-treated cells, but not by unphosphorylated FLAG-Sirt1 from cells treated with resveratrol and phosphatase (lane 4), suggesting that Sirt1 is activated by the phosphorylation .To further determine that the increased Sirt1 phosphorylation is coupled to Sirt1 activation, we performed no acids  . To reconfirm resveratrol-elicited Sirt1 phosphorylation is medicated by LKB1, we employed a structurally distinct Sirt1 activator SRT1720 . Previous studies have reported that AMPK mediates Sirt1 activation by increasing cellular NAD+ or GAPDH-Sirt1 interaction . Thus, we conclude that LKB1 kinase activity is responsible for Sirt1 phosphorylation and activation by resveratrol.Several kinases have been reported to phosphorylate Sirt1 , 22, 23.Figure 1 SRT1720 . As showeraction , 59; we in vitro kinase assay using recombinant LKB1-STRADα-Mo25α complex . In the presence of ATP, LKB1 directly phosphorylated full-length Sirt1 . Followed in vitro deacetylation assay with purified K382ac p53 peptide indicated that LKB1-directed phosphorylation is essential for Sirt1 deacetylase activity . No phosphorylation signal was detected in Sirt6 or Sirt7, two other nucleus-localized members of sirtuin family, suggesting the specificity of phosphorylation of Sirt1 by LKB1 .Because LKB1 is required for resveratrol-induced Sirt1 phosphorylation, we asked whether Sirt1 could be a novel substrate of LKB1. We first performed an  complex  by autorC–E, three residues  were identified, which were all located at the C-terminal domain of Sirt1 , suggesting that they are the major LKB1 phosphorylation sites on Sirt1. Consistently, when in vitro kinase assay demonstrated that Sirt1-3A mutant could not be phosphorylated by LKB1, it showed much weakened phosphorylation in resveratrol-treated cells . In addition, depletion of AMPK failed to change the phosphorylation level of wildtype and Sirt1-3A mutant , suggesting that Sirt1 is not a substrate of AMPK. Thus, we concluded that Sirt1 is a direct phosphorylation substrate of LKB1, and Ser615, Ser669, and Ser732 in the C terminus of Sirt1 are the main phosphorylation residues by LKB1.To identify the LKB1 phosphorylation site(s) on Sirt1, we analyzed the phosphorylated Sirt1 with mass spectrometry. As shown in of Sirt1 . We thenrylation C, suggesFig. 2D, , F and GF), and it was wildtype Sirt1, but not the 3A mutant, that reduced the acetylation level of p53 peptide in resveratrol-treated cells (G). These were confirmed by in vitro deacetylation assay using purified Sirt1 and K382ac p53 peptide as a substrate. As shown in Figure 2H, phosphor-disabled Sirt1-3A mutant exhibited a weaker, whereas the phosphorylation-mimetic Sirt1-3D mutant possessed a much higher, deacetylase activity than wt-Sirt1. Thus, we conclude that LKB1-dependent phosphorylation at Ser615, Ser669, and Ser732 promotes Sirt1 deacetylase activity.To further determine how LKB1-mediated phosphorylation influences Sirt1 deacetylase activity, we, respectively, expressed wildtype Sirt1 or the Sirt1 mutants in HEK293T cells and analyzed their activity using the fluorometric activity assay. All the mutants represented significantly reduced deacetylase activity F, and ited cells G. These A). DBC1 (Sirt1 endogenous inhibitor) competes with ESA to bind the core domain via its leucine zipper  domain . Deletion of ESA abolished the interaction of either the WT C terminal or the 3D C terminal with the deacetylase core domain (C and D). These results suggest that phosphorylation by LKB1 enhances the intramolecular interaction between the C-terminal domain and the core domain of Sirt1.The ESA region  at the C terminus is indispensable for Sirt1 activation by interacting with the core domain of Sirt1  Fig. S3Fig. S3A.) domain , 14, 60.e domain , C and DE). Intriguingly, coincubation of the 3D C terminus of Sirt1 reduced the association even more, whereas coincubation of the 3A C terminus showed no effect on the association (E). On lack of ESA, both WT C terminus and the 3D C terminus of Sirt1 lost their function to prevent DBC1 from interacting with the core domain of Sirt1 (F).We next examined the effect of LKB1-dependent phosphorylation on the binding of Sirt1 to its inhibitor DBC1. The purified recombinant LZ domain of DBC1 was used to pull down the core domain of Sirt1. Apparently, the DBC1 LZ domain showed a strong association with the Sirt1 core domain, which could be decreased by coincubation in the assay of the C terminus of Sirt1 E. Intrigociation E. On lacof Sirt1 F.C). To examine the binding capacity of Sirt1 mutants to its substrate peptide, aliquots of 293T cell lysates from transient transfection to wildtype FLAG-Sirt1 and various mutants were incubated with the acetylated p53 peptide followed by extensive washes and Western blotting analyses. We found that the p53 peptide bound more Sirt1-3D and less Sirt1-3A  and the ESA region was required for the binding (E and F). At last, we confirmed that the ESA region is necessary for resveratrol-stimulated Sirt1 activation (G). Thus, we conclude that LKB1-dependent phosphorylation activates Sirt1 through promoting its intramolecular interaction, which releases Sirt1 from DBC1 binding for concomitant substrate binding.In addition to the intramolecular core domain, the ESA region can also enhance the binding between Sirt1 and its substrates  Fig. S3Fig. S3C.tivation G. Thus, in vitro kinase assay by incubating immunoprecipitated FLAG-tagged LKB1, STRADα, and MO25α, with recombinant AMPK complex, in the presence of resveratrol. Adding resveratrol to the kinase reaction showed no effect on the level of AMPK phosphorylation at Thr172 (A). We then used LKB1 complex from resveratrol-treated cells and carried out similar experiments. Still, no detectable increase in AMPK phosphorylation was observed (B). These data suggest that resveratrol is not able to raise the kinase activity of LKB1 either in vitro or in cell.To further investigate the mechanism by which LKB1 activates Sirt1 in response to resveratrol stimulation, we first examined whether resveratrol directly activates LKB1. We performed an t Thr172 A. We theobserved B. These C and D, A–C). We also observed the increased interaction between LKB1 and Sirt1 with a parallel rise in phosphorylation of Sirt1 at various time points . Meanwhile, no LKB1 was detected in the FLAG-Sirt6 or FLAG-Sirt7 immunoprecipitates , indicating the specific binding between LKB1 and Sirt1. Consistent with the effect of resveratrol, treatment of cells with another Sirt1 activator SRT1720 also upregulated the interaction between LKB1 and Sirt1 . We further analyzed the subcellular distribution of LKB1 and Sirt1 in different cell lines. Compared with the very weak colocalization in untreated cells, LKB1 in resveratrol-treated cells showed strong colocalizations with Sirt1 in the nucleus (E–G). We then performed coimmunoprecipitation analysis in the nuclear fraction and cytoplasmic fraction, respectively. Immunoprecipitation of Sirt1, one of the sirtuin proteins primarily resided in nucleus, coprecipitated LKB1 in the nuclear fraction rather than in the cytoplasmic fraction, suggesting that LKB1 can associate with Sirt1 in the nucleus (H). In addition, resveratrol treatment markedly enhanced the coimmunoprecipitation of LKB1 with Sirt1 (H). Therefore, resveratrol can promote the interaction between LKB1 and Sirt1 in the cell nucleus. However, our enzymatic assay to assess the direct interaction between LKB1 and Sirt1 indicated that addition of resveratrol did not alter the kinetics of LKB1 kinase activity (G and H), suggesting that the resveratrol-elicited LKB1 interaction with Sirt1 seen in the nucleus is regulated by the signaling cascade. We further evaluate whether the interaction between LKB1 and the Sirt1 seen in resveratrol treated-cells can be recapitulated using different domains and/or fragments. To this end, aliquots of purified recombinant GST-Sirt1 proteins were used as affinity matrix to absorb recombinant MBP-LKB1. As shown in Figure 4I, GST-Sirt1-core, but not GST-Sirt1-N or GST-Sirt1-C, absorbed LKB1 protein . To test whether the Sirt1-core fragment interacts with LKB1, 293T cells were transiently transfected to express various GFP-Sirt1 deletion mutants and FLAG-LKB1. As shown in Fig. S4I, GFP-Sirt1-core selectively absorbed LKB1 (lane 7). Together, these data support the notion that resveratrol promotes the binding of LKB1 to Sirt1 without affecting LKB1 kinase activity. The deacetylase core region of Sirt1 provides a docking site for LKB1, enabling LKB1 to phosphorylate at the C terminus of Sirt1.We next tested whether resveratrol could promote the binding between LKB1 and Sirt1. With coimmunoprecipitation assay, we found that treatment of cells with resveratrol dose dependently increased the interaction between LKB1 and Sirt1 C and D, , A–C. We nucleus , E–G. We nucleus H. In addth Sirt1 H. TherefA and Fig. S5A), indicating that LKB1 acts upstream of coactivator transcription. Expression of wildtype Sirt1, but not the phosphorylation-disabled 3A mutant, reversed the effect of LKB1 depletion or Sirt1 depletion. In addition, expression of phosphorylation-mimetic Sirt1-3D mutant significantly reduced PGC-1α deacetylation, consistent with the expression of wt-Sirt1 in resveratrol-treated cells . These data support the notion that the phosphorylation of Sirt1 by LKB1 is required to mediate deacetylation of PGC-1α by activated Sirt1.PGC-1α is a master mediator of the metabolic effects of resveratrol , 61. It Fig. 5D), whereas LKB1 or Sirt1 knockdown prevented resveratrol-induced augmentation of the related mRNA levels (D and E). Next, we checked the restoration of Sirt1 depletion-induced inhibition of transcription by Sirt1-3A and Sirt1-3D. As expected, only the expression of wt-Sirt1, but not phosphorylation-disabled Sirt1-3A mutant, reversed the effect of Sirt1 knockdown on the transcription of genes related to mitochondrial biogenesis and respiration. Expression of phosphorylation-mimetic Sirt1-3D mutant reversed the effect of Sirt1 knockdown, consistent with the expression of wt-Sirt1 in resveratrol-treated cells . Taken together, these results suggest that phosphorylation of Sirt1 by LKB1 is essential for the transcription of genes related to mitochondrial function by activating PGC-1α.As a coactivator, PGC-1α induces transcription of various downstream genes, which comprise multiple genes in regulating mitochondrial function . ConsistFig. S5G and H, resveratrol treatment significantly increased the mitochondrial DNA content, whereas LKB1 or Sirt1 knockdown reversed this effect. Of note, expression of wt-Sirt1, but not Sirt1-3A mutant, rescued the mtDNA content in Sirt1-depleted cells. Expression of phosphorylation-mimetic Sirt1-3D mutant significantly increased mtDNA content, consistent with the expression of wt-Sirt1 in resveratrol-treated cells . Furthermore, mitochondrial density was measured by flow cytometry using MitoTracker Red CMXRos, a fluorescent probe that specifically labeled mitochondrial organelle. In Sirt1-depleted cells, expression of wt-Sirt1, but not the phosphorylation-disabled Sirt1-3A mutant, increases mitochondrial density with resveratrol treatment. Of interest, expression of phosphorylation-mimetic Sirt1-3D mutant evidently augmented mitochondrial density, consistent with the expression of wt-Sirt1 in resveratrol-treated cells . To establish mitochondria quality control, mitochondrial membrane potential was measured with a fluorescent probe, tetramethyl rhodamine methyl ester . Next, we tested the function of LKB1-dependent Sirt1 phosphorylation in mitochondrial respiration. Since phosphorylation-disabled Sirt1-3A mutant inhibited the transcription of components of mitochondrial electron transport chain, it is expected that the oxygen consumption rate could be enhanced when Sirt1 is phosphorylated by LKB1. Indeed, as in the Sirt1-depleted cells, resveratrol produced substantial increases in maximal respiration (FCCP-induced) and respiration capacity when re-expressing wt-Sirt1. Strikingly, none of the significant increases in mitochondrial function were observed in Sirt1-3A-expressing cells . The beneficial effects of resveratrol were clearly evident in Sirt1-3D-expressing cells, confirming that phosphorylation of S615, S669, and S732 was required for LKB1-mediated regulation of Sirt1 in mitochondrial function . Together, these results suggest that LKB1-dependent phosphorylation of Sirt1 is crucial for mitochondrial biogenesis and respiration through the activation of PGC-1α.To further confirm the function of LKB1-mediated Sirt1 phosphorylation in mitochondrial biogenesis, we first measured the mitochondrial DNA (mtDNA) content in LKB1-depleted or Sirt1-depleted C2C12 myotubes treated with resveratrol. As shown in yl ester . In SirtThe longevity-associated Sirt1 deacetylase orchestrates cell plasticity control and organ function. The natural product resveratrol has been recognized as a Sirt1 activator for a long time, but controversy exists around the mechanism of Sirt1 activation. Here, our identification of the resveratrol-elicited LKB1-Sirt1 signaling axis uncovered a new regulatory mechanism by which activation of LKB1-mediated phosphorylation of Sirt1 by resveratrol promotes the activity of Sirt1 for control of genomic stability . It woulin vivo and it may activate Sirt1 via an upstream target. LKB1 acts as an evolutionarily conserved regulator of cellular energy metabolism and functions as the major upstream kinase to phosphorylate AMPK and 12 other AMPK-related kinases , which suggests that S615 is more important in regulating Sirt1’s activity. Since these three sites influence the binding of DBC1 to Sirt1’s core domain, and S615 is closer to the core domain, we propose that S615 may have a greater blocking effect on DBC1 than the other two sites, which needs to be identified in future.Identification of the phosphorylation sites allows us to investigate the molecular nature of Sirt1 activation by LKB1. Utilizing the Sirt1 phosphorylation-disabled and phosphorylation-mimicking mutants, our analyses revealed that phosphorylation is essential and sufficient for the activation of Sirt1 deacetylase. In addition, this phosphorylation-based activation involves promotion of the intramolecular interaction of Sirt1. Structurally, Sirt1 possesses a long disordered C-terminal domain, which is indispensable for Sirt1 deacetylase activity , 80, 81.Fig. 2in vivo. However, resveratrol exhibits no direct effect on LKB1-Sirt1 interaction in vitro (G and H). The inconsistency between in vivo and in vitro assays suggests that signaling pathways regulating the binding of LKB1 and Sirt1 exist in resveratrol-treated cells. Future investigation using proximity ligation would enable us to consolidate the resveratrol-elicited Sirt1 activation into signaling cascades underlying LKB1-regulated mitochondrion activity and cellular plasticity control.Our present study revealed that the phosphorylation of Sirt1 at S615, S669, and S732 by LKB1 may regulate the electrostatic interactions between the C-terminal ESA region and deacetylase core domain, thereby dissociating Sirt1 from DBC1 and releasing Sirt1 activity. Our current analysis suggests that resveratrol treatment promotes the binding of LKB1 to Sirt1 in vitro , G and HIn sum, we report a previously unrecognized molecular mechanism that underlies Sirt1 activation and define a signaling axis that integrates LKB1 phosphorylation to mitochondrion dynamics. It is maybe helpful for us to understand and treat Sirt1 correlated ageing-related diseases.Site-specific mutants or deletion mutants of FLAG-Sirt1, HA-LKB1, FLAG-STRADα, GFP-MO25α, HA-p53, and GFP-p300 HAT (aa 3583–5280) were generated by PCR-based, site-directed mutagenesis kit from Vazyme (C112) according to the manufacturer’s instructions. GST-, His-, and MBP-tagged Sirt1 were generated by subcloning human Sirt1 cDNA into pGEX-6p-1, pET28a, and pMal-c2X, respectively. Mammalian coexpression plasmid HA-LKB1-STRADα was generated by inserting HA-LKB1 from the corresponding vector into MCS of pIRES2-ZsGreen1 vector and replacing ZsGreen1 coding frame with STRADα. Baculovirus expressed His-tagged Sirt1 deacetylase was generated by inserting human Sirt1  into pFastBac plasmid. Baculovirus-based coexpression plasmid His-LKB1-STRADα-MO25α was a gift from laboratory of Daan M. F. van Aalten . The p53in vivo K382 acetylation of p53, cells were treated with 1 μM doxorubicin for 1 h.C2C12 myoblast cells (ATCC), HeLa cells, and HEK293T cells were maintained as monolayers in advanced Dulbecco's modified Eagle's medium (DMEM) (Invitrogen) with 10% fetal bovine serum (HyClone) and 100 units/ml of penicillin plus 100 μg/ml of streptomycin (Invitrogen). To generate C2C12 myotubes, C2C12 cells were grown in DMEM with 2% horse serum for 4 days. For lentivirus infection, C2C12 cells were infected with packaged lentivirus particles for 12 h and were continuously grown in fresh medium for 48 h. After infection, the cells were grown in DMEM with 2% horse serum for 4 days to generate myotubes. Cells were treated the vehicle (dimethyl sulfoxide [DMSO]) or 25 μM resveratrol for 6 h. To investigate the effects of various polyphenols on Sirt1 activation, differentiated C2C12 cells were treated with DMSO (vehicle), 25 μM piceatannol (Sirt1 activator), 25 μM fisetin (Sirt1 activator), 25 μM quercetin (Sirt1 activator), 25 μM resveratrol (Sirt1 activator), 1 μM EX527 (Sirt1 inhibitor), or 10 mM nicotinamide (Sirt1 inhibitor) for 6 h. For Sirt1 dephosphorylation, immunoprecipitated Sirt1 was treated with lambda protein phosphatase at 30 °C for 30 min. To increase Anti-Sirt1 , anti-LKB1 , anti-acetyl-p53 K382 , anti-p53 , anti-phospho-AMPK T172 , anti-AMPK , anti-phospho-ACC Ser79 , anti-ACC , anti-MO25α , anti-acetylated-lysine , anti-Lamin B1 , anti-GST-tag , anti-His-tag , anti-HA-tag , and anti-MBP-tag  antibodies were from Cell Signaling Technology. Anti-phospho-Ser/Thr  and anti-α-tubulin  antibodies were from Abcam. Anti-STRAD (N-13) (sc-34102) was from Santa Cruz. Anti-FLAG-tag  antibody was from Sigma. Lambda Protein Phosphatase (P0753) was from New England Biolabs. Resveratrol, piceatannol, quercetin, fisetin, EX527, nicotinamide, NAD, N-ε-acetyl-L-lysine, SRT1720, 3xFLAG peptide, doxorubicin, phosphatase inhibitors, and protease inhibitors were from Sigma.The lentivirus-based vector pLKO.1 along with psPAX2 and pMD.2G were used for producing shRNA-packaged viral particles. The nucleotide sequence for shRNA against human Sirt1 was 5′-CAGGTCAAGGGATGGTATTTA-3′ (shSirt1-h1) and the sequence for shRNA against mouse Sirt1 was 5′-CAGATCAAGAGACGGTATCTA-3′ (shSirt1-m1). The nucleotide sequence for shRNA against human LKB1 was 5′-GGGTCACCCTCTACAACATCA-3′ (shLKB1-h4) and the sequence for shRNA against mouse LKB1 was 5′- GGGTCACACTTTACAACATCA-3′ (shLKB1-m4). The nucleotide sequence for shRNA against human AMPKα1α2 and mouse AMPKα1α2 was 5′-ATGATGTCAGATGGTGAATTT-3′. The nucleotide sequence for shRNA against CAMKKβ was 5′-GTGAAGACCATGATACGTAAA-3′, the sequence for shRNA against DYRK1A was 5′-GAACCTAACACGAAAGTTTGC-3′, the sequence for shRNA against DYRK3 was 5′-CTCCACCCAGAAGACTAAATA-3′, the sequence for shRNA against JNK1 was 5′-GCCCAGTAATATAGTAGTAAA-3′, the sequence for shRNA against JNK2 was 5′-CCAGATGCTTTGTGGTATTAA-3′, and the sequence for shRNA against CK2α was 5′-AGCCATCAACATCACAAATAA-3′. For producing shRNA-packaged viral particles, constructed pLKO.1 plasmid along with psPAX2 and pMD.2G were transfected into HEK293T cells with Lipofectamine 2000 reagent (Invitrogen) for 12 h according to manufacturer’s instructions. After transfection, cells were changed to fresh medium and 48 h later the lentivirus supernatant was collected. For protein expression, constructed pLVX-FLAG or pLVX-GFP plasmid along with psPAX2 and pMD.2G were transfected into HEK293T cells with Lipofectamine 2000 reagent for 12 h. After transfection, cells were changed to fresh medium, and 48 h later the lentivirus supernatant was collected to infect C2C12 myoblasts or HeLa cells. For stable expression, the lentivirus supernatant was used to infect cells. On the following day the cells were passaged and 24 h later selected with puromycin (2 μg/ml) for 2 weeks.E. coli strain Rosetta (DE3) was transformed with pACKRS and p53-382TAG-GST plasmids simultaneously. The bacteria were cultured in lysogeny broth medium supplemented with kanamycin (50 mg/ml) and spectinomycin (50 mg/ml) to A600 of 0.7. Acetyl-lysine  and NAM (20 mM) were then added, the culture was incubated for 0.5 h, and protein expression was induced with IPTG (0.2 mM) at 37 °C for 4 h. The bacteria were lysed by sonication in PBS buffer  and incubated with Glutathione agarose  for 1.5 h at 4 °C. The agarose was washed three times in PBS buffer and eluted with 10 mM reduced glutathione.For acetylated protein purification, E. coli strain Rosetta (DE3), and protein expression was induced with 0.2 mM IPTG at 16 °C. Bacteria expressing GST-tagged protein were suspended and lysed by sonication in PBS buffer supplemented with 0.1% Triton X-100. The preparation was incubated with glutathione-Sepharose 4B  for 1.5 h at 4 °C. The resin was washed three times, and GST-tagged protein was eluted with 10 mM reduced glutathione. Bacteria expressing MBP-AMPKα2 were lysed in MBP column buffer  and incubated with amylose resin (New England BioLabs) for 1.5 h at 4 °C. The resin was washed three times in MBP column buffer and eluted with MBP column buffer supplemented with 10 mM maltose. All purification procedures were performed at 4 °C, and protease inhibitor cocktail (Sigma) was added to prevent protein degradation.GST- and His-tagged Sirt1 truncations and site mutants, MBP-AMPKα2 were produced from bacteria as described . Basicalin vitro reactions, the FLAG beads were further washed twice with dialysis buffer, and the FLAG-tagged protein was eluted with dialysis buffer supplemented with 100 μg/ml 3×FLAG peptide (Sigma). For pull-down assays, GST or GST-tagged proteins were purified from bacteria and incubated with cell lysates or eluted His-tagged protein for 4 h. Four hours later, the bound fraction was washed with lysis buffer four times and analyzed by Western blotting.For immunoprecipitation, cells were treated with indicated reagents before being lysed in lysis buffer  supplemented with protease inhibitor cocktail (Sigma), phosphatase inhibitor cocktail (Sigma). After preclearing with protein A/G resin, the lysate was incubated with indicated antibody at 4 °C for 24 h with gentle rotation. Protein A/G resin was then added to the lysates, and they were incubated for another 4 h. The protein A/G resin was then spun down and washed four times with lysis buffer before being resolved by SDS-PAGE and immunoblotted with the indicated antibodies. For FLAG-tagged protein immunoprecipitation, the FLAG-M2 resin was added to the lysates and incubated for 4 h before washing. For Whole-cell extracts were prepared with SDS-PAGE sample buffer. Proteins were separated in SDS-PAGE with 10% gels and transferred onto nitrocellulose membranes (Millipore). Uniform transfer was confirmed by Ponceau S staining. Membranes were blocked in Tris-buffered saline with 0.1% Tween 20 (TBS-T buffer) and 5% dry milk (w/v) for 1 h at room temperature and washed three times in TBS-T. Membranes were blotted overnight at 4 °C with primary antibodies diluted at an appropriate dilution ratio in TBS-T with 5% dry milk and then with a secondary donkey anti-rabbit IgG antibody conjugated with horseradish peroxidase  for 1 h at room temperature. After washing, signals were developed with the ECL detection system (Thermo Scientific).2; 1 mM DTT) with purified Sirt1 (2 μg) substrate, LKB1 kinase , 50 μM ATP with or without 5 μCi of [γ-32P]-ATP. Reaction mixtures were incubated at 30 °C for 30 min, then terminated by 5× SDS-PAGE loading buffer . After the samples were boiled at 100 °C for 2 min, 50% of the sample was resolved by SDS-PAGE. For the reaction without [γ-32P]-ATP, samples were subjected to Western blotting with indicated antibodies to test phosphorylation levels. For the reaction in the presence of [γ-32P]-ATP, samples were stained by Coomassie Brilliant Blue R250 (CBB). For autoradiograms, CBB-stained SDS-PAGE gels were destained, scanned, and then dried between sheets of cellulose for 4 h. The semidried gels were then placed between intensifier and X-ray films for 12 to 24 h in a −80 °C freezer, and the levels of 32P incorporation into Sirt1 proteins was tested.The kinase reactions were performed in 40 μl kinase buffer  in Sirt1 assay buffer . The plate was incubated at 37 °C for 30 min, and the reaction was stopped with developer solution (Enzo Life Sciences) containing 2 mM nicotinamide to inhibit Sirt1 and protease to digest deacetylated p53 peptide. Sirt1 activity was assessed by measuring the fluorescent emission at 460 nm, following excitation at 360 nm.FLAG-Sirt1 was immunoprecipitated from cells infected with pLVX-FLAG-Sirt1 lentivirus particles and was eluted by FLAG peptide. Then 50 ng eluted FLAG-Sirt1 was incubated with 1 mM of NADKm and kcat values were calculated by the Michaelis–Menten equation as reported  according to the manufacturer’s instructions. In brief, 200 nM recombinant LKB1 kinase was incubated with gradient concentrations of purified Sirt1 protein in 20 μl kinase reaction buffer (ADP assay buffer) at 37 °C for 30 min; 20 μl ADP sensor buffer and 10 μl ADP sensor were added into the preparations to make a total 50 μl ADP assay volume. The reaction mixture was incubated at room temperature for 30 min. The amount of ADP produced from the kinase reaction assay was detected by monitoring the fluorescence intensity at Ex/Em = 540/590 nm. The background fluorescence was determined by measuring the fluorescence intensity in the absence of substrate and subtracted from the experiments. reported .E. coli was incubated with recombinant LKB1-STRADα-Mo25α complex in the presence of ATP and then separated by SDS-PAGE and depicted with colloidal Coomassie blue staining. Following reduction with dithiothreitol and alkylation with iodoacetamide, in-gel digestion of Sirt1 was performed with sequencing-grade Glu-C  at 1/50  ratio at 37 °C for 16 h in 50 mM NH4HCO3 (pH 8.0). The samples were then dried and dissolved in 0.1% formic acid (FA) water. LC-MS analysis was performed on ACQUITY UPLC M-Class (Waters) and XEVO G2-XS QTof Mass Spectrometer System (Waters). The Sirt1 protein samples were separated by a 120-min gradient with the linear gradient from 3% to 80% B  at a flow rate of 300 nl/min and the Sirt1-669-peptide samples were separated by a 30-min gradient with the linear gradient from 3% to 80% B  at a flow rate of 300 nl/min. The QToF mass spectrometer was operating in MSE mode or target MSMS mode, and the raw data were searched with Progenesis QI for proteomics V4.1 Software against the Human uniprot database.To prepare samples for mass spectrometric analysis of phosphorylation site(s) of Sirt1 by LKB1, Sirt1 protein purified from 2; 0.2% Triton X-100) supplemented with 3.7% paraformaldehyde. After blocking with PBST (PBS with 0.05% Tween-20) buffer containing 1% bovine serum albumin (Sigma) for 45 min at room temperature, the fixed cells were incubated with primary antibodies in a humidified chamber for 1 h at room temperature or overnight at 4 °C, followed by secondary antibodies for 1 h at 37 °C. The DNA was stained with DAPI from Sigma. Images were captured by Delta Vision softWoRx software (Applied Precision) and processed by deconvolution and z-stack projection.C2C12, HEK293T, or HeLa cells grown on coverslips were fixed by a pre-extraction method using PTEM buffer  according to the instructions and quantified using the NanoDrop 1000 spectrophotometer (Thermo Scientific). The cDNA was synthesized with the HiScript Ⅲ RT SuperMix for qPCR (Vazyme) using 1 μg of RNA. Quantitative RT-PCR reactions were performed using 1 μM of primers and LightCycler 480 SYBR Green Master (Roche) on a LightCycler 480 detection system (Roche). Calculations were performed by a comparative method (2-ΔΔCT) using GAPDH as an internal control. Primers used for PCR analysis are listed:GAPDH:Forward primer: 5′-ACATCATCCCTGCATCCACTG-3′Reverse primer: 5′-CCTGCTTCACCACCTTCTTG-3′Sirt1:Forward primer: 5′-GGCCGCGGATAGGTCCATA-3′Reverse primer: 5′-ACAATCTGCCACAGCGTCAT-3′LKB1:Forward primer: 5′-CTGGACTCCGAGACCTTATGC-3′Reverse primer: 5′-CAAGCTGGATCACATTCCGAT-3′PGC-1α:Forward primer: 5′-TATGGAGTGACATAGAGTGTGCT-3′Reverse primer: 5′-GTCGCTACACCACTTCAATCC-3′NRF1:Forward primer: 5′-AATGACCCAGGCTCAGCTTC-3′Reverse primer: 5′-GCTTGCAGCTTTCTTTCCCC-3′NRF2:Forward primer: 5′-TGACCATGAGTCGCTTGCC-3′Reverse primer: 5′-TCCTGCCAAACTTGCTCCAT-3′NDUFS8:Forward primer: 5′-GTTCATAGGGTCAGAGGTCAAG-3′Reverse primer: 5′-TCCATTAAGATGTCCTGTGCG-3′SDHb:Forward primer: 5′-GAGTCGGCCTGCAGTTTCA-3′Reverse primer: 5′-GGTCCCATCGGTAAATGGCA-3′Uqcrc1:Forward primer: 5′-GTGTCTCATTTGGATGGCACC-3′Reverse primer: 5′-AGCAAATGTCACGCAGCATC-3′Cox5b:Forward primer: 5′-GCTTCAAGGTTACTTCGCGG-3′Reverse primer: 5′-ATGGGTCCAGTCCCTTCTGT-3′ATP5a1:Forward primer: 5′-CATTGGTGATGGTATTGCGC-3′Reverse primer: 5′-TCCCAAACACGACAACTCC-3′For mtDNA analysis, total DNA was extracted with E.Z.N.A. Total DNA Kit (OMEGA). The mtDNA was amplified using primers specific for the mitochondrial cytochrome c oxidase subunit 2 (COX2) gene and normalized to genomic DNA by amplification of the ribosomal protein s18 (rps18) nuclear gene. Primers were designed using the IDT software (IDT) and the primer sequences are listed:COX2:Forward primer: 5′-ATAACCGAGTCGTTCTGCCAAT-3′Reverse primer: 5′-TTTCAGAGCATTGGCCATAGAA-3′Rps18:Forward primer: 5′-TGTGTTAGGGGACTGGTGGACA-3′Reverse primer: 5′-CATCACCCACTTACCCCCAAAA-3′2, 0.5 mM DTT, 0.2% Triton X-100, and protease inhibitors). Cell lysates were incubated on ice for 10 min and then centrifuged at 500g for 5 min at 4 °C; the supernatant was used as the cytoplasmic fraction. The pellet was washed twice with hypotonic buffer and resuspended in RIPA buffer . After centrifugation at 15,000g for 10 min, the supernatant was used as the nuclear fraction , and incubated for 30 min at 37 °C according to the manufacturer’s instructions. The fluorescence was excited at 561 nm in a flow cytometer . Data were analyzed using FlowJo software for 2 × 10escribed .3 cells.For measuring mitochondrial membrane potential, the cells were resuspended in PBS containing 20 nM tetramethyl rhodamine methyl ester (Invitrogen) and incubated for 30 min at 37 °C according to the manufacturer’s instructions. The fluorescence was excited at 561 nm in a flow cytometer . Data were analyzed using IDEAS 6.2 software for 2 × 104 cells/well in 80 μl high-glucose DMEM growth medium supplemented with 10% fetal bovine serum and 1% pen/strep. The following day, cells were infected with virus for 12 h and continuously cultured in fresh medium for 36 h. Then, cells were switched to low-serum media containing 2% horse serum and 1% pen/strep to induce differentiation. Cells were fed every 24 h for 4 days. On the day of testing, 25 μM resveratrol or DMSO as a vehicle control was suspended in fresh DMEM media and the cells were returned to the incubator for 6 h. After 6 h, cells were washed twice with 120 μl/well assay medium. A final volume of 180 μl of assay medium was added to each well, and cells were then transferred to a CO2-free incubator, maintained at 37 °C for 1 h before the start of the assay. Following assay calibration, measurements of oxygen consumption rate were performed with an XF96 Extracellular Flux Analyzer (Seahorse biosciences) as per the manufacturer’s protocol.C2C12 cells were seeded in XF 96-well cell culture microplates (Seahorse biosciences) at 1 × 10t test between two groups, one-way ANOVA with Tukey’s multiple comparison tests as a post hoc test for comparing every mean to every other mean, or one-way ANOVA with Dunnett’s multiple comparison as a post hoc test for comparing every mean to a control mean in each data set. A p-value <0.05 was considered statistically significant. Data are presented as means ± SD. Statistical tests used are indicated in the corresponding figure legends.Statistical analyses were performed using GraphPad Prism 7.0. Statistical differences were determined by unpaired two-tailed Student’s All data are contained in the article. Raw mass spectrometry data are deposited in a publicly accessible repository Zenodo. The DOI is 10.5281/zenodo.4775266.This article contains The authors declare that they have no conflicts of interest with the contents of this article.


Global climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming. Modeled SIA losses are highly correlated to global mean temperature increases, making it challenging to gauge if improvements in modeled sea ice derive from improved sea-ice models or from improvements in forcing driven by other GCM components. I use a set of five large GCM ensembles, and CMIP6 simulations, to quantify GCM internal variability and variability between GCMs from 1979–2014, showing modern GCMs do not plausibly estimate the response of SIA to warming in all months. I identify the marginal ice zone fraction (MIZF) as a metric that is less correlated to warming, has a response plausibly simulated from January–September (but not October–December), and has highly variable future projections across GCMs. These qualities make MIZF useful for evaluating the impact of sea-ice model changes on past, present, and projected sea-ice state. Climate models struggle to track the response of Arctic sea ice to warming, leading to efforts to improve sea-ice models. Here the author shows standard model metrics are poor judges of the impact of model improvements, but a new one, marginal ice zone fraction, is optimally suited to this task. Accordingly, the failure of GCMs to represent declining trends in observed SIE3 has contributed to efforts to improve the representation of sea ice in GCMs6. Still, a majority of GCMs submitting to phase 6 of the Coupled Model Intercomparison Project7 (CMIP6) remain unable to produce plausible estimates of the observed decline of September Arctic sea ice8.The rapid decline of the Arctic sea ice cover is a primary indicator of Earth’s changing climate. Yet the response of September sea-ice area or extent (SIA/SIE) to a unit increase in carbon emissions is underestimated compared to observations in nearly all global climate models (GCMs): only models that overestimate resulting global warming can reproduce observed trends in sea ice loss13, and references within. The underestimation of September SIA loss has improved across CMIP generations, but the link between SIA and GMT makes it challenging to judge whether improvements in modeled sea ice originate in improvements in sea-ice model physics or are due primarily to improvements in the external forcing of sea ice14, like changes to aerosol forcing17 noted that the inaccuracurate representation of SIA trends in CMIP5 models can be explained as the result of inherent internal variability in modeled climate, which in this study will be referred to as “model internal variability”. Because of the low number of ensemble members contributed by each model group in CMIP5, it was not possible to directly evaluate model internal variability over the global warming period. Instead,17 used year-to-year variance from a single model realization over a long pre-industrial simulation to represent model internal variability. The recent publication of large (20+ member) ensembles20, and the contribution of multiple historical runs to the 6th CMIP from a number of modeling centers , one that holds across sea ice and climate models of a wide range of complexities23, and can be observed using the same passive microwave observations used to estimate SIA/SIE24. MIZ location and variability is set through the coupled interaction of oceanic, atmospheric, thermodynamic and sea ice dynamical processes that are the focus of many recent modeling efforts29. However, September MIZ extent has generally been unchanged over 1979–201830, with September MIZ fraction (MIZF), the percentage of the September Arctic sea ice cover that is MIZ, increasing as SIA has declined.The marginal ice zone (MIZ) has been a point of focus as a critical region for polar climate and ecology20, CLIVAR-LE  from the ensemble-mean statistics of 8 climate models which submitted relatively large sets (10+ member) of historical simulations to CMIP6 , scattered against the trend in GMT  over the period 1979–2014. Because of the large difference between models in SIA and MIZF mean states, trends are expressed as a percentage change per year (this choice is examined in more detail in the Discussion below). While individual CLIVAR-LE models do not produce plausible estimates (see methods) of ΔSIA in September  with a bivariate correlation coefficient of r = −0.81: variation in ΔGMT across all simulations can explain 66% of the variance in ΔSIA.For each ensemble member, a normalized SIA trend is computed  between annual ΔGMT and monthly ΔSIA (red) and ΔMIZF (blue) trends, with significant correlations filled circles. SIA trends are significantly correlated with GMT trends in all months, whereas MIZF has no significant correlation (p > 0.01) with GMT in four: February, May, August, and September. The coefficient of determination of SIA exceeds that of MIZF in all months which can be expected as MIZF involves a ratio of MIZ area to SIA (see methods). In September, with a bivariate correlation coefficient of r = 0.17, only 3% of the variability in ΔMIZF across the simulations can be explained by ΔGMT variability.In Fig. ρSIA, is defined as the percent change in SIA per degree change in GMT (units %/K). A declining SIA with increasing GMT therefore yields a negative ρSIA.The preceding analysis demonstrated that September MIZF trends alone are plausibly estimated when considering both internal variability (in 4/5 CLIVAR-LE models) and inter-model variability (computed via the CMIP6-EME). September MIZF trends are also not correlated with GMT trends, and no members of the CLIVAR-LE plausibly estimate the observed change in September SIA. Still, the bias in modeled September SIA could plausibly be explained as a consequence of inter-model variability in the CMIP6-EME, though this does not take into account the correlation between GMT and September SIA. Here I investigate this correlation, examining SIA and MIZF sensitivity to warming across the seasonal cycle, where the sea ice sensitivity (or MIZF sensitivity), ρSIA, units %/K), for each of the 5 + 1 ensembles. Note that observed global warming over 1979–2014 was ~0.634 K, and therefore ρSIA = 50%/K would correspond to a −30% change in SIA over that period.Figure t-test . Red boxes plot where the null hypothesis—that model estimates and observational estimates come from distributions with the same mean—is rejected at the 5% significance level. Each of the ensembles reproduces the qualitative seasonal cycle of ρSIA, with the largest magnitude sensitivity to warming from June to September. In all months, and across all of the ensembles, observed values of ρSIA are more negative than model estimates. This includes an underestimation of September sea ice sensitivity, as previously observed by many8, e.g., However, controlling for internal variability (in the CLIVAR-LE) or inter-model variability (in the CMIP-EME), no models make plausible estimations of September ρSIA, or in more than 3 months of the year (blue boxes). This differs from the results of[ 17, who found that estimates of September sea ice sensitivity in CMIP5/6 class models were plausible when accounting for internal variability—although in those studies internal variability was quantified using different methodologies to account for having fewer ensemble members .The plausibility of a given model estimate of sensitivity is evaluated via a two-sided ρMIZF . These three months are those with the highest annual correlation between ΔMIZF and ΔGMT and therefore likely the most sensitive to forcing biases. The CMIP-EME does plausibly estimate ρMIZF in October and November.Despite producing generally plausible estimates of ρMIZF, but inter-model variability (expressed by the CMIP-EME) may. Thus MIZF may prove a useful benchmark for characterizing future model projections of sea ice change. Figure Accounting for internal variability, historical estimates of MIZF sensitivity are generally plausible from January to September. From October to December, internal variability cannot explain incorrect estimates of ∘/yr vs 0.0472∘/yr), and ensemble mean values of ΔMIZF vary by more than 600% (2.2%/yr vs 15.9%/yr).On the basis of September and December SIA projections, the models fall into three general groups. Two (GFDL-ESM2M and CSIRO-MK36) show MIZF trends that are ≈2%/yr in September, but are indistinguishable from zero in December. Two others (GFDL-CM3 and CANESM2) have near-zero trends in September but large (>10%/yr) ΔMIZF in December. The final, CESM, has a positive MIZF trend in September but weaker positive trend (ΔMIZF ≈ 2%/yr) in December. There is a difference in ΔMIZF for models with similar predicted ΔGMT. For example, between CESM and GFDL-CM3, ensemble-mean values of ΔGMT vary by <8%  is during the annual period where MIZF is most determined by GMT change Fig. , and (b)17. I suggest two main explanations for this difference. First, different data and methodology17: sought to solve the problem of assessing internal variability for small forced ensembles, using an ergodic assumption to convert control simulation variability to ensemble variability. Here, with a large number of members in each of the 5 + 1 ensembles, a more direct and simpler evaluation of internal variability is possible, one with a significantly tighter constraint on internal variability than is possible for smaller ensembles (see “Methods”) and therefore a less permissive test of plausibility. In addition, in ref. 8, an average estimate of model internal variability was defined by pooling all CMIP6 models that contributed three or more simulations, and here I compute the model internal variability for each CMIP6 model on its own. Second, we examine slightly different fields. I find that trends in SIA alone are plausibly produced by the CMIP6-EME , to warming Fig. , top rownot Fig. . With a not Fig. . This ch16. Accordingly observational uncertainty is highest in these months —reducing the ability to differentiate plausible model estimates of ρMIZF from implausible ones and potentially limiting the ability to differentiate model estimates from one another in the future. The wider error bounds for MIZF, however, do not affect the plausibility of modeled MIZF. The results presented here hold generally even if the uncertainty in observed MIZF is artificially reduced to be the same as for observed SIA , those predicting a large December MIZF expansion , and those predicting weak to no MIZF expansion over the next 35 years . The magnitude of projected trends in the two “expanding” cases is much larger than historical values of MIZ: for example, CESM projects a 10%/yr increase in MIZF from 2020 to 2055 following the RCP8.5 pathway, but MIZF expanded by <1%/yr from 1979–2014. Because the projected signals of MIZF expansion are high , and in September MIZF changes are not correlated with GMT changes, it may be possible to use MIZF forecasts in real time to assess and analyse the impact of changes to sea-ice model physics on Arctic sea ice, independent of whether models accurately represent warming.30. This could explain why MIZF trends are less correlated to GMT trends in several months. MIZF trends are also poorly predicted by the models considered here during the period of sea ice freeze-up and expansion, from October to December. This may point to the parameterization of new ice growth as an area of sea-ice model weakness, although the growth of new sea ice is highly tied to local temperature, and these months are also those with the highest correlation between MIZF trends and GMT trends. This period of the year is also that in which the effect of waves is most pronounced in Arctic sea ice38, and none of the CLIVAR-LE models nor any models submitting to CMIP6 incorporate the coupled evolution of sea ice and waves, one of the most significant drivers of MIZ variability40. Progress is being made at incorporating wave and responsive floe size distribution models43 in GCMs, and understanding how parameterizations of coupled wave, thermodynamic, and dynamic processes influence model estimates of MIZF will be a key area for future efforts.Processes that affect the variability and expansion of the MIZ are present around the sea ice margin, and may not vary greatly in effect over time but simply move north as sea ice retreats as suggested by ref. 20. Historical forcing is applied until 2014, at which point model forcing follows the RCP8.5 pathway.I use five contributing members to the CLIVAR large ensemble that provide sea ice concentration fields to the EarthSystemGrid data portal , which are listed in Table I also use eight available CMIP6 models with historical sea ice output available on the Earth System Frontier Grid as of 5/1/2020, that submitted 10 or more ensemble members, and are used to create the multi-model ensemble CMIP6-EME  rather than sea-ice extent to limit grid-related uncertainty∘N. Marginal ice zone area (MIZA) is computed as the sum of sea ice concentration for all grid cells with <80% concentration. The MIZA fraction is then MIZF = MIZA/SIA. While SIA should not be sensitive to a changing grid, MIZA may be, as whether a given geographical region is above or below 80% sea ice concentration can depend on how the grid is drawn16. I do not consider this source of grid-related uncertainty here.SIA is calculated as the sum of sea ice concentration multiplied by grid cell area in all grid cells above 09, for example, is the slope of this linear fit for September SIA. Sensitivites are computed as the negative of the ratio of sea ice trends to annual GMT trends, for example:To evaluate linear trends, I perform a linear regression of the 36-year (1979–2014) time series against time and a constant term for each month (for sea ice products) or the annual average (for GMT). The trend ΔSIAρX corresponds to a negative trend in X for a positive ΔGMT.Thus a negative value of σ0 over N ensemble members, this ultimately corrects σ upwards as:N = 3, as used in the pooled uncertainty metric by8, this corrects the sample standard deviation upwards by 38%. For N = 8, as used to compile the CMIP6-EME ensemble, the correction is 11%. For N = 20, the smallest size of a CLIVAR-LE ensemble set, the correction is 4.0%.The uncertainty associated with internal variability is quantified for each model using the unbiased sample standard deviation. Bessel’s correction is applied to the sample variance, and Cochran’s theorem corrects for the bias in the standard deviation. For an uncorrected sample standard deviation t-test, where the null hypothesis is that the model and observational distributions are drawn from a normal distribution with the same mean but not necessarily the same variance, evaluated at the 95% confidence interval. The unequal variance test is chosen as model variance  and observational variance  are not expected to be the same. Red values reject the null hypothesis at the 5% significance level. Whisker plots extend the model data three (unbiased) standard deviations from the mean, and dashed lines extend the observational estimate one (unbiased) standard deviation from the mean.For evaluating the plausibility of LE trends or statistics relative to observations, color coding in Figs. For visual comparison, assume the null hypothesis that ensemble statistics and observational statistics are drawn from normal distributions with different variances but the same mean. Then the likelihood that whiskers in Figs. ρMIZF estimates is due only to higher observational uncertainty, I create a synthetic dataset of ρMIZF composed of the observational ρMIZF mean and two-time series spaced at ±2 standard deviations from the mean, but where those standard deviations are computed instead as the observational uncertainty for ρSIA. I reproduce Fig. ρMIZF are qualitatively similar compared to using the true (higher) observational uncertainty: the CMIP-EME is plausible in September, though 4/5 CLIVAR-LE models are not.To test whether the increased plausibility of Supplementary InformationPeer Review File


Duck hepatitis A virus type 1 (DHAV-1) is one of the most deadly pathogens that endanger the duck industry. Most viruses usually turn off host translation after infection to facilitate viral replication and translation. For the first time report to our knowledge, DHAV-1 can induce eIF2α phosphorylation and inhibit cellular translation in duck embryo fibroblasts (DEFs). Moreover, the activity of DHAV-1 in the cells caused obvious eIF2α phosphorylation, which has nothing to do with the viral protein. Subsequently, we screened two kinases (PERK and GCN2) that affect eIF2α phosphorylation through inhibitors and shRNA. Notably, the role of GCN2 in other picornaviruses has not been reported. In addition, when the phosphorylation of eIF2α induced by DHAV-1 is inhibited, the translation efficiency of DEFs restores to a normal level, indicating that DHAV-1 induced cellular translation shutoff is dependent on eIF2α phosphorylation. Avihepatovirus genus of Picornaviridae family. Its genome is about 7,700 nt and consists of a 5' untranslated region (5' UTR), an open reading frame (ORF), a 3' untranslated region (3' UTR), and a poly(A) tail  is a rapidly spreading and highly lethal infectious disease caused by Duck hepatitis A virus (DHAV) in ducklings, which is very harmful to the duck industry. DHAV includes three genotypes: DHAV-1, DHAV-2, and DHAV-3. Among them, DHAV-1 is more pathogenic. It mainly infects ducklings of 1–4 weeks old and can cause more than 90% mortality. The main feature is liver swelling, bleeding spots, and neurological symptoms . DHAV-1 (A) tail . ORF is (A) tail .Met to the ribosomal 40S subunit in a GTP-dependent manner to initiate peptide chain synthesis. However, eIF2 activity is regulated by phosphorylation of its α subunit S51. Once eIF2α is phosphorylated, eIF2 competitively binds to eIF2B, and the function of eIF2B to convert eIF2-GDP to eIF2-GTP is weakened or disappeared, resulting in GTP that cannot recycle, which ultimately leads to translational inhibition. The four kinases  reported so far phosphorylate eIF2α by sensing different signals, thus regulating the cellular translation process. After picornaviruses invade cells, the accumulation of viral proteins in the endoplasmic reticulum and the production of double-stranded RNA during viral replication activate PERK and PKR, respectively  and inhibit cell translation. Moreover, we found that the activity of DHAV-1 in cells rather than viral protein is the cause of obvious eIF2α phosphorylation, and two kinases (PERK and GCN2) are involved in the eIF2α phosphorylation process. Besides, eIF2α phosphorylation inhibition can restore DEFs translation, which indicates that DHAV-1 inhibits DEFs translation through eIF2α phosphorylation.6 cells per well and the cells were grown in minimum essential medium (MEM) containing 10% newborn calf serum (Gibco) and incubated at 37°C with 5% CO2 in an incubator. Then, DEFs were infected with DHAV-1 for 2 h, and the unbound virus was removed by washing with phosphate-buffered saline (PBS) twice before the cells were overlaid with MEM containing 2% newborn calf serum. DEFs were transfected expression plasmids or poly(I:C) with TransIntro™ EL Transfection Reagent (TransGen Biotech). UV-DHAV-1 is obtained by irradiating DHAV-1 with UV light with a wavelength of 253.7 nm for 6 h.The DHAV-1 H strain (GenBank: JQ301467.1) was provided by the Institute of Preventive Veterinary Medicine at Sichuan Agricultural University. The primary DEFs were described previously . Six-welMouse anti-puromycin antibody was obtained from Millipore. Rabbit anti-phospho-eIF2α (S51) and mouse anti-Myc antibody were obtained from Cell Signaling Technology. Mouse anti-HA and mouse anti-Flag antibody were purchased from MBL. Mouse anti-GFP antibody was obtained from ABclonal. Rabbit anti-beta (β)-actin antibody was obtained from Proteintech. HRP-conjugated goat anti-mouse IgG and HRP-conjugated goat anti-rabbit IgG were purchased from Beyotime. Rabbit anti-VP3 antibody was prepared in our laboratory .C16 (PKR Inhibitor) and GSK2606414 (PERK inhibitor) were purchased from APExBIO, and GCN2-IN-1 (GCN2 inhibitor) was purchased from MCE. These inhibitors were dissolved in dimethyl sulfoxide (Sigma) and configured to 10 mmol/L, which were diluted to working concentration with MEM when used. Sodium arsenite was purchased from Sigma. Poly(I:C) was purchased from Invivogen.According to the manufacturer’s instructions, to construct plasmids expressing the viral protein, DHAV-1 RNA was isolated using RNAiso Plus Reagent (TaKaRa). According to the manufacturer’s instructions, genomic DNA was then removed, and reverse transcription was performed using a PrimeScriptTM RT Reagent Kit (TaKaRa). VP0, VP1, 2A, 2B, 2C, 3AB, and 3D sequences were amplified from DHAV-1 cDNA with PCR and primers . VP0, VPTransIntro™ EL Transfection Reagent (TransGen Biotech).The pGPU6/GFP/Neo-PERK#1/#2/#3 expression vectors and the pGPU6/GFP/Neo-GCN2#1/#2/#3 expression vectors were designed and constructed by GenePharma , and the sequences of shRNAs were as follows: PERK#1 5'-GCCAGTCATTAGAGGAAATTT-3', PERK#2 5'-GGCATGATAATGCAATTATTC-3', PERK#3 5'-GCAGGAAAGAGAACCTTAAAG-3', GCN2#1 5'-GCCTAAAGTTAGTGTTATAGC-3', GCN2#2 5'-GGACTATGATGAGTCAAATAT-3', GCN2#3 5'-GGTGCGAAATAAGCTTGATGG-3'. The DEFs were plated into six-well cell culture dishes and cultured overnight. The cells were then transfected with the recombinant plasmids using Total RNA was isolated using RNAiso Plus Reagent (TaKaRa) according to the manufacturer’s instructions. The number of viral copies in total RNA was measured using methods previously established in our laboratory . Three gRefer to the previous method, DEFs were treated with 1 μg/ml of puromycin for 30 min to detect the protein translation efficiency . After DCells were lysed in 200 μl cell lysis buffer (Beyotime) containing 1% PMSF. The cell lysate was centrifuged, and the supernatant was collected. Samples were fractionated by SDS-PAGE electrophoresis and then transferred to PVDF membrane, which was blocked with 5% non-fat dry milk at room temperature for 5–6 h. The membranes were incubated overnight at 4°C with primary antibodies diluted in blocking buffer. The membranes were washed three times with TBS-Tween and incubated for 1 h at 37°C with the respective secondary antibodies diluted in blocking buffer. The membranes were then washed three times with TBS-Tween, and bound proteins were detected using an ECL chromogenic kit (Beyotime).The cells were rinsed (three times) with phosphate-buffered saline (PBS) and then fixed in 4% paraformaldehyde overnight. The cells were permeabilized (0.2% Triton X-100 for 25 min), incubated with blocking solution [5% bovine serum albumin (BSA) in PBS with Tween 20 (PBST) for 60 min at 37°C], and then rinsed with PBST. The cells were then treated with DAPI (Roche). Images were captured using an 80i upright microscope (Nikon) and a SPOT Flex camera.The cell viability was measured using the Cell Counting Kit-8 (Biosharp) according to the manufacturer’s instructions.After many viruses infect host cells, they hijack the cell’s translation-related factors and reduce the overall cellular translation efficiency. Puromycin is a protein synthesis inhibitor. It has a structure similar to the end of the tRNA molecule. It can replace the aminoacylated tRNA to enter the ribosome A site and bind to the elongating polypeptide chain. When the elongating polypeptide is transferred to this abnormal A position, the polypeptide synthesis is blocked, and the immature C-terminal polypeptide containing puromycin is released. Therefore, we use the puromycin antibody to detect labeled elongating polypeptides, which can dynamically detect protein translation efficiency in a real-time manner. And, this method has been used in our previous report .DEFs were infected with DHAV-1 at MOI = 1, and samples were harvested at 6, 12, 24, and 48 h after infection, and control groups were set at each time point. Before harvesting the cells, puromycin (1 μg/ml) was added to the medium, and the cells were incubated at 37°C for 30 min . As showPhosphorylation of eIF2α causes total translation stagnation in the cells. The stalled messenger ribonucleoprotein (mRNP) complex aggregates under viral infection stress to form stress granules (SGs). Ras GTPase-activating protein-binding protein 1 (G3BP1) can regulate the SG core network through positive or negative cooperativity with other G3BP1-binding factors . TherefoIt was previously reported that the foot-and-mouth disease virus VP2 could cause the phosphorylation of eIF2α , so we sThe previous results have shown that the viral protein of DHAV-1 is not the cause of obvious phosphorylation of eIF2α. To further investigate whether the viral activity is the cause of eIF2α phosphorylation, we used UV to inactivate DHAV-1 and then infected DEFs at 1 MOI. As shown in Four known kinases phosphorylate eIF2α in cells, namely, PERK, PKR, GCN2, and HRI. However, we only found three kinases PERK, PKR, and GCN2, in duck-derived cells from National Center for Biotechnology Information (NCBI). To better understand which kinase plays a role during DHAV-1 infection, we used PERK, PKR, and GCN2 kinase inhibitors for screening. Due to the lack of antibodies against PERK, PKR, and GCN2, we can only indirectly reflect the three kinases’ activation through the phosphorylation of eIF2α. DEFs were infected with DHAV-1 at 1 MOI. At 22 h after infection, PERK, PKR, and GCN2 kinase inhibitors were added, and cells were harvested after 2 h of treatment . After aIn order to exclude the non-specific effects of inhibitors, we used shRNA to knock down PERK or GCN2. We designed three shRNAs against PERK or GCN2 and found that shRNAs could interfere with the transcription of PERK or GCN2 ,B. We aleIF2 plays an important role in eukaryotic cells, and its alpha subunit phosphorylation controls the cellular translation initiation. Many reports indicate that the virus causes cell translation inhibition through eIF2α phosphorylation, but recently dengue virus DENV) and mouse norovirus (MNV) have found that cell translation inhibition and eIF2α phosphorylation are not coupled with each other . Since tENV and mTo explore the direct relationship between eIF2α phosphorylation and DEFs translation inhibition, we used sodium arsenite (a reagent known to induce phosphorylation of eIF2α) as a positive control. As shown in the The results described above did not provide a clear answer to the role of eIF2α phosphorylation in translation shutoff. Therefore, we used inhibitors to treat DEFs and then labeled them with puromycin for 30 min to test whether the translation inhibition of DEFs was restored . As showSubsequently, we determined the effect of these kinase inhibitor treatments on DHAV-1 propagation. We treated DEFs with corresponding concentrations of kinase inhibitors for 2 h and then infected DEFs with 0.5 MOI or 1.0 MOI DHAV-1, respectively. The virus copy number was measured at 6, 12, and 18 h after infection, and the results showed that treatment with these kinase inhibitors would not affect DHAV-1 propagation .mRNA undergoes translation initiation, elongation, and post-translational modification to form a protein with a specific structure and function. Since eIF2 plays an important regulatory role in the translation initiation, inhibition of host cell translation by eIF2α phosphorylation is a common strategy for most viruses, but recently DENV and MNV have also reported translation shutoff independent of eIF2α phosphorylation. Therefore, to explore whether the translation shutoff of DEFs caused by DHAV-1 is dependent on eIF2α phosphorylation. After using inhibitors to weaken eIF2α phosphorylation, we found that DEFs infected with DHAV-1 recovered to the same translation level as the mock group, indicating that DHAV-1 inhibits DEFs translation by phosphorylation of eIF2α , 7B. In Under stress, eIF2α phosphorylation leads to an increase in the affinity of eIF2 and eIF2B, eIF2 competitively binds to eIF2B, and the function of eIF2B to convert eIF2-GDP to eIF2-GTP is weakened or disappeared. Therefore, how the virus synthesizes its protein in the presence of eIF2α phosphorylation is an interesting question. The accessory protein AcP10 of beluga whale coronavirus (Bw-CoV) can bind to eIF2B, hindering the eIF2 binding to eIF2B after eIF2α phosphorylation, thereby promoting the function of eIF2B-mediated conversion of eIF2-GDP to eIF2-GTP . SimilareIF2α phosphorylation plays an important role in viral infection . VirusesThe picornavirus infection causes cellular endoplasmic reticulum stress and activates PERK . Our resIt is a common phenomenon that PERK and GCN2 kinases simultaneously regulate the same substrate . We usedIn summary, our results show that DHAV-1 induces eIF2α phosphorylation-dependent cellular translation shutoff and that two kinases, PERK, and GCN2, are involved in eIF2α phosphorylation. These results provide basic data for further research on the pathogenic mechanism of DHAV-1.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.YaL conceived and carried out the experiments, analyzed the data, and wrote the manuscript. AC and MW conceived and supervised the study. SM, XO, QY, YW, QG, ML, SZ, JH, RJ, DZ, SC, XZ, YY, YnL, LZ, BT, and LP interpreted the data and revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.