Hepatic DNAJB9 Drives Anabolic Biasing to Reduce Steatosis and Obesity

Article

Hepatic DNAJB9 Drives Anabolic Biasing to Reduce Steatosis and Obesity Graphical Abstract

Authors Fangfang Sun, Yilie Liao, Xingfan Qu, ..., Haipeng Huang, Pingping Li, Suneng Fu

Correspondence [email protected]

In Brief Sun et al. show that DNAJB9 promotes SREBP1c degradation and mTORC2 assembly to stimulate protein synthesis and ATP production at the cost of lipogenesis. The anabolic biasing function of DNAJB9 drives cellular metabolism toward energy expenditure, and it provides a paradigm for tackling hyperphagia, obesity, and fatty liver diseases.

Highlights d

DNAJB9 adopts a dual topology to promote SREBP1c degradation and mTORC2 assembly

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DNAJB9’s dual-activity biases cellular metabolism against energy deposition

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The development of obesity is accompanied by hepatic DNAJB9 downregulation

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Hepatic DNAJB9 re-expression improves whole-body energy homeostasis

Sun et al., 2020, Cell Reports 30, 1835–1847 February 11, 2020 ª 2020 The Author(s). https://doi.org/10.1016/j.celrep.2020.01.043

Cell Reports

Article Hepatic DNAJB9 Drives Anabolic Biasing to Reduce Steatosis and Obesity Fangfang Sun,1,4 Yilie Liao,2,4 Xingfan Qu,1,4 Xia Xiao,4 Shaocong Hou,5,6,7 Zheqin Chen,3,4 Haipeng Huang,3,4 Pingping Li,5,6,7 and Suneng Fu1,2,3,4,8,* 1Center

for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China 3Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing 100084, China 4School of Life Sciences, Tsinghua University, Beijing 100084, China 5State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China 6Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China 7Diabetes Research Center of Chinese Academy of Medical Sciences, Beijing 100050, China 8Lead Contact *Correspondence: [email protected] https://doi.org/10.1016/j.celrep.2020.01.043 2MOE

SUMMARY

Nutrients stimulate the anabolic synthesis of proteins and lipids, but selective insulin resistance in obesity biases the anabolic program toward lipogenesis. Here, we report the identification of a DNAJB9-driven program that favors protein synthesis and energy production over lipid accumulation. We show there are two pools of DNAJB9 cochaperone. DNAJB9 in the ER lumen promotes the degradation of the lipogenic transcription factor SREBP1c through ERAD, whereas its counterpart on the ER membrane promotes the assembly of mTORC2 in the cytosol and stimulates the synthesis of proteins and ATP. The expression of Dnajb9 is induced by nutrients and downregulated in the obese mouse liver. Restoration of hepatic DNAJB9 expression effectively improves insulin sensitivity, restores protein synthesis, and suppresses food intake, accompanied by reduced hepatic steatosis and adiposity in multiple mouse models of obesity. Therefore, targeting the anabolic balance may provide a unique opportunity to tackle obesity and diabetes. INTRODUCTION The mechanistic target of rapamycin (mTOR) integrates a broad array of nutrients and growth factor signals to regulate energy homeostasis through the coordinated actions of mTOR complex 1 and complex 2 (mTORC1/2) (Albert and Hall, 2015; Caron et al., 2015; Haissaguerre et al., 2014; Howell and Manning, 2011; Jewell et al., 2013; Lamming and Sabatini, 2013; Tato et al., 2011; Wolfson and Sabatini, 2017). Among the two, mTORC1 is better studied, and it figures extensively in energy metabolism by reducing substrate influx through the inhibition of autophagy, promoting energy storage through enhanced lipid synthesis, or increasing energy production via

transcriptional and translational regulation of mitochondria €vel et al., biogenesis and dynamics (Cornu et al., 2014; Du 2010; Jung et al., 2009; Laplante and Sabatini, 2013; Morita et al., 2013). Functional characterization of mTORC2 is more recent, and its emerging role in energy metabolism includes inhibition of mitochondria biogenesis through PGC1a inactivation (Li et al., 2007), regulation of endoplasmic reticulum (ER)-mitochondria calcium flux and mitochondria membrane potential at the mitochondria-associated membrane (MAM) compartments (Betz et al., 2013), and stimulation of glycolysis and lipogenesis downstream of feeding and growth factor actions (Caron et al., 2015; Hagiwara et al., 2012; Javary et al., 2018; Kleinert et al., 2016; Yuan et al., 2012). Because of the conflicting action of mTOR in driving both energy-storing (lipid synthesis) and energy-consuming (protein and nucleotide synthesis) pathways, the overall impact of mTORC1/2 activation or deficiency on whole-body energy homeostasis can go both ways (Albert and Hall, 2015). For example, a positive role of mTORC2 in energy consumption was suggested by a mouse model with an adipose-specific deletion of Rictor, resulting in large body stature and organ size without affecting food consumption and adiposity (Cybulski et al., 2009). Infection and cold-induced adaptive thermogenesis also require intact mTORC2 function (Albert et al., 2016; Hallowell et al., 2017). However, the deletion of mTORC2 in the Myf5 lineage resulted in a more oxidative state and protected mice from obesity (Hung et al., 2014), suggestive of a negative role of mTORC2 in energy metabolism. Therefore, the impact of mTOR on energy homeostasis may depend upon its primary function in the target tissues being either promoting lipid synthesis (e.g., in the white adipose tissue and the liver) or driving protein synthesis and mitochondria biogenesis (e.g., in the skeletal muscle and the brown adipose tissue). Molecular chaperones play an important role in the maintenance of cellular proteostasis and mTOR signaling (Appenzeller-Herzog and Hall, 2012). Heat shock protein 90 (HSP90) binds directly to Raptor and assists the assembly of mTORC1, and HSP90 inhibition by geldanamycin impairs mTORC1 signaling and improves hepatic steatosis (Ambade et al., 2014; Ohji

Cell Reports 30, 1835–1847, February 11, 2020 ª 2020 The Author(s). 1835 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Figure 1. DNAJB9 Dictates Hepatic Feeding Response and Whole-Body Energy Homeostasis (A) Immunoblots of anabolic signaling pathways in control (LacZ) and Dnajb9-knockdown (shDnajb9) mouse liver. (B) Liver ratios of LacZ (n = 16) and shDnajb9 (n = 15) mice. (C and D) Measurement of glucose output (C; n = 6) and protein synthesis (D) in primary hepatocytes prepared from shDnajb9 and control mice. NT, not treated with additional substrates; Pyruvate, 1mM Pyruvate. (E) Growth curve of Dnajb9LKO and control (fl/fl) mice fed with NCD or HFD (n = 22). HFD started at 5 weeks after birth. Body weight was recorded after 6 h of food withdrawal. (F) Body composition of Dnajb9LKO and fl/fl mice fed with HFD (n = 22) at 18 weeks of age. (G) Liver H&E staining of fl/fl and Dnajb9LKO mice (5 months, HFD). (H) Immunoblots of anabolic signaling pathways in GFP- and DNAJB9-expressing mouse liver samples. (I–K) Measurement of liver ratio (I; n = 5), visceral fat (J; n = 5), and food intake (K; n = 10) of DNAJB9- and GFP-expressing mice. Mice were sacrificed 7 days after adenovirus administration, and food was withdrawn for the last 2 days followed by a bolus glucose (2 g/kg) injection 2 h before tissue collection. (L) Measurement of food intake in Dnajb9LKO and fl/fl mice (n = 4, 3 months, NCD) in metabolic cage studies. Scale bars, 100 mm. Data are represented as mean ± SEM. *p < 0.05, **p < 0.01, and ***p < 0.001. See also Figures S1–S3.

et al., 2006). GRP58/ERp57 also interacts with mTORC1 and regulates the phosphorylation of ER-associated S6K1 in response to growth factors and amino acids (Ramı´rez-Rangel et al., 2011). In another report, molecular chaperones were suggested to play a negative role in mTOR function by disassembling the complex (Qian et al., 2010). Importantly, molecular chaperones are broadly dysregulated in obese tissues (Boden and Merali, 2011; Ozcan et al., 2004; Yang et al., 2015), and supplementation of the chaperone function through overexpression of molecular chaperones (e.g., Grp78, Hsp72) or administration of chemical chaperones reduces hepatic steatosis and improves insulin and leptin resistance (Archer et al., 2018; Kammoun et al., 2009; O¨zcan et al., 2006). Therefore, the effect of molecular chaperones on overall energy homeostasis seems to be chaperone specific, and their potential involvement in mTOR-driven energy metabolism is of great interest to the field of obesity and diabetes. RESULTS DNAJB9 Dictates Feeding Response and Whole-Body Energy Homeostasis We are interested in the function of the J-class co-chaperone DNAJB9 as it was induced by feeding (Figure S1A) and capable of adopting two distinct topologies to function both in the ER lumen and in the cytosol (Figures S1B and S1C). As demon-

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strated in the Proteinase K protection assay, more than a quarter of the membrane-bound DNAJB9 had its C terminus exposed (type I topology) and degraded by proteinase treatment, while the rest was protected by the ER membrane, as expected from its luminal function in ER-associated protein degradation (ERAD; Figures S1B–S1D) (Dong et al., 2008; Lai et al., 2012). We constructed a series of whole-body and liver-specific Dnajb9 knockout (KO), knockdown, and overexpression mouse models as well as knockout cell lines to assess potential functions of DNAJB9 compatible with such a dual topology (Figure 1; Figures S2 and S3). Consistent with its feeding inducibility, adenovirus-mediated transient suppression of Dnajb9 expression by short hairpin RNA (shRNA) (shDnajb9; Figure S3A) in the wild-type mouse liver led to a dramatic suppression of feeding-activated signaling pathways (mTORC1: pmTOR-S2448, p-4EBP, and p-S6; mTORC2: pAKT-S473 and p-GSK3b-S9; Figures 1A and S3C) and a reduction in liver size (Figure 1B). Likely, both the elevation of gluconeogenesis (Figure 1C) and the suppression of protein synthesis (Figure 1D) contributed to the loss of liver mass. Despite the suppression of anabolism and elevation of catabolic signals (pACC-S79; Figures S3B–S3F), the protein levels of lipogenic enzymes (ACC, ACSL, and ACSS2) were unexpectedly upregulated (Figures 1A and S3C), accompanied by increased lipid accumulation in the liver of shDnajb9 mice (H&E and oil red O staining; Figure S3G), suggesting a metabolic

Figure 2. ER Luminal DNAJB9 Facilitates SREBP1c Degradation through ERAD (A) Schematic illustrations of DNAJB9 full-length (FL) and signal peptide deletion mutant (DSP). G/F, Gly/Phe-rich domain. (B) Subcellular localizations of FL- and DSPDNAJB9. Sec61B (red), ER marker; GFP (green), FL- and DSP-DNAJB9 fusion proteins. (C and D) Immunoblot (C) and quantification (D; n = 3) of SREBP1c protein levels in cells expressing indicated constructs. HSP70, loading control. (E and F) Immunoblot (E) and quantification (F; n = 2) of SREBP1c during the course of CHX (100 mg/mL) treatment in GFP- or DNAJB9-GFPexpressing cells. (G and H) Immunoblot (G) and quantification (H; n = 3) of SREBP1c protein levels in DMSO or NMS873 (2 mM, 24 h) treated vector- or DNAJB9-expressing cells. (I) Measurement of Srebp1c and lipogenic genes expression in mouse liver expressing GFP or DNAJB9 (n = 4 or 5). Normalized to 18s ribosome RNA. (J) Triglyceride levels in Srebp1 and Dnajb9 knockdown livers (n = 7). Cells used in (B)–(H) are HEK293A Dnajb9
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cells. Mice were food-withdrawn during the day, refed at 6 p.m., sacrificed, and tissues were collected at midnight (0 a.m.), at day-4 (I), or day-7 (J) days post adenovirus injection. Scale bars, 10 mm. Data are represented as mean ± SEM. NS, not significant; *p < 0.05, **p < 0.01, and ***p < 0.001. See also Figures S1 and S4.

switch from protein synthesis to lipid accumulation in the Dnajb9-deficient liver. Long-term Dnajb9 deficiency using albumin Cre-mediated deletion of Dnajb9 (Dnajb9LKO; Figures S2A and S2B) had no apparent effect on body weight under both normal chow diet (NCD) and high-fat diet (HFD) conditions (Figure 1E). However, a careful examination of body composition revealed a substantial loss of lean body mass and a corresponding increase of fat mass in the Dnajb9LKO mice (Figure 1F). The lipogenic transcription factor (TF) SREBP1c was strongly induced in the liver of Dnajb9LKO mice (Figure S3H), and they presented exacerbated hepatic steatosis under HFD conditions compared with control, wild-type mice (Figure 1G). Plasma and hepatic dyslipidemia were exacerbated, while no change in plasma insulin levels was observed (Figures S3I–S3M). Hepatic DNAJB9 overexpression (DNAJB9; Figure S3N), on the other hand, led to enhanced activation of anabolic signals (mTORC1: pmTOR-S2448 and pRictor-T1135; mTORC2: pACL-S455; Figures 1H and S3P) and a reduction in AMPK signaling (pACC-S79) (Figures 1H and S3O), but no induction of lipogenic enzymes was observed (Figure S3P). Liver mass was increased (Figure 1I), accompanied by a significant reduction in visceral adiposity (Figure 1J), suggesting a competing relationship between the liver and the adipose tissue in nutrient absorption. Surprisingly, hepatic activation of feeding signals resulted in potent suppression of food intake in the DNAJB9-overexpressing mice (Figure 1K), whereas Dnajb9 deficiency (Dnajb9LKO) increased food consumption (Figure 1L). Therefore, DNAJB9 possess potent capacity to bias the hepatic anabolic program toward augmented protein synthesis and hypertrophy, while reducing lipid accumulation and food intake.

ER Luminal DNAJB9 Facilitates SREBP1c Degradation through ERAD To dissect the mechanism of DNAJB9-driven anabolic bias, we first looked into how Dnajb9 loss of function may result in SREBP1c induction (Figure S3H) and lipid accumulation (Figures 1G and S3G). A potential link between DNAJB9 and lipogenesis is that SREBP precursor on the ER is degraded through ERAD (Hughes et al., 2009), and DNAJB9 traditionally functions as a cochaperone for HSPA5 and a rate-limiting factor in the ERAD pathway (Dong et al., 2008; Huang et al., 2019; Lai et al., 2012). It has also been reported that HSPA5 expression in the mouse liver reduced SREBP1c protein levels (Kammoun et al., 2009). Therefore, we hypothesized the hepatic steatosis observed in Dnajb9-deficient liver might have been resulted from SREBP1c stabilization. We tested this hypothesis by examining how the expression of DNAJB9 may regulate the level and stability of the recombinant SREBP1c protein levels in Dnajb9KO HEK293 cells (Figures S2C and S2D). As a control, we expressed a signal peptide-deleted form of DNAJB9 (DSP-DNAJB9) to differentiate its ERAD and non-ERAD functions (Figure 2A). As shown in Figure 2B, the full-length DNAJB9 was correctly localized to the ER, with some puncta signals presumably from Golgi-related compartments. In contrast, DSP-DNAJB9 was diffused throughout the cytosol (Figure 2B). Then we measured the protein levels of the co-expressed SREBP1c, and we found that restoration of the wild-type but not DSP-DNAJB9 significantly reduced SREBP1c protein levels in HEK293A Dnajb9KO cells (Figures 2C, 2D, S4A, and S4B). Cycloheximide chase analysis showed that the degradation of SREBP1c was almost doubled in the DNAJB9-overexpressing cells compared with the control, GFP-expressing cells (Figures 2E and 2F). More importantly, the addition of the ERAD inhibitor NMS-873 partially rescued

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Figure 3. Cytosolic DNAJB9 mTORC2 Complex Assembly

Assists

(A) Mass spectrometric identification of DNAJB9specific interacting proteins. Normalized to corresponding proteins in the eGFP pull-down. A5, HSPA5; B9, DNAJB9. (B) Co-IP followed by immunoblot to detect interactions between DNAJB9 and mTORC2. (C) Co-IP followed by kinase assay to measure mTORC2 activity in glycerol gradient fractionated samples. 33 Elutes (33) from DNAJB9-GFP pulldown were used for kinase assay and immunoblot. GGF, glycerol gradient fractionation. (D) Proposed model of DNAJB9-assisted assembly of mTORC2. (E) Immunoprecipitation measurement of RICTOR trapping with DNAJB9 (B9-GFP) in mTOR-deficient C2C12 cells. NC, scramble siRNAs. (F) Immunoprecipitation measurement of nascent RICTOR (RIC-Flag) assembly into mTORC2 complexes upon Dnajb9 deletion. Wild-type (WT), HEK293A cells; KO, HEK293A Dnajb9
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cells. (G–I) Reciprocal co-IP (G), blue native PAGE measurement (H), and quantification (I; n = 3) of mTORC2 complex abundance in GFP- and DNAJB9-GFP-expressing HEK293FT cells. Myctagged mTOR, SBP-tagged RICTOR, mSin1, and mLst8 were transfected at a ratio of 4:5:2:1 for 28 h. Cell lysates were subjected to reciprocal co-IP with either anti-Myc antibody or streptavidin-conjugated beads and blotted with antibodies against endogenous proteins, or cell lysates were supplemented with G250 for blue native PAGE. (J and K) BN-PAGE (J) and quantification (K; n = 4) of mTORC2 complex in fl/fl and Dnajb9LKO liver tissue samples (NCD, 5 months). (L and M) BN-PAGE (L) and quantification (M; n = 4) of mTORC2 complex abundance in GFP- and DNAJB9-overexpressing livers. (N and O) Measurement of mTORC2 signaling (N) in Dnajb9- (B9) and Rictor- (Ric) deficient C2C12 cells. Knockdown efficiency was determined by qRT-PCR (O; n = 4). Data are represented as mean ± SEM. ***p < 0.001. See also Figures S5–S7.

the DNAJB9-induced downregulation of SREBP1c (Figures 2G and 2H). Therefore, DNAJB9 expression can potently reduce SREBP1c protein levels through ERAD. The ability of DNAJB9 to degrade SREBP1c in vitro is consistent with the observed suppression of feeding-induced lipogenesis in the DNAJB9-expressing liver tissues (Figure 2I). More importantly, knockdown of SREBP1c suppressed Dnajb9 deficiency-induced hepatic steatosis (Figures 2J and S4C), suggesting a functional dependency of DNAJB9 on SREBP1c degradation in regulating hepatic lipid accumulation. Together, these data support SREBP1c as an ERAD substrate of DNAJB9 in regulating hepatic lipogenesis and lipid accumulation. Cytosolic DNAJB9 Assists mTORC2 Complex Assembly Next, we looked into how the cytosolic pool of DNAJB9 may regulate anabolic bias through co-immunoprecipitation (co-IP)

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and mass spectrometry identification of novel DNAJB9 client proteins. After normalization with GFP controls, mTORC2 components (e.g., RICTOR, mTOR, and mSin1) were significantly enriched in the DNAJB9 eluates (Figure 3A; Table S1). HSPA5 functions as the HSP70 of DNAJB9 in the ER lumen, and it exhibited no specific interaction with any of the mTORC2 components (Figures 3A and 3B). Domain truncation analysis confirmed the requirement of the canonical substrate-binding domain (G/F domain) for DNAJB9-RICTOR interaction (Figure S5). Therefore, mTORC2 represents a novel, cytosolic client of DNAJB9, functioning independent of HSPA5 in the ER lumen. We hypothesized that the dimer- and tetramer-forming capacities of the DNAJ proteins (Chen et al., 2017; Wu et al., 2005) might allow DNJAB9 to bring individual components of mTORC2 together for complex assembly. To detect the steps of mTORC2 assembly that may involve DNAJB9 chaperoning,

we fractionated the whole-cell lysate through glycerol gradients to separate proteins and protein complexes on the basis of their molecular weight differences and applied co-IP to measure the interaction between DNAJB9 and mTORC2 components. RICTOR co-IP was performed for glycerol gradient fractions to control for separation of the unassembled RICTOR and mTOR proteins from the assembled mTORC2 complexes. As shown in Figure 3C, RICTOR antibody could not pull down mTOR in the low-molecular weight fractions (i.e., fractions 7–10) but did so in the high-molecular weight fractions (i.e., fractions 11–18). In contrast, DNAJB9 pulled down both RICTOR and mTOR from all fractions (Figure 3C), suggesting that DNAJB9 binds to RICTOR and mTOR prior to their assembly into the mTORC2 complex. Overall, about one-third of the mTORC2 complex was associated with DNAJB9, and the DNAJB9-bound mTORC2 exhibits minimal kinase activity, likely in a pre-active state (Figure 3C). However, the DNAJ proteins may also function to disassemble protein complexes (Walsh et al., 2004), and DNAJB9 has been shown to monomerize the IRE1 oligomers under non-stress conditions (Amin-Wetzel et al., 2017). To differentiate the mechanism of disassembly from assembly (as illustrated in Figure 3D), we silenced mTOR expression and assayed for its impact on DNAJB9-RICTOR and DNAJB9-mTOR interactions. We reasoned that if the assembly model is correct, reducing the level of mTOR would necessitate an extended period for RICTOR to find its binding partner, and more RICTOR will remain in a DNAJB9-bound state. In contrast, the disassembly model would predict a proportional reduction in both RICTOR and mTOR interactions with DNAJB9 because of the lower abundance of the mTORC2 complex. In support of the assembly model, an increased amount of RICTOR was pulled down with DNAJB9 (B9-GFP) upon depletion of mTOR proteins (Figure 3E). Importantly, the amount of mSin1 pull-down by DNAJB9 was also significantly increased. Therefore, the absence of mTOR caused arrest of mTORC2 complex at the level of RICTOR-mSin1 heterodimer. We conducted a multitude of Dnajb9 gain- and loss-of-function experiments to further dissect the individual steps of DNAJB9 function involved in mTORC2 complex assembly. First, Dnajb9 deletion (HEK293A Dnajb9
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) greatly reduced the amount of mTOR pull-down by RICTOR (RIC-Flag), without affecting their protein levels (Figure 3F). Therefore, DNAJB9 is required to bring mTOR and RICTOR together. Additionally, Dnajb9 deletion led to reduced levels of mSin1 and its interaction with RICTOR (Figure 3F), which indicates the stabilization of mSin1 requires DNAJB9 function, possibly by stabilizing RICTOR-mSin1 interaction. Second, overexpression of DNAJB9 significantly enhanced the production of mTORC2 complexes, as determined by the increased amount of RICTOR, but not RAPTOR, that was pulled down by mTOR (Figures 3G and S6A, IP: Myc-mTOR panel) and vice versa (Figures 3G and S6B, IP: SBP-RICTOR panel). Again, the protein levels of mTOR and RICTOR were not altered by DNAJB9 expression (Figure 3G, input panel). The level of mSin1 pull-down by RICTOR was more than doubled under this context (Figure S6B), suggesting stabilization of RICTOR-mSin1 subcomplex by DNAJB9.

We further corroborated the contribution of DNAJB9 on mTORC2 complex assembly by assaying complex abundance and signaling transduction. As shown in Figures 3H–3M, S6C, and S6D, DNAJB9 gain of function significantly increased while Dnajb9 loss of function drastically reduced mTORC2 complex abundance in both cell culture and mouse liver tissues. The extended expression of Dnajb9 in mouse liver tissues (7 days compared with 24 h in cell culture) enabled a more potent increase in mTORC2 complex abundance: 3-fold in vivo (Figures 3L and 3M) versus 30% in vitro (Figures 3H and 3I). Because the majority of the wild-type DNAJB9 was imported into the ER lumen (Figures S1B and S1C), expression of a cytosolic DNAJB9 protein (DSP-DNAJB9) increased mTORC2 abundance in a more potent manner (Figures 2A, 2B, S6E, and S6F). Therefore, unlike its role in facilitating ERAD degradation of SREBP1c, the mTORC2 assembly function of DNAJB9 does not require ER residence. Functionally, transient silencing of Dnajb9 (siB9) resulted in a functional loss of mTORC2 in relaying insulin signaling toward AKT (Figures 3N, 3O, S6G, and S6H), as observed in Dnajb9-deficient liver tissues. Recent work suggests that PTEN deficiency may promote mTORC2 assembly by reducing the phosphorylation of T1135 of RICTOR, which was catalyzed by S6K1 as part of the feedback inhibition mechanism from mTORC1(Bhattacharya et al., 2016; Dibble et al., 2009). To understand the relationship between the PTEN-dependent and DNAJB9-dependent assembly processes, we silenced Dnajb9 expression in PTEN
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cells and assayed its impact on mTORC1/2 signaling. As expected, PTEN deficiency led to constitutive phosphorylation of AKT (S473) and, to a lesser degree, 4EBP1 under serum-deprived conditions (Figures S7A and S7B). Their phosphorylation depends on mTORC2 activation, as Rictor knockdown reduced both 4EBP1 and AKT S473 phosphorylation levels. Silencing the expression of Dnajb9 had a similar effect as that of Rictor deficiency (Figure S7A), suggesting that DNAJB9 functions downstream of PTEN in promoting the assembly of dephosphorylated RICTOR into a mTORC2 complex. mTORC2 Participates in DNAJB9-Driven Anabolic Programming As mTORC2 has an established role in growth factor signaling and feeding response (Hagiwara et al., 2012), we asked whether and what aspects of DNAJB9 function might be mediated through mTORC2 both in vitro and in vivo. As shown in Figure S8A, DNAJB9 expression potently stimulated protein synthesis, in contrast to its inhibitory role on lipogenesis. However, this ability was compromised when cells were acutely treated with Torin1 but not rapamycin (Figure S8A). Because Torin1 inhibits both mTOR complexes while rapamycin targets only mTORC1, our results suggest that the protein synthesis function of DNAJB9 depends on mTORC2. Consistent with this interpretation, expression of the constitutively active AKT rescued protein synthesis and the majority of mTORC1/2 signaling defects caused by Dnajb9 deficiency (Figures S8B and S8C). Another prominent aspect of DNAJB9 function is its ability to promote liver hypertrophy and suppress food intake (Figures 1I and 1K). Signaling cascades downstream of both mTOR complexes were sustainably upregulated in the DNAJB9

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gain-of-function mouse models, and Raptor deletion negated the anorexic effect of DNAJB9 overexpression (Figures S8D and S8E), suggesting the participation of hepatic anabolic programs downstream of mTORC1 in regulating food intake. However, because of the two-way regulation between DNAJB9 and mTORC2 (Figure S8F), potential involvement of pathways downstream of mTORC2 but independent of mTORC1 could not be examined at the genetic level. Therefore, global transcriptomic and metabolomic analyses were carried out to dissect the functional relationship between DNAJB9 and mTORC2 through statistical and informatic means. We first applied Pearson correlation analysis between the Dnajb9- and Rictor-knockdown transcriptome and metabolome with their respective controls (Figures S3A and S9A). Consistent with their two-way regulation on biochemical and molecular levels, the global impact of Dnajb9 and Rictor deficiency on the hepatic transcriptome and metabolome was highly concordant (differentially expressed genes [DEGs]: y =
0.0063 + 0.91x, R2 = 0.927 [Figure 4A; Table S2]; differentially expressed metabolites [DEMs]: y =
0.044 + 0.97x, R2 = 0.75 [Figure 4B; Table S3]). There was more than 50% overlap between the DEGs (Figures S9B and S9C) and DEMs; Figures S9D and S9E) from the Dnajb9- and Rictor-knockdown liver tissue samples. Follow-up pathway analysis of the up- and downregulated DEGs identified nearly 90% and 80% overlap in the overrepresented biological process gene ontologies (GO-BP) between the Dnajb9- and Rictor-deficient transcriptomes (Figures 4C and 4D). Established mTORC2 downstream pathways, including actin- and microtubule-based movement (Figures S9F and S9G) and DNA replication and mitotic nuclear division (Figure S9H), were broadly suppressed under both Dnajb9- and Rictor-deficient conditions. In contrast, genes suppressed by the insulin and the PI3K-AKT signaling pathways (e.g., G6pc, Pck1, Ppargc1a) were strongly induced (Figures S9I and S9J; Table S2). Intriguingly, many components upstream of mTORC2 in the insulin (e.g., Insr, Irs2) and PI3K-AKT (e.g., Egfr, Efna1, Fgfr4, Ghr, Gng10, Igf1) signaling pathways were upregulated in the Dnajb9/mTORC2-deficient transcriptome (Figures S9I and S9J), suggesting the presence of a compensatory mechanism to restore anabolic signaling. Similarly, the induction of the mitochondria electron transport chain (ETC) components (Figure S9K) is likely to compensate for energy deficiency, and it is consistent with the elevated mitochondria membrane potential observed in Rictor-deletion cells (Betz et al., 2013). We then looked more specifically into metabolic pathways. There were shared inductions of gluconeogenic and amino acid catabolism pathways and suppression of the nucleotide biosynthesis pathway between Dnajb9 and Rictor deficiency (Figures 4E and 4F). Concordantly, the intracellular amino acid pool and ATP levels were significantly reduced (Figures 4E and 4G). Therefore, the DNAJB9-mTORC2 axis is essential for maintaining an anabolic state in the mouse liver and preventing an excessive breakdown. DNAJB9-mTORC2 Promotes Anabolic Energy Metabolism through HKII and NRF1 Because regulation of liver mass in Dnajb9 mouse models was accompanied by changes in energy metabolism, we hypothe-

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sized that DNAJB9/mTORC2 might stimulate energy production to support protein synthesis and the maintenance of liver size. As an initial support of our hypothesis, small interfering RNA (siRNA)-mediated silencing of either Dnajb9 or Rictor led to a potent inhibition of both glycolysis and mitochondria oxidative phosphorylation, as measured by extracellular acidification rate (ECAR; Figures S6H and S10A) and oxygen consumption rate (OCR; Figure 5A), accompanied by mitochondria fragmentation (Figures 5B and S10B) and increased depolarization (Figure 5C). We further investigated the mechanism of DNAJB9/ mTORC2-driven energy metabolism by focusing on MAM components such as HK2, PACS2, and ITPR3, all of which were capable of regulating calcium flux and mitochondria membrane potential downstream of mTORC2-AKT signaling (Betz et al., 2013). Silencing the expression of these MAM proteins had a disabling effect on energy production as mTORC2 inhibition (Figures S10C–S10H), suggesting MAM integrity as a critical target of mTORC2 regulation in energy metabolism. To differentiate the role of HK2 in MAM and its more traditional role in glycolysis, we compared the effect of HK2 and glucokinase (GCK) on mitochondria function in the primary hepatocytes. As shown in Figures S10I and S10J, knocking down Gck had minimal impact on mitochondria respiration, while suppression of Hk2 reduced the mitochondria respiration capacity for more than 40%. We further constructed the TF network underlying the Dnajb9and Rictor-deficient transcriptome to reveal additional, upstream mediators of DNAJB9/mTORC2-driven energy metabolism (Figure 5D). Surprisingly, all predicted TFs binding to the promoter regions of the downregulated DEGs in the Dnajb9and Rictor-deficient transcriptome were placed in immediate proximity of the mTOR signaling module. In contrast, TFs associated with upregulated DEGs were represented by a group of closely related nuclear receptors connected to mTOR through PPARg or other secondary nodes (Figure 5D; Table S4). Therefore, the mTOR signaling cascade mostly activates gene expression, whereas genes induced in its absence are the result of secondary responses except a small group of highly related nuclear receptors. Intriguingly, knocking down the expression of both categories of TFs inhibited glycolysis and mitochondria oxygen consumption (Figure 5E; Figures S10K and S10L), suggesting the presence of two energy programs in the cell: one activated by mTORC2 (e.g., NRF1) and the other (e.g., PPARg/PGC1a) inhibited by it (Li et al., 2007; Suliman et al., 2003). The anabolic energy metabolism activated by mTORC2 is largely involved in glucose utilization (Figure 4E), whereas the catabolic arm inhibited by mTORC2 includes amino acid catabolism and mitochondria biogenesis pathways (Figure 4E; Figure S9K). Epistasis analyses showed that combined silencing of Rictor and Nrf1 resulted in the same level of inhibition on mitochondria function as Rictor single knockdown, whereas siRictor and siPPARg inhibited oxidative phosphorylation in an additive manner (Figure 5F). Together, these observations revealed two parallel programs of energy metabolism, and the mTORC2-activated program serves to produce ATP in support of macromolecule synthesis under fed state (Figure 5G).

Figure 4. System-Level Functional Overlap between DNAJB9 and mTORC2 (A and B) Spearman correlation of differentially expressed genes (A) and metabolites (B). (C and D) Gene Ontology analysis of biological pathways (GO-BP) enriched for upregulated (C) and downregulated (D) genes. (E) Heatmap display of differentially expressed genes (|log2 fold change [FC]| R 1, Benjamini-Hochberg [BH]-adjusted p < 0.01) and metabolites (|log2 FC| R 0.5, p < 0.05) in key metabolic pathways. Functional annotations were based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and compounds classification. Top 15 of differentially expressed genes for carbohydrate metabolism (glycolysis/gluconeogenesis and TCA) and top 10 for other pathways were included. In each pathway term, genes and metabolites were ranked by the sum of log2 FC from Dnajb9- and Rictor-knockdown groups. NS, no statistical significance. (F) Measurement of the transcript levels of genes in major metabolic pathways in mouse liver samples. Normalized to 18s ribosome RNA (n = 4). *p < 0.05. (G) Measurement of ATP levels in mouse liver tissues (n = 10 for shLacZ; n = 5 for shDnajb9) by liquid chromatography-mass spectrometry (LC-MS). Data are represented as mean ± SEM.**p < 0.01. See also Figures S8 and S9.

DNAJB9 Gain of Function Improves Hepatic Steatosis and Reduces Adiposity in Obese Mice We have previously established that obesity represents a state of pseudo-starvation (Fu et al., 2012), and the metabolome of the obese mouse liver partially resembled that of Dnajb9 deficiency (Figure S11). In addition, our studies above suggest that the signature participants of selective insulin resistance in obesity (i.e., impaired insulin signaling through the mTORC2-AKT axis and induction of the SREBP1c lipogenic program) may both be

downstream of DNAJB9. Therefore, we asked whether the function of DNAJB9 might be compromised in obesity and contribute to the disease pathology. We first confirmed the downregulation of Dnajb9 in liver samples from obese mice (Figure 6A), and it was accompanied by a reduction in intracellular ATP levels (Figure 6B), impairment of insulin-induced mTORC2 signaling (Figure 6C), and suppression of protein synthesis (Figure 6D). Adenovirus-mediated reexpression of DNAJB9 in the obese mouse primary hepatocytes

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Figure 5. DNAJB9-mTORC2 Promotes Anabolic Energy Metabolism through HKII and NRF1 (A) Measurement of mitochondria oxidative phosphorylation (OCR) in Dnajb9 (siB9) and Rictordeficient (siRic) 3T3-L1 cells (n = 6). OM, oligomycin; R/A, rotenone/antimycin A. (B and C) Confocal imaging of mitochondria (B) and fluorescence-activated cell sorting (FACS) measurement (%, C, n = 3) of JC-1 staining in Dnajb9- and Rictor-deficient HeLa cells. (D) STRING-based network analysis of shared transcription factors (TFs) for upregulated (red) and downregulated (blue) genes in Dnajb9- and Rictorknockdown livers. Components of the mTORC1/2 complex are labeled in green (stringency = 0.7), and TFs not connected to this network were removed. (E and F) Measurement of mitochondria respiration capacity upon silencing the TFs and PGC1a (E; n = 3–5) and upon single- or double-siRNA knockdown (F; n = 4 or 5) in 3T3-L1 cells. (G) A switch model of mTORC2 governing energy metabolism. On one hand, mTORC2 promotes anabolic energy metabolism through HKII and a network of TFs represented by NRF1. On the other hand, mTORC2 depresses catabolic energy metabolism and mitochondria biogenesis by inhibiting PPARG/PGC1a and their downstream nuclear receptor family TFs. Scale bars, 20 mm. Data are represented as mean ± SEM. NS, not significant. *p < 0.05, **p < 0.01, and ***p < 0.001. See also Figure S10.

effectively restored insulin-stimulated activation of mTORC2 within 24 h (Figure 6E). The level of protein synthesis was also increased (Figure 6F). The ability of DNAJB9 expression to rescue insulin signaling and protein synthesis ex vivo led us to ask whether it may recover metabolic and physiologic derangements in vivo. Reassuringly, re-expression of DNAJB9 in the obese mouse liver resulted in significant augmentation of both mTORC1 and mTORC2 signals, induction of glycolytic enzymes (Hk2 and Pkm), and inhibition of genes and proteins involved in gluconeogenesis (Pck1, Fbp1, and G6pc), amino acid catabolism (Agxt, Prodh, and Gls2), and de novo lipogenesis (Acc, Acl, Acsl, Acss2, Fasn, and Scd1) (Figures 7A and 7B). The potent suppression of Srebp1c but not Chrebp in DNAJB9-expressing samples (Figure 7B) suggests SREBP1c as the primary target of the DNAJB9-driven anti-lipogenic program. Consequently, the metabolome level perturbations observed in the obese mice were largely rescued (Figure S11; Table S5). Surprisingly, the persistent ‘‘pseudo-fed state’’ created by hepatic DNAJB9 expression was able to drastically reduce food consumption even in leptin-deficient, ob/ob mice (Figure 7C), suggesting the presence of a leptin-independent satiety signaling pathway downstream of hepatic DNAJB9. As a result, DNAJB9-re-expressing mice presented significant reductions in body weight, liver size, and adiposity (Figures 7D–7F). Plasma glucose levels were completely normalized, and hepatic steatosis was ameliorated (Figures 7G and 7H). Hyperinsulinemic-euglycemic clamp studies were carried out to evaluate insulin sensitivity on both systemic and tissue-specific levels (Figures 7I–7M and S12A). Hepatic DNAJB9 expression reduced basal insulin levels to almost half of control (Figure 7I), and increased glucose infusion rate (GIR) by 60% (Figure 7J), which can be attributed principally to the almost doubled capacity of insulin to suppress hepatic glucose produc-

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tion (HGP; Figure 7K). No significant changes in adipose and muscle insulin sensitivity were observed, as measured by the ability of insulin to suppress free fatty acid release from the adipose tissue (Figure 7L) and to stimulate glucose disposal by the muscle (Figure 7M). Efficacy studies of DNAJB9 overexpression were carried out in two additional obese mouse models, the HFD model and the leptin receptor-deficient (db/db) mouse model, and we observed improvement in insulin sensitivity, lowering of plasma glucose levels, and significant reduction in body weight and adiposity (Figures S12B–S12H). The ability of DNAJB9 to improve insulin signaling was dependent upon mTORC2, as knockdown of Akt1/2 abrogated this effect. In contrast, DNAJB9 likely exerted its anti-lipogenic function through pathways that are separate from and counter the action of mTORC2/AKT (Figures S13A– S13E; Figures 7N and 7O), further confirming the dual-function nature of DNAJB9. DISCUSSION Energy expenditure is correlated mainly with body lean mass (Carneiro et al., 2016), but the anabolic synthesis of proteins and lipids is generally considered to be under the control of the same master regulators, such as mTOR (Betz and Hall, 2013; Blenis, 2017; Howell et al., 2013; Laplante and Sabatini, 2012). The potential existence of a hierarchical order or balancing mechanism among the different arms of anabolic metabolism has not been addressed before. DNAJB9 represents an anabolic biasing system that triages between protein and lipid synthesis by promoting mTORC2 assembly in the cytosol while facilitating SREBP1c degradation through ERAD (Figures 7N and 7O). The positive feedback regulation between DNAJB9 and mTORC2 and the selective braking of lipogenesis allowed DNAJB9 to bias anabolic metabolism toward the energy-expending arms of metabolism (Figure 7O) and to

Figure 6. Dnajb9 Gain of Function Restores Insulin Signaling and Protein Synthesis in the Obese Primary Hepatocytes (A) Dnajb9 transcript levels in the liver of diet-induced (HFD, 6 months) and genetically obese (ob/ob, 10 weeks) mice (n = 4). (B–D) ATP levels (B; n = 3), mTORC2 signaling (C), and protein synthesis (D) in lean and obese (ob/ob) primary hepatocytes. Cells were fasted in HBSS for 50 mins and re-stimulated with 10nM insulin for 1 hour. (E and F) Insulin signaling (E) and protein synthesis (F) in ob/ob primary hepatocytes expressing LacZ (L) and DNAJB9 (B9). Data are represented as mean ± SEM. *p < 0.05 and **p < 0.01.

effectively revert weight gain. The ability of DNAJB9 to inhibit IRE1 activation and degrade SREBP1c has the added benefit of reducing insulin resistance and tumorigenesis that is frequently observed in obesity and in mTORC1/2 constitutively active mice (Amin-Wetzel et al., 2017; Guri et al., 2017; Hotamisligil, 2010; Menon et al., 2012; Ozcan et al., 2004). Although the participation of liver in appetite regulation has long been observed (Friedman and Tordoff, 1986; Langhans et al., 1985; Visinoni et al., 2012), the ability of DNAJB9 expression to suppress food intake in the ob/ob mice suggests that it is possible to overcome leptin resistance in subjects of obesity (Frederich et al., 1995; Friedman, 2016). We show that appetite regulation by DNAJB9 is closely associated with its ability to regulate mTORC1 activation and translation, and the appetite-suppressing function of DNAJB9 is lost in the Raptor-deficient mice. The observation that DNAJB9 may continue to sustain protein synthesis under rapamycin but not Torin1 treatment conditions suggests that DNAJB9 may regulate protein synthesis through additional mechanisms downstream of mTORC2 other than mTORC1. There are at least two mTORC1-independent mechanisms leading to insulin-stimulated protein synthesis: S6K1 activation by PDK1-dependent phosphorylation and eIF2B activation through GSK3b inhibition by AKT (Keshwani et al., 2009; Proud, 2006; Welsh et al., 1998). The ability of Torin1 treatment to fully suppress DNAJB9-driven protein synthesis implicates the engagement of the mTORC2-AKT-GSK3b-eIF2B axis. Alternatively, it is possible that the residual activity of mTORC1 toward 4EBP1 inhibition under rapamycin treatment conditions may be sufficient to support protein synthesis downstream of DNAJB9, which could reconcile the lack of effect of rapamycin

treatment on protein synthesis in vitro and the dependence of appetite regulation on mTORC1 in vivo. Although mTORC2 figures prominently into DNAJB9 biology, exploration of the ensemble of DNAJB9 client proteins remains at an early stage (Amin-Wetzel et al., 2017; Behnke et al., 2016; Huang et al., 2019). Therefore, the mechanistic underpinning of DNAJB9 in energy homeostasis, as well as the multitude phenotypes observed in DNAJB9 gain- and loss-of-function mice observed herein and reported elsewhere, likely involves additional DNAJB9 client proteins and crosstalk among them (Amin-Wetzel et al., 2017; Behnke et al., 2016; Berger et al., 2003; Dong et al., 2008; Fritz et al., 2014; Fritz and Weaver, 2014; van Galen et al., 2014; Lai et al., 2012; Shen et al., 2002). The transcriptomic (Tables S2 and S4; GSE93948), metabolomic (Tables S3 and S5), and proteomic data (Table S1) generated in this study may also serve as an entry point for examining how DNAJB9 may regulate mTORC1 assembly in vivo, how protein synthesis may regulate food intake, and how fatty acid oxidation and lipoprotein secretion may contribute to the hepatic lipid accumulation phenotypes. Additionally, DNAJB9 is not present in all eukaryotes (Qiu et al., 2006; Walsh et al., 2004). Therefore, anabolic biasing in lower eukaryotes, if it exists, may involve a pair of chaperones from the cytosol and the ER. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d

KEY RESOURCES TABLE

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d d

d

d d

LEAD CONTACT AND MATERIALS AVAILABILITY EXPERIMENTAL MODEL AND SUBJECT DETAILS B Mouse Husbandry and Phenotypic Analysis B Cell Culture, Transfection, and Antibodies B CRISPR/Cas9-mediated Dnajb9 deletion in HEK293A Cells METHOD DETAILS B Primary Hepatocyte Isolation and Culture B Constructs B Adenovirus Production B Adeno-associated Virus Production B Hyperinsulinemic-euglycemic Clamp B Immunoblots B Proteinase K Protection Assay B Immunoprecipitation B Glycerol Gradient Fractionation B Protein Synthesis Assay B Recombinant AKT Purification and in vitro Kinase Assay B Blue Native PAGE B Quantitative Reverse Transcribed Polymerase Chain Reaction (qRT-PCR) B Glucose and Insulin Tolerance Tests B Glucose, ATP, Cholesterol, and Triglyceride Measurements and Insulin ELISA B Body Composition B Seahorse Extracellular Flux Analysis B Mitochondrial Membrane Potential Measurement B Confocal Imaging B Histology Analysis B RNA Extraction and Transcriptomic Analysis B Metabolite Extraction and Metabolomic Analysis QUANTIFICATION AND STATISTICAL ANALYSES DATA AND CODE AVAILABILITY

SUPPLEMENTAL INFORMATION

mics Facility), Xuerui Yang (Gene Synthesis and Sequencing Facility), and Jinyu Wang (Cell Imaging Facility) at the Center for Biomedical Analysis and the Technology Center for Protein Research, Tsinghua University. We also thank Peng Li, Li Yu, Yiguo Wang, Yifu Qiu, Wei Guo, Yanhui Xu, Jie Na, Peng Jiang, Qiaoran Xi, Kehkooi Kee, and Xin Liang for reagents, equipment, and helpful discussions and Jia Xu for assistance with cartoons. This work is supported by National Science and Technology Major Project (2016YFA0502002, 2017YFA0504603), the National Natural Science Foundation of China (NSFC; 81471072 and 31671229), the National 1000 Junior Scholar Program, and the Tsinghua-Peking Center for Life Sciences to S.F. and NSFC grants 81622010 and 81770800 to P.L. AUTHOR CONTRIBUTIONS Conceptualization, F.S. and S.F.; Investigation, F.S., X.Q., Y.L., X.X., S.H., Z.C., and H.H.; Writing, F.S., X.Q., Y.L., S.H., Z.C., and S.F.; Supervision, S.F. and P.L. DECLARATION OF INTERESTS The authors declare no competing interests. Received: September 29, 2018 Revised: December 23, 2019 Accepted: January 14, 2020 Published: February 11, 2020 REFERENCES Albert, V., and Hall, M.N. (2015). mTOR signaling in cellular and organismal energetics. Curr. Opin. Cell Biol. 33, 55–66. Albert, V., Svensson, K., Shimobayashi, M., Colombi, M., Mun˜oz, S., Jimenez, V., Handschin, C., Bosch, F., and Hall, M.N. (2016). mTORC2 sustains thermogenesis via Akt-induced glucose uptake and glycolysis in brown adipose tissue. EMBO Mol. Med. 8, 232–246. Ambade, A., Catalano, D., Lim, A., Kopoyan, A., Shaffer, S.A., and Mandrekar, P. (2014). Inhibition of heat shock protein 90 alleviates steatosis and macrophage activation in murine alcoholic liver injury. J. Hepatol. 61, 903–911. Amin-Wetzel, N., Saunders, R.A., Kamphuis, M.J., Rato, C., Preissler, S., Harding, H.P., and Ron, D. (2017). A J-protein co-chaperone recruits BiP to monomerize IRE1 and repress the unfolded protein response. Cell 171, 1625–1637.

Supplemental Information can be found online at https://doi.org/10.1016/j. celrep.2020.01.043.

Appenzeller-Herzog, C., and Hall, M.N. (2012). Bidirectional crosstalk between endoplasmic reticulum stress and mTOR signaling. Trends Cell Biol. 22, 274–282.

ACKNOWLEDGMENTS

Archer, A.E., Rogers, R.S., Von Schulze, A.T., Wheatley, J.L., Morris, E.M., McCoin, C.S., Thyfault, J.P., and Geiger, P.C. (2018). Heat shock protein 72 regulates hepatic lipid accumulation. Am. J. Physiol. Regul. Integr. Comp. Physiol. 315, R696–R707.

We acknowledge the technical assistance of Zai Chang (Laboratory Animal Research Center), Haiteng Deng (Proteomics Facility), Xiaohui Liu (Metabolo-

Figure 7. Hepatic DNAJB9 Expression Restores Anabolic Signaling and Energy Homeostasis in Obese Mouse Models (A) Immunoblot measurement of metabolic proteins in the ob/ob mouse liver expressing GFP or DNAJB9-Flag. GAPDH was used as loading control. (B) qRT-PCR measurement of major metabolic genes expression in the GFP- and DNAJB9-overexpressing ob/ob mouse liver. Normalized to 18s ribosome RNA (n = 4). (C and D) Food intake (C) and body weight change (D) of GFP and DNAJB9-overexpressing obese mice. n = 6. (E–H) Liver ratio (E), visceral fat size (F), plasma glucose levels (G), and liver H&E staining (H) of GFP- and DNAJB9-overexpressing obese mice. n = 11 for GFP, n = 10 for DNAJB9. (I–M) Hyperinsulinemic-euglycemic clamp analysis of GFP and DNAJB9-overexpressing ob/ob mice (n = 7): insulin levels (I), glucose infusion rate (J), percentage suppression of HGP by insulin (K), percentage suppression of fasting fatty acid levels (L), and insulin-stimulated glucose disposal rate (M). (N) Model of DNAJB9 dual topology and functionality. The ER-resident DNAJB9 promotes SREBP1c degradation through ERAD, while the cytosolic DNAJB9 acts as a co-chaperone to promote mTORC2 complex assembly. (O) Model of anabolic biasing driven by DNAJB9. mTORC2 promotes both energy storage (SREBP1c-driven lipogenesis) and consumption (protein synthesis and ATP production, etc.). The anabolic response is augmented by the positive feedback loop between DNAJB9 and mTORC2. However, the selective braking of lipogenesis by DNAJB9 depressed energy saving. The potential presence of a direct activation mechanism linking DNAJB9 toward energy expenditure pathways is marked by a dashed arrow. Scale bars, 50 mm. Data are represented as mean ± SEM. *p < 0.05, **p < 0.01, and ***p < 0.001. See also Figures S11–S13.

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Cell Reports 30, 1835–1847, February 11, 2020 1847

STAR+METHODS KEY RESOURCES TABLE

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Antibodies Akt (pan) (40D4) Mouse mAb

Cell Signaling

Cat# 2920

Phospho-Akt (Ser473) (D9E) XP Rabbit mAb

Cell Signaling

Cat# 4060

Phospho-Akt (Thr308) (C31E5E) Rabbit mAb

Cell Signaling

Cat# 2965

4E-BP1(53H11) Rabbit mAb

Cell Signaling

Cat# 9644

Phospho-4E-BP1 (Thr37/46) (236B4) Rabbit mAb

Cell Signaling

Cat# 2855

mTOR (7C10) Rabbit mAb

Cell Signaling

Cat# 2983

Rictor (D16H9) Rabbit mAb

Cell Signaling

Cat# 9476

Raptor

Cell Signaling

Cat# 2800

Phospho-GSK-3b (Ser9) Antibody

Cell Signaling

Cat# 9336

Phospho -S6 Ribosome protein (S235/236)

Cell Signaling

Cat# 4858

Glycolysis Antibody Sampler Kit

Cell Signaling

Cat# 8337

Fatty Acid and Lipid Metabolism Antibody Sampler Kit

Cell Signaling

Cat# 8335

Phospho-PDK1 (Ser241) Antibody

Cell Signaling

Cat# 3061

MAPKAP1 Antibody

Protein tech

Cat# 15463-1-AP

GSK3B Rabbit Polyclonal Antibody

Protein tech

Cat# 22104-1-AP

GRP78/ BIP Rabbit Polyclonal Antibody

Protein tech

Cat# 11587-1-AP

GRP94 Mouse Monoclonal Antibody

Protein tech

Cat# 60012-1-Ig

HSP90AB1 Rabbit Polyclonal antibody

Protein tech

Cat# 11405-1-AP

HSP70 Rabbit Polyclonal Antibody

Protein tech

Cat# 10995-1-AP

Calnexin Polyclonal Antibody

Ayabio

Cat# AYA34411

Beta-Actin Antibody mouse mAb

Abgent

Cat# AM1021B

Anti-mTOR (phospho S2448) [EPR426(2)]

Abcam

Cat# ab109268

Anti-GAPDH Mouse Monoclonal Antibody

Bioeasy

Cat# BE0023

Anti-GFP tag (Rabbit)

Bioeasy

Cat# BE2002

Anti-GFP mAb-Magnetic Beads

MBL

Cat# D153-11

Anti-GFP (mouse)

Roche

Cat# 11814460001

Anti-Sec61B

Cell Signaling

Cat# 14648

Anti-Myc mAb-Magnetic Beads

MBL

Cat# M047-11

Anti-DYKDDDK antibody

Abmart

Cat# M20008L

Anti-FLAG M2 Magnetic Beads

Sigma-Aldrich

Cat# M8823

Goat Anti-Mouse IgG (H+L), HRP

Easybio

Cat# BE0102

Goat Anti-Rabbit IgG (H+L), HRP

Easybio

Cat# BE0101

Mouse Anti-rabbit IgG (Light chain), HRP Conjugated

Easybio

Cat# BE0107

Rabbit Anti-goat IgG (H+L), HRP

Easybio

Cat# BE0103

protein A/G Agarose beads

CMC-TAG

Cat# IF0001

Goat Anti-Mouse IgG H&L (Alexa Fluor 488)

Thermo Fisher

Cat# ab150113

Goat Anti-Rabbit IgG H&L (Alexa Fluor 568)

Thermo Fisher

Cat# ab175471

Streptavidin Beads (agarose beads)

Solarbio

Cat# YA2500

Chemicals, Peptides, and Recombinant Proteins Oligomycin A

Selleck

Cat# S1478

CHAPS

Amresco

Cat# 0465

TRITON X-100

Amresco

Cat# 0694

Cycloheximide

Amresco

Cat# 80059-086 (Continued on next page)

e1 Cell Reports 30, 1835–1847.e1–e9, February 11, 2020

Continued REAGENT or RESOURCE

SOURCE

IDENTIFIER

Insulin from bovine pancreas

Sigma-Aldrich

Cat# I6634

Akt1/PKBa Protein,unactive

Merck Milipore

Cat# 14-279

Humulin Insulin

Eli Lily and Company

Cat# HI0219

D-[3-3H] glucose

PerkinElmer

Cat# NET331C

NMS-873

Selleckchem

Cat# S7285

Torin1

Selleckchem

Cat# S2827

Rapamycin

Selleckchem

Cat# S1039

TRIzol Reagent

Thermo Fisher

Cat# 15596026

Fluoroshield with DAPI histology mounting medium

Sigma

Cat# F6057

Collagenase IV

Sigma

Cat# C5138

Gateway LR Clonase II Enzyme mix

Thermo Fisher

Cat# 11791100

OptiPrep

STEMCELL

Cat# 07820

EvaGreen qPCR MasterMix

ABM

Cat# mastermix-LR

Critical Commercial Assays Ribo-Zero Magnetic Core Kit

Epicenter

Cat# MRZ11124C

BREEZE 2 blood glucose meter

Bayer

Cat# 1440C

Glycogen ASSAY KIT

BIOASSAY

Cat# E2GN-100

Amplex Red Glucose/Glucose Oxidase Assay Kit

Thermo Fisher

Cat# A22189

ATP Determination Kit

Thermo Fisher

Cat# A22066

Protein Silver stain Kit

CWBIO

Cat# CW2012

L-Type TG M Color A

Wako

Cat# 461-08992

L-Type TG M Color B

Wako

Cat# 461-09092

Cholesterol Fluorometric Assay Kit

Cayman

Cat# 10007640-480

Triglyceride Assay Kit

Nanjing Jiancheng

Cat# A110-1-1

Total Cholesterol Assay Kit

Nanjing Jiancheng

Cat# A111-1-1

FastKing RT Kit(With gDNase)

TIANGEN Biotech

Cat# KR116-03

Seahorse XF Cell Mito Stress Test Kit

Agilent

Cat# 103015-100

Seahorse XF Glycolysis Stress Test Kit

Agilent

Cat# 103020-100

Seahorse XF Mito Fuel Flex Test Kit

Agilent

Cat# 103260-100

LabAssay NEFA kit

Wako Chemical

Cat#294-63601

Mouse Ultrasensitive Insulin ELISA kit

Alpco

Cat# 80-INSMSU-E10

BLOCK-iT U6 RNAi Entry Vector Kit

Thermo Fisher

Cat# K494500

pAd/BLOCK-iT-DEST RNAi Gateway Vector

Thermo Fisher

Cat# V49220

pAd/CMV/V5-DEST Gateway Vector Kit

Thermo Fisher

Cat# V49320

BCA Protein Assay Kit

Solarbio

Cat# PC0020

HeLa

ATCC

CCL-2

C2C12

ATCC

CRL-1772

Hepa1-6

ATCC

CRL-1830

Hepa1-6 mDNAJB9-GFP

This study

N/A

HEK293A

Thermo Fisher

R70507

HEK293A Dnajb9 knockout cells

This study

HEK293A Dnajb9
/


Experimental Models: Cell Lines

HEK293T

ATCC

CRL-3216

HEK293FT

ATCC

CRL-1573

Experimental Models: Organisms/Strains Mouse: C57BL/6J

The Jackson Laboratory

Stock No: 000664

B6.BKS(D)-Leprdb/J (db/db)

The Jackson Laboratory

Stock No: 000697

B6.Cg-Leprob/J (ob/ob)

The Jackson Laboratory

Stock No: 000632 (Continued on next page)

Cell Reports 30, 1835–1847.e1–e9, February 11, 2020 e2

Continued REAGENT or RESOURCE tm1.1Klg

)

IDENTIFIER

The Jackson Laboratory

Stock No: 020649

B6.Cg-Rptortm1.1Dmsa/J (Raptorfl/fl)

The Jackson Laboratory

Stock No: 013188

mDnajb9loxp/loxp: EGE-LRL-019 conditional (Dnajb9fl/fl)

Biocytogen

Dnajb9fl/fl

Biocytogen

Dnajb9
/


Clotech

Cat# 6085-1

Rictor

/SjmJ (Rictor

SOURCE

fl/fl


/


mDnajb9 : EGE-LRL-019 fragment (Dnajb9 whole body knockout) Recombinant DNA pEGFP-N1 pCMV6 entry HSPA5-Myc-DDK

Origene

Cat# MR209794

pCMV-Myc

Clontech

Cat# 631604

pCMV6 entry HSPA5-eGFP

This study

N/A

pCMV6 entry mDNAJB9-eGFP

This study

N/A

pENTR1A mDNAJB9-Flag

This study

N/A

pCMV6 entry mDNAJB9-myc-ddk

This study

N/A

pCMV6 entry mDNAJB9-N-myc-ddk

This study

N

pCMV6 entry mDNAJB9-NC-myc-ddk

This study

NC

pCMV6 entry mDNAJB9-NJ-myc-ddk

This study

NJ

pCMV6 entry mDNAJB9-J-myc-ddk

This study

J

pCMV6 entry mDNAJB9-DSP-myc-ddk

This study

DSP

pCMV6 entry mDNAJB9-N-G/F-myc-ddk

This study

N-G/F

pCMV6 entry mDNAJB9-NJ-G/F-myc-ddk

This study

NJ-G/F

pCMV6 entry eIF4E-myc-ddk

This study

eIF4E

pAd/CMV mDNAJB9-Flag

This study

N/A

pET15b-mAKT kinase dead K179M-N-terminal 8xHis

This study

N/A

pAd/CMV/AKT1-S473D

This study

AKT1-S473D

pCMV-Myc-DDK-hSREBP1c

This study

N/A

This study

N/A

Illustrator CC 2018

Adobe

https://www.adobe.com/products/ illustrator.html

Photoshop CS6

Adobe

https://www.adobe.com/cn/products/ photoshop.html

Oligonucleotides Refer to Table S6 Software and Algorithms

Alphaview (FluroChem FC3)

Proteinsimple

Prism 6

GraphPad

R 3.3.1

http://www.graphpad.com/scientificsoftware/prism/ https://www.r-project.org/

Deposited Data Raw and analyzed data

This study

GEO: GSE93948

Other Disposable PD-10 Desalting Column

GE Healthcare Life Sciences

Cat# 17085101

Silastic Tubing

Dow Corning

Cat# 508-001

Renathane Tubing

Braintree

Cat# MRE025

Pump 11 Elite Syringe Pump

Harvard Appratus

Cat# 704500

LEAD CONTACT AND MATERIALS AVAILABILITY Further information and requests for reagents may be directed to and will be fulfilled by the Lead Contact, Suneng Fu (fsneng@mail. tsinghua.edu.cn). All unique reagents generated in this study are available from the Lead Contact with a completed Materials Transfer Agreement.

e3 Cell Reports 30, 1835–1847.e1–e9, February 11, 2020

EXPERIMENTAL MODEL AND SUBJECT DETAILS Mouse Husbandry and Phenotypic Analysis Dnajb9 floxed (2nd and 3rd exon encompassing the whole open reading frame and 30 untranslated region, Figures S2A and S2B) and whole-body deletion mice were custom-ordered from BioCytogen and backcrossed to C57BL/6J. Liver-specific Dnajb9 deletion was generated by crossing Dnajb9fl/fl mice with Albumin-Cre transgenic mice (B6.Cg- Speer6-ps1Tg(Alb-cre)21Mgn /J, 003574, The Jackson Laboratory). Male leptin-deficient (ob/ob, strain B6.V-Lepob/J, stock number 000632)) and wild-type littermates in the C57BL/6J background were obtained from The Jackson Laboratory and bred in house. For the diet-induced obesity model, male Dnajb9LKO with corresponding control mice or wild-type C57BL/6J bred in-house or purchased from Beijing Vital River Laboratory Animal Technology were placed on HFD (D12492: 60% kcal% fat; Research Diets, Inc) starting from four weeks after birth. Raptorfl/fl (strain:013188 - B6Cg-Rptortm1.1Dmsa/J) and Rictor fl/fl (Strain: 020649 - STOCK Ptprcb Thy1a Rictortm1.1Klg/SjmJ) mice were obtained from The Jackson laboratory and bred in house. Liver-specific Raptor or Rictor knockouts were generated by tail-vein injected with adenovirus expressing CMV-Cre recombinase. All mice were maintained on a 12-h-light/12-h-dark cycle in a pathogen-free barrier facility at Tsinghua Laboratory Animal Research Center at controlled temperature (25 ± 1 C) with free access to water and food. All mice used in this study were male, and all the virus administrations in vivo were carried out by tail-vein injection. The metabolic and behavioral phenotype analysis of experimental mice were carried out in TSE Phenomaster metabolic cages (TSE Systems). Mice were individually caged with free access to food and water. After an initial adaptation of 24 hours, their physical movement, food consumption, body temperature, oxygen consumption and oxygen dioxide evolution were measured in real time for additional 48-72 hours. All animal procedures were in accordance with guidelines approved by the Tsinghua University Animal Care Committee. All mice used in this study were 8-12-week-old male and were sacrificed after 6hrs food withdrawal at 3:00pm without specification. Dnajb9 and Rictor knockdown in mice were generated by shRNA-driven adenovirus transduction, mice were sacrificed 7 days post virus administration after 6 hours of food withdrawal at 3 pm without specification. For liver triglycerides measurements upon Srebp1 and Dnajb9 knockdown, mice were fasted at 9 am, refed at 6 pm and sacrificed at midnight. In the hyperinsulinemic-euglycemic clamp studies, Flag-tagged DNAJB9 expression was generated by AAV transduction. All the other mice experiments in this study were transduced by adenovirus. For western blot determination of DNAJB9 overexpression in lean mice, the liver tissues were collected 7 days post adenovirus administration, food was withdrawn for the last two days followed by a bolus glucose (2g/kg) injection 2 hours before tissue collection. For the detection of lipogenic genes expression in DNAJB9 overexpressing lean mice, liver tissues were collected 4 days after adenovirus administration, mice were fasted at 9 am, refed at 6 pm and sacrificed at midnight. Purified adenovirus was injected approximately 0.1OD for lean mice, 0.2OD for obese mice. AAV was injected approximately 2 3 1011 genome copies per ob/ob mice (Ran et al., 2015). Measurement of food intake starts from day-3 post adenovirus injection, mice were caged individually since day-1 for initial adaptation. Food was removed at 9 am, refed at 6 pm, 3hrs and 12hrs of food intake was recorded. Food intake of DNAJB9 overexpression ob/ob mice and the Dnajb9 liver-specific knockout (Dnajb9LKO) mice was measured by TSE Phenomaster metabolic cage. Cell Culture, Transfection, and Antibodies HEK293FT, HEK293A, HeLa, 3T3-L1, Hepa1-6, C2C12 cells were obtained from ATCC. SW480 wild-type and PTEN knockout cells were a kind gift from Dr. Wei Guo. Cells were cultured in DMEM containing 10% fetal bovine serum (FBS). Plasmid DNA and small interference RNA were transfected with either polyethyleneimine (Sigma), Lipofectamine LTX, or Lipofectamine RNAiMAX (Thermo Scientific) according to manufacturer recommendations. Without specification, all siRNAs or plasmids were transfected for 24hr before analysis or further treatment. Refer to STAR methods for reagents and kits used in this study. CRISPR/Cas9-mediated Dnajb9 deletion in HEK293A Cells Optimal sgRNA targeting the first exon of human Dnajb9 was designed by CRISPR Design Tool (http://zlab.bio/ guide-design-resources, Feng Zhang Lab). Synthetic oligos were re-suspended in H2O to 100 mM final concentration, annealed, and ligated into linearized pX335 vector. Plasmids were transfected into HEK293A cells, passaged to 96 well plate at the concentration of 0.8 cell/well three days after transfection, expanded and positive clones were sequence verified. HEK293A Dnajb9 KO cells used in this paper containing an insertion of 79nt in exon2, 142nt away from the start codon, leading to a frameshift after the first 47 amino acids (Figures S2C and S2D). METHOD DETAILS Primary Hepatocyte Isolation and Culture For the isolation of primary hepatocytes, 8- to 10-week-old or indicated ages of wild-type, Dnajb9 knockout, and ob/ob mice were anesthetized with 250mg/kg avertin, and the liver was perfused through the portal vein with 50 mL of warm (37 C) HBSS buffer (Invitrogen, CA) supplemented with 1 mM EGTA and 5 mM glucose (HBSS-EGTA) at a speed of 10-15mL min-1 and then digested with

Cell Reports 30, 1835–1847.e1–e9, February 11, 2020 e4

40 mL of Collagenase IV (Sigma) prepared in HBSS buffer at the concentration of 0.25 mg/mL supplemented with 5 mM CaCl2 and 5 mM glucose. Primary hepatocytes were released in HBSS-EGTA using forceps and scissors, and sedimented at 500 rpm for 5 min. Cells were washed twice with Medium 199 (M&C GENE TECHNOLOGY), plated onto gelatin-coated 6- or 12-well plates at a confluency of 4 3 105 or 2 3 105 cells in BFM199 (0.2% BSA, 2% FBS, 1% Pen/Strep), respectively, incubated at 37 C with 5% CO2 for four hours, followed by a fresh media change and then cultured for designated analysis. For adenovirus-mediated knockdown or overexpression in vivo, wild-type or ob/ob mice were injected with indicated adenovirus 3 days before primary hepatocytes isolation. For adenovirus infection in vitro, adenovirus was added four hours after seeding, at a concentration of 0.001OD or 0.002OD per well in 12-well plate or 6-well plate. Cells were infected and cultured for 24hrs or 36hrs for designated analysis. Constructs pCMV Entry mDNAJB9-eGFP/Myc-DDK was constructed by PCR amplification of DNAJB9 ORF from mouse cDNA and cloned into pCMV6 Entry-eGFP/Myc-DDK vector. pCMV6 Entry mHSPA5-Myc-DDK was obtained from Origene, and the tag was changed into eGFP. meIF4E(control), Myc-DDK tagged DNAJB9 truncations including N(1-25[AA]), NJ(1-90), NC(1-25,91-222), J(26-90), DSP(24222) are cloned into pCMV6 Entry Myc-DDK by BglII/MluI, NJ-G/F(1-181) and N-G/F(1-25,91-181) by AsiSI/MluI.pCMV-Myc-DDKSREBP1c was constructed by PCR amplification of SREBP1c ORF from human(293A) cDNA and cloned into pCMV-Myc vector, DDK tag was added to the N-terminal of SREBP1c. All constructs of DNAJB9 and HSPA5 in this study were C-terminally tagged. All immunoblot determination of SREBP1c in this study was carried out with overexpressed Myc-DDK-tagged SREBP1c. Adenovirus Production DNAJB9 and LacZ cDNAs, Dnajb9, LacZ, Rictor, Srebp1, Gck, Hk2, Akt1 and Akt2 shRNAs, were cloned into the Adenoviral Gateway Expression System (Invitrogen). In brief, the cDNA of LacZ and Flag-tagged mouse DNAJB9 and knockdown oligos targeting mouse Dnajb9 and Rictor genes were first cloned into their respective entry vectors (pENTR1A for cDNA, pENTR-U6 vector for shRNA) and then transferred to their corresponding destination vectors (pAd/CMV-DEST for overexpression and pAd/BLOCK-iT-DEST for knockdown, Thermo Scientific) through LR recombination to generate cDNA or shRNA expression clones. Each construct was sequence confirmed and transfected into HEK293A cells for adenovirus packaging. After three rounds of amplifications, infected HEK293A cells from a total of ten 15cm dishes were harvested, lysed in 4mL PBS. The adenovirus was freeze-thawed for 3 times, centrifuged at 3,000 rpm for 10min to remove the debris. The adenoviral supernatant was laid on 6.5mL 55% cesium chloride, centrifuged at 25,000 rpm for 22hrs and desalted through PD10 column (GE Healthcare Life Sciences), concentration was determined by OD260 absorption. Adeno-associated Virus Production Flag-tagged DNAJB9 was cloned into the pCAG-GFP AAV shuttle vector (a gift from Dr. Xiaowei Chen) using KpnI and HindIII with primers: 50 -cgggtacctatggctactccacagtcag-30 and 50 - ataagcttgaCTACTTATCGTCGTCATCCT-30 . AAVs were prepared as described (McClure et al., 2011). Briefly, AAV shuttle vector, Delta F6 helper plasmid and RC2/8 were transfected into HEK293FT cells at a confluency of 80%–90%. Typically, a total of ten 15cm dishes were needed for one virus. Medium were changed 16hrs post transfection. At 60hrs after transfection, cells were collected and washed once with PBS at 1,000 rpm The AAV pellet was lysed in 5mL AAV lysis buffer containing 150mM NaCl, 20mM Tris pH8.0, freeze-thawed 3 times. MgCl2 and Benzonase were added into AAV lysate at a concentration of 1mM and 0.25KU/mL, respectively, and the lysate was incubated at 37 C for 15min to remove DNA and RNA. The AAV lysate was then centrifuged at 4,000 rpm, 4 C for 30min to remove cellular debris. The AAV supernatant was then loaded onto a discontinuous iodixanol gradient of 17%, 25%, 40%, s and 60% and centrifuged at 53,000 rpm for 2h 40min. After centrifugation, the viral fraction (the 40% layer) was collected and concentrated using concentrators (Vivaspin Turbo 15, 100,000 MWCO, Sartorius, VS15T42) to remove iodixanol. Virus quality and titer were determined by SDS-PAGE and qRT-PCR. Hyperinsulinemic-euglycemic Clamp Five days after AAV injection, hyperinsulinemic-euglycemic clamps were performed on ob/ob mice as previously described (Li et al., 2016). Mice were anesthetized and one catheter (Silastic 508-001, Dow Corning) was implanted in the right jugular vein. The catheter was then tunneled subcutaneously to the back of the neck and exteriorized. The mice were then allowed to recover for 3 days prior to clamp experiments. Mice losing < 5% of their pre-surgery weight were included in the following test. After a 6-hr fasting, blood was drawn from tail vein for blood glucose level determination and subsequent FFA and insulin analysis. Clamp was started by infusion with D-[3-3H] glucose (PerkinElmer Life Sciences, MA, USA) at a constant rate of 5 mCi/h. After 90 min of tracer equilibration, blood sample was taken for basal glucose turnover measurements. Then glucose (50% D-glucose, variable infusion rate) and tracer (5 mCi/h) plus insulin (Humulin, Eli Lily and Company. 12 mU/kg/min) were infused into the jugular vein at t = 0 min. Small blood samples were drawn from the tail vein at 10 min intervals and achievement of steady-state conditions (150 mg/dl ± 5 mg/dl) was confirmed at the end of the clamp by maintaining glucose infusion and plasma glucose concentration for a minimum of 30 min. Blood samples at t = 0(basal), 110 and 120 (end of experiment) min were taken to determine glucose-specific activity, FFA and insulin concentration. Tracer-determined rates were quantified by using the Steele equation for steady-state conditions. At steady state, the rate

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of glucose disappearance, or total GDR, is equal to the sum of the rate of endogenous glucose productions (HGP) plus the exogenous(cold) GIR. The IS-GDR is equal to the total GDR minus the basal glucose turnover rate. Immunoblots Cultured cells or liver specimens were snap-frozen in liquid nitrogen and stored at
80 C. For whole cell protein lysate extraction, 50mg of liver tissues were homogenized in 1mL of cold RIPA lysis buffer (20mM Tris, pH7.5, 150mM NaCl, 1mM EDTA, 1mM EGTA, 1% NP-40, 1% sodium deoxycholate) supplemented with protease and phosphatase inhibitors, homogenized by a polytron homogenizer at 2,800 rpm for 30 s, centrifuged 10min at 13,000 3 g, 4 C to remove unlysed cells and debris. Protein concentrations were determined by BCA quantification. A total of 25mg proteins were subjected to SDS-PAGE and transferred onto nitrocellulose membrane. Western blot was performed according to standard protocols recommended by antibody vendors, detected by HRPconjugated secondary antibodies. Proteinase K Protection Assay Cells were lysed in homogenization buffer containing 10mM HEPES PH 7.4, 220mM mannitol and 70mM sucrose for 30min, and disrupted by passing through 29-gauge needles 30 times. Cell lysates were centrifuged at 300 3 g for 5 min at 4 C to get rid of unlysed cells and debris. The supernatant was centrifuged at 10,000 3 g for 20 min at 4 C. After that, the pellet was washed with homogenization buffer once and then resuspended for protection assay in aliquots.A final concentration of 100 mg/mL proteinase K, with or without 0.5% Triton X-100 was added to each reaction and kept at 4 C for 30min. Proteins were precipitated with trichloroacetic acid. Mix on ice for 30min, then centrifuged at 12,000 3 g for 10min at 4 C. Supernatant was discarded and pellets were washed twice with ice cold acetone, air dried and dissolved in 1 3 loading buffer supplemented with 3M urea and denatured at 95 C for 5min. Immunoprecipitation Cells were lysed in 0.3% CHAPS lysis buffer (40mM HEPES, 120mM NaCl, 0.3%CHAPS) supplemented with protease and phosphatase inhibitors (Pierce) for 15min and cleared at 1,000 3 g for 10 minutes. For RICTOR immunoprecipitation, crude lysates were incubated with RICTOR or IgG antibody for 2hrs at 4 C, followed by the addition of protein-A/G-Agarose Beads (CMCTAG) for 90 more minutes, pelleted down by 100 3 g for 1 minute. For GFP, Myc and Flag IP, crude lysates were incubated with GFP-, Myc- or Flagconjugated beads for 2hrs and magnetically captured. Immunoprecipitation with Streptavidin-beads were carried out similarly as the magnetic beads, expect for centrifugation at 4 C, 50 3 g, 2mins to pellet the beads. Beads were washed three times with 1 mL of lysis buffer and resuspended in mTORC2 kinase assay buffer (25mM HEPES, 150mM KAc, 5mM MgCl2) for in vitro kinase assay or boiled in 1 3 loading buffer for SDS-PAGE. For identification of DNAJB9-interacting proteins, two 15-cm dishes of cells stably expressing either GFP, DNAJB9-GFP, or HSPA5-GFP were harvested for immunoprecipitation, and 200 mL GFP beads were used for each IP reaction. The elution was run on 8% gel for 30min, and the whole lane was sent to Tsinghua Proteomics Facility for Mass Spectrum analysis. Glycerol Gradient Fractionation Cell lysates were prepared as described in immunoprecipitation. A total of 0.5-1mL whole-cell lysate was loaded on the top of an 11.5mL 10%–40% (v/v) continuous glycerol gradient column and centrifuged at 35,000 rpm, 4 C for 15hrs using SW41 rotor (HITACHI CP 80WX). The column was fractionated into 0.5mLs by BioComp Piston Gradient Fractionator. Fractions were diluted four times with CHAPs lysis buffer for Immunoprecipitation (See also Immunoprecipitation) or mixed with five volumes of pre-chilled
20 C acetone for precipitation and the precipitates were dissolved in 1 3 SDS loading buffer for further immunoblot analysis. Protein Synthesis Assay Protein synthesis was measured by puromycin chasing. Cells were incubated with puromycin containing medium at the concentration of 0.5 mg/mL for 30min, then washed twice with pre-chilled PBS, and lysed for immunoblots. Recombinant AKT Purification and in vitro Kinase Assay Kinase-dead AKT (K179D) was cloned into pcDNA3.1+ backbone with an N-terminal 8x His-tag. HEK293T cells were transfected and harvested 48hrs after transfection. Recombinant His-AKT (K179D) was purified with a Nickel column (Ni-NTA, QIAGEN) followed by gel filtration (SD200, GE). For mTORC2 kinase assay, 200ng of dephosphorylated His-AKT protein was used for each reaction, supplemented with 500nM ATP at a final volume of 20 ml, incubated at 37 C for 30min. Reactions were stopped by the addition of 2x SDS loading buffer. Proteins were resolved by SDS-PAGE and analyzed by the pAKT-S473 antibody. Blue Native PAGE Gradient gels (3%–12%) were prepared and BN-PAGE was performed as described previously (Fiala et al., 2011; Wittig et al., 2006). Briefly, 50mg of liver tissues were homogenized in 1mL of 0.3% CHAPS lysis buffer supplemented with protease and phosphatase inhibitors, by a polytron homogenizer at 2,800 rpm for 30 s, and centrifuged for 10min at 10,000 3 g, 4 C to remove tissue debris. Cell

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lysate was prepared as described in immunoprecipitation, except for centrifugation at a speed of 10,000 3 g. Both liver and cell lysates were supplemented with Coomassie blue G250 at a detergent/dye ratio of 8 (gram/gram). A total of 35 mg protein lysates were loaded for each sample and run until the dye front reaches the end of the gel. The BN-PAGE gel was then denatured in 2x SDS Loading Buffer for 10 min at room temperature and boiled for no more than 20sec, incubated in the hot SDS Sample Buffer for another 15 min. The protein constituents of the denaturedBN-gel was then transferred onto a PVDF membrane, fixed in 5% Acetate, and destained in methanol. The de-stained PVDF membrane was then used for Western Blot according to standard protocols. Quantitative Reverse Transcribed Polymerase Chain Reaction (qRT-PCR) Total RNA was extracted with TRIzol Reagent (Invitrogen) according to manufacturer’s recommendations, reverse transcribed with FastKing RT Kit (KR116, Tiangen). Quantitative PCR was performed with the EvaGreen mastermix on an Applied Biosystems StepOne or Vii7 Real-Time PCR System (Applied Biosystems). Duplicate runs of each sample were normalized to Gapdh, 18 s or 36b4 to determine relative gene expression levels. Values plotted represent averages from at least four different biological samples. The nucleotide sequence of the primer pairs used in this study is listed in the Table S6. Glucose and Insulin Tolerance Tests Glucose tolerance tests (GTT) were performed by intraperitoneal glucose injection (1.5 or 2 g kg
1 body weight for obese and lean mice respectively) after an overnight fasting (16hrs), and insulin tolerance tests (ITT) were performed by intraperitoneal insulin injection (1 or 1.5 IU kg
1 for lean and obese mice) after a 6-hour food withdrawal. Plasma glucose was measured before and after the injection at indicated time points. Glucose, ATP, Cholesterol, and Triglyceride Measurements and Insulin ELISA For glucose output assay, primary hepatocytes were fasted with glucose-free DMEM (M&C GENE TECHNOLOGY) for 3hrs to deplete glycogen. Glucose-free DMEM with indicated substrates were added onto the cells for another 3 hr, media were collected for glucose measurement. For ATP measurement, primary hepatocytes were lysed with 2% TCA, neutralized with 2 volumes of 1M Tris-Acetate (pH7.4), cleared at 10,000 3 g for 10min, and the supernatants were collected and measured according to manufacturer’s recommendations. Glucose and ATP were determined by Amplex Red Glucose/Glucose Oxidase Assay Kit and ATP Determination Kit (Thermo Fisher), respectively. Blood glucose was measured with a Bayer glucometer (Breeze2, Bayer). For insulin measurement, blood was collected after 6hrs of food withdrawal, and measured by Ultrasensitive Mouse Insulin ELISA kit (Alpco). Lipids were extracted from frozen liver samples with methanol:chloroform (1:2). Briefly, 50mg of liver tissues were homogenized in 20 volumes of prechilled (
80 C) methanol:chloroform with polytron homogenizer (2,800 rpm for 30 s), vortexed vigorously for 30 s, and a 1/4 volume of H2O were added to the homogenate, vortexed for another 30 s, centrifuged at 3,000 rpm for 10 minutes, and the bottom layer was collected, dried in Speed-Vac, and stored at
80 C, or dissolved immediately in 5% NP-40 for cholesterol and triglyceride determination. Cholesterol and triglyceride of plasma and resolved liver lipids were determined by Cholesterol Fluorometric Assay Kit (Cayman) and Triglyceride Assay Kit (Nanjing Jiancheng). Body Composition Mice were anaesthetized with Avertin (250mg/kg). Body composition (% of fat and lean mass) was determined by dual-energy X-ray absorptiometry (DEXA) (Lunar PIXImus2, GE). Seahorse Extracellular Flux Analysis For Seahorse flux analysis, 3T3-L1 cells (2,000 cells/well for RNAi and 4,000 cells/well for compounds treatment) or primary hepatocytes (3,500 cells) were plated in XF 96-well microplates. After a proper time, medium was completely replaced with XF assay medium (10 or 5.5mM glucose, 2mM pyruvate, 4mM glutamine) or pyruvate only medium (2mM pyruvate). The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured with XFe96 Extracellular Flux Analyzer (Seahorse Biosciences) according to manufacturer’s recommendations. Reagents were added at the following final concentrations: glucose (10mM), 2-DG (100mM), oligomycin (2 or 3.5 mM), FCCP (1 or 0.5 mM), Rotenone (1 or 2 mM), Antimycin A (1 or 2 mM). Measured values were normalized to total protein amount quantified by BCA quantification or cell numbers measured by the CyQUANT Cell Proliferation Assay Kit (Life Technologies). Mitochondrial Membrane Potential Measurement Mitochondrial membrane potential was determined by JC1 staining. HeLa cells were transfected with indicated siRNA for 24h. Cells were digested, and resuspended into 1 3 106 cells/mL, then incubated with 20 mM JC1 for 15min at 37 C, 5% CO2, after which cells were washed twice with PBS and analyzed by FACS (BD). Confocal Imaging For mitochondria imaging, HeLa cells were first transfected with indicated siRNA (50nM) followed by Mito-GFP adenovirus infection for 24hrs, and fixed. Images were captured with Nikon A2 confocal microscope.

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For immunostaining of DNAJB9 mutants, HEK293A Dnajb9
/
cells grown on coverslips were transfected with C-terminal GFPtagged DNAJB9 truncations for 24hrs, fixed, blocked with 5% goat serum and immuno-stained with a-GFP (mouse, Millipore) and a-Sec61B (rabbit, CST) antibodies overnight. Cells were then washed for three times, incubated with anti-mouse Alexa Fluor Plus 488 and anti-rabbit Alexa Fluor Plus 568 secondary antibodies for 1hr, and washed again for three times. Coverslips were mounted with DAPI-containing mounting medium, and sealed with nail polish. Images were captured with Olympus FV1200 microscope. Histology Analysis For hematoxylin and eosin (H&E) staining, liver specimens were promptly fixed in 4%PFA after harvesting, dehydrated, and embedded. Paraffin-embedded tissues were cut into 5-mm-thick sections and stained with hematoxylin/eosin according to the standard protocols. For Oil-Red-O staining, PFA fixed samples were sequentially dehydrated in 15% and 30% sucrose solutions, snapfrozen in OCT (Sakura), cryo-sectioned into 8-mm thick-slice, and stained with Oil-Red-O (Sigma) solutions. RNA Extraction and Transcriptomic Analysis Total RNA was extracted from frozen tissues with TRIzol Reagent according to manufacturer’s instructions. RNA samples were treated with DNase I (Thermo Scientific) and cleaned with the Ribo-Zero Magnetic Core Kit (Epicenter). A total of 500 ng RNA was used for library preparation with TruSeq Stranded Total RNA with Ribo-Zero Gold kit (Illumina, RS-123-2201). Cutadapt and RSeQC were executed for adaptor trimming and quality control of paired-end RNA sequencing data then aligned to the UCSC mm10 mouse reference genome with TopHat2.0 (Trapnell et al., 2012; Wang et al., 2012). Read-pairs were counted with HTSeq and differential gene expression was determined by DESeq2 (Love et al., 2014). The criteria for differentially expressed genes across conditions were defined as more than two-fold change in normalized counts and less than 0.01 in adjusted P values (Benjamini– Hochberg). Gene ontology and KEGG enrichment analysis were performed with Bioconductor package clusterProfiler (Yu et al., 2012) and gene sets with an adjusted P value less than 0.05 were considered to functionally enriched. Putative transcription factors targeting differentially expressed genes were predicted in Pscan (Zambelli et al., 2009) with the Jaspar database. The TF binding site analysis was focused on the region between nucleotides
950 and +50 relative to transcription start site (TSS), and a TF binding site was considered to be significantly overrepresented with a BH adjusted P value is less than 0.05. Network analysis was performed in STRING with a threshold of 0.7(Szklarczyk et al., 2017). Metabolite Extraction and Metabolomic Analysis Metabolites were extracted from frozen liver samples with methanol. Briefly, 50mg of liver tissues were homogenized in 20 volumes of prechilled (
80 C) 80% methanol with polytron homogenizer (25,000 rpm for 30 s), centrifuged at 14,000 3 g for 5 minutes to pellet the debris, and re-extracted with another 500 mL of 80% methanol, pooled and dried in Speed-Vac and stored at
80 C. Untargeted metabolomics were implemented with a Q Exactive orbitrap mass spectrometer (Thermo, CA). In a positive mode, mobile phase A was prepared by dissolving 0.63 g of ammonium formate in 50mL of HPLC-grade water, then adding 950mL of HPLC-grade acetonitrile and 1 mL of formic acid. Mobile phase B was prepared by dissolving 0.63 g of ammonium formate in 500mL of HPLC-grade water, followed by 500mL of HPLC-grade acetonitrile and 1 mL formic acid. In a negative mode, mobile phase A was prepared by dissolving 0.77 g of ammonium acetate in 50mL of HPLC-grade water, then adding 950mL of HPLC-grade acetonitrile. Adjust pH to 9.0 with ammonium hydroxide solution. Mobile phase B was prepared by dissolving 0.77 g of ammonium acetate in 500ml HPLC-grade water, subsequently, adding 500mL HPLC-grade acetonitrile and the pH was adjusted to 9.0 with ammonium hydroxide solution. Untargeted lipidomics were also implemented with a Q Exactive orbitrap mass spectrometer (Thermo, CA). In this case, mobile phase A was prepared by dissolving 0.77 g of ammonium acetate to 400mL of HPLC-grade water, followed by adding 600mL of HPLC-grade acetonitrile,and mobile phase B was prepared by mixing 100mL of acetonitrile with 900mL isopropanol. Targeted metabolomics were implemented with a TSQ Quantiva Triple Quadrupole mass spectrometer (Thermo, CA) with positive/ negative ion switching. Mobile phase A was prepared by adding 2.376mL tributylamine and 0.858mL acetic acid to HPLC-grade water, then adding HPLC-grade water to 1L volume. Mobile phase B was HPLC-grade methanol. For multivariate model analysis, between-group differences in metabolite abundant were assessed by Welch’s two-sample t-test. Differences were considered statistically significant for p < 0.05. Chemometric statistical analysis was performed http://cran. r-project.org/ in RStudio (v. 0.99.896) for Windows. Hierarchical clustering (HCL) and unsupervised principal components analysis (PCA) were implemented to assess data quality and detect sample outliers. Raw data from the metabolic analysis were normalized by the median of each sample. Missing values were assumed to be below the limit of detection and were imputed with half of minimum of the whole dataset. Metabolites with more than two-fold change in normalized values and less than 0.05 in p-values were considered as being differentially regulated. Metabolite Set Enrichment Analysis (MSEA) was then implemented in MPINet (Li et al., 2014).

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QUANTIFICATION AND STATISTICAL ANALYSES Data are presented as Mean ± SEM. Statistical significance was evaluated by student’s unpaired t-test unless specified otherwise. Between-group differences for GTT and ITT were assessed by ANOVA. A p-value of less than 0.05 was considered as significant. Immunoblot quantification was performed by Alphaview (FluroChem FC3). All charts were drawn in Graphpad Prism6. Cartoons were drawn in Adobe Illustrator 2018. DATA AND CODE AVAILABILITY The accession number for the raw and processed RNA-seq data reported in this manuscript is GEO: GSE93948.

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