The transcription factors TFEB and TFE3 link the FLCN-AMPK signaling axis to innate immune response and pathogen resistance

Abstract

TFEB and TFE3 are transcriptional regulators of the innate immune response, but the mechanisms regulating their activation upon pathogen infection are poorly elucidated. Using C. elegans and mammalian models, we report that the master metabolic modulator 5’-AMP-activated protein kinase (AMPK) and its negative regulator Folliculin (FLCN) act upstream of TFEB/TFE3 in the innate immune response, independently of the mTORC1 signaling pathway. In nematodes, loss of FLCN or overexpression of AMPK confers pathogen resistance via activation of TFEB/TFE3-dependent antimicrobial genes, while ablation of total AMPK activity abolishes this phenotype. Similarly, in mammalian cells, loss of FLCN or pharmacological activation of AMPK induces TFEB/TFE3-dependent pro-inflammatory cytokine expression. Importantly, a rapid reduction in cellular ATP levels in murine macrophages is observed upon lipopolysaccharide (LPS) treatment accompanied by an acute AMPK activation and TFEB nuclear localization. These results uncover an ancient, highly conserved and pharmacologically actionable mechanism coupling energy status with innate immunity.

Introduction

Innate immune responses constitute the first line of defense against pathogenic infections in simple metazoans, invertebrates, and mammals (Akira et al., 2006, Hoffmann, 2003, Irazoqui et al., 2010b, Medzhitov, 2007). While much effort has been put into elucidating the functions of downstream mediators of immune response including antimicrobial peptides, C-type lectins, cytokines and chemokines, less is known regarding how host cells recognize foreign infections and trigger the activation of transcription factors that coordinate the anti-microbial response. Among the few well-characterized transcription factors, NF-κB, was shown to be an important factor in controlling host defense gene expression, mediated through toll like receptor (TLR) and nucleotide-binding leucine-rich repeat containing (NLR) ligand pathways (Medzhitov, 2009). However, another under-appreciated host-defense transcription factor was recently identified in Caenorhabditis elegans (C. elegans), which lacks the NF-κB pathway (Visvikis et al., 2014). Using this model, HLH-30, the C. elegans ortholog of TFEB and TFE3, was identified as an important evolutionarily conserved transcriptional regulator of the host response to infection (Lapierre et al., 2013, Visvikis et al., 2014, Rehli et al., 1999). TFEB and TFE3 are basic helix–loop–helix leucine zipper transcription factors that multi-task in regulating a similar set of genes involved in lipid metabolism, autophagy, lysosomal biogenesis and stress response genes (David, 2011, Raben and Puertollano, 2016, Sardiello, 2016, Settembre et al., 2013, Settembre et al., 2011). Several studies have reported a similar mechanisms underlying TFEB/TFE3 activation in response to nutrient deprivation and metabolic stress. In nutrient-rich environments, the kinases ERK2 and mTORC1 phosphorylate TFEB/TFE3 on specific serine residues and retain them in the cytoplasm in an inactive state (Martina et al., 2012, Martina et al., 2014, Roczniak-Ferguson et al., 2012, Settembre et al., 2011, Settembre et al., 2012). The mTORC1-dependent phosphorylation of TFEB (S211) and TFE3 (S321) promotes binding to 14-3-3. It has been suggested that this interaction masks the Nuclear Localization Signal (NLS), thus inhibiting TFEB and TFE3 nuclear translocation (Roczniak-Ferguson et al., 2012, Martina et al., 2012). Conversely, under starvation, this repressive phosphorylation is lifted, resulting in their translocation to the nucleus and activation of their downstream transcriptional targets that encode components of the lysosomal biogenesis and autophagy pathways (Martina et al., 2012, Roczniak-Ferguson et al., 2012, Settembre et al., 2011, Settembre et al., 2012). Despite these remarkable similarities between TFEB and TFE3, it is still unclear whether these transcription factors have cooperative, complementary, or partially redundant roles under different environmental conditions. Importantly, in murine macrophages, both TFEB and TFE3 were shown to be activated and translocated to the nucleus upon pathogen infection or stimulation with TLR ligands, where they collaborate in mediating the transcriptional upregulation of several cytokines and chemokines involved in antimicrobial immune response (Pastore et al., 2016, Samie and Cresswell, 2015, Visvikis et al., 2014). This functional conservation of the TFEB/TFE3 pathway is further supported by a recent study showing that bacterial membrane pore-forming toxin induces cellular autophagy in an HLH-30-dependent manner in C. elegans (Chen et al., 2017). However, the mechanisms by which nematode and mammalian TFEB/TFE3 are activated during infection are still poorly understood. Recently, TFEB activation was found to involve phospholipase C and protein kinase D pathways both in C. elegans and mammals upon pathogen infection (Najibi et al., 2016). Subsequent studies showed that lipopolysaccharide (LPS)-stimulated TFEB/TFE3 activation in murine macrophages induced cytokine production and secretion independent of mTORC1, but the specific pathway by which their activation was mediated was not elucidated (Pastore et al., 2016).

Folliculin (FLCN) is a binding partner and negative regulator of 5’-AMP-activated protein kinase (AMPK) (Baba et al., 2006, Takagi et al., 2008), which was identified as a tumor suppressor protein responsible for the Birt-Hogg-Dubé (BHD) neoplastic syndrome in humans (Tee and Pause, 2013). Importantly, the interaction of FLCN with AMPK is mediated by two homologous FLCN-binding proteins FNIP1 and 2 [22,23]. Pathogenic mutations from BHD patients lead to a loss of FNIP/AMPK binding pointing to the functional significance of this interaction in tumor suppression [22]. AMPK is a heterotrimeric enzyme, which monitors the energy status and maintains energy homeostasis under metabolic stress by activating catabolic processes and inhibiting anabolic pathways (Hardie, 2015, Hardie and Ashford, 2014, Hardie et al., 2012). We have previously shown that loss of FLCN or expression of a FLCN mutant unable to bind FNIP/AMPK led to chronic AMPK activation, resulting in increased ATP levels through an elevated glycolytic flux, oxidative phosphorylation and autophagy (Possik et al., 2015, Possik et al., 2014, Possik and Pause, 2016). Importantly, we have shown that loss of FLCN mediates resistance to oxidative stress, heat, anoxia, obesity, and hyperosmotic stresses via AMPK activation in C. elegans and mammalian models (Possik et al., 2015, Possik et al., 2014, Possik and Pause, 2016, Yan et al., 2014).

While a role for FLCN in regulating immune responses has not been reported, the functional role for AMPK in innate immunity seems to be context and cell-type dependent (Blagih et al., 2015, Prantner et al., 2017). In this study, we demonstrate an evolutionarily conserved pathogen resistance mechanism mediated by FLCN and AMPK via TFEB/TFE3. Specifically, we show that loss of flcn-1 in C. elegans, which leads to chronic AMPK activation, enhances the HLH-30 nuclear translocation and induces the expression of hlh-30-dependent antimicrobial genes upon infection, mediating resistance to bacterial pathogens. Using RNA-seq, we show that many hlh-30-dependent antimicrobial genes are regulated by AMPK upon S. aureus infection. AMPK loss reduces HLH-30 nuclear translocation and abrogates the increased resistance of flcn-1(ok975) mutant animals to pathogens. Furthermore, we show that constitutive activation of AMPK C. elegans nematodes leads to an HLH-30-dependent increase in pathogen resistance, similar to what we observe upon loss of flcn-1. Importantly, we show that this pathway of regulation is evolutionarily conserved and that FLCN and AMPK regulate TFEB/TFE3-driven cytokine and inflammatory genes in mouse embryonic fibroblasts and macrophages. Overall our data suggest an essential role of the FLCN/AMPK axis in the regulation of host-defense response via TFEB/TFE3, highlighting a possible mechanism likely to contribute to tumor formation in BHD patients. Our findings also shed light on the potential use of AMPK activators in the stimulation of the innate immune response and defense against pathogens.

Results

Loss of flcn-1 in C. elegans increases the expression of anti-microbial genes and confers resistance to bacterial pathogens

To understand the physiological role of FLCN-1, we compared gene expression profiles of wild-type and flcn-1(ok975) mutant animals. Among differentially expressed genes, 243 transcripts were up-regulated in flcn-1(ok975) mutant animals compared to wild-type animals at basal level (Table S3-Sheet 1) and were classified based on their biological functions (Table 1 and Table S3-Sheets 1 and 2). Genes associated with stress response, innate immune response, defense mechanisms and response to stimulus processes, including heat shock proteins, C-type lectins, lysozymes and cytochrome P450 genes, were induced in flcn-1(ok975) unstressed mutant animals compared to wild-type animals (Table 1, S1–3, Figure 1A). Selected genes were validated using RT-qPCR (Figure 1B and Table S3-Sheet 3). On the other hand, 704 genes were shown to be downregulated in flcn-1(ok975) mutant animals (Table S3-Sheet 4) and are involved in various processes that control proliferation and growth (Table S3-Sheet 5). These results indicate that a differential gene expression might be providing advantage to the flcn-1 mutant worms prior to stress or pathogen attacks. This is in accordance with our previously reported results where loss of flcn-1(ok975) conferred resistance to oxidative stress, heat stress, anoxia and hyperosmotic stress in C. elegans (Possik et al., 2015, Possik et al., 2014, Possik and Pause, 2015, Possik and Pause, 2016, Yan et al., 2014). Since it was demonstrated that the osmo-sensitive gene expression mimics the transcriptional profiles of pathogen infection (Rohlfing et al., 2010), we compared the overlap between genes upregulated in flcn-1(ok975) mutant animals and genes induced by infection of C. elegans nematodes with pathogens (Irazoqui et al., 2010a, Troemel et al., 2006). Indeed, we found a significant overlap of the transcriptome especially upon Staphylococcus aureus (S. aureus) (Figure S1A and Table S3-Sheet 6) and Pseudomonas aeruginosa (P. aeruginosa) infection (Figure S1B and Table S3-Sheet 7).

Table 1:

Genes classified according to family functions and upregulated in flcn-1(ok975) mutant animals

Gene ID	Gene	Description	Fold change	p-value
C-type lectins
H16D19.1	dec-13	C-type lectin	8.77	7.37E-07
T20B3.8	clec-23	C-type lectin	3.00	8.39E-04
F49A5.4	clec-24	C-type lectin	2.32	2.99E-03
T20B3.16	dec-36	C-type lectin	1.69	2.19E-03
T20B3.13	dec-40	C-type lectin	2.68	3.41E-04
ZK666.6	clec-60	C-type lectin	3.10	7.05E-04
ZK666.7	clec-61	C-type lectin	2.67	2.21E-03
F35C5.6.2	clec-63	C-type lectin	1.42	7.74E-04
F35C5.8.1	clec-65	C-type lectin	1.36	1.00E-03
Y46C8L.8	clec-74	C-type lectin	3.48	3.16E-03
Y45G2A.8a	clec-82	C-type lectin	2.04	1.07E-03
C54D1.2	clec-86	C-type lectin	1.79	9.79E-03
Y54G2A.6	clec-85	C-type lectin	1.59	1.05E-04
R13F6.2	clec-159	C-type lectin	2.56	2.79E-03
F38A1.5	dec-166	C-type lectin	1.62	8.68E-04
F59A7.5	clec-206	C-type lectin	2.36	1.35E-03
F38A5.3a	lee-11	Galectin	1.64	2.87E-04
Heat shock protein family
T27E4.8	hsp-16.1	Heat shock protein	1.76	1.65E-05
Y46H3A.3	hsp-16.2	Heat shock protein	2.83	1.01E-05
Y46H3A.2	hsp-16.41	Heat shock protein	3.39	5.58E-07
T27E4.3	hsp-16.48	Heat shock protein	1.81	1.01E-05
Cytochrome P 450 family
T10B9.1	cyp-13A4	Cytochrome P450 family	2.30	1.43E-03
T10B9.2	cyp-13A5	Cytochrome P450 family	2.10	4.59E-04
K09A11.3	cyp-14A2	Cytochrome P450 family	1.60	1.24E-03
C36A4.1	cyp-25Al	Cytochrome P450 family	1.88	5.18E-04
B0213.14	cyp-34A8	Cytochrome P450 family	1.48	1.73E-03
B0213.15b	cyp-34A9	Cytochrome P450 family	1.61	2.95E-03
C03G6.15	cyp-35A2	Cytochrome P450 family	1.81	7.40E-06
K09D9.2	cyp-35A3	Cytochrome P450 family	1.92	4.18E-05
C49G7.8	cyp-35A4	Cytochrome P450 family	2.04	1.06E-04
Lysosyme family
Y22F5A.5	lys-2	Lysosyme	1.40	1.08E-03
Y22F5A.6	lys-3	Lysosyme	6.15	6.96E-03
C02A12.4	lys-7	Lysosyme	2.54	5.96E-03
F17E9.11	lys-10	Lysosyme	3.08	3.72E-04
Serpentine receptor
Y105C5B.10	srv-16	Serpentine receptor, class V	2.25	3.08E-03
C01B4.5	srd-61	Serpentine receptor, class D	2.30	1.81E-03
F36G9.5	sru-22	Serpentine receptor, class U	2.33	3.23E-04
F36G9.6	sru-23	Serpentine receptor, class U	2.65	1.89E-04
C25E10.3B	srsx-34	Serpentine receptor, class SX	2.81	7.37E-04
R52.7	srh-195	Srepentine receptor, class H	2.98	2.80E-03
K12D9.5	srw-120	Serpentine receptor, class W	3.03	2.07E-04
T25E12.13	srz-99	Serpentine receptor, class Z	2.35	1.71E-03
T25E12.11	srz-100	Serpentine receptor, class Z	3.58	1.56E-04
K12D9.7	srw-119	Serpentine receptor, class W	5.52	2.33E-07
C13D9.1	srr-6	Serpentine receptor, class R	5.75	4.95E-06
F-box family
K05F6.1	FBXB-49	F-box B protein	2.28	1.27E-03
F10A3.3	FBXA-18	F-box A protein	2.14	1.79E-03
C08E3.10A	FBXA-158	F-box A protein	5.03	8.97E-06
F42G2.4	FBXA-182	F-box A protein	1.84	2.77E-03
Thaumatin family
F28D1.3	thn-1	Thaumatin family	2.37	7.83E-08
F28D1.5	thn-2	Thaumatin family	2.73	2.16E-11
Tetraspanin family
C02F5.8	TSP-1	Tetraspanin family	4.11	3.71E-06
C02F5.11	TSP-2	Tetraspanin family	3.57	1.65E-05
Arrestin domain family
F48F7.7	ARRD-24	Arrestin domain protein	7.76	2.87E-04
Y17G7B.14	ARRD-8	Arrestin domain protein	3.96	2.65E-04
M176.1	ARRD-3	Arrestin domain protein	3.53	4.90E-04

Figure 1: Loss of flcn-1 increases the expression of antimicrobial genes and confers resistance to bacterial pathogens.

(A) Pie chart of functional gene ontology analysis of the genes upregulated in flcn-1(ok975) mutant animals at basal level. (B) Relative mRNA expression of stress response and antimicrobial peptide genes in wild-type and flcn-1 mutant animals. Data represents the average of three independent experiments, each done in triplicates ± SEM. Significance was determined using student’s t-test (*p<0.05, **p<0.01, ***p<0.001). (C-E) Percent survival of indicated strains upon infection with S. aureus and P. aeruginosa. Refer to Table S1 for details on number of animals utilized and number of repeats. The Statistical analysis was obtained using Mantel Cox test on the pooled curve.

Next, we asked whether flcn-1(ok975) mutant animals display enhanced resistance to pathogens. Strikingly, we found that the flcn-1(ok975) mutant animals are more resistant than wild-type animals to S. aureus and P. aeruginosa infection (Figure 1C–D, Table S1). These phenotypes were rescued using a transgenic flcn-1 mutant animal re-expressing flcn-1 (Figure 1E, Table S1). These results demonstrate an important role for flcn-1 in the induction of antimicrobial peptides and stress response genes mediating the resistance to infection with bacterial pathogens.

Loss of flcn-1 increases pathogen resistance via HLH-30 activation

HLH-30, the worm ortholog of TFEB/TFE3, has been reported to modulate longevity and pathogen resistance in C. elegans through activation of autophagy and expression of antimicrobial genes (Lapierre et al., 2013, Settembre et al., 2013). Importantly, we found a significant overlap between genes that were upregulated in flcn-1(ok975) mutant animals and downregulated in hlh-30(tm1978) mutant animals (Table S3-Sheet 8) (Visvikis et al., 2014). Thus, we asked whether HLH-30 is induced in flcn-1 mutants using an hlh-30::GFP transgenic reporter strain (Lapierre et al., 2013, Visvikis et al., 2014). Upon infection with S. aureus, as shown in this study and others (Visvikis et al., 2014), HLH-30 translocated to the nucleus (Figure 2A). In particular, about 40% of the wild-type animals displayed an HLH-30 nuclear localization after 20 min of infection with S. aureus. Importantly, we observed that the percentage of animals displaying a constitutive nuclear HLH-30 translocation in uninfected worms was significantly higher upon loss of flcn-1 (Figure 2B, time 0). Strikingly, we show that upon S. aureus infection, the percentage of animals with HLH-30 nuclear translocation increased further in flcn-1 mutant animals. Specifically, we found that after 20 min of infection with S. aureus, more than 80% of the flcn-1 mutant animals displayed an HLH-30 nuclear localization in comparison to less than 40% for wild-type animals. Overall, this highlights an important role for HLH-30 in the increased pathogen resistance conferred by loss of flcn-1 (Figure 2B).

Figure 2: Loss of flcn-1 increases pathogen resistance via HLH-30 activation.

(A) Representative micrographs of HLH-30::GFP at basal level or after infection with S. aureus for 30 min. The signal is found in the nuclei of enterocytes, a cell-type in which lipids are stored in nematodes. Scale bars in i, iii and in ii, iv represent 100 μm and 50 μm respectively. (B) Percent animals showing HLH-30 nuclear translocation in hlh-30p::hlh-30::GFP and flcn-1; hlh-30p::hlh-30::GFP worm strains upon S. aureus infection for indicated time points to determine HLH-30 nuclear localization upon flcn-1 loss at basal level (time 0) and upon S. aureus infection. Data represents the mean ± SEM from three independent repeats, n ≥ 30 animals/condition for every repeat. Significance was determined using student’s t-test (*p<0.05, **p<0.01, ***p<0.001). (C, D) Percent survival of indicated worm strains upon infection with S. aureus and P. aeruginosa. Refer to Table S1 for details on number of animals utilized and number of repeats. Statistics obtained using Mantel-Cox analysis on the pooled curve. (E-K) Relative mRNA expression of indicated target genes in wild-type, flcn-1(ok975), flcn-1(ok975); hlh-30 (tm1978), and hlh-30 (tm1978) animals at basal level and after treatment with S. aureus for 4 h. Data represents the average of three independent experiments done in triplicates ± SEM. Significance was determined using one-way ANOVA with the application of Bonferroni correction (*p<0.05, **p<0.01, ***p<0.001).

To determine whether hlh-30 is required for the increased survival of flcn-1 mutant animals to pathogens, we generated a flcn-1(ok975); hlh-30(tm1978) double mutant strain. Importantly, loss of hlh-30 significantly impaired the survival advantage upon both S. aureus (Figure 2C) and P. aeruginosa infections (Figure 2D) that was conferred by loss of flcn-1, demonstrating its involvement in pathogen resistance (Table S1). Accordingly, loss of hlh-30 also suppressed the increased resistance of flcn-1 to hyperosmotic stress (Possik et al., 2015) supporting that the adaptation to the two stresses requires a similar transcriptional profile dictated by HLH-30 (Figure S2).

To further assess the involvement of HLH-30 in the transcriptional response downstream of FLCN-1, we measured the gene expression of known HLH-30 target genes (Visvikis et al., 2014). Using RT-qPCR, we found a significant upregulation in many hlh-30-dependent antimicrobial and infection-associated genes in uninfected flcn-1 mutant worms (Figures 2E–K). Furthermore, after 4 h of infection with S. aureus, we show that loss of hlh-30 strongly reduced the expression of antimicrobial peptide genes and infection-related genes in both wild-type and flcn-1 mutant animals (Figures 2E–K), supporting a role for HLH-30 in the pathogen transcriptional signature downstream of flcn-1. Collectively, we found that loss of flcn-1 activates the transcription of HLH-30 antimicrobial peptide genes at basal level, which is further induced upon S. aureus infection.

The regulation of TFEB/TFE3 by FLCN is evolutionarily conserved through an mTOR independent mechanism

Because the role of HLH-30 in host defense is evolutionarily conserved (Visvikis et al., 2014), we tested whether the FLCN-HLH-30 axis that we uncovered in C. elegans is conserved from worms to mammals. Indeed, we observed that Flcn deletion in mouse embryonic fibroblasts (MEFs) promoted TFEB and TFE3 nuclear localization at basal levels compared to wild-type MEFs as detected by subcellular fractionation and immunofluorescence assays (Figure 3A–C). The difference in the cytosolic TFEB molecular weight can be attributed to the phosphorylation forms of TFEB [17]. Consequently, known TFEB and TFE3 targets were upregulated at the mRNA level upon Flcn deletion (Figure 3B), including genes involved in innate host response, such as IL-6. Addition of Torin1, a specific inhibitor of mTORC1 and mTORC2, induced TFEB nuclear localization in Flcn knockout (KO) MEFs to a higher extent than wild-type MEFs (Figure 3C), evoking an mTOR independent pathway. Moreover, loss of Flcn did not affect mTOR signaling, as measured by immunoblotting for the phosphorylated form of the S6 ribosomal protein (S6), a well-described mTORC1 downstream target (Figure 3D). In line with this, in C. elegans, inhibition of let-363, the C. elegans TOR homolog, increased the HLH-30 nuclear translocation at basal level similar to what has been previously reported (Lapierre et al., 2013) (Figure 3E). Importantly, loss of flcn-1 further increased the HLH-30 nuclear translocation upon inhibition of let-363 at basal level supporting a TOR-independent pathway governing HLH-30 regulation (Figure 3E). Moreover, infection with S. aureus increased to a similar extent the HLH-30 nuclear localization both in wild-type and flcn-1 (ok975) animals fed with let-363 RNAi, presumably because the infection happens rapidly masking the effects of let-363 RNAi on HLH-30 translocation (Figure 3E). These findings suggest that loss of FLCN drives HLH-30/TFEB/TFE3 nuclear localization through a mechanism distinct from the canonical mTOR pathway both in nematodes and mammalian cells.

Figure 3: The regulation of TFEB/TFE3 by FLCN is evolutionarily conserved through mTOR independent mechanisms.

(A) Immunoblot of isolated cytosolic-soluble fractions and nuclear fractions of wild-type and FLCN KO mouse embryonic fibroblasts (MEFs). (B) Relative mRNA levels measured by qRT-PCR of indicated genes in wild-type and FLCN KO MEFs. Data represent the average of three independent experiments done in triplicates ± SEM. Significance was determined using student’s t-test (**p<0.01, ***p<0.001). (C) Quantification of the percentage of cells showing TFEB nuclear staining treated with mTORC1 inhibitor; Torin1 (1 μM) for 2 h. Data represents the average of three independent experiments, each done in triplicates ± SEM. Significance was determined using one-way ANOVA with the application of Bonferroni correction (*p<0.05). (D) Immunoblot analysis of whole cell extracts with or without Torin1 (1 μM) for 2 h. (E) Percent animals showing HLH-30 nuclear translocation in indicated hlh-30p::hlh-30::GFP worm strains treated with or without let-363 RNAi at basal level or upon S. aureus infection. Data represent the mean ± SEM with 3 independent repeats, n ≥ 30 animals/condition for every repeat. Significance was determined using one-way ANOVA with the application of the Bonferroni correction (**p<0.01, ***p<0.001). (F) Relative IL-6 mRNA levels measured by qRT-PCR in empty vector (EV) or shTFEB/TFE3-treated wild-type or FLCN KO MEFs, stimulated with or without 10 ng/ml TNF-α for 2 h. Data represents the average of three independent experiments, each done in triplicates ± SEM. Significance was determined using one-way ANOVA with the application of Bonferroni correction (*p<0.05, **p<0.01). (G) Immunoblot analysis of wild-type and FLCN KO MEFs transfected with EV or shTFEB/TFE3. (H) Immunoblot analysis of RAW 264.7 cells transfected with EV or shFLCN. (I) Relative IL-6 mRNA levels measured by qRT-PCR in EV or shFLCN-treated RAW264.7 cells, stimulated with or without 1 μg/ml LPS for 3 h. (J) Quantification of IL-6 levels of conditions described in (I) using Mouse Protein Cytokine Array. Data represent the average of three independent experiments ± SEM. Significance was determined using one-way ANOVA with the application of Bonferroni correction (*p<0.05, **p<0.01). (K) Hierarchical clustering of cytokine and chemokine secretion in the supernatant using Mouse Protein Cytokine Array in EV or shFLCN-treated RAW264.7 cells stimulated with 1 μg/ml LPS for 3 and 24 h. Each square in a column represents the average of triplicate experiments, and each column represents an independent replicate. Fold increase was normalized against EV and color-coded (dark red indicates 2 or more-fold increase, dark blue indicates no change). (L) Fold increase in cytokine and chemokine secretion levels as described in (J). Data represent the average of three independent experiments, each done in triplicates ± SEM. Significance was determined using student’s t-test in comparison to the EV stimulated with LPS for 3h and 24, respectively (*p<0.05, **p<0.01, ***p<0.001).

To further assess whether the transcriptional up-regulation of cytokines and chemokines upon loss of Flcn was mediated by TFEB and TFE3, we knocked down their endogenous expression simultaneously using shTFEB and shTFE3 in wild-type and Flcn KO MEFs and determined the expression of IL-6 following TNFα stimulation (Figure 3F–G). Notably, we found that the significant induction of IL-6 mRNA levels upon TNF-α stimulation in both wild-type and Flcn KO MEFs was abrogated to levels observed in unstimulated cells upon knockdown of TFEB/TFE3 (Figure 3F). To confirm the observed effects in a relevant cellular system for innate immune response, we used RAW264.7 murine macrophages and reduced the endogenous expression of Flcn using shRNA-mediated knockdown approaches. Importantly, we show a significant increase in IL-6 production, at both mRNA (Figure 3H) and protein levels (Figure 3I), in FLCN KD macrophages compared to empty vector (EV) in response to LPS stimulation. To further assess the role of FLCN in inflammation and innate immune response, we determined the cytokine and chemokine secretion profiles in wild-type and shFLCN macrophages after 3h and 24h of LPS stimulation using mouse protein cytokine arrays. Notably, we show a significant and prominent increase in many cytokines in FLCN KD macrophages as compared to EV upon LPS stimulation (Figures 3J–K). These cytokines encompass key mediators of the inflammatory response.

FLCN depletion in macrophages enhances their energy metabolism and phagocytic potential

Next, we investigated the metabolic consequences of FLCN depletion in RAW264.7 macrophages. We found that glucose consumption and lactate production were increased in FLCN KD macrophages compared to control macrophages (Figure 4A–B), and this was accompanied by an augmented extracellular acidification rate (ECAR) (Figure 4C–D) and oxygen consumption rate (OCR) (Figure 4E–F) at basal level and upon the sequential addition of oligomycin (an ATP synthase inhibitor), Carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) (for maximum respiratory capacity), followed by rotenone/antimycin A (to block mitochondrial electron transport). In line with these results, we also report an increase in ATP production in FLCN KD macrophages compared to controls (Figure 4G). Next, we investigated whether FLCN depletion enhances the phagocytic potential in macrophages (Figure 4I). Using pHrodo Red S. aureus Bioparticles, we report a 30% increase in phagocytic capacity of FLCN KD macrophages compared to control cells, as shown by the fold change in the mean florescence intensity (Figure 4I). To test whether this increase in phagocytic activity in FLCN KD macrophages is dependent on TFEB/TFE3 activation, we knocked down FLCN in TFEB/TFE3 DKO RAW macrophages (Figure 4H), and showed that the phagocytic activity of these cells decreased by almost 50% compared to FLCN depleted macrophages, upon stimulation with pHrodo Red S. aureus Bioparticles (Figure 4I). Taken together, we show that depletion of FLCN in macrophages prompts a metabolic transformation toward increased cellular bioenergetics, accompanied by an augmented TFEB/TFE3-dependent phagocytic capacity, which might further enhance the innate immune response.

Figure 4: FLCN depletion in macrophages enhances their energy metabolism and phagocytic potential.

(A) Glucose production and (B) lactate consumption levels measured using NOVA Bioanalysis flux analyzer in empty vector (EV) or shFLCN RAW264.7 at basal level. (C, D) Extracellular acidification rate (ECAR) and (E, F) oxygen consumption rate (OCR) of EV or shFLCN RAW264.7 at basal level as measured by Seahorse Bioscience XF96 extracellular flux analyzer. After establishing a baseline, oligomycin (10 μM), FCCP (15 μM), and rotenone/antimycin A (1 μM, and 10 μM, respectively) were added. (G) Fold change in ATP levels in EV or shFLCN RAW264.7 after 24 h of seeding as measured by CellTiter-Glo Luminescent Cell Viability Assay. Data represent the average of three independent experiments, each done in triplicates ± SEM. Significance was determined using student’s t-test (**p<0.01, ***p<0.001). (H) Immunoblot analysis of EV and TFEB/TFE3 DKO RAW264.7 cells transfected with EV or shFLCN. (I) Phagocytic activities of EV, TFEB/TFE3 DKO, and TFEB/TFE3 DKO shFLCN RAW264.7 cells measured using Red pHrodo S.aureus BioParticles by flow cytometry. Data represents the average of three independent experiments, each done in triplicates ± SEM. Significance was determined using one-way ANOVA with the application of Bonferroni correction (**p<0.01, ****p<0.0001).

AMPK regulates HLH-30 activation and antimicrobial response upon infection with bacterial pathogens.

Given that we have previously reported that loss of flcn-1 leads to chronic AMPK activation, which increases resistance to energy (Possik et al., 2014) and hyperosmotic stresses (Possik and Pause, 2015) in nematodes, we tested whether flcn-1 mutant animals confer pathogen resistance via AMPK-mediated regulation of HLH-30. Importantly, simultaneous loss of aak-1 and aak-2 (C. elegans orthologs of AMPK α1/α2) completely abolished the increased survival to both S. aureus and P. aeruginosa in wild-type and flcn-1 mutant animals, demonstrating that this phenotype requires AMPK (Figures 5A–B and Table S1). Furthermore, transgenic overexpression of a constitutively active catalytic subunit of AMPK (aak-2 oe) in nematodes confers pathogen resistance similar to flcn-1(ok975) mutants, which is mostly dependent on HLH-30 (Figure 5C and Table S1). Moreover, loss of both AMPK catalytic subunits significantly reduced the nuclear translocation of HLH-30 upon S. aureus infection (Figure 5D). Additionally, we found that loss of aak-2(ok524) alone was insufficient to reduce the nuclear translocation of HLH-30 upon S. aureus infection, suggesting that complete abrogation of both AMPK catalytic activities is required for this phenotype (Figure S3B). To further elaborate the role of AMPK in pathogen response and specifically in the transcription of antimicrobial and stress response genes upon infection, we used RNA-seq technology to measure differential gene expression in wild-type and aak-1(tm1944); aak-2(ok524) mutant animals at basal level and after 4 h infection with S. aureus (Figure S3A, Table S3-Sheets 9–13). We identified more than 800 genes induced upon S. aureus infection that are dependent on AMPK (Figure 5E and S3A, Table S3-Sheets 15 and 15). Furthermore, we found a significant overlap of 112 genes down-regulated in aak-1(tm1944); aak-2(ok524) mutant animals and genes regulated by hlh-30 upon S. aureus infection (Visvikis et al., 2014) (Figure 5F, Table S3–Sheet 16). Gene ontology classification highlights important pathways regulated by AMPK during S. aureus infection, including defense response and stress response pathways (Figure 5G and Table S3-Sheets 14–17). Using RT-qPCR, we validated several genes obtained by RNA-seq (Figures 5H–R), all of which have been reported to be involved in defense mechanisms against bacterial pathogens (Irazoqui et al., 2010a, Irazoqui et al., 2010b). Overall, these results indicate that AMPK regulates the nuclear translocation of HLH-30 and the HLH-30 driven antimicrobial response upon infection with bacterial pathogens.

Figure 5: AMPK regulates HLH-30 activation and antimicrobial response upon infection with bacterial pathogens.

(A-C) Percent survival of indicated worm strains upon infection with S. aureus or P. aeruginosa. Refer to Table S1 for details on number of animals utilized and number of repeats. Statistics obtained by Mantel-Cox analysis on the pooled curve. (D) Percentage of animals showing HLH-30 nuclear translocation in indicated hlh-30p::hlh-30::GFP worm strains upon infection with S. aureus for the indicated amount of time. Data represent the mean ± SEM with 3 independent repeats, n ≥ 30 animals/condition for every repeat. Significance was determined using student’s t-test (*p<0.05, **p<0.01, ***p<0.001). (E) Venn diagram of the overlapping set of genes between S. aureus-induced genes in wild-type animals and genes downregulated in aak-1(tm1944); aak-2(ok524) mutant animals upon infection. (F) Venn diagram and (G) pie chart of functional gene ontology analysis of AMPK-dependent genes obtained by the overlap analysis between genes downregulated in aak-1(tm1944); aak-2(ok524) mutant animals in comparison to wild-type animals upon S. aureus infection and the hlh-30-dependent list of genes published in (40). Comparisons were performed using the “compare two lists” online software and the significance was obtained using “nemates” software. (H-R) Relative mRNA levels measured by qRT-PCR of AMPK-dependent genes in wild-type and aak-1(tm1944); aak-2(ok524) mutant animals infected with or without S. aureus for 4 h. Results are normalized to non-treated wild-type animals. Validation of RNA-seq using three biological replicates per condition and three technical replicates per biological repeat. Significance was determined using one-way ANOVA with the application of the Bonferroni correction (*p<0.05, **p<0.01, ***p<0.001, ****p<0.001).

AMPK regulates TFEB/TFE3-mediated innate immune response

Based on our previous and current data, we tested whether the transcriptional innate immune response observed upon loss of FLCN is similar to a gain in AMPK activity in mammalian cells using GSK-621, a specific AMPK activator (Chen et al., 2016, Jiang et al., 2016). In MEFs, we show that GSK-621 activated AMPK, as shown by increased downstream target p-ACC, without inhibiting mTOR signaling as measured by immunoblotting for the phosphorylated forms of p70S6K and 4EBP1 (Figure 6A). Such activation was accompanied by a significant increase in the nuclear translocation of TFEB and TFE3 (Figures 6B–C), which was lost in AMPKα1/α2 double knock out (DKO) MEFs (Figures 6B–C), confirming the specific activation of AMPK by GSK-621. Additionally, we show that IL-6, a TFEB/TFE3 target, was transcriptionally upregulated when treated with GSK-621 and its expression was abrogated upon down-regulation of TFEB/TFE3 using shTFEB/TFE3 (Figure 6D) implying that AMPK impinges on TFEB/TFE3-mediated transcription in mammalian cells similarly to what we have observed in C. elegans. Moreover, treatment of RAW264.7 macrophages with GSK-621 activated AMPK without affecting mTOR signaling (Figure 6E), promoted the nuclear translocation of TFEB (Figure 6F), and led to a strong increase in production and secretion of various cytokines and chemokines even in the absence of LPS treatment or pathogen infection (Figure 6G). To substantiate our findings in a more physiological context, we tested whether acute LPS treatment of macrophages could affect cellular bioenergetics, which could be sensed by AMPK. Indeed, we observed an acute reduction in cellular ATP levels (Figure 6H), accompanied by AMPK activation (Figure 6I), and a significant increase in TFEB nuclear localization (Figure 6J) as early as 30 minutes after addition of LPS in RAW macrophages.

Figure 6: AMPK regulates TFEB/TFE3-mediated innate immune response.

(A) Immunoblot of wild-type or AMPKα1/α2 double knock out (DKO) MEFs stimulated with the AMPK activator; GSK-621 (30 μM) for 1 h. (B) Representative images of TFEB and TFE3 staining in wild-type and AMPK DKO MEFs before and after treatment with GSK-621 (30 μM) for 1 h. Scale bars represent 20 μm. (C) Quantification of the percentage of cells showing TFEB and TFE3 nuclear staining of the conditions described in (B). (D) Relative IL-6 mRNA levels measured by qRT-PCR in wild-type MEFs transfected with EV or shTFEB/TFE3, stimulated with GSK-621 for 2 h. Data represents the average of three independent experiments, each done in triplicates ± SEM. Significance was determined using one-way ANOVA with the application of Bonferroni correction (*p<0.05, ***p<0.001). (E) Immunoblot analysis of RAW264.7 macrophages treated with GSK-621 (30 μM) for 2 h. (F) Quantification of the percentage of RAW264.7 cells showing TFEB nuclear staining of the conditions described in (D). (G) Quantification of the significant fold increases in cytokine and chemokine protein levels in RAW264.7 macrophages, treated with GSK-621 (30 μM) for 2 h as compared to control. (H) Fold change in ATP levels in RAW264.7 treated with LPS (1 μg/ml) for up to 1 h as measured by CellTiter-Glo Luminescent Cell Viability Assay. (I) Immunoblot analysis of RAW264.7 macrophages treated with LPS (1 μg/ml) for up to 1 h. (J) Quantification of the percentage of RAW264.7 cells with TFEB nuclear localization of the conditions described in (H). Data represent the average of three independent experiments, each done in triplicates ± SEM. Significance was determined using student’s t-test (*p<0.05, **p<0.01, ***p<0.001).

Collectively, both the mammalian and worm results demonstrate an important role for AMPK in the regulation of the innate host immune response through TFEB/TFE3 activation.

Discussion

We have previously shown that loss of FLCN activates AMPK, increasing the resistance to oxidative stress, heat, anoxia, hyperosmotic stresses, and obesity in C. elegans and mammalian models (Possik et al., 2015, Possik et al., 2014, Possik and Pause, 2016, Yan et al., 2016, Yan et al., 2014). Here, we report evidence supporting an evolutionary conserved role of FLCN in innate host defense mediated through AMPK and TFEB/TFE3 activation.

Given that the gene profile upon osmotic stress mimics that of pathogen infection (Irazoqui et al., 2010a), we found a significant overlap in the transcriptional profile in flcn-1 mutant animals when compared to wild-type animals infected with pathogens. We report that flcn-1 mutant animals confer resistance to pathogen infection through nuclear localization and activation of HLH-30. Increased nuclear localization and activation of TFE3 were previously reported in renal tumors from Birt Hogg-Dubé syndrome patients, a syndrome associated with a germline mutation of the FLCN gene (Hong et al., 2010). Subsequent studies further supported a role for FLCN in the cytoplasmic retention of TFE3 and TFEB (Betschinger et al., 2013, Martina and Puertollano, 2013, Petit et al., 2013, Wada et al., 2016). The mechanisms through which TFEB and TFE3 are regulated in response to nutrient status have been characterized. Most studies to date suggested that mTORC1-dependent phosphorylation of TFEB causes cytoplasmic retention of this transcription factor under nutrient-rich conditions. Inhibition of mTORC1 activity upon nutrient starvation has been associated with hypo-phosphorylated forms and nuclear accumulation of TFEB and TFE3 inducing the up-regulation of genes involved in autophagy and lysosomal biogenesis, and thus favoring cell survival and adaption to stress (Efeyan et al., 2013, Martina et al., 2012, Martina and Puertollano, 2013, Petit et al., 2013, Roczniak-Ferguson et al., 2012, Settembre et al., 2012). The link between FLCN and mTOR has been previously proposed, where FLCN was identified as a GTP-Activating Protein (GAP) for Ras-related GTPase (Rag)C/D, and a Guanine Exchange Factor (GEF) for RagA/B, which ultimately activates mTOR (Petit et al., 2013, Tsun et al., 2013). The yeast ortholog of FLCN, Lst7, also acts as a GAP for yeast RagC/D ortholog Gtr2 (Peli-Gulli et al., 2017). Conversely, FLCN-deficient tumors were shown to exhibit activated mTOR while acute loss of FLCN in cellular systems led to mTOR inhibition (Baba et al., 2006, Hasumi et al., 2014, Peli-Gulli et al., 2017, Petit et al., 2013, Tsun et al., 2013), suggesting that FLCN’s role in this process is cell and context-dependent, and might vary in response to different environmental signals.

Our current work reveals that pathogen-induced regulation of TFEB and TFE3 activation appears to have different dynamics than that of starvation-induced regulation. Using both C. elegans and mammalian models, we show that the FLCN/AMPK axis and the mTOR axis impinge differently and independently on TFEB and TFE3 activation status. We show in nematodes that AMPK regulated the nuclear localization of HLH-30 and the transcription of anti-microbial genes. In mammalian cells, we show that AMPK activation led to the transcriptional up-regulation of pro-inflammatory cytokines through the nuclear translocation and activation of TFEB/TFE3. AMPK has been shown to govern lineage specification by promoting autophagy and lysosomal biogenesis through transcriptional mechanisms including TFEB (Young et al., 2016). Although no direct link between AMPK and TFEB has been reported, AMPK was thought to activate TFEB through inhibition of mTORC1 (Young et al., 2016). Conversely, and in support of our observed results herein, it has been shown that while starvation-mediated activation of TFEB/TFE3 involved mTORC1, their pathogen-induced activation appeared to be mTORC1 independent (Pastore et al., 2016).

Bacterivorous nematodes, such as C. elegans induce the expression of transcriptional host-defense responses including the HLH-30/TFEB pathway that promote organismal survival (Engelmann et al., 2011, O’Rourke et al., 2006, Sinha et al., 2012, Troemel et al., 2006, Visvikis et al., 2014). However, these invertebrates appear to lack the NLR and TLR pathogen sensing pathways as well as NF-κB and other transcription factor pathways that regulate innate immunity in higher organisms (Irazoqui et al., 2010b, Ishii et al., 2008). Our findings shed light on an ancient, highly conserved pathogen sensing and signal transduction mechanism, which involves AMPK and the transcription factor TFEB/TFE3. LPS, which is part of the outer membrane of Gram-negative bacteria, was shown to inhibit respiration and energy production in cells and isolated mitochondria (Frisard et al., 2015, Hansen et al., 2015, Kato, 1972, McGivney and Bradley, 1980). We show here, that LPS treatment of macrophages leads to an acute reduction of cellular energy levels resulting in AMPK activation, induction of TFEB/TFE3 and inflammatory cytokines (Figure 6H–K; Figure 3H, I). Therefore, this ancient pathogen-sensing pathway may have evolved by the fact that pathogen infection leads to an energy shortage, which is sensed by cellular AMPK. Activated AMPK will in turn promote TFEB/TFE3 nuclear translocation and induction of an innate host defense.

It remains to be elucidated how exactly loss of FLCN leads to AMPK activation on a mechanistic level. FLCN is a GAP for RagC/D, which ultimately activates mTOR (Petit et al., 2013, Tsun et al., 2013). Loss of FLCN leads to permanent mTOR inhibition with respect to activation of lysosomal biogenesis and autophagy via TFEB/TFE3, whereas mTOR-mediated signaling towards protein synthesis appears not to be affected (Wada et al., 2016). We showed previously that loss of binding of FLCN to FNIP/AMPK via introduction of a phospho-mutant of FLCN as well as knockdown or complete loss of FLCN leads to permanent activation of AMPK with respect to autophagy via ULK-1, mitochondrial biogenesis via PGC-1α, glycolysis and angiogenesis via HIF-1α, glycogen metabolism upon osmotic stress, and resistance to obesity via induction of functional beige adipose tissue (Possik et al., 2015, Possik et al., 2014, Yan et al., 2016, Yan et al., 2014). However, more detailed work needs to be performed to fully understand the role of FLCN binding to FNIP and AMPK and its regulation in the role of AMPK activation.

How AMPK activates TFEB and TFE3 upon depletion of FLCN remains unknown. However, it is likely that AMPK activation under pathogen-induced conditions regulates TFEB and TFE3 activation distinctly from the mTOR pathway. Recent studies have shown that the kinases PLC-1 and DKF-1, the C. elegans orthologs of mammalian PLC and PRKD1/PKD, respectively, are required for HLH-30 activation during infection of nematodes with S. aureus (Najibi et al., 2016). A similar mechanism of TFEB activation in mouse macrophages infected with pathogens involved the PRKCA/PKCa axis demonstrating that TFEB activation in response to pathogen infection is conserved throughout evolution (Najibi et al., 2016). It appears that TFEB/TFE3 are controlled by a panel of kinases and phosphatases that depending on the environmental cues exhibit different downstream responses. Quantitative proteomics have identified over 20 phosphorylation sites on TFEB and TFE3, and although not directly tested in this current study, assessing their direct phosphorylation by AMPK could provide information about the contribution of AMPK in TFEB regulation.

The mechanisms through which TFEB and TFE3 confer pathogen resistance are still being deciphered. While TFEB/TFE3 activation were previously reported not to affect pathogen burden over the course of infection, they appear to regulate the mechanisms of tolerance to infection through autophagy/lysosomal pathways that enhance ability of the host to survive upon pathogen invasion (Visvikis et al., 2014). Moreover, induction of lysosomal pathways have been demonstrated to enhance the phagocytic capacity of innate immune effector cells (Gordon, 2016). In this study, we show that down-regulation of FLCN in murine macrophages enhances their phagocytic activity and prompts a metabolic transformation toward increased cellular bioenergetics, which might further enhance the innate immune response. FLCN/AMPK-mediated increase in autophagic flux and AMPK/TFEB-mediated increase in lysosomal biogenesis are likely to contribute to metabolic fitness of infected cells and increased phagocytosis in macrophages. Interestingly, and in line with our results, it was recently proposed that the activation of the Fcγ receptor in macrophages enhances lysosome-based proteolysis and killing of phagocytosed E. coli and this activation induces the nuclear translocation of TFEB accompanied by an increase the expression of specific lysosomal proteins. Notably, TFEB silencing represses the Fcγ-receptor-mediated enhancements in degradation and bacterial killing (Gray et al., 2016). Hence, further studies are required to elucidate precisely how TFEB/TFE3 regulation through FLCN/AMPK axis affects host tolerance of infection in nematodes and in mammals.

Patients affected with BHD syndrome are at risk of developing bilateral, multifocal renal tumors, skin tumors and lung cysts (Schmidt and Linehan, 2015). In addition, chromosomal translocations leading to TFE3 or TFEB over-activation were reported in sporadic juvenile and advanced renal cell carcinoma (RCC) (Kauffman et al., 2014). Hence, it is tempting to speculate whether loss of FLCN and AMPK activation in humans induce a chronic inflammatory response and thereby promoting cancer progression, similar to reported cancer cases where innate immune response pathway such as NF-κB is over-activated (Karin, 2009).

Furthermore, in this study we place AMPK at the center of FLCN-TFEB/TFE3 axis. Several direct AMPK activators are being developed for treatment of type-2 diabetes, obesity, and metabolic syndrome (Zhang et al., 2009). We propose that some of these specific AMPK activating compounds could be repurposed to enhance host defense against pathogens or treat other immunodeficiency syndromes through AMPK-mediated activation of TFEB/TFE3, providing druggable strategies in innate immune modulation and therapy of bacterial infections. To this end, pharmacological inhibition of mTOR is currently being investigated in human clinical trials to treat age-associated immune dysfunction, also dubbed “immune senescence” (resTORbio, Inc).

STAR methods

Contact for Reagent and Resource Sharing

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Arnim Pause (arnim.pause@mcgill.ca).

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Mice

C57BL/6 Flcn floxed mice (generously provided by Dr. Laura S. Schmidt, NCI, Bethesda, MD, USA) were used in to generate primary MEFs used in this study. Mice were housed in the McGill Animal Care Facility in standard cages with food and water ad libidum. Mice were maintained at 22–24°C on a 12-hr light/12-hr dark cycle. All studies were approved by the Animal Resource Centre at McGill University and comply with guidelines set by the Canadian Council of Animal Care. To generate the MEFs, pregnant mice were sacrificed at day 12.5 of the pregnancy by cervical dislocation to prevent embryos death. The embryos were then dissected out, sacrificed by cutting the head using standard procedures and isolated MEFs were then cultured for in vitro analysis. The sex of the cell lines was not determined as they were isolated from mice at embryonic stage.

C. elegans strains, maintenance, and RNAi treatments

Strains used in this study: Wild-type Bristol (N2), flcn-1(ok975) II, aak-1(tm1944)III; aak-2(ok524)X, flcn-1(ok975)II; aak-1(tm1944)III; aak-2(ok524)X, hlh-30(tm1978)IV, flcn-1(ok975)II; hlh-30(tm1978)IV, sqIs19[hlh-30p::hlh-30::gfp, rol-6(su1006), N2; uthIs202 [Paak-2c:: aak2 (aa1–321)::Tomato::unc-54 3’UTR, rol6], flcn-1(ok975); flcn-1::GFP. Nematodes were maintained and synchronized using standard culture methods (Brenner, 1974). The RNAi feeding experiments were performed, and bacteria transformed with empty vector were used as control. Briefly, young adult animals were placed on NGM plated containing seeded bacteria expressing dsRNA against indicated genes and L4 / young adult F1 progeny was used for phenotypic scoring in all RNAi experiments. Plates were incubated at 20°C.

Bacterial strains

The bacterial strains used in this study are: Escherichia coli OP50; Staphylococcus aureus (MW2); and Pseudomonas aeruginosa (PA14).

Cell Lines and Cell Culture

Primary MEFs were isolated from C57BL/6 E12.5 Flcn floxed mice and were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin and 100 μg/ml streptomycin (Invitrogen). Flcn wild-type and knockout MEFs were generated after immortalization of primary Flcn Flox/Flox MEFs with retroviral infection of SV40 large T (hygromycin B) and retroviral infection of CD8 or CD8-Cre recombinase, followed by FACS sorting of CD8 positive cells. AMPK DKO MEFs and their wild-type counterpart cells were generously provided by Dr. Benoit Viollet (Institut Cochin INSERM, Paris, France). RAW 264.7 cells (termed RAW cells), a murine macrophage cell line (ATCC CRL-24) were generously provided by Dr. C. Krawczyk (McGill University, Montreal, Canada). RAW 264.7 TFEB/TFE3 DKO cells and their EV counterpart cells were generously provided by Dr. Rosa Puertollano (National Institutes of Health, Bethesda, MD, USA). Cell lines were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) (Wisent), 100 U/ml penicillin and 100 μg/ml streptomycin (Invitrogen) in 5% CO₂ at 37°C. MEFs and RAW cells were stably downregulated for TFEB/TFE3 or FLCN, respectively, using the Mission lentivirus shRNA empty vector (shEV), shTFEB (TRCN0000013110; Sigma-Aldrich), shTFE3 (TRCN0000232151; Sigma-Aldrich), or shFLCN (TRCN0000301434; Sigma-Aldrich).

METHOD DETAILS

Pathogen resistance assay

To measure pathogen stress resistance, synchronized L4 worms were transferred to Tryptic Soya Agar (TSA) plates with 8 μg/ml Nalidixic acid that were seeded with 1:50 S. aureus MW2 bacteria incubated at 37°C for 3 h (Powell and Ausubel, 2008). Survival was measured daily by transferring worms onto new plates. To measure stress resistance to P. aeruginosa PA14, synchronized L4 worms were transferred to Slow Killing (SK) plates (Powell and Ausubel, 2008). Worms were transferred to new plates every 24 h to monitor survival. Worms that responded with movement upon being touched by a platinum wire were considered alive. Assays were performed in triplicate plates per condition with 30 animals per plate, in three independent experiments.

HLH-30 nuclear translocation assay

The hlh-30p::hlh-30::GFP was kindly provided by Malene Hansen’s Lab. The flcn-1(ok975); hlh-30p::hlh-30::GFP, aak-2(gt-33); hlh-30p::hlh-30::GFP and aak-1(tm1944);aak-2(gt-33); hlh-30p::hlh-30::GFP strains, respectively, were obtained using standard genetic crossing strategies. 30–40 worm eggs were transferred to 35 mm regular NGM plates seeded with OP50. Synchronized young adult animals expressing the HLH-30:GFP transgene were transferred to TSA plates seeded with 1:20 S. aureus MW2 bacteria on the day of the experiment. Worms displaying HLH-30 translocation were scored. For the let-363 RNAi based experiments, synchronized animals were grown on RNAi plates and were used at the young adult stage for infection with S. aureus MW2 bacteria. Translocation was counted using a fluorescent dissecting microscope at indicated timepoints and imaged using Zeiss confocal laser scanning microscope. Images were taken within the first 5 min because mounting stress also induces HLH-30 nuclear translocation.

RNA extraction and real-time PCR in C. elegans

Synchronized young adult nematodes were exposed to pathogenic S. aureus bacteria or OP50 seeded plates for 4 h, harvested and washed with M9 buffer. Pellets were flash frozen in liquid nitrogen. Total RNA was extracted with Trizol. iScript Supermix from Bio-Rad was used to reverse transcribe 1 μg RNA samples. Bio-Rad SYBR Green mix was used and qPCRs were performed on the Roche LightCycler 480 machine. Three housekeeping genes were used to confirm changes in gene expression, cdc-42, pmp-3 and Y45FD10.4 (Hoogewijs et al., 2008). Primers are available upon request.

Microarray experiment and gene overlap analysis

Synchronized L4/young adult wild-type and flcn-1(ok975) animals were harvested and RNA was extracted using Trizol and purified on Qiagen RNeasy columns. Total RNA samples were then hybridized onto Agilent gene chips. Fold change values were calculated using the mean of both data sets. Agilent files were uploaded into the FlexArray software at Genome Quebec for analysis. Three replicates were normalized and analyzed for each condition. Fold change was determined and p-value was obtained using a standard student’s t-test. Differentially expressed genes were compared to other studies; hyperosmotic stress (Rohlfing et al., 2010) and pathogen infection (Irazoqui et al., 2010a, Troemel et al., 2006) using the “compare two lists” online tool at http://www.nemates.org/MA/progs/Compare.html. The significance of the overlap and enrichment scores was determined via hypergeometric distribution method using http://nemates.org/MA/progs/overlap_stats.html. The number of genes in the C. elegans genome was considered 19,735.

RNA sequencing method

Synchronized wild-type and aak-1(tm1944); aak-2(ok524) animals were harvested at the late L4 stage, washed with M9, and flash frozen in liquid nitrogen. RNA was extracted using Trizol and purified using Qiagen RNeasy columns. RNA samples were processed for RNA-seq analysis at Novogene Inc.

RNA sequencing analysis

RNA samples were processed for RNA sequencing analysis at Novogene Inc. Briefly, the clustering of the index-coded samples was performed on a cBot Cluster Generation System using HiSeq PE Cluster Kit cBot-HS (Illumina). Quality control Raw data of fastq format were firstly processed through in-house perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. All the downstream analyses were based on the clean data with high quality. Reads mapping to the reference genome. Index of the reference genome was built using Bowtie v2.2.3 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. TopHat was selected as the mapping tool. Quantification of gene expression level HTSeq v0.6.1 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. For DESeq with biological replicates, differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq R package (1.18.0). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the GOseq R package, in which gene length bias wascorrected. GO terms with corrected Pvalue less than 0.05 were considered significantly enriched by differential expressed genes. KOBAS software was used to test the statistical enrichment of differential expression genes in KEGG pathways. PPI analysis of differentially expressed genes was based on the STRING database, which known and predicted Protein-Protein Interactions. For the species existing in the database, the networks were constructed by extracting the target gene list from the database; Otherwise, Blastx (v2.2.28) was used to align the target gene sequences to the selected reference protein sequences, and then the networks were built according to the known interaction of selected reference species.

Reagents, chemicals, and antibodies

LPS derived from Escherichia Coli endotoxin (0111:B4, InvivoGen, San Diego, CA, USA) was dissolved in PBS (5 mg/ml) by sonication for 2 min, aliquoted and stored at −80°C until use. All LPS preparations were free of protein or lipoprotein contaminants. LPS was used at a final concentration of 1 μg/ml. Recombinant mouse TNF-α was obtained from Biolegend (#575206) with a stock concentration of 0.2 mg/ml and was used at a final concentration of 10 ng/ml dissolved in 10% DMEM. GSK-621 was obtained from APExBIO or Selleckchem (Houston, Texas, USA) and dissolved in DMSO to a stock concentration of 30 mM and used at a final concentration of 30 μM for MEFs. Torin1 was obtained from Tocris Bioscience (Bristol, UK) and dissolved in DMSO to a stock concentration of 1 mM and used at a final concentration of 1 μM. The final DMSO concentration never exceeded 0.1% and this concentration was shown to have no detrimental effect on all the studied cells.

The mouse FLCN polyclonal antibody was generated by the McGill animal resource center services through injecting purified GST-FLCN recombinant protein in rabbits. ß-Actin (SC-47778; Santa Cruz Biotechnology), Tubulin (T9026; Sigma-Aldrich), LaminA (SC-71481; Santa Cruz Biotechnology), AMPKα (2532; Cell Signaling Technology), human FLCN 3697; Cell Signaling Technology), p-AMPKα (Thr172) (2531; Cell Signaling Technology), ACC (3676; Cell Signaling Technology), p-ACC (S79) (3661; Cell Signaling Technology), TFEB (A303–673A; Bethyl Laboratories), TFE3 (14779S; Cell Signaling Technology and HPA023881; Sigma-Aldrich), p70S6K (2708; Cell Signaling Technology), p-p70S6K (9205; Cell Signaling Technology), S6 (2217; Cell Signaling Technology), p-S6 (4858; Cell Signaling Technology), 4EBP1 (9644; Cell Signaling Technology), p-4EBP1 (9456; Cell Signaling Technology) antibodies are commercially available.

Quantitative real-time RT-PCR in mammalian cells

FLCN wild-type, FLCN KO MEFs, and RAW 264.7 cells were seeded in triplicates in 6-well plates at 3 × 10⁵ cells per well in DMEM medium supplemented with 10% FBS. After incubation for 24 h at 37°C, 5% CO₂, cells were treated with TNF-α, LPS, GSK-621 or vehicle for 2, 3, or 24 h. Cells were then collected, and total RNA was isolated and purified using Total RNA Mini Kit (Geneaid) according to the manufacturer’s instructions. For quantitative real-time PCR analysis, 1 μg of total RNA was reverse-transcribed using the SuperScript III kit (Invitrogen). SYBR Green reactions using the SYBR Green qPCR supermix (Invitrogen) and specific primers (available upon request) were performed using an AriaMX Real-time PCR system (Agilent Technologies). Relative expression of mRNAs was determined after normalization against housekeeping gene RPLP0 or B2M.

Mouse Protein Cytokine Array

RAW 264.7 cells were seeded in triplicates in 6-well plates at 1 × 10⁶ cells per well in DMEM medium supplemented with 10% FBS. After incubation for 24 h at 37 °C, 5% CO₂, cells were treated with LPS or GSK-621 or vehicle for 3, or 24 h, and the conditioned medium was harvested and centrifuged at 1,500 × g to remove cell debris. 32 cytokine/chemokine/growth factor biomarkers were simultaneously quantified by using a Discovery Assay® called the Mouse Cytokine Array/Chemokine Array 32-Plex (Eve Technologies Corp, Calgary, AB, Canada). The multiplex assay was performed by using the Bio-Plex™ 200 system (Bio-Rad Laboratories, Inc., Hercules, CA, USA), and a Milliplex Mouse Cytokine/Chemokine kit (Millipore, St. Charles, MO, USA) according to the manufacturers protocol. The 32-plex consisted of Eotaxin, G-CSF, GM-CSF, IFNγ, IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-9, IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-15, IL-17, IP-10, KC, LIF, LIX, MCP-1, M-CSF, MIG, MIP-1α, MIP-1β, MIP-2, RANTES, VEGF. The change in the cytokine levels in FLCN KO medium was normalized against their respective wild-type medium.

Protein extraction and immunoblotting

For AMPK immunoblotting, cells were washed twice with cold PBS, lysed in AMPK lysis buffer (10 mM Tris-HCl (pH 8.0), 0.5 mM CHAPS, 1.5 mM MgCl₂ 1 mM EGTA, 10% glycerol, 5 mM NaF, 0.1 mM Na₃VO₄, 1 mM benzamidine, 5 mM NaPPi), supplemented with complete protease inhibitor (Roche) and DTT (1 mM), and cell lysates were cleared by centrifugation at 13000 × g. For all other immunoblotting, cells were washed twice with cold PBS and lysed directly in Laemmli buffer (62.5 mM Tris-HCl (pH 6.8), 2% (w/v) sodium dodecyl sulphate, 10% (v/v) glycerol, 5% (v/v) β-mercaptoethanol and 0.01% (w/v) bromophenol blue). Proteins were separated on SDS-PAGE gels and revealed by western blot using the antibodies listed above.

Subcellular fractionation

Cells were washed and resuspended in PBS and pelleted 5 min at 300 × g at 4°C. The pellet was lysed in NP-40 lysis buffer (50 mM Tris-HCl (pH 7.4), 0.5 % NP-40, 5 mM MgCl₂ 140 mM KCl, 5 mM NaF, 0.1 mM Na₃VO₄, 1 mM benzamidine, 5 mM NaPPi, 1x protease inhibitors) and pelleted 5 min at 1000 × g. Supernatant was collected as the cytosolic fraction. The pellet was lysed in nuclear lysis buffer (25 mM Tris-HCl (pH 7.4), 0.5 % Triton X-100, 0.5% SDS, 5 mM MgCl₂ 140 mM KCl, 5 mM NaF, 0.1 mM Na₃VO₄, 1 mM benzamidine, 5 mM NaPPi, 1 × protease inhibitors), sonicated 15 min with 30 sec bursts and collected as the nuclear + membrane fraction.

Immunofluorescence

Cells were washed with PBS and fixed in petri dishes with 3.7% formaldehyde at room temperature for 30 min. After fixation, cells were washed twice with PBS and then permeabilized with 0.3% Triton X-100 in PBS at room temperature for 5 min. Cells were incubated in 10% BSA in PBS for 1 h and then with TFEB or TFE3 primary antibody in 1.5% BSA in PBS for 2 h at 37°C. Cells were washed three times with PBS and incubated with the corresponding secondary antibodies conjugated to Alexa Fluor 488 in 1.5% BSA in PBS for 30 min at 37°C. Cells were washed three times with PBS and incubated with DAPI (0.1 μg/ml) in PBS for 15 min at room temperature. PBS-washed dishes were covered with cover slips and observed with Axioskop microscope (Zeiss).

Metabolic Assays

Glucose production and lactate consumption was measured using a NOVA Bioanalysis flux analyzer or the Eton Bioscience kit (Eton Bioscience, Charlestown, MA, USA). Briefly, cells were plated at 500,000 cells/well in triplicates in 6-well plates in DMEM growth medium for 24 h. Then The conditioned media was collected, spun down at 13×g for 5 mins, and transferred to new tubes were the media was analyzed using the NOVA Bioanalysis flux analyzer. OCR and the ECAR of cells were measured using an XF96 Extracellular Flux Analyzer (Seahorse Bioscience, Boston, MA, USA). In brief, EV or shFLCN RAW264.7 were plated at 100,000 cells/well in growth medium for 24 h. After 24 h, cells were incubated in non-buffered DMEM containing 25 mM glucose and 2 mM glutamine in a CO2-free incubator at 37°C for 2 h to allow for temperature and pH equilibration before loading into the XF96 apparatus. XF assays consisted of sequential mix (3 min), pause (3 min), and measurement (5 min) cycles, allowing for determination of OCR/ECAR every 10 min. After establishing a baseline, Oligomycin (10 uM), FCCP (15 μM), and Rotenone/Antimycin A (1 μM, and 10 μM, respectively) were added sequentially.

ATP quantification

Cells were plated at 4,000 cells/well in triplicates in 96-well plates. After 24 h, cells were lysed and mixed for 10 min according to manufacturer’s instructions (CellTiter-Glo luminescent cell viability assay, Promaga). Luminescence was measured using Fluostar Omage (BMG Labtech) directly in plates.

Phagocytosis Assay

Phagocytosis in EV or shFLCN RAW264.7 cells was assessed using Red pHrodo S.aureus BioParticles conjugate assay (Thermofisher) according to the manufacturer’s protocol. In brief, EV or shFLCN RAW264.7 were plated in triplicates in 96-well plates at 80,000 cells/well in growth medium for 2 h before treatment. After 2 h, cells were treated with the BioParticles (after homogenization in serum-free DMEM) at a final concentration of 1 mg/ml and incubated at 37 °C for 3 h. Subsequently, cells were collected and analyzed using FACSDiva analyzer (Becton Dickison).

QUANTIFICATION AND STATISTICAL ANALYSIS

Data are expressed as mean ± SEM. Statistical analyses for all data were performed using student’s t-test for comparisons between 2 groups, one-way ANOVA for comparisons between 3 or more groups, and Log-rank Mantel Cox test for survival plots, using GraphPad Prism 7 software. The data is assumed normal as tested by the Shapiro and Wilk normality test. Statistical significance is indicated in figures (*p<0.05, **p<0.01, ***p<0.001, ****p<0.0001) or included in the supplemental tables. In vitro studies were biologically repeated at least three times in triplicates. The numbers of animals in each experiment are indicated in the figure legends.

DATA AND SOFTWARE AVAILABILITY

The RNA sequencing and microarray data are deposited at Gene Expression Omnibus (GEO) accession number: GSE126656.

Supplementary Material

Table S1

Table S1, related to Figures 1, 2, and 5: Mean survival on pathogens: results and statistical analysis

Sheet 1: Mean survival on S. aureus: results and statistical analysis.

Sheet 2: Mean survival on P. aeruginosa: results and statistical analysis.

Table S3

Table S3, related to Figures 1, 2, and 5: Gene expression analysis

Sheet 1: List of genes upregulated in flcn-1(ok975) mutant animals in comparison to wild-type at basal level.

Sheet 2: GO analysis of genes upregulated in flcn-1(ok975) mutant animals at basal level.

Sheet 3: List of genes with known antimicrobial and defense functions upregulated in flcn-1(ok975) at basal level; selection based on GO annotations.

Sheet 4: List of genes downregulated in flcn-1(ok975) mutant animals at basal level.

Sheet 5: GO analysis of genes downregulated in flcn-1(ok975) mutant animals at basal level.

Sheet 6: Overlapping genes upregulated in flcn-1(ok975) animals and animals infected with

S. aureus.

Sheet 7: Overlapping genes upregulated in flcn-1(ok975) animals and animals infected with

P. aeruginosa.

Sheet 8: List of overlapping genes upregulated in flcn-1(ok975) mutant animals at basal level and S. aureus hlh-30-dependent genes.

Sheet 9: Genes upregulated in wild-type animals treated with S. aureus for 4 h in comparison to animals grown on OP50 E. Coli.

Sheet 10: List of genes downregulated in wild-type animals treated with S. aureus for 4 h in comparison to animals grown on OP50.

Sheet 11: List of genes upregulated in aak-1(tm1944); aak-2(ok524) animals in comparison to wild-type animals at basal level.

Sheet 12: List of genes downregulated in aak-1(tm1944); aak-2(ok524) animals in comparison to wild-type animals at basal level.

Sheet 13: List of genes downregulated in aak-1(tm1944); aak-2(ok524) animals treated with

S. aureus in comparison to wild-type animals treated with S. aureus.

Sheet 14: AMPK-dependent genes determined by overlap between genes upregulated in wild-type animals upon S. aureus infection and downregulated in infected aak-1(tm1944); aak-2(ok524) mutant animals.

Sheet 15: GO analysis of AMPK-dependent genes obtained by overlap between genes upregulated in wild-type animals upon S. aureus infection and downregulated in infected aak-1(tm1944); aak-2(ok524) mutant animals. This sheet includes a histogram of functional gene ontology analysis of genes induced by S. aureus in wild-type animals and downregulated in aak-1(tm1944); aak-2(ok524) mutant animals upon infection.

Sheet 16: Genes downregulated in aak-1(tm1944); aak-2(ok524) and hlh-3(tm1978) mutant animals upon S. aureus infection in comparison to wild-type.

Sheet 17: GO analysis of the overlap between infection genes regulated by AMPK and HLH-30.

Table S2

Table S2, related to Figure 2: Mean survival on 400mM NaCl: results and statistical analysis

Key Resource Table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Mouse monoclonal ß-Actin	Santa Cruz	Cat#47778
Mouse monoclonal Tubulin	Sigma-Aldrich	Cat#T9026
Rabbit polyclonal Lamin A	Santa Cruz	Cat#71481
Rabbit polyclonal AMPKα	Cell Signaling	Cat#2532
Rabbit polyclonal p-AMPKα (Thr172)	Cell Signaling	Cat#2531
Rabbit monoclonal ACC	Cell Signaling	Cat#3676
Rabbit polyclonal p-ACC (S79)	Cell Signaling	Cat#3661
Rabbit polyclonal TFEB	Bethyl Laboratories	Cat#A303–673A
Rabbit polyclonal TFE3	Cell Signaling	Cat#14779S
Rabbit polyclonal TFE3	Sigma-Aldrich	Cat#HPA023881
Rabbit monoclonal p70S6K	Cell Signaling	Cat#2708
Rabbit polyclonal p-p70S6K (Thr389)	Cell Signaling	Cat#9205
Rabbit monoclonal S6	Cell Signaling	Cat#2217
Rabbit monoclonal p-S6	Cell Signaling	Cat#4858
Rabbit monoclonal 4EBP1	Cell Signaling	Cat#9644
Rabbit monoclonal p-4EBP1	Cell Signaling	Cat#9456
Rabbit monoclonal FLCN	Cell Signaling	Cat#3697
Mouse polyclonal FLCN	McGill animal resource center, Yan et. al., 2014	N/A
Bacterial and Virus Strains
Staphylococcus aureus MW2	Laboratory of Dr. Joaquin Madrenas	NCBI:txid196620
Pseudomonas aeruginosa PA14	Laboratory of Dr. Eric Deziel	NCBI:txid652611
Escherichia coli OP50	Laboratory of Dr. Richard Roy	NCBI:txid 637912
pLKO.1-puro Empty Vector	(Stewart et. al., 2003)	RRID:Addgene_8453)
shRNA against TFEB cloned into the pLKO.1-puro non-Mammalian vector	Sigma-Aldrich	TRCN0000013110
shRNA against TFE3 cloned into the pLKO.1-puro non-Mammalian vector	Sigma-Aldrich	TRCN0000232151
shRNA against FLCN cloned into the pLKO.1-puro non-Mammalian vector	Sigma-Aldrich	TRCN0000301434
Chemicals, Peptides, and Recombinant Proteins
LPS from Escherichia Coli endotoxin	InvivoGen	tlrl-eblps
Recombinant mouse TNF-α	Biolegend	CASt# 575206
GSK-621	APExBIO	CAS# 1346607-05-3
GSK-621	Selleckchem	CHIR-99021
Torin1	Tocris Bioscience	CAS#1222998-36-8
Critical Commercial Assays
Mouse Protein Cytokine Array	Eve Technologies Corp, Calgary, Canada	N/A
NOVA Bioanalysis flux analyzer	Eton Bioscience, Charlestown, MA, USA	N/A
XF96 Extracellular Flux Analyzer	Seahorse Bioscience, Boston, MA, USA	N/A
CellTiter-Glo luminescent cell viability assay	Promega	CAT#TB288
Red pHrodo S.aureus BioParticles conjugate assay	Thermofisher	CAT#A10010
Deposited Data
RNA-seq data (Raw data)	Gene Expression Omnibus (GEO)	GSE126656
RNA-seq data (analyzed data)	GEO	GSE126656
Microarray data (Raw data)	GEO	GSE126656
Microarray data (analyzed data)	GEO	GSE126656
Experimental Models: Cell Lines
Mouse: FlcnFlox/Flox MEFs	Laboratory of Dr. Laura S. Schmidt	N/A
Embryonic Mouse: Flcn+/+ and Flcn−/− MEFs	Yan et. al., 2014	N/A
Embryonic Mouse: Flcn+/+ and Flcn−/− MEFs + shTFEB/TFE3	This paper	N/A
Embryonic Mouse: Flcn+/+ and Flcn−/− MEFs + shEV	This paper	N/A
Embryonic Mouse: AMPK+/+ and AMPK−/− MEFs	Laboratory of Dr. Benoit Viollet	N/A
Male Mouse: RAW 264.7	ATCC	TIB-71
Male Mouse: RAW 264.7 + shFLCN	This paper	N/A
Male Mouse: RAW 264.7 + shEV	This paper	N/A
Male Mouse: TFEB−/−/TFE3−/− RAW 264.7	Laboratory of Dr. Rosa Puertollano (Pastore et. al, 2016)	N/A
Male Mouse: RAW 264.7 + lenticrisprv2-EV	Laboratory of Dr. Rosa Puertollano (Pastore et. al, 2016)	N/A
Male Mouse: RAW 264.7 + lenticrisprv2-EV + shFLCN	This paper	N/A
Male Mouse: TFEB−/−/TFE3−/− RAW 264.7 + shFLCN	This paper	N/A
Experimental Models: Organisms/Strains
C.elegans: wild-type Bristol (N2)	Laboratory of Dr. Richard Roy	N/A
C.elegans: flcn-1 (ok975) II	Caenorhabditis Genetics Center	RB1035
C.elegans: aak-1(tm1944)III; aak-2(ok524)X	Laboratory of Dr. Richard Roy	AGD397
C.elegans: flcn-1(ok975)II; aak-1(tm1944)III; aak-2(ok524)X	Possik et al., 2015	N/A
C.elegans: hlh-30(tm1978)IV	Laboratory of Dr. Malene Hansen	JIN1375
C.elegans: flcn-1(ok975)II; hlh-30(tm1978)IV	This paper	N/A
C.elegans: sqIs19[hlh-30p::hlh-30::gfp, rol-6(su1006	Laboratory of Dr. Malene Hansen	MAH235
C.elegans: N2; uthIs202 [Paak-2c:: aak2 (aa1-321)::Tomato::unc-54 3’UTR, rol6]	Caenorhabditis Genetics Center	WBM60
C.elegans: flcn-1(ok975); flcn::GFP	Overexpression of FLCN-1 co-injected with pRF4 (rol-6) and pCFJ90 (Pmyo-2::mCherry::unc-54utr)	N/A
Oligonucleotides
Primers	See Table S4	N/A
Recombinant DNA
Software and Algorithms
FlexArray	N/A	Genome Quebec
GraphPad Prism 7	N/A	N/A
Nemates Software	N/A	http://nemates.Org/MA/progs/overlap_stats.html
WBcel235 (Ensembl release 67)	Novogene inc.	N/A
Illumina bcl2fastq software	Novogene inc.	N/A
DEGseq 1.12.0	Novogene inc.	N/A
GOseq R package	Novogene inc.	N/A
KEGG	Novogene inc.	http://www.genome.jp/kegg/)
Blastx (v2.2.28)	Novogene inc.	N/A
Other