bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 TFAP2 transcription factors are

2 regulators of lipid droplet biogenesis 3 4 5 Cameron C Scott1, Stefania Vossio1, Jacques Rougemont2, Jean Gruenberg1,3 6 7 8 9 10 11 12 1. Department of Biochemistry, University of Geneva, 30 quai Ernest Ansermet 13 1211 Geneva 4, Switzerland 14 2. Department of Theoretical Physics, University of Geneva, 24 quai Ernest Ansermet 15 1211 Geneva 4, Switzerland 16 17 3. To whom correspondence should be addressed: [email protected] 18 19 20 21 22

1 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 Abstract

2 How trafficking pathways and organelle abundance adapt in response to metabolic

3 and physiological changes is still mysterious, although a few transcriptional regulators of

4 organellar biogenesis have been identified in recent years. We previously found that the Wnt

5 signaling directly controls lipid droplet formation, linking the cell storage capacity to the

6 established functions of Wnt in development and differentiation. In the present paper, we

7 report that Wnt-induced lipid droplet biogenesis does not depend on the canonical TCF/LEF

8 transcription factors. Instead, we find that TFAP2 family members mediate the pro-lipid

9 droplet signal induced by Wnt3a, leading to the notion that the TFAP2 transcription factor

10 may function as a “master” regulator of lipid droplet biogenesis.

11 12 13

14 Main Text

15 Cellular adaptation to a changing local environment is imperative for survival and

16 proliferation. This is affected through a collection of sensing and signalling pathways that

17 integrate information about the local environment and induce the requisite changes in various

18 cellular programs that control organelle abundance and function, through multiple routes,

19 including the modulation of transcription. In recent years, several transcriptional ‘master

20 regulators’ of organellar biogenesis have been reported for mitochondria (Jornayvaz and

21 Shulman, 2010), autophagosomes (Kang et al., 2012, Chauhan et al., 2013) and lysosomes

22 (Sardiello et al., 2009). While the functional details of this control are still under

23 investigation, coordinated transcriptional control of specific organelles is an emerging theme

24 in cell biology.

25 Lipid droplets are primary storage organelle for neutral lipids in the cell (Meyers et

26 al., 2017). Intriguingly, the number and nature of these organelles vary greatly, both over

2 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 time within a cell, and between cell types (Thiam and Beller, 2017). While a major function

2 of lipid droplets is clearly as the storehouse of triglycerides and sterol esters, the diversity and

3 variation of this organelle likely reflect the number of reported alternate functions of lipid

4 droplets such as regulation of inflammation, general metabolism, and host-pathogen interplay

5 (Barisch and Soldati, 2017, Melo and Weller, 2016, Konige et al., 2014). Despite the

6 recognized importance of this organelle in health and disease, little is known of the signalling

7 systems or proximal transcriptional regulators that control lipid droplet biogenesis, function

8 and turnover in cells.

9 Recently, we used genome-wide, high-content siRNA screens to identify genes that

10 affect cellular lipids. This analysis revealed that the Wnt ligand can potently stimulate lipid

11 droplet accumulation in multiple cell types through upstream elements of the Wnt signalling

12 pathway, including the canonical surface receptors and adenomatous polyposis coli (APC), a

13 component of the destruction complex (Scott et al., 2015). Therefore, to further characterize

14 the signalling cascade leading to the accumulation of lipid droplets after Wnt addition, we

15 tested the key components from the canonical Wnt signalling pathway for a role in lipid

16 droplet regulation, starting with the core enzyme of the destruction complex, GSK3B (Fig.

17 1A). We found that overexpression of both the wild-type, and the constitutively-active S9A

18 (Stambolic and Woodgett, 1994) mutant of GSK3B were capable of attenuating lipid droplet

19 accumulation in response to Wnt3a-treatment (Fig. 1B), consistent with the function of

20 GSK3B activity as a negative regulator of Wnt signalling. Further, siRNAs to GSK3B were

21 sufficient to induce lipid droplet accumulation (Fig. 1C) — much like we had shown after

22 gene silencing of APC, another member of the destruction complex (Scott et al., 2015).

23 We next investigated the possible role of the downstream targets of the Wnt pathway

24 at the transcription level. Surprisingly, siRNAs to TCF/LEF transcription factors relevant in

25 canonical Wnt signalling, failed to affect lipid droplet accumulation - and yet there is no

3 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 doubt that lipid droplet accumulation in response to Wnt3a is transcriptionally mediated.

2 Indeed, the expression of SOAT1, DGAT1 and DGAT2, which encode key-enzymes of lipid

3 droplet formation, increased in response to Wnt3a-treatment (Scott et al., 2015). In addition,

4 silencing these genes inhibited lipid droplet accumulation in response to Wnt3a, most

5 potently in combination with each other (Fig. 1D), or with specific inhibitors to DGAT1 or

6 SOAT (Fig. 1E, Fig. 1 - Fig.Sup. 1A). Finally, Wnt3a-induced lipid droplets were decreased

7 after treatment with the general inhibitor of transcription Actinomycin D (Fig. 1E). While

8 these data altogether confirmed that the Wnt pathway was mediating the pro-lipid droplet

9 signal, our inability to link lipid droplet induction to TCF/LEF led us to consider the

10 possibility that a branching signalling path, under the control of GSK3B and/or ß-catenin but

11 not the canonical Wnt transcription factors TCF/LEF, was inducing the accumulation of lipid

12 droplets in cells. To explore this possibility, we initiated several parallel and complementary

13 systems-level analyses with the aim to identify the transcriptional regulators proximal to lipid

14 droplet biogenesis.

15 To better understand the nature of the pro-lipid droplet signal induced by Wnt, we

16 revaluated the involvement of individual components of the Wnt signalling pathway in the

17 induction of lipid droplet accumulation. First, we tested the ability of each of the 19 human

18 Wnt ligands to induce lipid droplet accumulation in L Cells by transfection and autocrine or

19 paracrine induction of the Wnt pathway (Fig. 1F-G). Wnt ligands displayed a broad, but not

20 universal capacity to induce lipid droplet accumulation that paralleled both their evolutionary

21 pedigree (Fig. 1F), and previously reported abilities to activate canonical Wnt signalling as

22 measured by a TCF/LEF reporter system (Najdi et al., 2012). This confirmed that the pro-

23 lipid droplet signal was indeed transiting, at least initially, through canonical Wnt signalling

24 components.

4 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 To systematically assess the involvement of the remaining components of the Wnt

2 pathway for involvement in the lipid droplet response, we performed a targeted screen for

3 factors influencing lipid droplet accumulation using a library of 73 compounds selected for

4 known interactions with elements of the Wnt pathway (see Methods). We tested the library in

5 both, Wnt3a-stimulated conditions to assess any inhibitory activity of lipid droplet

6 accumulation, and unstimulated conditions to identify compounds with the ability to induce

7 the phenomenon (Fig. 1 – Source Data 1). Indeed, treatment with several compounds

8 reported to activate the Wnt pathway induced lipid droplet accumulation, such as BML-284

9 (activator of ß-catenin (Liu et al., 2005)), doxorubicin (activator of Wnt signalling (Dai et al.,

10 2009)), and forskolin (activation via PKA-Wnt crosstalk (Zhang et al., 2014)) (Fig. 1H-I).

11 Conversely, known inhibitors of the pathway such as Tricostatin A (epigenetic regulator of

12 DKK1 (Vibhakar et al., 2007)) hexachlorophene (ß-catenin inhibitor (Park et al., 2006)), and

13 niclosamide (inducer of LRP6 degradation (Lu et al., 2011)) significantly decreased the

14 appearance of lipid droplets in response to Wnt3a (Fig. 1H-I).

15 While these data certainly confirm the role of Wnts in regulating lipid droplets, they

16 did not reveal the pathway linking the Wnt destruction complex to the transcriptional changes

17 we observed (Fig. 1A-E, (Scott et al., 2015)). Given these results, and the large number of ß-

18 catenin-independent targets of the destruction complex (Kim et al., 2009), we initiated

19 several strategies to identify candidate regulators, in particular the proximal transcription

20 factors directly upstream from lipid droplet biogenesis. Our aim was to identify factors linked

21 to Wnt signalling and to characterize the signalling pathway from ligand-stimulation to lipid

22 droplet accumulation.

23 We first started by taking the subset of genes annotated as ‘transcription factor

24 activity’ (GO:0000988) from our primary genome-wide siRNA screen data (Scott et al.,

25 2015) to identify transcription factors that influenced cellular cholesterol levels in the cell

5 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 (Fig. 2 - Fig.Sup. 1A; Fig. 2 – Source Data 1). While several of these candidates have well

2 established roles in regulating general proliferation (i.e. MACC1, JDP2, SP3, TP53, ZNF217,

3 TAF1), or links to the WNT pathway in keeping with our findings (i.e. GLI3, SIX2, SOX9,

4 FOXK2, BARX1), we were particularly interested in identifying candidates with reported

5 functions in lipid metabolism. The latter subset included ARNT2, STAT3, KDM3A, ATF5,

6 KLF5, KLF6 and members of the TFAP2 (AP-2) transcription factor family (see below).

7 As a second approach to search for candidate transcriptional regulators of lipid

8 droplets we compared existing transcriptome data of Wnt3a-treated cells with that of other

9 conditions known to induce the accumulation of lipid droplets in tissue culture cells. It is

10 well-established in the formation of lipid droplets is stimulated artificially by the addition of

11 exogenous fatty acids, and the process has been studied at the transcriptional level in multiple

12 studies. We therefore combined our Wnt3a gene array data (Scott et al., 2015) with three

13 published datasets of mRNA levels after treatment of cells with fatty acids or knockout of

14 lipid droplet regulatory factors (Li et al., 2010, Lockridge et al., 2008, Shaw et al., 2013). Our

15 rationale was to identify the relevant transcription factors required for the induction of lipid

16 droplet biogenesis by inferring from the expression data what is the common set of

17 transcription factors active in response to Wnt3a and / or to the modulation of lipid droplets

18 by fatty acid treatment or gene knockout. This analysis involved testing for over-

19 representation of genes annotated to be regulated by a specific transcription factor in the set

20 of the most perturbed genes after either treatment. Wnt3a-stimulation influenced a larger

21 number of transcriptional regulators (172) as compared to lipid droplet modulation (91), but

22 the vast majority of this subset (>75%) were also changed by Wnt3a (Fig. 2 - Fig.Sup. 1B).

23 With this approach, we found that many candidate transcription factors known to function in

24 both lipid metabolism and adipogenesis were influenced by Wnt3a-treament and fatty acid

6 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 perturbation (Fig. 2 – Source Data 2), including TFAP2A (p-value Wnt3a treatment: 3.5 x 10-

2 9; p-value fatty acid perturbation: 4.8 x 10-4).

3 As a third systems-level approach, we undertook a direct examination of the promotor

4 regions of annotated lipid droplet proteins as was used by Sardiello and colleagues to identify

5 the transcription factor TFEB and the CLEAR element as master regulators of lysosome

6 biogenesis (Sardiello et al., 2009). We collected and examined the upstream promoter

7 sequences for the 145 proteins annotated as ‘Lipid Droplet’ (GO:0005811; Fig. 2 – Source

8 Data 3) and tested for over-represented sequence motifs. Among the most enriched motifs in

9 the upstream promotor region of lipid droplet genes were motifs identified as TFAP2A

10 (pValue: 9.0 x 10-3) and TFAP2C (pValue: 1.8 x 10-5) binding sites (Fig. 2 - Fig.Sup. 2A).

11 Further analysis revealed that 74 of the ‘Lipid Droplet’ proteins contained at least one of the

12 TFAP2A or TFAP2C annotated binding sites (Fig. 2 - Fig.Sup. 2B, Fig. 2 - Source Data 3)

13 which were generally present within the first few hundred base-pairs from the start site (Fig.

14 2 - Fig.Sup. 2C).

15 The TFAP2 (AP-2) family of basic helix-span-helix transcription factors have been

16 long recognized to play key roles during development. Yet, little else is known regarding

17 their function in adult animals where these proteins are expressed, although various TFAP2

18 homologs have been linked to tumour progression in cancer models (Eckert et al., 2005). The

19 family consists of five proteins in human and mouse, and are thought to form homo- and

20 heterodimers that bind to similar promotor sequences albeit with different affinities. Given

21 our observation that downstream genes known to be regulated by TFAP2 family members

22 change in response to Wnt3a (Fig. 2 - Fig.Sup. 1B), that the silencing of TFAP2 genes can

23 regulate cholesterol amounts in cells (Fig. 2 - Fig.Sup. 1A), and that TFAP2 consensus sites

24 are over-enriched in the promoter sequences of genes encoding for lipid droplet proteins (Fig.

25 2 - Fig.Sup. 2A), we began to suspect that TFAP2 proteins mediate the pro-lipid droplet

7 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 signal induced by Wnt3a. This notion was further buttressed by previous studies showing that

2 TFAP2A directly interacts with both ß-catenin and APC (Li et al., 2009, Li et al., 2015)

3 making this family of transcription factors our leading candidate for mediating the pro-lipid

4 droplet signalling activity of Wnt3a.

5 To gain a detailed description of the transcriptional changes in the context of TFAP2,

6 Wnt and lipid droplets, we performed an RNAseq determination of mRNA levels in cells

7 treated with Wnt3a for short times (2h and 6h) with the aim to identify early factors of the

8 transcriptional control relevant for lipid droplet biogenesis (Scott and Gruenberg, 2018). As

9 expected, a pathway analysis of the most responsive genes at 2h found over-representation of

10 terms related to mRNA processing, DNA binding and transcriptional regulation (Fig. 2A),

11 consistent with the expected nature of the early Wnt3a-responsive genes. By 6h post-Wnt3a

12 treatment, transcriptional regulators were still over-represented, but additional terms

13 reflecting downstream effector pathways were present, including those related to glucose

14 metabolism and endosomal trafficking, as well as fatty acid and cholesterol related genes

15 (Fig. 2A) consistent with our previous findings (Scott et al., 2015). Indeed, the RNAseq

16 analysis found that SREBF1 (Sterol Regulatory Element Binding Transcription Factor 1)

17 mRNA levels were the most decreased of any transcription factor (0.60 of control) at the later

18 time point (Fig. 2B).

19 Intriguingly, the most upregulated transcription factor at early times was DDIT3 (DNA

20 Damage Inducible Transcript 3, also known as CHOP) — and increased DDIT3 could readily

21 be detected at the mRNA and protein level (Fig. 2 - Fig.Sup. 3A-B). This member of the

22 CCAAT/enhancer-binding (CEBP) protein family is a potent and direct inhibitor of SREBF1

23 transcription (Chikka et al., 2013) and a known regulator of cellular lipid metabolism. While

24 this interaction may contribute to the decrease in cellular free cholesterol and the

25 downregulation of cholesterol metabolic enzymes after Wnt3a treatment (Fig. 2A, (Scott et

8 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 al., 2015)), overexpression of constitutively-active SREBF1 truncations (Shimano et al.,

2 1997) had essentially no effect on the formation of lipid droplets, whether Wnt3a was present

3 or not (Fig. 2 - Fig.Sup. 4A). Neither did treatment with the S1P/SREBF1 inhibitor PF-

4 429242 (Hawkins et al., 2008) (Fig. 2 - Fig.Sup. 4B) in either Wnt3a-stimulated, or naïve

5 conditions. These observations suggest that while relevant to the observed changes in cellular

6 cholesterol homeostasis generated by Wnt3a, putative DDIT3-induced changes in SREBF1

7 expression have no significant role in lipid droplet accumulation in response to Wnt3a.

8 Along with DDIT3 and SREBF1, our list of early Wnt3a-responsive transcription

9 factors includes several that have known functions in regulating cellular lipid homeostasis

10 such as CEBPB and CEBPE, KLF5, KLF10, PPARG, MLXIPL, and PER2. Our list also

11 included the TFAP2 family member TFAP2C, which our datamining strategies had already

12 identified as involved in lipid homeostasis, and as a candidate transcription factor controlling

13 lipid droplet biogenesis. In fact, 6h post-Wnt3a TFAP2C (1.72-fold) was among the most

14 upregulated transcription factors (Fig. 2B).

15 Given that our datamining efforts identified TFAP2 family members as putative

16 transcription factors regulating lipid droplet proteins and that TFAP2C was among the most

17 upregulated transcription factors in response to Wnt3a (Fig. 2B), we next investigated

18 whether TFAP2 family members played a direct role in regulating lipid droplets. To this end,

19 we tested whether Wnt3a retained the ability to induce lipid droplets after TFAP2 depletion

20 by RNAi. While knock-down of either TFAP2A or TFAP2C had no or only a modest effect,

21 tandem silencing of both homologs produced a marked reduction in the number of lipid

22 droplets present in cells in response to Wnt3a (Fig. 3A-B). In keeping with this finding, these

23 siRNA treatments diminished the mRNA levels of SOAT1, a key enzyme proximal to the

24 production of lipid droplets (Fig 3C) that mediate the production of cholesteryl esters (Chang

25 et al., 2001).

9 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 As an alternative approach, we used CRISPR/Cas9 gene knockout to generate HeLa-

2 MZ cells clones lacking TFAP2A (Fig. 3D-E). While tandem depletion by RNAi was

3 necessary to reduce lipid droplet production after Wnt3a addition in L Cells, two knockout

4 clones of TFAP2A demonstrated a complete lack of change in lipid droplet number after

5 Wnt3a stimulation (Fig. 3D-E), indicating that TFAP2 family members exhibit

6 complementary functions. Together, these results imply that TFAP2A/TFAP2C are

7 necessary for mediating the pro-lipid droplet signal of the Wnt pathway.

8 We next sought to determine if the TFAP2 family is sufficient for the induction of

9 lipid droplets. For this, we fused full-length TFAP2A, TFAP2B, and TFAP2C to mCherry

10 and overexpressed these as exogenous chimeras. As transcription factors the TFAP2 family

11 members function in the nucleus, the mCherry-tagged TFAP family members exhibited a

12 somewhat heterogeneous distribution between cytoplasm and nuclei, presumably because of

13 variations in expression levels. Impressively, the number of lipid droplets per cell increased

14 with increased nuclear localization of each TFAP2 family member (Fig. 3H), and, in cells

15 with TFAP2 nuclear localization, the expression of each family member was clearly

16 sufficient to cause lipid droplet biogenesis (Fig. 3F-G). Moreover, the expression of TFAP2C

17 was also able to trigger the expression of lipid droplet enzymes (Fig. 3I), further supporting

18 the notion that TFAP2 family members function as transcriptional regulators of lipid droplet

19 biogenesis.

20 In totality, these findings demonstrate TFAP2A, TFAP2B and TFAP2C are sufficient

21 to induce biogenesis of lipid droplets when expressed in cells, and members of the TFAP2

22 family are required to mediate the accumulation of lipid droplets seen in response to Wnt-

23 stimulation. This finding is in keeping with a previous report that targeted overexpression of

24 TFAP2C in mouse liver induced steatosis, accumulation of fat, and eventual liver failure

25 (Holl et al., 2011), and given the enrichment in TFAP2 binding sites in lipid droplets proteins

10 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 (Fig. 2 - Fig.Sup. 2), implicate the TFAP2 family as central regulators of lipid droplet

2 biogenesis.

3 Given our observation that TFAP2C expression correlates with DDIT3 expression in

4 cells after Wnt3a treatment (Fig. 2B), we investigated the role of DDIT3 in control of lipid

5 droplets. Indeed, while changes in SREBF1 expression induced by DDIT3 do not seem to be

6 involved in lipid droplet accumulation (Fig. 2 - Fig.Sup. 4), this process may be regulated by

7 DDIT3 itself. We started by examining the promotor region of the transcription factors whose

8 mRNA levels were impacted by Wnt3a-treatment (Fig. 2B) for reported DDIT3::CEBPA

9 consensus sites (Ubeda et al., 1996). Intriguingly, not only did we find such a site upstream

10 of the TFAP2C gene, but there was a DDIT3::CEBPA consensus site in 7 out of the 10 lipid-

11 related transcription factors identified in our RNAseq (Fig. 4 - Fig.Sup. 1A), a 1.8-fold

12 enrichment over the frequency in the total Wnt3a-influenced transcription factor set. This

13 lends further support for the view of DDIT3 as an important transcriptional regulator of lipid

14 homeostasis in cells (Chikka et al., 2013).

15 To address this notion directly, we next tested whether DDIT3 itself was able to

16 influence both TFAP2 family members, and the number of lipid droplets in cells. Indeed,

17 overexpression of DDIT3-mCherry chimera resulted in an increase in the mRNA levels of

18 lipid droplet enzymes (Fig. 4A). Further, silencing of DDIT3 both diminished SOAT1

19 mRNA levels (Fig. 4B) and lipid droplet accumulation in response to Wnt3a (Fig. 4C). These

20 observations suggest that DDIT3 plays a regulatory role in lipid droplet biogenesis,

21 presumably in concert with TFAP2.

22 We next tested the requirement of DDIT3 for the lipid droplet response directly by

23 interfering with DDIT3 expression by both RNAi and CRISPR/Cas9. Silencing of DDIT3

24 expression diminished accumulation of lipid droplets both in response to Wnt3a stimulation

25 (Fig. 4C; Fig. 4 - Fig.Sup. 1B), and after silencing of APC (Fig. 4 - Fig.Sup. 1B), an

11 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 alternative strategy previously shown to be sufficient to induce lipid droplets (Scott et al.,

2 2015). Further, knock-out clones lacking DDIT3 were completely non-responsive to Wnt3a

3 with regards to lipid droplet number. In the context of the significant increase in DDIT3

4 message and protein (Fig. 2 - Fig.Sup. 3), these results suggest that induction of DDIT3

5 transcription in response to Wnt3a is necessary for lipid droplet biogenesis.

6 Given that the presence of DDIT3 was necessary to convey the pro-lipid droplet

7 signal, we next tested whether over-expression of the transcription factor is sufficient to

8 induce lipid droplet accumulation. As with TFAP2, overexpression of mCherry-tagged

9 fusions of DDIT3 was sufficient to increase lipid droplet numbers (Fig. 4E; Fig. 4 - Fig.Sup.

10 1C) in transfected cells as compared to the control. In total, these data suggest that in addition

11 to regulation of cholesterol metabolism (Chikka et al., 2013), DDIT3 may function as a more

12 global regulator of cellular lipid homeostasis in part through regulation of the TFAP2 family

13 of transcription factors.

14

15 CONCLUSIONS 16 In conclusion, our data show TFAP2 family members function to modulate

17 expression of lipid droplet proteins and induce the accumulation of lipid droplets in cells. We

18 found TFAP2C is necessary to potentiate the pro-lipid droplet signal induced by Wnt3a, and

19 expression of TFAP2 family members is sufficient to induce lipid droplet accumulation in

20 cells (Fig. 3).

21 Not only do these data support the view that the TFAP2 family of transcription factors

22 can function as regulators of lipid droplet biogenesis, they provide insight into the

23 transcriptional network directing changes in lipid homeostasis and the accumulation of lipid

24 droplets in response to Wnt stimulation. Our observations indicate that, in addition to the

25 canonical TCF/LEF transcriptional response, Wnt signalling via APC, GSK3, and ß-Catenin

12 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 induces transcription-mediated lipid changes. These include decreased levels of SREBF1,

2 which is likely to contribute to the observed reduction in total membrane cholesterol levels

3 (Scott et al., 2015), and increased expression of DDIT3 and TFAP2, which triggers lipid

4 droplet biogenesis with storage of cholesterol esters and triglycerides. These observations

5 also lead to the notion that via TFAP2 transcription factors, Wnt exerts pro-proliferative

6 effects in developmental and in pathological contexts by leading to the accumulation of lipid

7 droplets.

8 In addition, Wnt3a has been recently identified as an intra-cell synchronizer of

9 circadian rhythms in the gut, controlling cell-cycle progression, and the mRNA of Wnt3a

10 itself exhibits circadian oscillations under the control of the master clock regulators Bmal1

11 and Per (Matsu-Ura et al., 2016). Given this function, it is tempting to speculate that Wnts

12 serve a more general role as a mid- or long-range circadian signalling intermediates, linking

13 lipid metabolism to the master transcriptional clocks that coordinate metabolic functions with

14 the day-night cycle. Because of its circadian nature, and capacity to induce lipid droplet

15 accumulation in a broad range of cells types, Wnts could serve as a fundamental signal for

16 cells to store lipids during times when nutrients are expected to be in excess for later use

17 during periods of rest or fasting.

18 Both lipid droplets enzymes (Solt et al., 2012), and the volume of lipid droplets

19 themselves (Uchiyama and Asari, 1984), vary in a circadian fashion, which is completely

20 consistent with a role as neutral lipid storage sites. TFAP2 binding sites are over-represented

21 in the promotors of circadian-controlled genes (Bozek et al., 2009), suggesting that the

22 Wnt/TFAP2 control of lipid droplets is one mechanism by which the daily storage of lipids in

23 lipid droplets, and cellular lipid homeostasis itself, is coordinated. Further, targeted

24 overexpression of TFAP2C induced the equivalent phenotype of steatosis in mouse liver

13 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 (Holl et al., 2011), underscoring the role of TFAP2 proteins in the regulation of neutral lipid

2 metabolism.

3 The Wnt pathway is also not the first developmental signalling pathway found to

4 exert key regulatory functions in directing energy metabolism. The FOXA family of

5 transcription factors, fundamental for early embryogenesis, play core roles in directing

6 glucose metabolism (Friedman and Kaestner, 2006), while SOX17 has been shown to have

7 additional, non-developmental functions regulating lipid metabolism in the liver of adult

8 animals (Rommelaere et al., 2014). Given the overlapping requirement for a molecular queue

9 to synchronize energy storage during both embryo development and daily metabolic activity,

10 it is not surprising that some of these systems have evolved to serve dual roles in both

11 biological contexts. These findings support the view that both Wnt signalling, and the TFAP2

12 family of transcription factors have important (and possibly linked) roles in development and

13 circadian lipid metabolism of the cell.

14

15 Materials and Methods

16 Cells, media, reagents and antibodies 17 18 HeLa-MZ cells, a line of HeLa cells selected to be amiable to imaging, were provided by

19 Prof. Lucas Pelkmans (University of Zurich). HeLa cells are not on the list of commonly

20 misidentified cell lines maintained by the International Cell Line Authentication Committee.

21 Our HeLa-MZ cells were authenticated by Microsynth (Balgach, Switzerland), which

22 revealed 100% identity to the DNA profile of the cell line HeLa (ATCC: CCL-2) and 100%

23 identity over all 15 autosomal STRs to the Microsynth’s reference DNA profile of HeLa. L

24 cells (ATCC: CRL-2648) and L Wnt3A cells (ATCC: CRL-2647) were generously provided

25 by Prof. Gisou van der Goot (École Polytechnique Fédérale de Lausanne; EPFL) and cultured

14 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 as per ATCC recommendations. Cells are mycoplasma negative as tested by GATC Biotech

2 (Konstanz, Germany). Wnt3A-conditioned, and control-conditioned media was prepared

3 from these cells by pooling two subsequent collections of 24h each from confluent cells.

4 Reagents were sourced as follows: Hoechst 33342 and BODIPY 493/503 from Molecular

5 Probes (Eugene, OR); The DGAT1 inhibitor A-922500 and the Membrane Bound

6 Transcription Factor Peptidase, Site 1 (S1P/SREBF) inhibitor PF-429242, and the mTOR

7 inhibitor Torin-2 were from Tocris Bioscience (Zug, Switzerland); lipoprotein-depleted

8 serum (LPDS) was prepared as previously described (Havel et al., 1955); anti-DDIT3

9 antibodies were from Cell Signaling (L63F7; Leiden, The Netherlands); anti-TFAP2A

10 antibodies were from Abcam (ab52222; Cambridge, UK); fluorescently labeled secondary

11 antibodies from Jackson ImmunoResearch Laboratories (West Grove, PA); 96-well Falcon

12 imaging plates (#353219) were from Corning (Corning, NY); oligonucleotides and small

13 interfering RNA (siRNA) from Dharmacon (SmartPool; Lafayette, CO) or QIAGEN (Venlo,

14 The Netherlands) and the siRNAs used in this work were: GSK3B (S100300335) ; DGAT1

15 (S100978278); DGAT2 (S100978278); SOAT1 (S101428924); TFAP2A (J-062799);

16 TFAP2C (J-048594); DDIT3 (J-062068); APC (S102757251).

17 Other chemicals and reagents were obtained from Sigma-Aldrich (St. Louis, MO).

18 Transfections of cDNA and siRNAs were performed using Lipofectamine LTX and

19 Lipofectamine RNAiMax (Invitrogen; Basel, Switzerland) respectively using the supplier’s

20 instructions.

21 Plasmids encoding GSK3B and GSK3BS9A, and the SREBF truncations were from Addgene

22 (Cambridge, MA); TFAP2A, TFAP2B, and DDIT3 coding sequences were obtained from the

23 Gene Expression Core Facility at the EPFL, and TFAP2C from DNASU (Arizona State

24 University) and cloned into appropriate mammalian expression vectors using Gateway

25 Cloning (Thermo Fisher Scientific). Knock-out cell lines of TFAP2A and DDIT3 were

15 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 obtained by clonal isolation after transfection of a pX330 Cas9 plasmid (Cong et al., 2013)

2 with appropriate guide sequences (see Supplementary Methods).

3

4 Lipid Droplet Quantitation 5 6 L Cells were seeded (6 000 cells/well) in imaging plates the day before addition of control-

7 conditioned, or Wnt3a-conditioned media for 24h, before fixation with 3% paraformaldehyde

8 for 20 min. Where necessary, cells were treated with siRNAs and Lipofectamine RNAiMax

9 Reagent (Thermo Fisher) per the manufacturer’s instructions for 48 hours before seeding into

10 imaging plates. Lipid droplets and nuclei were labelled with 1µg/mL BODIPY 493/503

11 (Invitrogen; D3922) and 2µg/mL Hoechst 33342 (Invitrogen; H3570) for 30 min, wash with

12 PBS, and the plate sealed and imaged with ImageXpress Micro XLS (Molecular Devices,

13 Sunnyvale, CA) automated microscope using a 60X air objective. Images were segmented

14 using CellProfiler (Carpenter et al., 2006) to identify and quantify nuclei, cells, and lipid

15 droplets.

16

17 Wnt Ligand Screen

18 L Cells cells were seeded into image plates (6 000 cells/well) the morning before transfection

19 with Lipofectamine 3000 (Invitrogen) as per the manufacturer’s instructions with a subset of

20 untagged Wnts from the open source Wnt Project plasmid library (Najdi et al., 2012). Cells

21 were fixed with 3% PFA after 48h, and lipid droplets quantified as above. Data were

22 normalized to the number of lipid droplets in the empty vector condition. The circular

23 phylogenetic tree was constructed using human Wnt sequences and the Lasergene (v12.1;

24 DNAStar, Madison WI) bioinformatics software.

25

16 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 Wnt Compound Screen

2 L Cells were seeded into imaging plates (6 000 cells/well) the day before addition of the Wnt

3 Pathway Library (BML-2838; Enzo Life Sciences, Farmingdale, NY) at either 1µM or

4 10µM, with either control-conditioned or Wnt3a-conditioned media for 24h, before fixation

5 and lipid droplet quantitation as above. Data were normalized by z-score and the average of

6 quadruplicates.

7

8

9 mRNA Determination

10 RT-PCR was carried out essentially as described (Brankatschk et al., 2012, Scott et al.,

11 2015), after total RNA extraction using TRIzol Reagent (Life Technologies AG; Basel,

12 Switzerland) according to manufacturer’s recommendation from monolayers of HeLa-MZ or

13 L cells.

14 15 Western Blotting 16 17 18 For Western blotting, cells were lysed with RIPA buffer plus protease inhibitors (1mM

19 phenylmethylsulfonyl fluoride, 10µg/ml aprotinin, 1µM pepstatin, 10µM leupeptin) for 20

20 min. The lysates were sonicated, clarified by centrifugation at (14 000 RCF for 10 min at

21 4°C) and were separated by SDS-PAGE and blotted on nitrocellulose or polyvinylidene

22 fluoride membranes.

23

24 RNAseq

25 HeLa-MZ cells were treated in triplicate with control-conditioned, or Wnt3a-conditioned

26 media for 2h or 6h before cells were collected and RNA isolated. Purification of total RNA

17 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 was done with the RNeasy Mini Kit from Qiagen according to the manufacturer’s

2 instructions. The concentration, purity, and integrity of the RNA were measured with the

3 Picodrop Microliter UV-Vis Spectrophotometer (Picodrop), and the Agilent 2100

4 Bioanalyzer (Agilent Technologies), together with the RNA 6000 Series II Nano Kit

5 (Agilent) according to the manufacturer’s instructions. Purified RNAs were sequenced with a

6 HiSeq 4000 (Illumina). The datasets are available at EMBL-EBI ArrayExpress

7 (https://www.ebi.ac.uk/arrayexpress/) under accession number E-MTAB-6623.

8 Sequencing reads were mapped to the hg38 genome using bowtie2 in local alignment mode.

9 Then reads were attributed to known exons as defined by the ensembl annotation, and

10 transcript-level read counts were inferred as described in (David et al., 2014). Differential

11 expression was then evaluated by LIMMA (Law et al., 2014) using the log of rpkm values.

12 The fold induction was determined as a ratio of mRNA amounts of Wnt3a to control. Genes

13 with message levels increase more than 1.5-fold, or decreased less than 0.8-fold were

14 collected and tested for pathway enrichment using DAVID bioinformatics resources (v6.8)

15 (Huang da et al., 2009) and the resulting data compiled and plotted using Cytoscape

16 (Shannon et al., 2003) and previously described Matlab scripts (Mercer et al., 2012).

17 18 Wnt siRNA Screen Analysis

19 20 Data from the genome-wide siRNA screen (Scott et al., 2015) was used to produce the subset

21 of annotated transcription factors (GO:0003700) cellular cholesterol levels after gene

22 silencing provided by AmiGO 2 (version 2.4.26) (Carbon et al., 2009). Data were filtered for

23 genes that increased or decreased total cellular cholesterol a z-score of 1.5 or greater.

24

25 Transcription Factor Enrichment 26 27

18 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 Transcript data from cells treated with Wnt3a (E-MTAB-2872 (Scott et al., 2015)), or fatty

2 acids (GSE21023 (Lockridge et al., 2008), GSE22693 (Xu et al., 2015), GSE42220 (Shaw et

3 al., 2013)) were collected and mRNAs that significantly changed in response to treatment

4 were compiled and analyzed for transcription factor enrichment using GeneGo (MetaCore)

5 bioinformatics software (Thomson Reuters). Data from both conditions was compared and

6 ordered by a sum of ranking.

7

8 Promotor Analysis 9 10 Promotor sequences from the current (GRCh38) human genome were collected using

11 Regulatory Sequence Analysis Tools (RSAT) software (Medina-Rivera et al., 2015) to collect

12 the non-overlapping upstream promotor sequences (<3 000 bp) of genes of interest which

13 were tested for enriched sequences using either the RSAT’s oligo-analysis or Analysis of

14 Motif Enrichment (AME) module of MEME Suite (v.4.12.0) (McLeay and Bailey, 2010).

15 Genes with no non-overlapping upstream region were discarded from the analysis.

16 Identification and quantitation of consensus binding sites was done with either RSAT’s

17 matrix-scan or MEME Suite’s Motif Alignment and Search Tool (MAST) module using a

18 cut-off of <0.0001 to define a consensus site. Consensus sites from JASPAR (Mathelier et al.,

19 2016) were: DDIT3:CEBPA (MA0019.1), TFAP2A (MA0003.2, MA0810.1), TFAP2C

20 (MA0524.1, MA0524.2).

21

22 23 Construction of Gene Edited Knock Out Cell Lines 24 25 26 The guide sequences used to construct specific CRISPR/Cas9 vectors were determined using

27 the CRISPR Design Tool(Ran et al., 2013) and were:

28

19 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 TFAP2A

2

3 Fwd: CACCGGAGTAAGGATCTTGCGACT

4 Rev: AAACAGTCGCAAGATCCTTACTCC

5

6 DDIT3

7

8 Fwd: CACCGGCACCTATATCTCATCCCC

9 Rev: AAACGACTGATCCAACTGCAGAGAC

10

11 These sequences were used to create insert the target sequence into the pX330 vector using

12 Golden Gate Assembly (New England Biolabs) and transfected into cells as described in the

13 Methods. Knock-out clones were isolated by serial dilution and confirmed by Western

14 blotting and activity assays.

15

16

20 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 Figure and Table Legends 2

3 Figure 1. The Wnt pathway and the regulation of lipid droplets.

4 A. HeLa-MZ cells were transfected with plasmids encoding wt or S9A mutant of GSK3B 24h

5 before the addition of Wnt-3a- or control-conditioned media for a further 24h. Cells were

6 fixed, labeled with BODIPY (lipid droplets, green) and Hoechst 33342 (nuclei, magenta), and

7 imaged by light microscopy. B. The number of lipid droplets was quantified by automated

8 microscopy (bar graph), and the data are presented as the mean number of lipid droplets per

9 cell of 5 independent experiments ± SEM, normalized to the control condition.

10 C-D. As in (A), except that HeLa-MZ cells (C) or L Cells (D) were transfected with siRNAs

11 against the indicated targets for 48h, before the addition of Wnt3a. Efficient silencing was

12 confirmed by qPCR (Fig. 1 - Fig.Sup. 1B) and the data are presented as the mean number of

13 lipid droplets per cell of 3 independent experiments ± SEM, normalized to the control

14 condition.

15 E. L cells were incubated with the indicated compounds together with Wnt3a for 24h,

16 processed and analyzed as in (A) and the data are presented as the mean number of lipid

17 droplets per cell of 5 independent experiments ± SEM, normalized to the control condition.

18 F. Evolutionary relationship of the 19 Wnt ligands. Colour indicates ability to induce lipid

19 droplets as detailed in (G).

20 G. L Cells were transfected with plasmids containing each of Wnt ligand for 48h, imaged and

21 analyzed as in (A). Data are normalized to the empty vector control and were tested for

22 significance (*, <0.05; **, <0.005, n.s., not significant) and are presented as the mean number

23 of lipid droplets per cell of 2 independent replicates of the screen ± SEM, normalized to the

24 control condition. The data are color-coded from a high (light) to a low (dark) number of

25 lipid droplets induced by each Wnt ligand.

21 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 H-I. High content image-based screen of a library of compounds that affect the Wnt pathway

2 in HeLa-MZ cells. Cells were incubated for 24h with Wnt-3a- or control-conditioned media

3 for 24h in the presence of the compounds at 1µM and 10µM, fixed, labeled with BODIPY

4 (lipid droplets) and Hoechst 33342 (nuclei) and imaged by automated microscopy. The

5 number of droplets per cell was counted and the zscores established, in order to quantify the

6 ability of each compounds to induce lipid droplets in untreated cells (G, left panel), or to

7 inhibit lipid droplet formation in Wnt3a-treated cells (G, right panel). Panel H illustrates the

8 effects of compounds that induce droplet formation (left column) or that do (Trichostatin A)

9 or do not (niclosamide, hexachlorophene) inhibit droplet formation (right column) in Wnt3a-

10 treated cells. Nuclei are in magenta, and lipid droplets in green.

11 Green bars, control-conditioned media; Red bars, Wnt3a-conditioned media. (*) indicates a

12 p-value <0.05. See also Figure 1 – Figure Supplement 1, and Figure 1 – Source Data 1.

13

14 Figure 2. mRNA profiling and analysis of gene expression of cells treated with Wnt3a.

15 A-B. HeLa-MZ cells were treated with control- or Wnt3a-conditioned media for 2h or 6h

16 before RNA isolation and RNAseq analysis. Panel (A) shows the pathway enrichment of

17 perturbed mRNAs. Node size indicates number of genes in each ontology and colour the

18 statistical strength of the enrichment. Edge thickness indicates the strength of overlap of

19 related ontologies. From (A), the perturbation of transcription factors amounts in response to

20 Wnt3a is shown in panel B. See also Figure 2 – Figure Supplement 1 to 4, and Figure 2 –

21 Source Data 1 to 3.

22

23 Figure 3. The TFAP2 family of transcription factors are both necessary and sufficient to

24 mediated lipid droplet accumulation.

22 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 A-B. L Cells were treated with siRNA against the indicated targets for 48h before the

2 addition of Wnt3a-conditioned medium for an additional 24h. Cells were then fixed, labeled,

3 imaged and analyzed by automated microscopy as in Fig 1A. In (A), data are presented as the

4 normalized mean number of lipid droplets per cell of 5 independent experiments ± SEM. (*)

5 indicates a p-value <0.05. Cells treated with non-target siRNAs or with siRNAs to both

6 TFAP2A and TFAB2C are shown in panel B (nuclei in magenta; lipid droplets in green).

7 C. L cells were treated with siRNAs against the indicated targets as in (A), before the

8 addition of Wnt3a- or control-conditioned media for an additional 24h. RNA was isolated

9 and analyzed by qPCR, and that data are expressed relative to the non-target control and are

10 presented as the mean number of lipid droplets per cell of 2 independent experiments ± SEM.

11 D-E. HeLa-MZ cells were transfected with targeted CRISPR/Cas9 plasmids against

12 TFAP2A. The corresponding knock-out clones as well as control cells were treated with

13 Wnt3a conditioned-media for 24h. In D, the number of lipid droplets was quantified as in Fig

14 1A and is expressed as fold induction relative to the control cells in 5 independent

15 experiments ± SEM. Inset: TFAP2A protein levels of each clone determined by Western blot.

16 Arrow indicate position of 50 kDa marker. Representative images are shown in E (nuclei in

17 magenta; lipid droplets in green).

18 F-H. L Cells were transfected or not with mCherry-tagged TFAP2 family members for 48h

19 before fixation, labeling and imaging as in Fig 1A. The mean number of lipid droplets per

20 cell expressing each mCherry-tagged TFAP2 protein was counted, and is expressed, as in

21 panel (D), as fold induction relative to the control cells in 6 independent experiments ± SEM.

22 (*) indicates a p-value <0.05. Panel G shows cells expressing each mCherry-tagged TFAP2

23 protein (Blue, nucleus; Green, lipid droplets; Red, TFAP2-mCherry fusion proteins), and

24 panel H shows the number of lipid droplets per cell in cells overexpression TFAP2C-

23 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 mCherry, binned by their nuclear:cytoplasmic distribution. Data are the mean lipid droplets

2 per cell ± SEM for 450 cells.

3 I. L Cells were treated as in F before extraction and determination of the indicated mRNAs

4 by qPCR. Data are representative of two independent experiments.

5

6 Figure 4. DDIT3 is both necessary and sufficient to mediated lipid droplet accumulation

7 A-B. qPCR of DDIT3, DGAT2 and SOAT1 after overexpression of TFAP2C-mCherry (A),

8 DDIT3-mCherry (B), or siRNAs to DDIT3 (C). Data are representative of two independent

9 experiments.

10 C. L cells were treated, processed and analyzed like in Fig 3A, except that they were

11 transfected with siRNAs to DDIT3 before stimulation with Wnt3a. Data are presented as the

12 normalized mean number of lipid droplets per cell of 3 independent experiments ± SEM. (*)

13 indicates a p-value <0.05. Panel E shows cells treated with non-target or anti-DDIT3 siRNAs

14 (magenta, nucleus; green, lipid droplets).

15 D. HeLa-MZ CRISPR/Cas9 clones were prepared and analysed as in Fig 3D. Data are

16 expressed as fold induction relative to the control cells in 5 independent experiments ± SEM.

17 Inset: DDIT3 protein levels of each clone determined by Western blot. Arrow indicate

18 position of 25 kDa marker. Representative images are shown (nuclei in magenta; lipid

19 droplets in green).

20 E. L cells were treated, processed and analyzed like in Fig 3F, except that they were

21 transfected with a plasmid encoding DDIT3-mCherry. Data are presented as the normalized

22 mean number of lipid droplets per cell of 6 independent experiments ± SEM. (*) indicates a

23 p-value <0.05. Panel E shows cells expressing or not DDIT3-mCherry (blue, nucleus; green,

24 lipid droplets, red, DDIT3-mCherry fusion protein).

25 See also Figure 4 – Figure Supplement 1.

24 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 Figure 1 – Figure Supplement 1. Lipid droplet accumulation in response to Wnt3a:

2 combinatorial treatments against lipid droplet enzymes by RNAi and chemical

3 inhibitors.

4 A. L cells were treated with the indicated siRNAs for 48h before addition of the indicated

5 compounds together with control, or Wnt3a, conditioned media and incubation for an

6 additional 24h. Cells were then fixed, stained and lipid droplet number quantified as in Fig

7 1A. Data are presented as the normalized mean number of lipid droplets per cell of 3

8 independent experiments ± SEM.

9 B. L Cells were treated as in Fig. 1A with the indicated siRNAs and the amounts of the

10 corresponding mRNA were determined by qPCR.

11

12 Figure 2 – Figure Supplement 1. Datamining for putative lipid droplet transcriptional

13 regulators.

14 A. Transcription factors that influence cellular cholesterol amounts from a genome-wide

15 screen of cholesterol regulatory genes (Scott et al., 2015). Node size is proportional to the

16 absolute z-score difference from the control, and the colour indicates increased (green) or

17 decreased (blue) cellular cholesterol levels.

18 B. Existing mRNA profiling experiments of cells treated with Wnt3a (blue), or perturbations

19 likely to induce lipid droplet accumulation (see methods) (pink), were analyzed for

20 enrichment of genes linked to known transcriptions factors to identify candidate transcription

21 factors linking Wnt3a to lipid droplet biogenesis.

22

23 Figure 2 – Figure Supplement 2. The consensus binding sites of TFAP2 family members

24 are overrepresented in lipid droplet genes.

25 A. Enrichment of known transcription factor consensus sequences in lipid droplet genes.

25 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 B. TFAP2A and TFAP2C consensus binding site motifs.

2 C. Distribution of TFAP2 consensus sites in the promotor region of genes annotated to be

3 lipid droplet related.

4 5 Figure 2 – Figure Supplement 3. Effect of Wnt3a on DDIT3 protein and mRNA

6 amounts in L Cells.

7 Cells were treated with control, or Wnt3a conditioned media for the indicated times before

8 lysis.

9 A. mRNA was extracted from L Cells cells and amounts were determined by qPCR.

10 B. Cell extracts were analyzed by SDS-PAGE and Western blot using antibodies against the

11 indicated proteins.

12

13 Figure 2 – Figure Supplement 4. SREBF activity does not significantly influence lipid

14 droplet number.

15 A. L Cells were transfected or not with constitutively-active truncation mutants of SREBF1a,

16 SREBF1c, and SREBF2 for 24h before addition of control or Wnt3a conditioned media for

17 an additional 24h. Cells were fixed, labeled and imaged by automated microscopy as in Fig

18 1A. Data are box-and-whisker plots of a representative experiment.

19 B. L Cells were treated with the indicated chemical inhibitors simultaneously with the

20 conditioned media, and processed as (A). PF-429242; inhibitor of the SREBF site 1 protease

21 (S1P). Torin-2; mTOR inhibitor.

22

23 Figure 4 – Figure Supplement 1. DDIT3 and lipid homeostasis.

24 A. Location of DDIT3:CEBPA consensus sites in the promotor regions of the transcription

25 factors Fig. 2B reported to be involved in lipid homeostasis. Indicated is the pvalue of the

26 match of the consensus motif.

26 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 B. HeLa-MZ cells were treated with siRNAs to the indicated target proteins for 48h, and then

2 further incubated for 24h in the presence of control, or Wnt3a, conditioned media. Cells were

3 then fixed, stained and the number of lipid droplets was quantified as in Fig 1A. Data are

4 presented as the normalized mean number of lipid droplets per cell of 3 independent

5 experiments ± SEM.

6 C. DDIT3-mCherry was overexpressed in HeLa-MZ cells for 24h and then further incubated

7 for 24h in the presence of control, or Wnt3a, conditioned media. Cells were then fixed and

8 labeled, and the number of lipid droplets per cell expressing DDIT3-mCherry was quantified

9 as in Fig 4F. Data are presented as the normalized mean number of lipid droplets per cell of 5

10 independent experiments ± SEM.

11

27 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 Acknowledgements 2

3 We are grateful to Christian Iseli, Nicolas Guex, and Ioannis Xenarios from Vital-IT and the

4 Swiss Institute of Bioinformatics for indispensable guidance on the interpretation of the

5 transcriptomics data and critical reading of the manuscript. RNA was sequenced at the iGE3

6 Genomics Platform. of the University of Geneva (https://ige3.genomics.unige.ch). Support

7 was from the Swiss National Science Foundation, the NCCR in Chemical Biology and

8 LipidX from the Swiss SystemsX.ch Initiative, evaluated by the Swiss National Science

9 Foundation (to J. G.). C. C. S. has been supported by fellowships from the Human Frontier

10 Science Program and the Canadian Institutes of Health Research.

11

12 Author Contributions 13

14 CCS carried out most of the experiments and analyses and wrote the manuscript; SV

15 contributed the mRNA determinations and assisted with lipid droplet quantitation; JR

16 contributed to the analysis of the RNAseq data; and JG supervised the entire project and

17 wrote the manuscript.

18

19 Competing Interests 20

21 The authors declare no competing interests.

22

23 Materials & Correspondence 24

25 Correspondence and requests to Jean Gruenberg.

28 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 2 Figure Supplements 3

4 Figure 1 – figure supplement 1: Lipid droplet accumulation in response to Wnt3a:

5 combinatorial treatments against lipid droplet enzymes by RNAi and chemical inhibitors.

6 Figure 2 – figure supplement 1: Datamining for putative lipid droplet transcriptional

7 regulators.

8 Figure 2 – figure supplement 2: The consensus binding sites of TFAP2 family members are

9 overrepresented in lipid droplet genes.

10 Figure 2 – figure supplement 3: Effect of Wnt3a on DDIT3 protein and mRNA amounts in L

11 Cells.

12 Figure 2 – figure supplement 4: SREBF activity does not significantly influence lipid droplet

13 number.

14 Figure 4 – figure supplement 1: DDIT3 and lipid homeostasis.

15

16 Figure Source Data 17

18 Figure 1 – Source Data 1: Effect of Wnt pathway related compounds on lipid droplet

19 induction.

20 Figure 2 – Source Data 1: Effect of silencing transcription factors on cellular cholesterol

21 amounts.

22 Figure 2 – Source Data 2: Comparative enrichment of transcriptional targets in cells treated

23 with Wnt3a or fatty acid perturbation.

24 Figure 2 – Source Data 3: TFAP2 family member consensus binding sites in lipid droplet

25 genes.

26

29 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 References 2 3 4 5 BARISCH, C. & SOLDATI, T. 2017. Breaking fat! How mycobacteria and other intracellular 6 pathogens manipulate host lipid droplets. Biochimie. 7 BOZEK, K., RELOGIO, A., KIELBASA, S. M., HEINE, M., DAME, C., KRAMER, A. & HERZEL, H. 8 2009. Regulation of clock-controlled genes in mammals. PLoS One, 4, e4882. 9 BRANKATSCHK, B., WICHERT, S. P., JOHNSON, S. D., SCHAAD, O., ROSSNER, M. J. & 10 GRUENBERG, J. 2012. Regulation of the EGF transcriptional response by endocytic 11 sorting. Sci Signal, 5, ra21. 12 CARBON, S., IRELAND, A., MUNGALL, C. J., SHU, S., MARSHALL, B., LEWIS, S., AMI, G. O. H. & 13 WEB PRESENCE WORKING, G. 2009. AmiGO: online access to ontology and 14 annotation data. Bioinformatics, 25, 288-9. 15 CARPENTER, A. E., JONES, T. R., LAMPRECHT, M. R., CLARKE, C., KANG, I. H., FRIMAN, O., 16 GUERTIN, D. A., CHANG, J. H., LINDQUIST, R. A., MOFFAT, J., GOLLAND, P. & 17 SABATINI, D. M. 2006. CellProfiler: image analysis software for identifying and 18 quantifying cell phenotypes. Genome Biol, 7, R100. 19 CHANG, T. Y., CHANG, C. C., LU, X. & LIN, S. 2001. Catalysis of ACAT may be completed 20 within the plane of the membrane: a working hypothesis. J Lipid Res, 42, 1933-8. 21 CHAUHAN, S., GOODWIN, J. G., CHAUHAN, S., MANYAM, G., WANG, J., KAMAT, A. M. & 22 BOYD, D. D. 2013. ZKSCAN3 is a master transcriptional repressor of autophagy. Mol 23 Cell, 50, 16-28. 24 CHIKKA, M. R., MCCABE, D. D., TYRA, H. M. & RUTKOWSKI, D. T. 2013. C/EBP homologous 25 protein (CHOP) contributes to suppression of metabolic genes during endoplasmic 26 reticulum stress in the liver. J Biol Chem, 288, 4405-15. 27 CONG, L., RAN, F. A., COX, D., LIN, S., BARRETTO, R., HABIB, N., HSU, P. D., WU, X., JIANG, 28 W., MARRAFFINI, L. A. & ZHANG, F. 2013. Multiplex genome engineering using 29 CRISPR/Cas systems. Science, 339, 819-23. 30 DAI, C., STOLZ, D. B., KISS, L. P., MONGA, S. P., HOLZMAN, L. B. & LIU, Y. 2009. Wnt/beta- 31 catenin signaling promotes podocyte dysfunction and albuminuria. J Am Soc 32 Nephrol, 20, 1997-2008. 33 DAVID, F. P., DELAFONTAINE, J., CARAT, S., ROSS, F. J., LEFEBVRE, G., JAROSZ, Y., SINCLAIR, 34 L., NOORDERMEER, D., ROUGEMONT, J. & LELEU, M. 2014. HTSstation: a web 35 application and open-access libraries for high-throughput sequencing data analysis. 36 PLoS One, 9, e85879. 37 ECKERT, D., BUHL, S., WEBER, S., JAGER, R. & SCHORLE, H. 2005. The AP-2 family of 38 transcription factors. Genome Biol, 6, 246. 39 FRIEDMAN, J. R. & KAESTNER, K. H. 2006. The Foxa family of transcription factors in 40 development and metabolism. Cell Mol Life Sci, 63, 2317-28. 41 HAVEL, R. J., EDER, H. A. & BRAGDON, J. H. 1955. The distribution and chemical composition 42 of ultracentrifugally separated lipoproteins in human serum. J Clin Invest, 34, 1345- 43 53. 44 HAWKINS, J. L., ROBBINS, M. D., WARREN, L. C., XIA, D., PETRAS, S. F., VALENTINE, J. J., 45 VARGHESE, A. H., WANG, I. K., SUBASHI, T. A., SHELLY, L. D., HAY, B. A., LANDSCHULZ, 46 K. T., GEOGHEGAN, K. F. & HARWOOD, H. J., JR. 2008. Pharmacologic inhibition of 47 site 1 protease activity inhibits sterol regulatory element-binding protein processing

30 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 and reduces lipogenic enzyme gene expression and lipid synthesis in cultured cells 2 and experimental animals. J Pharmacol Exp Ther, 326, 801-8. 3 HOLL, D., KUCKENBERG, P., WOYNECKI, T., EGERT, A., BECKER, A., HUSS, S., STABENOW, D., 4 ZIMMER, A., KNOLLE, P., TOLBA, R., FISCHER, H. P. & SCHORLE, H. 2011. Transgenic 5 overexpression of Tcfap2c/AP-2gamma results in liver failure and intestinal 6 dysplasia. PLoS One, 6, e22034. 7 HUANG DA, W., SHERMAN, B. T. & LEMPICKI, R. A. 2009. Systematic and integrative analysis 8 of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4, 44-57. 9 JORNAYVAZ, F. R. & SHULMAN, G. I. 2010. Regulation of mitochondrial biogenesis. Essays 10 Biochem, 47, 69-84. 11 KANG, Y. A., SANALKUMAR, R., O'GEEN, H., LINNEMANN, A. K., CHANG, C. J., BOUHASSIRA, 12 E. E., FARNHAM, P. J., KELES, S. & BRESNICK, E. H. 2012. Autophagy driven by a 13 master regulator of hematopoiesis. Mol Cell Biol, 32, 226-39. 14 KIM, N. G., XU, C. & GUMBINER, B. M. 2009. Identification of targets of the Wnt pathway 15 destruction complex in addition to beta-catenin. Proc Natl Acad Sci U S A, 106, 5165- 16 70. 17 KONIGE, M., WANG, H. & SZTALRYD, C. 2014. Role of adipose specific lipid droplet proteins 18 in maintaining whole body energy homeostasis. Biochim Biophys Acta, 1842, 393- 19 401. 20 LAW, C. W., CHEN, Y., SHI, W. & SMYTH, G. K. 2014. voom: Precision weights unlock linear 21 model analysis tools for RNA-seq read counts. Genome Biol, 15, R29. 22 LI, D., ZHANG, Y., XU, L., ZHOU, L., WANG, Y., XUE, B., WEN, Z., LI, P. & SANG, J. 2010. 23 Regulation of gene expression by FSP27 in white and brown adipose tissue. BMC 24 Genomics, 11, 446. 25 LI, Q., LOHR, C. V. & DASHWOOD, R. H. 2009. Activator protein 2alpha suppresses intestinal 26 tumorigenesis in the Apc(min) mouse. Cancer Lett, 283, 36-42. 27 LI, X., WANG, W., WANG, J., MALOVANNAYA, A., XI, Y., LI, W., GUERRA, R., HAWKE, D. H., 28 QIN, J. & CHEN, J. 2015. Proteomic analyses reveal distinct chromatin-associated and 29 soluble transcription factor complexes. Mol Syst Biol, 11, 775. 30 LIU, J., WU, X., MITCHELL, B., KINTNER, C., DING, S. & SCHULTZ, P. G. 2005. A small-molecule 31 agonist of the Wnt signaling pathway. Angew Chem Int Ed Engl, 44, 1987-90. 32 LOCKRIDGE, J. B., SAILORS, M. L., DURGAN, D. J., EGBEJIMI, O., JEONG, W. J., BRAY, M. S., 33 STANLEY, W. C. & YOUNG, M. E. 2008. Bioinformatic profiling of the transcriptional 34 response of adult rat cardiomyocytes to distinct fatty acids. J Lipid Res, 49, 1395-408. 35 LU, W., LIN, C., ROBERTS, M. J., WAUD, W. R., PIAZZA, G. A. & LI, Y. 2011. Niclosamide 36 suppresses cancer cell growth by inducing Wnt co-receptor LRP6 degradation and 37 inhibiting the Wnt/beta-catenin pathway. PLoS One, 6, e29290. 38 MATHELIER, A., FORNES, O., ARENILLAS, D. J., CHEN, C. Y., DENAY, G., LEE, J., SHI, W., SHYR, 39 C., TAN, G., WORSLEY-HUNT, R., ZHANG, A. W., PARCY, F., LENHARD, B., SANDELIN, 40 A. & WASSERMAN, W. W. 2016. JASPAR 2016: a major expansion and update of the 41 open-access database of transcription factor binding profiles. Nucleic Acids Res, 44, 42 D110-5. 43 MATSU-URA, T., DOVZHENOK, A., AIHARA, E., ROOD, J., LE, H., REN, Y., ROSSELOT, A. E., 44 ZHANG, T., LEE, C., OBRIETAN, K., MONTROSE, M. H., LIM, S., MOORE, S. R. & HONG, 45 C. I. 2016. Intercellular Coupling of the Cell Cycle and Circadian Clock in Adult Stem 46 Cell Culture. Mol Cell, 64, 900-912.

31 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 MCLEAY, R. C. & BAILEY, T. L. 2010. Motif Enrichment Analysis: a unified framework and an 2 evaluation on ChIP data. BMC Bioinformatics, 11, 165. 3 MEDINA-RIVERA, A., DEFRANCE, M., SAND, O., HERRMANN, C., CASTRO-MONDRAGON, J. A., 4 DELERCE, J., JAEGER, S., BLANCHET, C., VINCENS, P., CARON, C., STAINES, D. M., 5 CONTRERAS-MOREIRA, B., ARTUFEL, M., CHARBONNIER-KHAMVONGSA, L., 6 HERNANDEZ, C., THIEFFRY, D., THOMAS-CHOLLIER, M. & VAN HELDEN, J. 2015. RSAT 7 2015: Regulatory Sequence Analysis Tools. Nucleic Acids Res, 43, W50-6. 8 MELO, R. C. & WELLER, P. F. 2016. Lipid droplets in leukocytes: Organelles linked to 9 inflammatory responses. Exp Cell Res, 340, 193-7. 10 MERCER, J., SNIJDER, B., SACHER, R., BURKARD, C., BLECK, C. K., STAHLBERG, H., PELKMANS, 11 L. & HELENIUS, A. 2012. RNAi screening reveals proteasome- and Cullin3-dependent 12 stages in vaccinia virus infection. Cell Rep, 2, 1036-47. 13 MEYERS, A., WEISKITTEL, T. M. & DALHAIMER, P. 2017. Lipid Droplets: Formation to 14 Breakdown. Lipids, 52, 465-475. 15 NAJDI, R., PROFFITT, K., SPROWL, S., KAUR, S., YU, J., COVEY, T. M., VIRSHUP, D. M. & 16 WATERMAN, M. L. 2012. A uniform human Wnt expression library reveals a shared 17 secretory pathway and unique signaling activities. Differentiation, 84, 203-13. 18 PARK, S., GWAK, J., CHO, M., SONG, T., WON, J., KIM, D. E., SHIN, J. G. & OH, S. 2006. 19 Hexachlorophene inhibits Wnt/beta-catenin pathway by promoting Siah-mediated 20 beta-catenin degradation. Mol Pharmacol, 70, 960-6. 21 RAN, F. A., HSU, P. D., WRIGHT, J., AGARWALA, V., SCOTT, D. A. & ZHANG, F. 2013. Genome 22 engineering using the CRISPR-Cas9 system. Nat Protoc, 8, 2281-2308. 23 ROMMELAERE, S., MILLET, V., VU MANH, T. P., GENSOLLEN, T., ANDREOLETTI, P., 24 CHERKAOUI-MALKI, M., BOURGES, C., ESCALIERE, B., DU, X., XIA, Y., IMBERT, J., 25 BEUTLER, B., KANAI, Y., MALISSEN, B., MALISSEN, M., TAILLEUX, A., STAELS, B., 26 GALLAND, F. & NAQUET, P. 2014. Sox17 regulates liver lipid metabolism and 27 adaptation to fasting. PLoS One, 9, e104925. 28 SARDIELLO, M., PALMIERI, M., DI RONZA, A., MEDINA, D. L., VALENZA, M., GENNARINO, V. 29 A., DI MALTA, C., DONAUDY, F., EMBRIONE, V., POLISHCHUK, R. S., BANFI, S., 30 PARENTI, G., CATTANEO, E. & BALLABIO, A. 2009. A gene network regulating 31 lysosomal biogenesis and function. Science, 325, 473-7. 32 SCOTT, C. C., VOSSIO, S., VACCA, F., SNIJDER, B., LARIOS, J., SCHAAD, O., GUEX, N., 33 KUZNETSOV, D., MARTIN, O., CHAMBON, M., TURCATTI, G., PELKMANS, L. & 34 GRUENBERG, J. 2015. Wnt directs the endosomal flux of LDL-derived cholesterol and 35 lipid droplet homeostasis. EMBO Rep, 16, 741-52. 36 SCOTT, C. C. & GRUENBERG, J. 2018. Data from: HeLa cells treated with Wnt3a. 37 https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6623. Publicly available 38 at the at EMBL-EBI ArrayExpress under accession number E-MTAB-6623. 39 SHANNON, P., MARKIEL, A., OZIER, O., BALIGA, N. S., WANG, J. T., RAMAGE, D., AMIN, N., 40 SCHWIKOWSKI, B. & IDEKER, T. 2003. Cytoscape: a software environment for 41 integrated models of biomolecular interaction networks. Genome Res, 13, 2498-504. 42 SHAW, B., LAMBERT, S., WONG, M. H., RALSTON, J. C., STRYJECKI, C. & MUTCH, D. M. 2013. 43 Individual saturated and monounsaturated fatty acids trigger distinct transcriptional 44 networks in differentiated 3T3-L1 preadipocytes. J Nutrigenet Nutrigenomics, 6, 1- 45 15.

32 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 SHIMANO, H., HORTON, J. D., SHIMOMURA, I., HAMMER, R. E., BROWN, M. S. & GOLDSTEIN, 2 J. L. 1997. Isoform 1c of sterol regulatory element binding protein is less active than 3 isoform 1a in livers of transgenic mice and in cultured cells. J Clin Invest, 99, 846-54. 4 SOLT, L. A., WANG, Y., BANERJEE, S., HUGHES, T., KOJETIN, D. J., LUNDASEN, T., SHIN, Y., LIU, 5 J., CAMERON, M. D., NOEL, R., YOO, S. H., TAKAHASHI, J. S., BUTLER, A. A., 6 KAMENECKA, T. M. & BURRIS, T. P. 2012. Regulation of circadian behaviour and 7 metabolism by synthetic REV-ERB agonists. Nature, 485, 62-8. 8 STAMBOLIC, V. & WOODGETT, J. R. 1994. Mitogen inactivation of glycogen synthase kinase- 9 3 beta in intact cells via serine 9 phosphorylation. Biochem J, 303 ( Pt 3), 701-4. 10 THIAM, A. R. & BELLER, M. 2017. The why, when and how of lipid droplet diversity. J Cell Sci, 11 130, 315-324. 12 UBEDA, M., WANG, X. Z., ZINSZNER, H., WU, I., HABENER, J. F. & RON, D. 1996. Stress- 13 induced binding of the transcriptional factor CHOP to a novel DNA control element. 14 Mol Cell Biol, 16, 1479-89. 15 UCHIYAMA, Y. & ASARI, A. 1984. A morphometric study of the variations in subcellular 16 structures of rat hepatocytes during 24 hours. Cell Tissue Res, 236, 305-15. 17 VIBHAKAR, R., FOLTZ, G., YOON, J. G., FIELD, L., LEE, H., RYU, G. Y., PIERSON, J., DAVIDSON, 18 B. & MADAN, A. 2007. Dickkopf-1 is an epigenetically silenced candidate tumor 19 suppressor gene in medulloblastoma. Neuro Oncol, 9, 135-44. 20 XU, L., XIA, X., ARSHAD, M. & ZHOU, L. 2015. Gene expression profile in the fat tissue of 21 Fsp27 deficient mice. Genom Data, 5, 326-8. 22 ZHANG, M., MAHONEY, E., ZUO, T., MANCHANDA, P. K., DAVULURI, R. V. & KIRSCHNER, L. S. 23 2014. Protein kinase A activation enhances beta-catenin transcriptional activity 24 through nuclear localization to PML bodies. PLoS One, 9, e109523. 25

33 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder,Control CM who has granted bioRxiv a license to display the preprint in perpetuity. It is made Wnt3aavailable CM under C.aCC-BY 4.0 InternationalD. license. E. * A. B. * * Control CM Wnt3a CM * 1.25 5 * 1.5 3

4 1.0 *

GSK3B 1.0 2 3 0.75 Wnt3a CM

wt 2 0.5 0.5 GSK3B 1 1 0.25 Normalized Number of Lipid Droplets Normalized Number of Lipid Droplets Normalized Number of Lipid Droplets Normalized Number of Lipid Droplets GSK3B S9A siRNA 0 0 0 0

LPDSLPDS SOAT Control GSK3B ControlControl GSK3B DGAT1DGAT2 DGAT Inh. GSK3BS9A Non-target Non-targetNon-target Actinomycin D DGAT1/DGAT2 WNT2B2 G. WNT1 F. WNT9B WNT2 2.5 3.0 ** WNT9A WNT5A ** 2.5 WNT11 WNT5B * ** ** ** WNT16 2.0 n.s. WNT8A ** * ** * 1.5 n.s. n.s. * n.s. n.s. n.s. n.s. WNT8B WNT7A n.s. 1.0

WNT7B Relative Number of Lipid Droplets WNT10B 0.5 Relative Number of Lipid Droplets WNT10A WNT3A 0 Wnt1 Wnt2 Wnt2B2 Wnt3 Wnt3A Wnt4 Wnt5A Wnt5B Wnt6 Wnt7A Wnt7B Wnt8A Wnt8B Wnt9A Wnt9B Wnt10A Wnt10B Wnt11 Wnt16 Empty Vector WNT6 WNT3 WNT1 WNT4 0.95

H. Control CM Wnt3a CM Control CM + Compound Library Wnt3a CM + Compound Library I. 4 2 Control Pyrvinium pamoate BML-284 Doxorubicin·HCl 1

3 - Control Pyrvinium pamoate 0

WAY-262611 Trichostatin A 2 Niclosamide Forskolin −1 PP2 TWS-119 BML-285 Bafilomycin A1 Niclosamide PGE2 Imatinib mesylate BIO Bosutinib Decitabine −2 WAY-262611 1 Hexachlorophene BML-284 - −3 chlorophene

10µM (zScore) 0 10µM (zScore) −4 Bosutinib Hexa-

−1 −5 Doxorubicin

−6 Niclosamide −2 Trichostatin A −7 −2 −1 0 1 2 3 −6 −5 −4 −3 −2 −1 0 1 2

1µM (zScore) 1µM (zScore) Forskolin

Figure 1 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made A. available under aCC-BY 4.0 International licenseB.. 5.0 Stress Response Cell Division MAPK Signalling Zinc Binding Protein

Significance 0.5 Phosphorylation Wnt Signaling Innate Immune Enrichment Response Chromosome Virus Entry -log(pValue) Organization mRNA Transcriptional Processing Regulation Hydrolase 90 Cell Migration Response to FoxO Signaling Drug Proto-oncogene 5 Autophagy Number Epithelial to Mesenchymal of Cell Cycle Arrest Transition Regulation of genes Gene Expression 2h

Protein TGF-beta Protein Refolding Signaling Multivesicular Phosphorylation Body Cholesterol Protein Protein Ubiquitin Biosythesis Phosphatase Epithelial to Mesenchymal Fatty Acid Fatty Acid Transition Degradation Metabolism Autophagy Transcriptional Cell Cycle DNA Damage Regulation mRNA Export One-carbon Metabolism DNA Synthesis TCA Cycle Translocation Cancer mRNA Splicing G2/M Osteoclast Aminoacyl-tRNA Chromosome Checkpoint Differentiation Cardiomyopathy Telomere Focal Adhesion Maintenance Tyrosine Kinase 6h

Figure 2 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license2.0. Control CM Non-target * B. C. A. * ) 1.5 2-ddCt Non-target 1 1.0 Wnt3a CM

TFAP2A/TFAP2C 0.5

Relative mRNA Expression( Relative mRNA Control CM Normalized Number of Lipid Droplets Wnt3a CM 0 0 TFAP2A TFAP2C TFAP2A/ TFAP2C TFAP2A/ TFAP2C Non-target Non-target

* G. TFAP2A- D. 2 E. F. * mCherry * * 2.0 * Control Control CM

1.5 Clone #1 Control Clone #2 TFAP2B- mCherry Control 1 1.0 Clone #1 Wnt3a CM 0.5 TFAP2C- mCherry Induction of Lipid Droplet Number

Fold Induction of Lipid Droplets by Wnt3a 0 0 Clone #2

Control Control Clone #1 Clone #2 TFAP2A TFAP2B TFAP2C TFAP2A CRISPR K/O H. I. DGAT2 SOAT1

5 2.5 2.5 4 2.0 ) ) 2-ddCt 2.0 2-ddCt 3 1.5

1.5 2 1.0

1.0 1 0.5 Relative mRNA Expression( Relative mRNA Relative mRNA Expression( Relative mRNA

0.5 0 0 Mean Number of Lipid Droplets Per Cell Control Control TFAP2C 0 TFAP2C <1 1 to 2 2 to 3 3 to 4 4+ Control CM TFAP2C Ratio Nucleus:Cytoplasm Wnt3a CM Figure 3 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. TheSOAT1 copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has grantedDGAT2 bioRxiv a license to display the preprint in perpetuity. It is made 8 2.5 ) 2.0 ) available under aCC-BY 4.0 International license. ) ) 2-ddCt 2-ddCt B. C. 2-ddCt A. 4 2-ddCt 2.0 6 1.5 3 1.5 4 1.0 2 1.0

0.5 2 1 0.5 Relative mRNA Expression( Relative mRNA Control CM Expression( Relative mRNA Wnt3a CM Relative SOAT1 mRNA Expression( mRNA Relative SOAT1 Relative DDIT3 mRNA Expression( Relative DDIT3 mRNA 0 0 0 0 DDIT3 DDIT3 DDIT3 Control Control Control TFAP2C D. E. Non-target * 1.0 2.0 *

0.8 Control 1.5 Non-Target 0.6 1.0 0.4 DDIT3 KO DDIT3 0.5 0.2

0 0 Normalized Number of Lipid Droplets Mock DDIT3 Non- Normalized Number of Lipid Droplets DDIT3 Mock Target DDIT3 F. * 2.0

1.5

1.0

0.5

0

Normalized Number of Lipid Droplets Mock DDIT3

Figure 4 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Control CM available under aCC-BY 4.0 International license. A. Wnt3a CM

1.75

1.5

1.25

1

0.75

0.5 Normalized Number of Lipid Droplets

0.25

0 Sandoz 58-035 - + + + + - - - - A 922500 - - - - - + + + +

SOAT DGAT1 DGAT2 DGAT2

Non-target Non-target Non-target

DGAT2/SOAT DGAT1/DGAT2

DGAT2 SOAT1 100 DGAT1 100 100 B. ) ) ) 2-ddCt 2-ddCt 75 2-ddCt 75 75

50 50 50 Relative mRNA Expression( Relative mRNA Relative mRNA Expression( Relative mRNA 25 Expression( Relative mRNA 25 25

0 0 0 Figure 1 –

DGAT1 DGAT2 SOAT1 Non-target Non-target Non-target Figure Supplement 1 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

A. CXXC1 ETV5 EN1 KDM3A HOXC10 ASCL2 ETV4 JMJD1C SKIL ATF5 MACC1 RXRB SMARCD2 STAT3 TP53 GMEB2 ELF4 VAX1 ZBTB16 TFAP2B SP3 KLF5 CNBP NKX2-8 SPI1 ZNF217 LIF ARNT2 TAF1 MYOCD KLF6 TFAP2E ZNF704 CREBRF JDP2 OVOL1 ZNF277 PAX2 Lipid Metabolism Proliferation EGR2 SIN3A TGIF1

SOX9 THRA SMARCC1 IRF8

RARG z score GLI3 3.3 HSF2 ATF1 YY1 FOXK2

ZBTB7B BCL6 BARX1 SIX2 ZKSCAN3 STAT1 ELF3 CUX1 PITX3 1.5 FOXQ1 PBX3 Wnt Associated ATF2 KDM6A TFAM Increased Decreased Cholesterol Cholesterol B.

102 70 21

Fatty Acid Wnt3a

Figure 2 – Figure Supplement 1 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. A. B. FOXP1 TFAP2A TFAP2C FOXC1 FOXC2 SP2 2 MA0003.2 2 MA0524.1 FOXL1 FOXD3

1 bits 1 IRF1 bits MEF2D MEF2C FOXJ3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 FOXJ2 2 MA0810.1 position 2 MA0524.2 position MEF2B MEF2A

FOXJ1 bits 1 bits 1 EGR1 KLF4 HEY2 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 SP3 EGR2 position position FOXI1 RREB1 PAX5 FOXK1 TFAP2C ZBTB3 C. TCFAP2A TBX1 75 KLF5 KLF1 NR2F1 SOX21 POU3F1 TCF4 50 POU3F3 KLF7 SP8 HEY1 ID4 FOXG1 ZFX 25

NR2F6 Sites TFAP2 E2F6 FOXA1 FOXD2 POU3F2 ATG CPEB1 0 ORF ERG -3000 -2500 -2000 -1500 -1000 -500 ETS1 MGA HIF1A-ARNT KLF16 FOXB1 RARG SOX8 SOX7 IRF7 TFAP2A SOX12 SP4 0 1 2 3 4 5 6 7 8 9 10 11 12 -log(pValue)

Figure 2 – Figure Supplement 2 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

A. 15 B. Control CM Wnt3a CM

2 6 10 24 2 6 10 24 Time (h)

10 DDIT3

5 Normalized mRNA amouts Normalized mRNA

Tubulin 0 0 5 10 15 20 25 Time (hours)

Figure 2 – Figure Supplement 3 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. A. B.

A. 30 B. 30

25 25

20 20

15 15

10 10 Number of Lipid Droplets Per Cell Number of Lipid Droplets Per Cell 5 5

0 0

Control Control DMSO Torin-2 DMSO Torin-2 SREBP2 SREBP2 SREBP1aSREBP1c SREBP1aSREBP1c PF-429242 PF-429242

+ Control CM + Wnt3a CM + Control CM + Wnt3a CM

Figure 2 – Figure Supplement 4 bioRxiv preprint doi: https://doi.org/10.1101/282376; this version posted March 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. A. p-value 9.0x10-6 TFAP2C 1.8x10-5 KLF10 1.8x10-5 SREBF1 2.0x10-5 PER2 2.9x10-5 CEBPE 2.9x10-5 MLXIPL 3.5x10-5 KLF5 PPARG CEBPB DDIT3 -2500 -2000 -1500 -1000 -500 0

* * B. * C. 5 4 Control CM Wnt3a CM * 4 3 * * 3

2 2

1 1

0 0

Normalized Number of Lipid Droplets Mock DDIT3 Normalized Number of Lipid Droplets

APC Non- APC/ DDIT3 Target DDIT3

Figure 4 – Figure Supplement 1