ABSTRACT
Perturbations in mitochondrial function and homeostasis are pervasive in lysosomal storage diseases, but the underlying mechanisms remain unknown. Here, we report a transcriptional program that represses mitochondrial biogenesis and function in lysosomal storage diseases Niemann-Pick type C (NPC) and acid sphingomyelinase deficiency (ASM), in patient cells and mouse tissues. This mechanism is mediated by the transcription factors KLF2 and ETV1, which are both induced in NPC and ASM patient cells. Mitochondrial biogenesis and function defects in these cells are rescued by the silencing of KLF2 or ETV1. Increased ETV1 expression is regulated by KLF2, while the increase of KLF2 protein levels in NPC and ASM stems from impaired signaling downstream sphingosine-1-phosphate receptor 1 (S1PR1), which normally represses KLF2. In patient cells, S1PR1 is undetectable at the plasma membrane and thus unable to repress KLF2. This manuscript provides a mechanistic pathway for the prevalent mitochondrial defects in lysosomal storage diseases.
INTRODUCTION
Lysosomal storage diseases are a group of severe diseases caused by mutations in genes encoding for lysosomal proteins, and are referred to as storage diseases because one common phenotype is the accumulation of undigested substrates in the lysosomes, with the consequent enlargement and loss of function of the organelle (Parenti et al., 2015). The lysosomes have far-reaching roles beyond the “recycling bin” paradigm, and are key players in nutrient sensing and metabolic regulation (Ballabio, 2016; Lim and Zoncu, 2016; Settembre et al., 2013). Furthermore, lysosomes are essential for the process of macroautophagy, and thus for the selective autophagy of mitochondria, the main mechanism to degrade dysfunctional mitochondria (Pickles et al., 2018). Mitochondrial perturbations have been widely reported in several lysosomal storage diseases (Platt et al., 2012; Plotegher and Duchen, 2017), including neuronal ceroid lipofuscinosis, Gaucher and Niemann-Pick diseases (Jolly et al., 2002; Lim et al., 2015; Osellame et al., 2013; Torres et al., 2017; Wos et al., 2016). Nevertheless, it remains unclear why mitochondrial dysfunction is so prevalent in lysosomal storage diseases.
In this study, we focus on two lysosomal storage diseases, Niemann-Pick type C (NPC) and acid sphingomyelinase deficiency. NPC is caused by mutations in the gene NPC1 or, less commonly, NPC2 (Patterson and Walkley, 2017; Schuchman and Wasserstein, 2016). NPC1 and NPC2 encode proteins involved in sphingomyelin and cholesterol efflux from the lysosome (Platt, 2014). Acid sphingomyelinase deficiency, also known as Niemann-Pick A/B, is caused by mutations in the gene SMPD1 encoding acid sphingomyelinase (ASM). ASM catalyzes the breakdown of sphingomyelin into ceramide and phosphorylcholine (Schuchman and Wasserstein, 2016). Interestingly, accumulation of cholesterol, sphingosine, sphingomyelin and glycosphingolipids in the lysosomes are observed both in Niemann-Pick and acid sphingomyelinase deficiency cells and tissues (Leventhal et al., 2001; Vanier, 1983).
The NPC1 knock-out mouse (NPC1−/−) and a knock-in of the most common NPC1 patient mutation I1061T (Praggastis et al., 2015) are established models of Niemann-Pick type C disease (Loftus et al., 1997). Both NPC1−/− and NPC1I1061T mice recapitulate most of the neuropathological phenotypes of the disease, with the disease onset occurring earlier in the NPC1−/− mice. The ASM knock-out mouse (ASM−/−) is a widely used model of ASM deficiency (Horinouchi et al., 1995).
Mitochondria are fundamental metabolic organelles in the cell, harboring key pathways for aerobic metabolism such as the citrate cycle, the key integrator metabolic pathway, as well as the respiratory chain and oxidative phosphorylation, Fe-S cluster and heme synthesis (Pagliarini and Rutter, 2013). They are also recognized as a major cellular signaling platform, with far-reaching implications on cell proliferation, stem cell maintenance, cellular immunity and cell death (Kasahara and Scorrano, 2014; Raimundo, 2014). Mitochondria are composed of about 1000 proteins, of which only 13 are encoded by mitochondrial DNA (mtDNA) (Pagliarini et al., 2008). The other ~1000 proteins are encoded by nuclear genes, and imported to the different sub-mitochondrial compartments (e.g., matrix, inner membrane, outer membrane, intermembrane space) by dedicated pathways (Wiedemann and Pfanner, 2017).
The large number of proteins that are nuclear-encoded and imported to mitochondria imply the need for regulatory steps that ensure the coordination of the process of mitochondrial biogenesis. This is often regulated at transcript level, by transcription factors that promote the expression of nuclear genes encoding for mitochondrial proteins (Scarpulla et al., 2012). One of the best characterized is the nuclear respiratory factor 1 (NRF1), which stimulates the expression of many subunits of the respiratory chain and oxidative phosphorylation, but also of genes necessary for mtDNA maintenance and expression, such as TFAM (Evans and Scarpulla, 1989, 1990). Other transcription factors, such as estrogen-related receptor α (ERRα) and the oncogene myc, also act as positive regulators of mitochondrial biogenesis (Herzog et al., 2006; Li et al., 2005). Several co-activators also participate in the regulation of mitochondrial biogenesis, of which the co-activator PGC1α (peroxisome proliferator-activated receptor-gamma, co-activator 1 α) is the best characterized (Wu et al., 1999). PGC1a can interact with NRF1 or ERRα and stimulate mitochondrial biogenesis (Scarpulla et al., 2012). No transcriptional repressors of mitochondrial biogenesis have so far been described. Impaired or uncoordinated mitochondrial biogenesis often results in impaired mitochondria leading to pathological consequences (Cotney et al., 2009; Raimundo et al., 2012).
Here, we identify the transcription factors KLF2 and ETV1 as transcriptional repressors of mitochondrial biogenesis. The up-regulation of these two proteins in patient cells and mouse tissues of two lysosomal diseases, Niemann-Pick type C and ASM deficiency, underlies the mitochondrial defects observed in these syndromes. The silencing of ETV1 and, particularly, KLF2, is sufficient to return mitochondrial biogenesis and function to control levels.
RESULTS
Expression of mitochondria-related genes is decreased in NPC1−/− tissues
Mitochondrial homeostasis and function is impaired in many lysosomal storage diseases. The two main axes of mitochondrial homeostasis are biogenesis and demise (by selective autophagy, designated mitophagy). Given that lysosomal diseases are characterized by impaired autophagy (Settembre et al., 2008), it is expectable that mitophagy is also impaired. However, it remains unknown how mitochondrial biogenesis is affected in lysosomal storage diseases.
To assess mitochondrial biogenesis at transcript level in a systematic manner, we resorted to a publicly-available transcriptome dataset of NPC1−/− mice liver and brain, the two tissues most affected in Niemann-Pick type C. The dataset included both pre-symptomatic and symptomatic animals (Alam et al., 2012). To monitor the effects of Niemann-Pick disease on transcriptional regulation of mitochondrial biogenesis, we started by establishing a comprehensive list of mitochondria-related genes. We used a published mitochondrial proteome (MitoCarta, (Pagliarini et al., 2008) see methods for details), and converted the protein names to the corresponding ENSEMBL gene name to generate the “mitochondria-associated gene list”. The process is illustrated in Figure 1A. We prepared a second list which included only the respiratory chain and oxidative phosphorylation subunits (“RC/OXPHOS gene list”). As controls, we prepared “gene lists” for lysosomes, peroxisomes, Golgi and endoplasmic reticulum using the same strategy. The proteomes used to build the organelle-specific gene lists are detailed in the methods section (Table I). Next, we used transcriptome data from asymptomatic and symptomatic brain and liver of NPC1−/− and corresponding WT littermates to determine how the organelle gene lists were affected.
First, we assessed the average expression of lysosomal genes in NPC1−/− brain and liver, to verify the validity of our “organelle gene list” approach in this dataset. We have shown earlier that the average expression level of an organelle-gene list is a good indicator of the activity of the transcriptional program of biogenesis for that organelle (Fernandez-Mosquera et al., 2017). The average expression of lysosomal genes was significantly increased in the asymptomatic NPC1−/− brain and liver (Supplementary Figure S1A), and increased further with the onset of the disease in NPC1−/− brain and liver (Supplementary Figure S1A), in agreement with the expected increase in the expression of lysosomal genes in lysosomal storage diseases.
Then, we measured the average expression of the “mitochondrial gene list” in NPC1−/− brain and liver. Mitochondria-associated genes were up-regulated in pre-symptomatic NPC1−/− brain, and down-regulated in symptomatic brain (Figure 1B). In the liver, the average expression of mitochondria-associated genes was not significantly changed in the pre-symptomatic group, but was robustly decreased in the symptomatic NPC1−/− mice (Figure 1B). When looking only at the “RC/OXPHOS gene list”, the pattern was similar but the magnitude of the changes was more robust (Figure 1C). These results are not due to a small number of genes skewing the whole population, since the proportion of mitochondrial genes in the differentially expressed gene lists for NPC1−/− brain (Supplementary Figure S2A-C) and liver (Supplementary Figure S2D-F) increases robustly (about 5-fold) with disease onset. These results highlight a general trend towards a global down-regulation of mitochondrial genes under chronic lysosomal malfunction.
In order to determine if this effect was specific to mitochondria or also observed in other organelles, we tested how the average expression of peroxisomal-, endoplasmic reticulum-and Golgi-specific genes was affected. The expression of peroxisomal genes was not affected in NPC1−/− brain, but was down-regulated in both asymptomatic and symptomatic NPC1−/− liver (Supplementary Figure S1B). The expression of endoplasmic reticulum-related and Golgi-related genes was not significantly altered (Supplementary Figure S1B). These results suggest that lysosomal stress caused by absence of NPC1 in multiple tissues specifically affects the expression of mitochondrial genes, although disease onset also results in a liver-specific repression of peroxisomal genes.
Mitochondrial biogenesis and function are impaired in NPC and ASM patient cells and tissues
To verify the results from the large-scale transcriptional analysis of NPC1−/− tissues, we tested the expression of several genes encoding for mitochondrial proteins in the livers of NPC1−/− mice. The genes tested encode for subunits of the respiratory chain complex I (NDUFS3 and ND6), complex II (SDHA), complex III (CYTB) and complex IV (COX5A, COX1). ND6, CYTB and COX1 are encoded by mtDNA, while all the others are nuclear-encoded. We observed a robust and consistent decrease in the transcript levels of mitochondria-related genes in the livers of NPC1−/− mice (Figure 2A) compared to their respective WT littermates. A similar reduction on the expression of mitochondria-associated genes was also observed in NPC patient fibroblasts (Figure 2B) whose lysosomal phenotype has already been characterized (Kirkegaard et al., 2010).
The accumulation of cholesterol and sphingomyelin in the lysosomes is common to both NPC and acid shingomyelinase (ASM) deficiency (Pentchev et al., 1984; Reagan et al., 2000; Leventhal et al., 2001; Tamura et al., 2006; Lloyd-Evans et al., 2008; Tamasawa et al., 2012; Lee et al., 2013; Platt. 2014). However, while mitochondria in NPC also present increased levels of cholesterol, this does not happen in ASM deficiency (Torres et al., 2017). Since excessive mitochondrial cholesterol can impair mitochondrial function (Torres et al., 2017), we tested if ASM deficiency would also have a repressive effect on mitochondrial biogenesis. Similar to the NPC findings, we observed a decrease in the expression of mitochondria-associated genes in the ASM−/− liver compared to the wt littermates (Figure 2C) as well as in patient fibroblasts of ASM deficiency (Figure 2D).
To assess if this down-regulation of mitochondrial biogenesis in NPC and ASM deficiency had functional consequences for respiratory chain efficiency, we measured the amounts of mitochondrial superoxide, a by-product of the mitochondrial respiratory chain known to be produced in higher amounts when mitochondria are not functioning optimally (Raimundo et al., 2012; Raimundo, 2014), which can be estimated using a superoxide-sensitive mitochondria-targeted dye, MitoSox. We observed an increase in MitoSox intensity in patient fibroblasts with NPC (Figure 2E) and ASM deficiency (Figure 2F) denoting increased superoxide levels which are indicative of poor mitochondrial performance. Altogether, these results show that the biogenesis of mitochondria is repressed in NPC- and ASM-deficient cells and tissues, and that the existing mitochondria are not functioning optimally. Furthermore, the mitochondrial impairments are likely unrelated to the levels of cholesterol in mitochondria (known to be high in NPC but normal in ASM; Torres et al., 2017), and seem rather a consequence of the lysosomal saturation in NPC and ASM deficiency.
Impaired mitochondrial respiration in NPC and ASM deficiency
To further characterize the impact of lysosomal disease on mitochondrial function, we focused on the ASM-deficient fibroblasts, which showed a more robust decrease of mitochondrial biogenesis than NPC and do not have the confounding factor of excessive mitochondrial cholesterol. We used cells from two patients of ASM deficiency, one of which had the lysosomal phenotype already characterized (Corcelle-Termeau et al., 2016). Additionally, we also employed a line from a patient with compound heterozygous loss-of-function mutations in SMPD1 (the gene encoding ASM), which have severe ASM deficiency (5% activity left). The lysosomal impairments in this line have not yet been characterized besides patient diagnosis, therefore we first evaluated lysosomal function in these fibroblasts. One of the consequences of lysosomal dysfunction is the accumulation of autophagic substrates, such as the protein p62 (also known as Sequestosome 1, SQSTM1) as well as autophagosomes (Settembre et al., 2008). We assessed the levels of p62/SQSTM1 and LC3B-II, a marker of autophagosomal mass, by Western blot, and found both sharply increased in the ASM-deficient fibroblasts, as expected (Supplementary Figure S3A). We also assessed the lysosomal proteolytic capacity, by measuring the degradation of the lysosomal substrate DQ-BSA. DQ-BSA is a polymer of fluorescently-tagged bovine serum albumin, which accumulates in the lysosomes. The fluorescence is quenched in the polymeric form and detectable in the monomers. As the lysosomal proteases start cleaving DQ-BSA and releasing monomers, fluorescence starts increasing, and the rate of fluorescence increase is proportional to the activity of lysosomal proteases. We observed a strong decrease in DQ-BSA degradation rate in the ASM fibroblasts (Supplementary Figure S3B). These results support a strong impairment of lysosomal function in ASM-deficient cells used in this study.
We then set to characterize mitochondrial function. First, we monitored the oxygen consumption rate (OCR). This was done with a high-throughput real-time respirometer, which allows the measurement under multiple conditions, such as basal medium, inhibition of oxidative phosphorylation (when OCR is inhibited) and uncoupled respiratory chain (when OCR occurs unrestrained). We observed a robust decrease in OCR in ASM-deficient fibroblasts which lasted across all conditions tested: basal medium, inhibition of the oxidative phosphorylation with oligomycin, and uncoupling of respiratory chain and oxidative phosphorylation by FCCP (Figure 3A). We determined that the ASM-deficient fibroblasts have 80% decrease in the OCR compared to the control cells in basal conditions (Figure 3B), and about 70% decrease in maximal (uncoupled) conditions (Figure 3B). This result shows a very robust decrease of mitochondrial OCR in ASM-deficient cells. We further observed that the ASM-deficient cells also had a lower amount of TFAM (Figure 3C), a nuclear-encoded protein needed for the stability and transcription of mitochondrial DNA (mtDNA). mtDNA is present at multiple copies per cell, and the copy number is tightly correlated with the amount of TFAM. Accordingly, we find that the mtDNA copy number is decreased in ASM-deficient cells (Figure 3D). These results further support that the ASM-deficient fibroblasts have decreased mitochondrial mass. Overall, ASM-deficient cells present decreased mitochondrial biogenesis, decreased mitochondrial mass and impaired mitochondrial function.
Acid sphingomyelinase generates ceramide, which is itself a powerful signaling lipid, and can be metabolized by acid ceramidase into sphingosine and other signaling lipids. To clarify if the activity of acid ceramidase was affecting the mitochondrial phenotypes observed in ASM-deficiency, we treated control cells with desipramine, a pharmacological inhibitor of both acid sphingomyelinase and acid ceramidase (Zeidan et al, 2006), and measured mitochondrial biogenesis, OCR and superoxide. The transcript levels of mitochondria-related genes were overall down-regulated in desipramine-treated fibroblasts (Suppl. Fig. S4A), which also presented decreased respiratory activity (Suppl. Fig. S4B), with a ~50% decrease in basal OCR and ~60% decrease in uncoupled OCR (Suppl. Fig. S4C). An increase in the levels of superoxide was also observed (Suppl. Fig. S4D).
Since one of the known consequences of ASM deficiency is accumulation of cholesterol in the lysosomes (Lloyd-Evans et al., 2008; Oninla et al., 2014), and given that we observed similar perturbations on mitochondrial homeostasis in ASM- and NPC-deficient patient fibroblasts, we tested if pharmacological inhibition of NPC1 would also be sufficient to impact mitochondrial biogenesis and function. We treated control cells with the NPC1 inhibitor U18666A (Lu et al., 2015), and observed decreased expression of mitochondria-associated genes (Supplementary Figure S5A), lower respiration (Suppl. Fig. S5B) with ~30% lower basal OCR and ~50% lower uncoupled OCR (Suppl. Fig. S5C). Finally, the treatment with U18666A resulted in increased superoxide levels (Suppl. Fig. S5D). Thus, pharmacological inhibition of ASM or NPC1, resulting in both cases in accumulation of lysosomal sphingomyelin and cholesterol, is sufficient to cause decreased expression of mitochondrial genes and impaired mitochondrial respiratory chain activity. This effect seems to be independent of acid ceramidase activity.
KLF2 and ETV1 are up-regulated in NPC1−/− tissues and repress transcription of mitochondria-associated genes
Having established a clear mitochondrial phenotype in NPC and ASM deficiency, we set out to identify the underlying mechanism. The robust decrease in the expression of hundreds of mitochondria-related genes in NPC1−/− brain (Suppl. Figure S1C) and NPC1−/− liver (Suppl. Figure S1F) suggests the involvement of a coordinated transcriptional program, and therefore of transcriptional regulators, such as transcription factors. To determine which transcription factors might be mediating the repression of mitochondria-associated genes, we took an unbiased bottom-up approach to determine potential transcriptional regulators. Given that the whole mitochondrial gene list has ~1000 genes, we focused on the RC/OXPHOS list, which shows the same behavior as the complete mitochondrial gene list (as shown in Figures 1 and Suppl. Figure S2) and has a more manageable size (~100 genes). Using the Genomatix Gene2Promoter tool, we obtained the genomic sequences (Mus musculus) of the promoter regions of the RC/OXPHOS genes, from −500 base pairs upstream the transcription start site, to +100 base pairs downstream. This region is sufficient to account for the regulation of gene expression by transcription factors in many promoters of mitochondrial genes (Gleyzer et al., 2005; Virbasius and Scarpulla, 1994). We then used Genomatix Matinspector tool to analyze the gene promoters for transcription factor binding sites (cis-elements), and identified those statistically enriched (illustrated in Figure 4A). The most overrepresented cis-elements in the promoters of RC/OXPHOS genes are the transcription factor families SP1, E2F, Krueppel-like factors (KLF) and ETS factors (Table II). In parallel, as control, we carried out a similar approach for the lysosomal gene list (whose expression is increased, in contrast with the mitochondrial genes) and observed that the SP1 and E2F families were also significantly enriched in the promoters of lysosomal genes (Supplementary Table I). Given that the expression of lysosomal genes and mitochondrial genes is affected in opposite ways, we reasoned that it would be unlikely that the same transcription factors were driving two opposite processes. For this reason, we proceeded only with the KLF and ETS families, which only scored as significantly enriched in the mitochondrial promoters (Table II).
Next, we again resorted to the transcriptome dataset of NPC1−/− brain and liver to determine if any transcription factors in the KLF2 and ETS families were predicted to have increased or decreased activity during NPC disease progression. Using Ingenuity Pathway Analysis, we determined which transcription factors scored as significant regulators in these tissues (Supplementary Table II). The only transcription factor of the KLF family meeting the criteria was KLF2. Several ETS family transcription factors have redundant binding sites (Hollenhorst et al., 2007), so we tested the three members that scored in the Genomatix promoter analysis, SPI1, ELK1 and ETV1. SPI1 is expressed in macrophages and not expressed in fibroblasts (Feng et al., 2008; Suzuki et al., 2012), and accordingly we could not detect the expression of SPI1 in control or patient fibroblasts, either at transcript or protein (data not shown). While ELK1 was not changed at transcript level (Supplementary Figure S6A), ETV1 was significantly increased in ASM deficiency patient fibroblasts (Supplementary Figure S6A). The transcript levels of KLF2 were not changed in ASM deficiency (Supplementary Figure S6B).
We then focused on KLF2 and ETV1 (Figure 4A). First, we tested if the levels of these proteins were affected in NPC- or ASM-deficient fibroblasts, by Western blotting, and found that both KLF2 and ETV1 were robustly up-regulated in both conditions, including the in-house as well as the previously characterized ASM-deficient line (Figure 4B). We again compared the ASM-deficient fibroblasts with control fibroblasts treated with the inhibitor of both ASM and acid ceramidase. Desipramine-treated fibroblasts yielded a similar result: both KLF2 and ETV1 are up-regulated at protein level (Supplementary Figure S7A, quantified in S7B) but only ETV1 transcript levels are significantly changed (Supplementary Figure S7C). Altogether, these results suggest that the accumulation of KLF2 in response to lysosomal lipid storage is regulated post-translationally, while ETV1 is regulated at transcript level.
Given that ETV1 and KLF2 are predicted by our promoter analysis to have binding sites in the promoters of the genes encoding for respiratory chain subunits, and that increased expression of these two transcription factors correlates with repression of respiratory chain genes, we reasoned that KLF2 and ETV1 might be mediating this repression. To explore this possibility, we took advantage of another publicly available transcriptome dataset of erythroid cells of KLF2−/− and WT mice (GSE27602). We observed an increase in the average transcript levels of the “mitochondria gene list” in the KLF2−/− cells compared to the WT littermates (Supplementary Figure S8). The effect is also observed, with higher magnitude, when measuring the average expression of the genes encoding for respiratory chain subunits (Supplementary Figure S8). These results suggest that KLF2 is able to repress mitochondrial biogenesis in vivo. In addition, it is noteworthy that several known ETV1 targets are mitochondrial genes, as previously shown by chromatin immunoprecipitation (Baena et al., 2013) and illustrated in Supplementary Figure 9. To test the effect of ETV1 on the expression of mitochondria-related genes, we expressed full length ETV1 (ETV1FL) as well as ETV1 lacking the DNA-binding domain (ETV11-334) in control fibroblasts (Figure 4C) and evaluated the effect on the expression of mitochondria-related genes. The overexpression of ETV1FL elicited a decrease in the transcript levels of most mitochondria-associated genes (Figure 4D). However, ETV11-334 did not repress the transcript levels of these mitochondrial-related genes. This result is coherent with the role of ETV1 as a repressor of mitochondrial biogenesis, and further demonstrates that this repression occurs via direct binding of ETV1 to DNA (Janknecht, 1996), thus validating our in silico promoter analysis. The unexpected increase in the transcript levels of mitochondria-related genes under overexpression of ETV11-334, unable to bind DNA, may be explained by ETV1 functioning as a homodimer (Poon, 2012). Therefore, overexpression of a mutant unable to bind DNA might titrate out the wild-type ETV1, thus effectively functioning as a dominant-negative ETV1 isoform, with the consequent activation of mitochondrial biogenesis.
Silencing of KLF2 and ETV1 in ASM deficiency rescues mitochondrial biogenesis and function
To test if KLF2 and ETV1 were indeed repressing mitochondrial biogenesis in ASM-deficient cells, we knocked-down ETV1 (Figure 5A) and KLF2 (Figure 5B), independently, in ASM-deficient fibroblasts. Both knock-downs were effective (Figures 5A-B), and both resulted in an increase of mitochondrial protein levels back to control levels, as exemplified for TFAM (Figures 5A-B). Interestingly, transcription factor nuclear respiratory factor 1 (NRF1), a known inducer of mitochondria-related gene expression, was also sharply down-regulated in ASM-deficient fibroblasts, and was rescued by the silencing of ETV1 or of KLF2. This result suggests a compound effect of repression of mitochondria-related genes by KLF2 and ETV1, combined with decreased activation of the expression of the same genes by NRF1. Notably, the transcript levels of mitochondria-associated genes, which are down-regulated in ASM-deficient fibroblasts, were increased by the silencing of ETV1 and even more robustly increased by KLF2 silencing (Figure 5C). This includes NRF1 and its closely related protein nuclear respiratory factor 2 (NRF2, also known as GABPA), again suggesting that these two transcription factors may be repressed by KLF2 and ETV1. Importantly, the improvement in the expression of mitochondria-associated genes by silencing KLF2 or ETV1 is not due to an improvement of the lysosomal phenotype. We measured readouts of lysosomal function such as the accumulation of autophagosomal marker LC3BII or autophagy substrate p62, by Western blot, and found that silencing of KLF2 or ETV1 had no impact on the lysosomal dysfunction in ASM-deficient cells (Supplementary Figure S10A-B). Finally, mitochondrial respiration was partly rescued in ASM-deficient fibroblasts by the knock-down of ETV1 and robustly rescued by KLF2 silencing (Figure 5D). Altogether, these results show that KLF2 and ETV1, two transcription factors that are increased in ASM and NPC deficient fibroblasts and hyperactive in NPC1−/− tissues, repress mitochondrial biogenesis and that their silencing restores mitochondrial biogenesis and function in ASM fibroblasts.
KLF2 regulates ETV1 in an ERK-dependent manner
The silencing of KLF2 had a more robust effect on the recovery of mitochondrial function than the silencing of ETV1. For this reason, we set to understand if these transcritption factors work in parallel pathways or if they are epistatic. First, we checked if these two transcription factors were epistatic, and observed that the silencing of KLF2 in ASM-deficient fibroblasts results in the ablation of ETV1 (Figure 6A). ETV1 silencing has no effect on KLF2 (Figure 6A). Since we have shown above that ETV1 is regulated at transcript level, this result implies that KLF2 regulates (activates) the transcription of the gene encoding ETV1, in agreement with the increased transcript levels of ETV1 in ASM-deficient fibroblasts.
Next, we tested known signaling modulators of KLF2 or ETV1 in ASM-deficient fibroblasts. Akt signaling down-regulates KLF2 (Skon et al., 2013), and we observed that Akt seems deactivated in ASM-deficient fibroblasts, as assessed by decreased phosphorylation of Akt Serine 473 (Figure 6B). ERK is a positive effector of ETV1 (Janknecht, 1996), and we found ERK signaling increased in ASM-deficient fibroblasts (Figure 6B). mTORC1 signaling is often involved in lysosomal stress signaling, and we found it activated in ASM-deficient fibroblasts, as assessed by the phosphorylation of p70S6 kinase (P70S6K) Threonine 389 (Figure 6B). AMPK signaling, which regulates mTORC1 as well as biogenesis of mitochondria and lysosomes, was not affected, as assessed by phosphorylation of acetyl-CoA carboxylase (ACC) (Figure 6B). Inhibition of mTORC1 signaling in ASM-deficient fibroblasts by treatment with the mTORC1 inhibitor torin1 had no effect on the expression of mitochondria-related genes or mitochondrial function (data not shown).
We next tested if the activation of ERK signaling was related to the increased levels of ETV1. We treated the ASM-deficient fibroblasts with the ERK inhibitor U0126, which led to the ablation of ERK signaling, as expected (Figure 6C). KLF2 was mostly unaffected by ERK inhibition (Figure 6C). However, ETV1 was returned to control levels (Figure 6C), suggesting that induction of ETV1 by KLF2 requires active ERK signaling.
S1PR1 signaling dynamically regulates KLF2 and mitochondrial biogenesis and function
Next, we sought to identify the mechanism leading to KLF2 up-regulation. Since one of the consequences of lysosomal malfunction is the stalling of the autophagy pathway, we tested if KLF2 could be induced by perturbations in autophagy, such as inhibition of autophagosome formation (Atg5 silencing) or inhibition of the fusion of autophagosomes to lysosomes (syntaxin 17 silencing). However, no effect was observed in KLF2 (data not shown).
KLF2 is known to be negatively regulated by Akt signaling (Skon et al., 2013), which is down in ASM-deficient fibroblasts (Figure 6B). Interestingly, one of the genes induced by KLF2 is the sphingosine-1-phosphate receptor 1 (S1PR1) (Skon et al., 2013), which we find up-regulated at transcript level in ASM-deficient fibroblasts (Supplementary Figure S11). S1PR1 and KLF2 are part of a signaling network in which the activity of the receptor represses its own expression by activating Akt, which then phosphorylates KLF2 and marks it for proteasomal degradation (Sinclair et al., 2008; Skon et al., 2013). Interestingly, the S1PR1 receptor has been previously shown to affect mitochondrial function in T cells, but the mechanisms remained unexplored (Mendoza et al., 2017). Furthermore, the levels of sphingosine-1-phosphate (S1P) are decreased in the plasma of NPC patients (Fan et al., 2013), suggesting that signaling elicited by S1P may be down-regulated.
Given the connections between S1PR1, KLF2 and our findings implicating KLF2 in the regulation of mitochondrial-related gene expression, we decided to test if perturbation of the S1PR1 pathway in Niemann-Pick could explain the up-regulation of KLF2 and, accordingly, the expression of mitochondria-related genes. To this end, we treated control fibroblasts with either a selective agonist (Sew2871) or with a selective inhibitor (W146) of S1PR1. Next, we measured the effects on mitochondria. We observed that the activation of S1PR1 by the agonist Sew2871 results in increased transcript levels of mitochondria-related genes (Figure 7A). Reciprocally, inhibition of S1PR1 by W146 leads to decreased transcript levels of these genes. Furthermore, activation of S1PR1 results in increased mitochondrial OCR under basal and uncoupled conditions (Figure 7C-D), while the inhibition of the receptor results in a robust inhibition of mitochondrial OCR (Figure 7E-F). Finally, we observed that KLF2 responds as expected to S1PR1 activity. When S1PR1 is activated, KLF2 levels decrease (Figure 7G), while inhibition of S1PR1 results in increased KLF2 abundance (Figure 7H). Notably, the protein levels of mitochondrial proteins TFAM, cytochrome oxidase I (mtCOI) and succinate dehydrogenase subunit b (SDHB) are all increased when KLF2 is down-regulated (S1PR1 activation, Figure 7G), and all decreased when KLF2 levels are increased (S1PR1 inhibition, Figure 7H). These results underscore that the S1PR1-KLF2-mitochondrial biogenesis pathway can be dynamically regulated in control fibroblasts. Furthermore, these data suggest that the S1PR1 pathway is down-regulated in ASM-deficient fibroblasts, given the increased levels of KLF2 and the decreased expression of mitochondria-related genes. Interestingly, the expression of sphingosine kinase 1 (SPHK1), which generates S1P that can be exported to the extracellular space, is down-regulated in ASM-deficient fibroblasts (Supplementary Figure S11). Similarly, SPHK2, which generates S1P intracellularly, in mitochondria and endoplasmic reticulum, is also down-regulated in ASM-deficient fibroblasts (Supplementary Figure S11). Altogether, these results suggest that S1P signaling via S1PR1 is profoundly down-regulated in ASM-deficient fibroblasts, and that this event is at the root of the up-regulation of KLF2 and its downstream consequences, particularly ETV1 induction and inhibition of mitochondrial biogenesis.
S1PR1 is mislocalized in ASM-deficient cells and unresponsive to activators
Given the apparent down-regulation of S1PR1 signaling in ASM deficiency, we set to test if reactivation of the S1PR1 pathway in ASM-deficient fibroblasts would rescue the expression of mitochondria-related genes as well as mitochondrial function. We treated control and ASM-deficient fibroblasts with the S1PR1 agonist Sew2871, and in agreement with our data shown above, we found an increase in the expression of mitochondria-related genes in control fibroblasts (Figure 8B). However, and surprisingly, the ASM-deficient fibroblasts did not respond to the treatment with the S1PR1 agonist: no change was observed in the transcript levels of mitochondria-related genes (Figure 8B). Similar results were obtained when using S1P instead of the agonist (data not shown). These results suggest that the S1PR1 receptor is absent or inaccessible to extracellular cues, implying that it may be sequestered away from the plasma membrane. The protein levels of S1PR1 are not changed in ASM-deficient fibroblasts (Figure 8C). Therefore, we tested if S1PR1 localization at the plasma membrane was affected in ASM-deficient cells. We used a PE-conjugated antibody against S1PR1 for flow cytometry, in non-permeabilized cells, and determined the amount of plasma membrane labelling in control and ASM-deficient fibroblasts. As negative control, we treated cells with FTY720, which antagonizes S1PR1 signaling by promoting its endocytosis. The treatment with FTY720 reduced the levels of S1PR1 at the plasma membrane, which were robustly decreased in ASM-deficient cells. Thus, the mislocalization of S1PR1 in ASM-deficient cells, and consequent decreased signaling, explain the increase in KLF2 signaling and its downstream consequences.
DISCUSSION
This study addresses a novel mechanism by which mitochondria are impaired in lysosomal lipid storage diseases. We show here that the transcription factors KLF2 and ETV1 repress the expression of genes encoding mitochondrial proteins. Both KLF2 and ETV1 are up-regulated in patient cells from Niemann-Pick type C and acid sphingomyelinase deficiency, and their silencing, particularly KLF2, is sufficient to return mitochondrial biogenesis and function to control levels. Decreased signaling through sphingosine-1-phosphate receptor 1 (S1PR1) activates KLF2, which induces the expression of ETV1, culminating in the down-regulation of mitochondrial biogenesis.
The transcriptional regulation of mitochondrial biogenesis is known since the identification of the transcription factor nuclear respiratory factor 1 (NRF1), which induces the expression of many respiratory chain and mtDNA maintenance genes (Scarpulla et al., 2012). Several other transcription factors have been shown to stimulate mitochondrial biogenesis, such as estrogen related receptor α (ERRα) or the oncogene myc (Scarpulla et al., 2012). The role of the co-activator PGC1α (peroxisome proliferation activated receptor gamma, co-activator 1α) has also been shown to promote NRF1- and ERRα-mediated mitochondrial biogenesis (Wu et al., 1999). However, to our knowledge, no transcription factor has previously been shown to repress mitochondrial biogenesis. Thus, the roles of KLF2 and ETV1 as repressors of mitochondrial biogenesis, shown in this manuscript, open a new paradigm on the transcriptional regulation of the mitochondrial biogenesis. Notably, the transcription factor NRF1, which is a known positive regulator of mitochondrial biogenesis and is down-regulated in fibroblasts with acid sphingomyelinase deficiency, is also repressed by KLF2 and ETV1. It therefore seems that KLF2, ETV1 and NRF1 may form a transcriptional regulatory network that dynamically regulates mitochondrial biogenesis, with accelerator (NRF1) and brakes (KLF2 and ETV1). The transcriptional network between KLF2, ETV1 and NRF1, as well as the involvement of other transcription factors such as ERRα, myc, or co-activators such as PGC1α, warrants further research. Interestingly, another Krüppel-like factor, KLF4, was recently shown to promote mitochondrial biogenesis in the heart (Liao et al., 2015), implying that the repressive behavior of KLF2 is a specificity of this transcription factor and not a characteristic transversal to the whole Krüppel-like factor family.
It is particularly interesting that a transcriptional network repressing mitochondrial biogenesis appears robustly active in lysosomal diseases. The role of lysosomes in cellular function has been subject of increasing attention, both regarding its physiological roles as a signaling platform as well as the pathological consequences of lysosomal defects in lysosomal storage diseases (Settembre et al., 2008; Ballabio and Gieselmann, 2009; Perera and Zoncu, 2016; Platt et al., 2012). Numerous studies describe the impact of lysosomal defects on the function of other organelles, particularly mitochondria, in several lysosomal storage diseases (Diogo et al., 2017; Plotegher and Duchen, 2017; Raimundo et al., 2016; Torres et al., 2017). Mitochondria are usually impaired in cells and tissues with primary lysosomal defects, with decreased oxygen consumption and increased production of superoxide and other reactive oxygen species (Jolly et al., 2002; Plotegher and Duchen, 2017). However, this is usually attributed to a decrease in autophagy (and mitophagy), with the consequent accumulation of damaged mitochondria in the cytoplasm. Our data in cellular and mouse models of Niemann-Pick-C disease and acid sphingomyelinase deficiency shows, however, that in addition to defective autophagy there is a signaling mechanism based on the induction of two transcription factors, KLF2 and ETV1, which repress mitochondrial biogenesis. This may represent a signaling circuit in which the cells with lysosomal defects repress the generation of an organelle whose degradation requires lysosomal function.
Interestingly, the up-regulation of KLF2 in ASM-deficient cells seems to be a consequence of impaired sphingosine-1-phosphate (S1P) signaling through S1P receptor 1 (S1PR1). This receptor had previously been implicated in the regulation of mitochondrial function in T cells, but the mechanism remained unclear (Mendoza et al., 2017). We show in this study that S1PR1 is a bona fide bi-directional regulator of mitochondrial function via the effect of KLF2 and ETV1 on mitochondrial biogenesis. Indeed, both the activation and the inhibition of S1PR1 impacted the expression of genes encoding mitochondrial proteins in control cells. Interestingly, the acid sphingomyelinase-deficient fibroblasts were non-responsive to agonists of S1PR1, which suggests that the receptor may be sequestered away from the plasma membrane in the patient cells. In support of this hypothesis, the amount of S1PR1 in the plasma membrane of the ASM-deficient cells is negligible, while the total protein levels of S1PR1 are similar to control cells. This result implies a mistargeting of S1PR1 in acid sphingomyelinase deficiency and Niemann-Pick disease, which is akin to other proteins aberrantly mislocalized away from the plasma membrane in these diseases, such as Met receptor tyrosine kinase or K-Ras (Zhu et al., 2016; Cho et al., 2015).
The accumulation of lipids such as sphingomyelin and cholesterol in the lysosomes in Niemann-Pick type C and acid sphingomyelinase deficiency is likely to result in deficiency of those lipids in other cellular locations. Thus, one conceivable cellular adaptation would be shutting down the mitochondrial respiratory chain and citrate cycle, which would allow to shunt citrate to the cytoplasm, where it can be converted by acetyl-CoA lyase to acetyl-CoA and used for lipid synthesis (Bauer et al., 2005; Wellen et al., 2009).
The interplay between mitochondria and lysosomes is a relatively novel concept that only now starts being grasped (Diogo et al., 2017; Raimundo et al., 2016). Our study contributes to the understanding of how mitochondria and lysosomes are interdependent. By identifying a molecular mechanism involving a transcriptional network, we highlight that the communication between these two organelles goes beyond metabolic cues, and involves complex cellular signaling. This is akin to what is observed in mitochondrial malfunction, which also impacts the transcriptional programs of lysosomal biogenesis (Fernandez-Mosquera et al., 2017; Nezich et al., 2015).
The contribution of the signaling pathways mediating communication between mitochondria and lysosomes for pathology certainly warrants further exploration, not just in mitochondrial and lysosomal diseases but also in the context of neurodegenerative diseases that arise from defects of either of these organelles.
MATERIAL AND METHODS
Drugs and cellular treatments
The following drugs were used for cellular treatments: 50μM Chloroquine (Sigma, C6628), 1μM Oligomycin (Sigma, O4876), 2μM Carbonyl cyanide 3-fluorophenylhydrazone (FCCP) (Sigma, C2920), 1μM Rotenone (Sigma, R8875), 1μM Antimycin (Sigma, A8674), 40μM Desipramine (Biotrend, BG0162), 5μM Sew2871 (Cayman, 10006440), 20μM U0126 (Millipore, 662005), 10μM U18666A (Cayman, 10009085), 10μM W146 (Sigma-Aldrich, W1020) and 2μM FTY720 (Selleckchem, S5002).
Cell culture and transient transfections
Control and Niemann-Pick patient fibroblasts were grown in DMEM high glucose medium (Gibco, 11965) supplemented with 10% fetal bovine serum and 1% Penicillin/Streptomycin at 37°C and 5% CO2, in a humidified incubator, unless otherwise stated. Patient fibroblasts retained about 5% of the control activity of acid sphingomyelinase, and were collected and maintained according to the ethical guidelines of the UMG. Control and patient fibroblasts were transfected with siRNAs for ETV1 or KLF2 using electroporation (Amaxa kit, Lonza, V4XP-1024) or with scrambled control siRNA following manufacturer’s protocol. Additional control, human, adult primary fibroblasts were obtained ATCC (PCS-201-012). NPC1 patient cells were obtained from Coriell Institute for Medical Research (GM18398). The use of human cells for these studies was approved by the Ethical Commision of the Universitätsmedizin Göttingen.
XF medium
XF assay medium (Seahorse Bioscience, 100965-000) was supplemented with sodium pyruvate, glutamax and glucose following manufacturer’s recipe and the pH of medium was adjusted to 7.4.
Oxygen consumption rate measurements
OCR was measured in fibroblasts using the XF96 Extracellular Flux analyzer (Seahorse Bioscience) as described (53). Briefly, cells were seeded at 20000 cells per well in XF96 cell culture multi-well plates in DMEM medium and incubated for 24 hours in the growth conditions stated for all cell cultures. XF96 cartridges were incubated overnight in XF calibrant at 370C in a non-CO2 incubator. Prior to OCR measurements, the growth medium of cells was exchanged with XF medium and incubated at 370C in a non-CO2 incubator for 1hour. Inhibitors were diluted to appropriate concentrations in XF medium and loaded into corresponding microwells in the XF96cartridge plate. Following equilibration of sensor cartridges, XF96 cell culture plate was loaded into the XF96 Extracellular Flux analyzer at 370C and OCR was measured after cycles of mixing and acquiring data (basal) or inhibitor injection, mixing and data acquisition.
Western Blotting
Whole cell extracts of cultured fibroblast were prepared in 1.5% n-dodecylmaltoside (Roth, CN26.2) in PBS supplemented with protease and phosphatase inhibitor cocktail (Thermoscientific, 78442) as described (Raimundo et al, 2009). Protein concentrations of whole cell extracts were determined using a Bradford assay (Bio-Rad, 500-0006). 50μg of sample proteins per well were subjected to Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes (Amersham, Life Technologies). After blocking in 5% Milk in TBS tween, membranes were immunoblotted with the following antibodies: SQSTM1 (Abcam, ab110252), HPRT (Abcam, ab10479), KLF2 (Abcam, ab203591), ETV1 (Abcam, ab184120), LC3B (Cell signaling, 3868), Pan Akt (Cell signaling, 4691), Phospho Akt (Cell signaling, 4060), Total Erk1/2(Cell signaling, 4695), Phospho Erk1/2 (Cell signaling, 4376), TFAM (Abcam, ab138351), P70S6K1 (Cell signaling, 2708) phospho P70S6K1 (9234), ACC (Cell signaling, 3676), phospho ACC (Cell signaling, 3661), Nrf1 (Abcam, ab175932), Nrf2 (Proteintech, 21542-1-AP) and Uqcrc1 (Abcam, ab110252), Tfeb (Bethyl, A303-672A), Ndufs3 (Invitrogen, 459130), Sdhb, mtCO1 OXPHOS cocktail (Abcam, ab110413) and S1PR1 (Abcam, ab125074).. Band densitometric quantifications were determined using ImageJ software 1.48v.
Quantitative RT-PCR
RNA extraction and purification from fibroblasts were performed using Crystal RNA mini Kit (Biolab, 31-01-404). RNA extraction from mouse liver was performed using Trizol as described (Fernandez-Mosquera et al., 2017) followed by purification using Crystal RNA mini kit (Biolab, 31-01-404). RNA concentration and quality were determined using Nanodrop (PeqLab) and cDNA was synthesized with iScript cDNA synthesis kit (Bio-Rad, 178-8991) following manufacturer’s protocol. Each 8μl q-PCR was made of 4μl diluted cDNA, 0.2μl of each primer (from 25μM stock) and 3.6μl of iTaq Universal Sybr Green Supermix (Bio-Rad, 172-5124) and ran on the QuantStudio 6 Flex Real-Time PCR system (Applied Biosystems).
Flow Cytometry
Measurement of mitochondrial superoxide levels using MitoSOX Red Mitochondrial superoxide indicator (Molecular Probes, M36008) was performed by flow cytometry according to the manufacturer’s instructions. For S1PR1 plasma membrane localization, 1×106 control and ASM deficient fibroblasts treated with or without 2μM FTY720 were labelled in suspension with 10μL of PE-conjugated S1PR1 antibody (R and D systems, FAB2016P) for 1hour, washed twice in isotonic PBS supplemented with 1% BSA, resuspended in 200-400uL of buffer and subjected to flow cytometry analyses for the surface expression of S1PR1.
Statistical Analysis
The results obtained from at least three independent replicates were presented as mean ± SD unless otherwise indicated. P-values were determined using Student’s t test for two group comparisons or ANOVA for multi-group comparisons. *p<0.05 **p<0.01 ***p<0.001.
Dataset selection
In order to identify transcriptional signatures mediating interactions between organelles in Niemann-Pick pathology, we mined for microarray data involving Niemann-Pick mouse models from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo). Criteria for dataset selection included datasets with multiple replicates from several tissues. The dataset selected was GSE39621, which includes samples of brain, liver and spleen of mice before and after 6 weeks of age, when the symptoms of the disease start manifesting. Given that the spleen may contain immune cells in addition to splenocytes, and likely to have many more of non-splenocytes in the disease case, since spleen enlargement is a hallmark of the disease, we considered that the control and NPC1−/− were not directly comparable and thus used only the data relative to brain and liver.
Organelle-specific gene lists
We obtained organelle proteomes from up-to-date and comprehensive databases for mitochondrial (and respiratory chain subunits), lysosomal, peroxisomal, endoplasmic reticulum and Golgi proteomes (Table I). These protein IDs were converted to NCBI gene symbols, which were then used to identify the corresponding probeset names for different microarray matrices.
Microarray data analysis
We obtained mouse NPC1 wildtype, NPC1+/− and NPC1−/− in asymptomatic (less than 6 weeks old) and symptomatic (more than 6 weeks old) brain, liver and spleen from the GEO database (Alam et al., 2012). The controls for the NPC1 dataset are the wt mice in the brain but the heterozygous mice in the other tissues. We used the software GeneSpring (Agilent Technologies, Santa Clara, CA) to normalize the datasets by robust multi-array averaging (RMA) to normalize datasets (Raimundo et al., 2009). The datasets for all tissues originating from the same knock-out mouse and corresponding controls were normalized together. After normalization, we determined which transcripts had significantly different expression between NPC1−/− and controls for each individual tissue, using ANOVA. We also calculated the fold change from probe expression values between lysosomal disease and control mice for each tissue. The statistical filter was set at p-value<0.05, and the transcripts that pass the filter for each tissue represent the corresponding transcriptional signature.
To calculate the average expression of organelle-specific gene lists, we normalized each transcript to the average of the control samples, and calculated the average of the expression levels of all genes in each organelle-specific gene list. To determine if the difference observed between NPC−/− and controls was significant, we calculated the t-test p-value (unpaired, unequal variance) for the whole gene set using Microsoft Excel. Given that the lists have hundreds of genes, we performed a Bonferroni post-hoc correction. The adjusted p-values<0,05 were considered significant.
Pathway analysis and identification of transcriptional regulators
We employed a multi-dimensional strategy aimed at the identification of signaling pathways, as described (Raimundo et al., 2012; Raimundo et al., 2009; Schroeder et al., 2013; West et al., 2015). The transcriptional lists were imported to the software Ingenuity Pathway Analysis (IPA) (http://www.ingenuity.com), which then determines which pathways and transcriptional regulators are statistically enriched, using Fisher’s exact test. The statistical threshold was set at p<0.01.
Promoter analysis
To perform promoter analysis on the respiratory chain genes, we imported the respiratory chain gene list to the software Genomatix Suite (www.genomatix.de). Then we set a pipeline within the software suite, by first defining the promoters of the respiratory chain genes and then determining which transcription factors (TF) had binding sites on them. To locate the promoters, we use the Genomatix tool Gene2Promoter, and defined the promoter region from 500 base pairs upstream (−500) the transcription start site (TSS) until 100 base pairs downstream the TSS (+100). Given that some genes may have more than one promoter due to alternative splicing, we selected only the promoters that drive the expression of the transcript leading to the protein that functions as a respiratory chain subunit. The promoter sequences were then used to determine cis-elements and identify the corresponding TF, limiting the search to those TF that had at least a binding site in at least 85% of the promoters. The software provides a statistical assessment of the enrichment of the binding sites for each TF family in the promoters under analysis. We set a threshold of p<0.05 for the Fisher exact test p-value for each TF family enrichment. Then, we determine, for each significantly enriched family, which individual TF are included, and select as relevant TF those that have a binding site in at least 50% of the promoters under analysis.
Accession numbers
The publicly-available dataset used in this study is GSE39621 for Niemann Pick’s disease mouse model (NPC1−/−) (Alam et al., 2012).
Measurement of lysosomal proteolytic capacity
Lysosomal proteolytic capacity was measured using the DQ Red BSA Dye (Molecular Probes, D-12051) following manufacturer’s protocol. 1mg of dye is resuspended in 1mL of PBS and 100ul of the resuspended dye is added to 10ml of warm DMEM medium. Previously plated cells in a transparent 96 well-plate are loaded with 100ul per well each of the dye containing medium and incubated at 37°C for 1 hour. Cells are then washed twice with warm PBS and the medium is replaced with 100 μL/well of warm EBSS medium. The kinetics of DQ Red BSA digestion are recorded at respective excitation and emission maxima of 590nm and 620nm in a multi-plate reader over a 4h period.
CONFLICT OF INTEREST STATEMENT
The authors have no conflict of interest.
ACKNOWLEDGEMENTS
ERC Starting Grant 337327 and AMDA Research Grant (NR); Deutsche Forschungsgemeinschaft Emmy-Noether Award and Schram Stiftung Grant (IM); Deutsche Forschungsgemeinschaft SFB1190 (NR and IM).
We thank Dr. Ralf Janknecht for the ETV1 constructs.