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. 2018 Sep 25;7:e38839. doi: 10.7554/eLife.38839

The cargo receptor SURF4 promotes the efficient cellular secretion of PCSK9

Brian T Emmer 1,2, Geoffrey G Hesketh 3, Emilee Kotnik 2, Vi T Tang 2, Paul J Lascuna 2, Jie Xiang 2, Anne-Claude Gingras 3,4, Xiao-Wei Chen 2,5, David Ginsburg 1,2,6,7,8,✉
Editors: Randy Schekman9, Randy Schekman10
PMCID: PMC6156083  PMID: 30251625

Abstract

PCSK9 is a secreted protein that regulates plasma cholesterol levels and cardiovascular disease risk. Prior studies suggested the presence of an ER cargo receptor that recruits PCSK9 into the secretory pathway, but its identity has remained elusive. Here, we apply a novel approach that combines proximity-dependent biotinylation and proteomics together with genome-scale CRISPR screening to identify SURF4, a homologue of the yeast cargo receptor Erv29p, as a primary mediator of PCSK9 secretion in HEK293T cells. The functional contribution of SURF4 to PCSK9 secretion was confirmed with multiple independent SURF4-targeting sgRNAs, clonal SURF4-deficient cell lines, and functional rescue with SURF4 cDNA. SURF4 was found to localize to the early secretory pathway where it physically interacts with PCSK9. Deletion of SURF4 resulted in ER accumulation and decreased extracellular secretion of PCSK9. These findings support a model in which SURF4 functions as an ER cargo receptor mediating the efficient cellular secretion of PCSK9.

Research organism: Human

Introduction

PCSK9 is a proprotein convertase that acts as a negative regulator of the LDL receptor (Seidah et al., 2014). PCSK9 is synthesized primarily in hepatocytes and secreted into the bloodstream. Circulating PCSK9 binds to the LDL receptor and diverts it to lysosomes for degradation, thereby leading to decreased LDL receptor abundance at the hepatocyte cell surface, decreased LDL clearance, and hypercholesterolemia. PCSK9 was originally implicated in cardiovascular disease when human genetic studies identified gain-of-function PCSK9 mutations as a cause of familial hypercholesterolemia (Abifadel et al., 2003). Subsequently, loss-of-function PCSK9 variants were associated with decreased plasma cholesterol and lowered lifetime incidence of cardiovascular disease (Cohen et al., 2006; Benn et al., 2010). Therapeutic inhibitors of PCSK9 have been recently developed that exhibit potent lipid-lowering effects and are associated with a reduction in cardiovascular events (Open-Label Study of Long-Term Evaluation against LDL Cholesterol (OSLER) Investigators et al., 2015; ODYSSEY LONG TERM Investigators et al., 2015).

A critical early sorting step for secreted proteins is their incorporation into membrane-bound vesicles that transport cargoes from the ER to the Golgi apparatus (Zanetti et al., 2011). The formation of these vesicles is driven by coat protein complex II (COPII), which includes the SAR1 GTPase, heterodimers of SEC23/SEC24, and heterotetramers of SEC13/SEC31. Secreted cargoes are incorporated into COPII vesicles by two mechanisms. ‘Cargo capture’ refers to concentrative, receptor-mediated, active sorting of selected cargoes, in contrast to ‘bulk flow’, by which cargoes enter COPII vesicles through passive diffusion. These mechanisms are not mutually exclusive, as cargoes may exhibit basal rates of secretion that are enhanced by receptor-mediated recruitment. It remains unclear to what extent protein recruitment into the secretory pathway is driven by selective cargo capture versus passive bulk flow (Barlowe and Helenius, 2016).

The active sorting of secreted cargoes into COPII-coated vesicles is driven primarily by SEC24, with the multiple SEC24 paralogs observed in vertebrates thought to accommodate a diverse and regulated repertoire of cargoes. Genetic deficiency in the mouse for one of these paralogs, SEC24A, results in hypocholesterolemia due to reduced secretion of PCSK9 from hepatocytes (Chen et al., 2013). This finding suggested an active receptor-mediated mechanism for PCSK9 recruitment into COPII vesicles. A direct physical interaction between SEC24A and PCSK9, however, is implausible since SEC24A localizes to the cytoplasmic side of the ER membrane and PCSK9 to the luminal side, with neither possessing a transmembrane domain. This topology instead implies the presence of an ER cargo receptor, a transmembrane protein that could serve as an intermediary between the COPII coat and luminal PCSK9.

Although COPII-dependent ER cargo receptors were first identified in yeast nearly two decades ago, few examples of similar cargo receptor interactions have been reported for mammalian secreted proteins (Barlowe and Helenius, 2016). Previous investigation of the ER cargo receptor LMAN1 demonstrated no specificity for SEC24A over other SEC24 paralogs, making this unlikely to serve as a PCSK9 cargo receptor (Wendeler et al., 2007). Earlier analyses of PCSK9-interacting proteins (Ly et al., 2016; Xu et al., 2012; Denis et al., 2011) did not identify a clear receptor mediating PCSK9 secretion. Here, we developed a novel strategy for ER cargo receptor identification that combines proximity-dependent biotinylation with CRISPR-mediated functional genomic screening. This approach led to the identification of the ER cargo receptor SURF4 as a primary mediator of PCSK9 secretion in HEK293T cells.

Results

Identification of candidate PCSK9 cargo receptors by proximity-dependent biotinylation

To identify PCSK9-interacting proteins, we first engineered cells expressing a fusion of PCSK9 and a mutant biotin ligase, E. coli BirA*(R118G), that catalyzes proximity-dependent biotinylation of primary amines on neighboring proteins within an estimated ~10 nm radius (Roux et al., 2012; Kim et al., 2014), effectively converting transient interactions into covalent modification (Figure 1A). The high affinity of the biotin-streptavidin interaction in turn allows for stringent detergent and high salt conditions during purification. Quantitative mass spectrometry of streptavidin-purified interacting proteins from cells expressing PCSK9-BirA* identified 162 prey proteins that were specifically labeled (Bayesian FDR ≤ 1%) by PCSK9-BirA* relative to control bait proteins (Supplementary file 1).

Figure 1. Proximity-dependent biotinylation with a PCSK9-BirA* fusion.

Figure 1.

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(A) Proximity detection by mass spectrometry of streptavidin-purified prey proteins biotinylated by a fusion of BirA* to a bait protein of interest. (B) Immunoblotting of lysates of cells expressing various BirA*-fusion proteins. (C) Spectral counts of prey proteins identified from lysates of cells expressing PCSK9-BirA* relative to A1AT-BirA*. (D) Spectral counts of prey proteins purified from lysates of cells expressing PCSK9-BirA* relative to the maximum spectral count from lysates of cells expressing either SAR1A-BirA* or SAR1B-BirA*. (E) Venn diagram of identified prey proteins from lysates of cells expressing BirA* fusions with PCSK9, A1AT, or the maximum for either Sar1A or Sar1B. (F) Heat map of spectral counts for candidate proteins demonstrating interaction with both PCSK9-BirA* and either SAR1A-BirA* or SAR1B-BirA*. Spectral count values represent averages of 2 biologic replicates. Only prey proteins that exhibit BFDR ≤0.01 for one or more bait proteins are displayed. Source data is provided in Supplementary file 1.

To refine the candidate list of PCSK9-interacting proteins, we next analyzed cells expressing a fusion of BirA* with a control secreted protein, alpha-1 antitrypsin (A1AT). The interactome of A1AT showed substantial overlap with that of PCSK9 (108/162 proteins, Figure 1C). The A1AT cargo receptor LMAN1 was similarly labeled by both PCSK9-BirA* and A1AT-BirA*, suggesting that the restricted environment of the COPII vesicle may lead to nonspecific labeling of adjacent cargo receptors. We next compared the interactome of PCSK9 to that of SAR1A and SAR1B (Figure 1D), COPII proteins that localize to the cytoplasmic surface of budding COPII vesicles, identifying a total of 35 candidate ER cargo receptors interacting with both PCSK9 and either SAR1A or SAR1B (Figure 1E–F, Supplementary file 1). The majority of these candidates were annotated as integral membrane proteins (32/35, p=3×10−16) with localization in the ER (24/35, p=1.6×10−18), as would be expected for an ER cargo receptor (Supplementary file 1).

A genome-scale CRISPR screen identifies SURF4 as a putative ER cargo receptor for PCSK9

We next developed a functional screen to identify genes involved in PCSK9 secretion (Figure 2A). We reasoned that mutants with reduced PCSK9 exit from the ER would accumulate intracellular PCSK9, and that fusion of PCSK9 to eGFP would allow for a cell-autonomous, scalable, and selectable readout of PCSK9 accumulation. We generated clonal HEK293T cell lines stably co-expressing both a PCSK9-eGFP fusion and, as an internal control, alpha-1 antitrypsin fused with mCherry. Immunoblotting verified the efficient secretion of both fusion proteins from clonal reporter cell lines (Figure 2—figure supplement 1).

Figure 2. Whole genome CRISPR mutagenesis screen for PCSK9 secretion modifiers.

(A) Strategy for whole genome screen. (B) Flow cytometry of reporter cells stably expressing PCSK9-eGFP-2A-A1AT-mCherry, treated with 1 µg/mL brefeldin A or a sgRNA targeting LMAN1. (C) Normalized abundance of each sgRNA in the library in eGFP high and eGFP low populations. (D) MAGeCK gene-level enrichment scores for each gene targeted by the library arranged by chromosome number and transcription start site. The diameter of the bubble is proportional to the number of unique sgRNAs targeting each gene that demonstrate significant enrichment in GFP high cells. Source data is provided in Supplementary files 2 and 3.

Figure 2.

Figure 2—figure supplement 1. Analysis of PCSK9-eGFP-2A-A1AT-mCherry reporter cell clones.

Figure 2—figure supplement 1.

(A) Construct for CMV-promoter-driven expression of PCSK9-eGFP-2A-A1AT-mCherry expression. (B) Immunoblotting of HEK293T cells transfected with the PCSK9-eGFP-2A-A1AT-mCherry vector, and four individual drug-resistant clonal cell lines, relative to parental untransfected cells.
Figure 2—figure supplement 2. Whole genome screen analysis.

Figure 2—figure supplement 2.

(A) Cumulative distribution function of mapped reads for each sgRNA in the whole genome library. (B) Comparison of read counts for every pairwise combination of 4 biologic replicates with Pearson correlation coefficient.
Figure 2—figure supplement 3. Validation experiments for additional candidate genes.

Figure 2—figure supplement 3.

The top-scoring sgRNA for each of the next 21 most enriched genes after SURF4 was individually cloned into lentiCRISPRv2 and tested in PCSK9-eGFP-2A-A1AT-mCherry reporter cells. Flow cytometry was performed for each of 3 biologic replicates. The mean fluorescent intensity of PCSK9-eGFP and A1AT-mCherry for sgRNA against each candidate gene relative to nontargeting sgRNA controls is shown. Error bars represent standard deviations.
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Disruption of ER-Golgi transport by treatment of these reporter cells with brefeldin A, an Arf1 inhibitor, resulted in intracellular accumulation of both PCSK9-eGFP and A1AT-mCherry (Figure 2B). CRISPR-mediated inhibition of the ER cargo receptor for A1AT, LMAN1 (Zhang et al., 2011), resulted in intracellular accumulation of A1AT-mCherry with no effect on PCSK9-eGFP (Figure 2B). To screen for specific modifiers of PCSK9 secretion, we next sought to identify single guide RNAs (sgRNAs) that would induce accumulation of PCSK9-eGFP with no change in A1AT-mCherry fluorescence. We mutagenized the PCSK9-eGFP-2A-A1AT-mCherry reporter cell line with the GeCKOv2 pooled library of 123,411 sgRNAs that includes six independent sgRNAs targeting nearly every coding gene in the human genome (Sanjana et al., 2014) (Figure 2A). Mutants with aberrant PCSK9-eGFP fluorescence but normal A1AT-mCherry fluorescence were then isolated by flow cytometry, with integrated lentiviral sgRNA sequences quantified by deep sequencing and analyzed for enrichment in PCSK9-eGFP high cells. The coverage and distribution of sgRNA sequencing reads demonstrated maintenance of library complexity and high reproducibility between biological replicates (Figure 2—figure supplement 2).

Strikingly, the four most enriched sgRNAs in the PCSK9-eGFP high cell population all targeted the same gene, SURF4 (Figure 2C, Supplementary file 2). The enrichment of SURF4-targeting sgRNA in eGFP-high cells was consistent across each of 4 biologic replicates and, after adjustment for multiple hypothesis testing, statistically significant for 5 of the 6 SURF4-targeting sgRNAs in the library (p<10−13 – 10−36, Figure 3A). Gene-level analysis confirmed the strongest enrichment for SURF4-targeting sgRNA (Figure 2D, Supplementary file 3). SURF4 is a homologue of yeast Erv29, an ER cargo receptor that mediates the secretion of glycosylated pro-alpha-factor (Belden and Barlowe, 2001). Comparison of candidate PCSK9 cargo receptors identified by either CRISPR functional screening (Figure 2D) or proximity-dependent biotinylation (Figure 1F) demonstrated that SURF4 was the only candidate identified by both approaches.

Figure 3. SURF4 mutagenesis causes an accumulation of intracellular PCSK9-eGFP.

(A) Individual sgRNA sequencing counts for SURF4-targeting sgRNA in eGFP high and eGFP low populations for each of 4 biologic replicates. Adjusted p values were calculated using DESeq2. (B) Flow cytometry histograms of PCSK9-eGFP and A1AT-mCherry fluorescence in reporter cells transfected with plasmids delivering Cas9 and SURF4-targeting sgRNA or empty vector. (C) Quantification of intracellular fluorescence for cells treated with empty vector (n = 3) or unique SURF4-targeting sgRNAs (n = 6). (D) Quantification of intracellular fluorescence for clonal cell lines each containing frameshift-causing indels at two different SURF4 target sites (n = 7 wild-type clones, n = 3 clones generated from each SURF4-targeting sgRNA). (E) Flow cytometry histograms for cells expressing PCSK9-eGFP-2A-A1AT-mCherry and deleted for SURF4 with or without stable expression of a wild-type or FLAG-tagged SURF4 cDNA. (F) Time course of intracellular accumulation of tetracycline-inducible PCSK9-eGFP on WT, SURF4-deficient, or SURF4 rescue background (n = 3 biologic replicates for each cell line at each time point). *p<0.05 by Student’s t-test. Error bars represent standard deviations.

Figure 3.

Figure 3—figure supplement 1. ER stress markers.

Figure 3—figure supplement 1.

Clonal cell lines with a stably integrated tetracycline-inducible PCSK9 cDNA on either WT or SURF4 mutant background were treated with tetracycline or vehicle control and analyzed by immunoblotting for various markers of ER stress (A), quantified by densitometry (B). Error bars represent standard deviations.
Figure 3—figure supplement 2. SURF4-deficient genotypes.

Figure 3—figure supplement 2.

Clonal cell lines were genotyped by PCR amplification of sgRNA target site and Sanger sequencing of the amplicon with either TIDE decomposition of individual alleles from the amplicon, or ligation of the amplicon into cloning plasmids and Sanger sequencing of multiple individual clones. Sequence corresponding to sgRNA is underlined and expected double-strand break site is indicated (^).
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SURF4 deletion causes intracellular accumulation of PCSK9-eGFP in HEK293T cells

To validate the functional interaction of SURF4 with PCSK9-eGFP, we generated plasmids encoding Cas9 and 6 independent SURF4-targeting sgRNAs (three from the original screen and three additional unique sgRNAs). Reporter cells were transiently transfected with each of these 6 SURF4-targeting constructs and analyzed by FACS, with all six sgRNAs resulting in accumulation of intracellular PCSK9-eGFP fluorescence with no effect on intracellular A1AT-mCherry fluorescence (Figure 3B–C) or induction of ER stress markers (Figure 3—figure supplement 1). Similarly, six clonal cell lines carrying sequence-verified indels in either SURF4 exon 2 or exon 5 (Figure 3—figure supplement 2) all exhibited specific PCSK9-eGFP accumulation with no effect on A1AT-mCherry (Figure 3D). This phenotype was rescued by stable expression of wild-type SURF4 cDNA (Figure 3E). The intracellular accumulation of PCSK9 upon SURF4 disruption was confirmed in an independently-derived HEK293T cell line carrying an inducible PCSK9-eGFP allele. Accumulation of PCSK9-eGFP in SURF4 mutant cells relative to wild-type cells was detectable within 4 hr of induction (Figure 3F).

To identify other potential cellular components also required for efficient PCSK9 secretion, we next examined the 21 genes in addition to SURF4 which exhibited potentially significant enrichment scores (FDR ≤ 10%) in the whole genome CRISPR screen. PCSK9-eGFP-2A-A1AT-mCherry reporter cells were transduced with individual lentiviral CRISPR constructs targeting each of these genes and analyzed by FACS. None of the sgRNA targeting these additional genes were found to result in a significant effect specifically on PCSK9-eGFP fluorescence (Figure 2—figure supplement 3). Thus, within the statistical power of our screen, SURF4 emerges as the single gene out of the ~19,000 human genes targeted by the GeCKOv2 library (Sanjana et al., 2014) whose inactivation results in specific intracellular retention of PCSK9 in HEK293T cells.

SURF4 localizes to the early secretory pathway where it physically interacts with PCSK9

HEK293T cells were engineered to stably express SURF4 with an N-terminal FLAG epitope tag. FLAG-tagged SURF4 demonstrated similar rescue of PCSK9-eGFP fluorescence in SURF4-deficient cells compared to native, untagged SURF4 (Figure 3E), demonstrating that this tag does not interfere with SURF4 function. Consistent with previous reports (Mitrovic et al., 2008; Saegusa et al., 2018) and compatible with a role for SURF4 as an ER cargo receptor, immunofluorescence of FLAG-SURF4 demonstrated colocalization with a marker of the ER and, to a lesser extent, the ERGIC compartment (Figure 4A–B). FLAG-tagged SURF4 and GFP-tagged PCSK9 were found to co-immunoprecipitate from cell lysates prepared in the presence of the chemical crosslinker dithiobis(succinimidyl propionate), with no detectable nonspecific co-immunoprecipitation for several control ER and ERGIC-localized proteins (Figure 4D).

Figure 4. SURF4 localizes to the early secretory pathway where it physically interacts with PCSK9.

Figure 4.

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(A) Immunofluorescence of FLAG-SURF4 together with markers of the ER (calnexin), ERGIC (LMAN1), and Golgi (GM130). Scale bar = 10 µm. (B) Quantification of colocalization (n = 8 cells analyzed for each combination of antibody staining). (C) Spectral counts for SURF4 in streptavidin-purified eluates from cells expressing various BirA* fusion proteins. (D) Immunoprecipitations were performed using antibodies directed against FLAG or GFP from lysates of cells expressing FLAG-SURF4, PCSK9-eGFP, both, or neither. Error bars represent standard deviations.

Loss of SURF4 results in decreased PCSK9 secretion and ER accumulation of PCSK9

Fluorescence assays on both extracellular conditioned media and intracellular lysates prepared from both wild-type and SURF4-deficient cells demonstrated a significantly decreased ratio of extracellular to intracellular PCSK9-eGFP fluorescence (Figure 5A), consistent with a defect in extracellular secretion. Pulse-chase labeling confirmed a decreased rate of PCSK9-eGFP secretion into the conditioned media in SURF4-deficient cells (Figure 5—figure supplement 1).

Figure 5. SURF4 mutagenesis causes a decrease in PCSK9 extracellular secretion and an accumulation of PCSK9 in the ER.

(A) Fluorescence detection of PCSK9-eGFP in extracellular conditioned media relative to cellular lysate in WT (n = 7) and clonal SURF4-deficient (n = 5) fluorescent reporter cell lines. (B) Immunoblotting of tetracycline-inducible PCSK9 in extracellular conditioned media and cellular lysates from WT, SURF4-deficient, or SURF4 rescued cells. (C) Quantification of densitometry of native PCSK9 relative in conditioned media and cellular lysates, normalized to GAPDH, for WT, SURF4-deficient, or SURF4 rescued cells (n = 4 biologic replicates for each cell line). (D) Quantitative PCR of SURF4 and PCSK9 transcript levels from RNA isolated from a SURF4 WT or mutant fluorescent reporter cell line (n = 3 measurement replicates). (E) Live cell fluorescence microscopy of fluorescent reporter cells, either WT or SURF4-deficient, transfected with the ER marker ERoxBFP. Scale bar = 10 µm. (F) Quantification of PCSK9-eGFP signal intensity in pixels positive for ERoxBFP fluorescence (n = 14 for each cell line). (G) EndoH-sensitivity of PCSK9 expressed in WT, SURF4-deficient, or SURF4 rescued cells. (H) Quantification of endoH-sensitivity, normalized to average intensity of average WT band intensity (n = 4 biologic replicates for each cell line). *p<0.05 by Student’s t-test. Error bars represent standard deviations.

Figure 5.

Figure 5—figure supplement 1. Pulse-chase labeling of PCSK9-eGFP secretion.

Figure 5—figure supplement 1.

A SURF4 mutant cell line with a tetracycline-inducible PCSK9-eGFP construct and with (SURF4 rescue) or without (SURF4 mutant) a stably integrated SURF4 cDNA were radiolabeled with tracer containing 35S-Met and 35S-Cys. Conditioned media and cellular lysates were harvested at the indicated time points and PCSK9-eGFP immunoprecipitated and analyzed by SDS-PAGE electrophoresis and autoradiography.
Figure 5—figure supplement 2. EndoH-sensitivity of PCSK9 expressed in WT, SURF4-deficient, or SURF4 rescued cells for each of 4 independent biologic replicates.

Figure 5—figure supplement 2.

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To exclude interaction of SURF4 with the GFP portion of the fusion rather than PCSK9 itself, we also examined SURF4-dependence for the secretion of untagged PCSK9. Stable cell lines were generated by Flp/FRT-mediated knock-in of PCSK9 coding sequence into a tetracycline-inducible locus of HEK293T cells that were either wild-type or SURF4-deficient, the latter with or without stable integration of a wild-type SURF4 cDNA expression construct. Consistent with the reduced secretion observed above for the PCSK9-eGFP fusion, untagged PCSK9 also exhibited a significant decrease in the ratio of extracellular to intracellular levels in SURF4-deficient cells that was rescued by expression of a SURF4 transgene (Figure 5B–C). Quantitative PCR demonstrated equivalent levels of PCSK9 mRNA levels in wild-type and SURF4-deficient cells (Figure 5D), excluding an indirect effect on PCSK9 transcription or mRNA stability.

To characterize the compartmental localization of intracellular PCSK9, we performed live cell fluorescence microscopy on wild-type and SURF4-deficient cells. PCSK9-eGFP fluorescence demonstrated increased colocalization with an ER marker in SURF4-deficient cells, consistent with ER retention (Figure 5E–F). Similarly, the predominant form of native PCSK9 in SURF4-deficient cells was sensitive to endoglycosidase H (Figure 5G–H, Figure 5—figure supplement 2), consistent with its localization in the ER (Freeze and Kranz, 2010). The relative intensity of endoH-resistant PCSK9 was unchanged, consistent with continued normal post-Golgi transport and secretion. Collectively these results indicate that SURF4 promotes the efficient ER exit and secretion of PCSK9.

Discussion

It is estimated that ~3000 human proteins are extracellularly secreted through the COPII pathway (Uhlén et al., 2015), though few ER cargo receptors for these proteins have been identified and the proportion of secreted proteins that depend upon cargo receptor interactions is unknown (Barlowe and Helenius, 2016). Our findings suggest that SURF4 actively recruits PCSK9 into COPII vesicles, providing additional support for the cargo capture model of protein secretion. SURF4-deleted cells exhibit only a partial defect in PCSK9 secretion and whether the residual SURF4-independent secretion is due to bulk flow, or interactions with alternative ER cargo receptors, remains unknown.

To potentially circumvent the expected transient and/or low affinity nature of interactions between cargoes and their receptors, we applied purification by BirA*-mediated proximity-dependent biotinylation, which converts transient protein-protein interactions into permanent modifications (Roux et al., 2012). Although this approach led to the detection of SURF4 interactions with PCSK9, SAR1A, and SAR1B, SURF4 was similarly marked by A1AT. Likewise, the A1AT cargo receptor LMAN1 was marked by both A1AT and PCSK9. These data suggest that BirA*-mediated proximity-dependent biotinylation may efficiently label ER cargo receptors, but lack the spatiotemporal resolution to distinguish incorporation within the same COPII vesicle from specific cargo – cargo receptor interactions.

CRISPR/Cas9-mediated gene editing has emerged as an efficient, programmable, and high-throughput tool for forward genetic screening in mammalian cells (Shalem et al., 2015). Our discovery of the PCSK9-SURF4 interaction demonstrates the feasibility of identifying ER cargo receptors by functional genomic screening and suggests that pooled CRISPR screening may prove to be a generalizable strategy applicable to the thousands of other secreted proteins for which a cargo receptor has not yet been identified. A limitation of our approach is its reliance on an immortalized cell line with the potential for accumulated somatic genetic and epigenetic changes, as well as restriction to a single cell-type gene expression program. Though LMAN1 cargoes appear to retain their cargo receptor-dependence for secretion in a variety of distantly related cell types (Nyfeler et al., 2008; Zhang et al., 2005; Nyfeler et al., 2006), the same many not apply to SURF4.

Of note, SEC24A and/or SEC24B were previously shown to be required for efficient PCSK9 secretion in mice (Chen et al., 2013). Though these genes were not identified in the current screen, this observation could be explained by the 5 – 10 fold greater expression of SEC24A than SEC24B in mouse liver, in contrast to the similar expression of both paralogs in HEK293T cells (Sultan et al., 2008).

In addition to a cargo receptor facilitating ER exit, our screening strategy should also survey other segments of the secretory pathway. Though sortilin has previously been implicated in post-Golgi transport of PCSK9 (Gustafsen et al., 2014), sortilin dependence was not confirmed in a subsequent study (Butkinaree et al., 2015) and also was not detected in the current screen. However, our data do not exclude a broader role for sortilin that also affected A1AT secretion, consistent with its contribution to the secretion of other proteins, including gamma-interferon (Herda et al., 2012) and ApoB-100 (Kjolby et al., 2010).

The yeast homologue of SURF4, Erv29p, was originally identified by proteomic analysis of purified COPII vesicles (Otte et al., 2001). Erv29p promotes the secretion and concentrative sorting of the yeast mating factor gpαf into COPII vesicles through interaction with a hydrophobic I-L-V signal in gpαf (Belden and Barlowe, 2001; Malkus et al., 2002; Otte and Barlowe, 2004). In HeLa cells, SURF4 has been shown to cycle in the early secretory pathway and to interact with LMAN1 and members of the p24 family. RNAi-mediated knockdown of SURF4 alone resulted in no overt phenotype, though when combined with knockdown of LMAN1 caused morphologic changes to the ERGIC and Golgi compartments (Mitrovic et al., 2008). SURF4 has also been shown to interact with STIM1 and modulate store-operated calcium entry, though the mechanism underlying this observation is unclear (Fujii et al., 2012). Our screen did not identify significant enrichment of sgRNAs targeting p24 proteins, LMAN1, or STIM1, suggesting that these interactions are not required for efficient PCSK9 secretion in HEK293T cells.

Taken together with the marked reductions in plasma cholesterol associated with genetic or therapy-induced reductions in plasma PCSK9 (ODYSSEY LONG TERM Investigators et al., 2015), our findings raise the possibility that SURF4 could represent an additional novel therapeutic target for the treatment of hypercholesterolemia. However, SURF4 has not been identified in genome-wide association studies for human lipid phenotypes (Global Lipids Genetics Consortium et al., 2013; Lange et al., 2015), suggesting that partial reduction of SURF4 expression does not limit PCSK9 secretion, consistent with the normal PCSK9 secretion and cholesterol profiles of Sec24A+/- mice (Chen et al., 2013) and the normal levels of LMAN1 cargoes (coagulation factors V, VIII, and A1AT) in LMAN1+/- mice (Zhang et al., 2011). Though a loss-of-function variant (p.Gln185Ter) is present in ~1:500 individuals (Exome Aggregation Consortium et al., 2016), no human diseases have been associated with SURF4 deficiency and no mouse models for Surf4 deletion have been reported (Online Mendelian Inheritance in Man, OMIM, 2018). Erv29, the SURF4 homolog in yeast, is required for gpαf secretion (Belden and Barlowe, 2001), with a recent report demonstrating a role for the C. elegans homolog (SFT-4) in facilitating the secretion of yolk lipoproteins, as well as mammalian SURF4 in mediating apolipoprotein B secretion in HepG2 cells (Saegusa et al., 2018). The latter result, together with our findings, suggest a potentially broader role for SURF4 in the complex regulation of mammalian lipid homeostasis in vivo.

Materials and methods

Key resources table.

Reagent
type (species)
or resource		 Designation Source or reference Identifiers Additional

information
Gene (Homo
sapiens)		 PCSK9 NA Uniprot Q8NBP7
Gene (Homo
sapiens)		 A1AT NA Uniprot P01009
Gene (Homo
sapiens)		 SURF4 NA Uniprot O15260
Cell line (Homo
sapiens)		 HEK293T ATCC RRID:CVCL_1926
Cell line (Homo
sapiens)		 T-Rex-293 Invitrogen RRID:CVCL_D585
Antibody anti-PCSK9 (rabbit
polyclonal)		 Cayman Chemical RRID:AB_569536 (1:1000)
Antibody anti-GFP (rabbit
monoclonal)		 Abcam RRID:AB_303395 (1:5000)
Antibody anti-mCherry (rabbit
polyclonal)		 Abcam RRID:AB_2571870 (1:1000)
Antibody anti-GAPDH (rabbit
monoclonal)		 Abcam RRID:AB_2630358 (1:10,000)
Antibody anti-β-actin (mouse
monoclonal)		 Santa Cruz RRID:AB_2714189 (1:10,000)
Antibody anti-LMAN1 (rabbit
monoclonal)		 Abcam RRID:AB_10973984 (1:5000)
Antibody anti-calnexin (rabbit
monoclonal)		 Cell Signaling RRID:AB_2228381 (1:2000)
Antibody anti-GM130 (rabbit
monoclonal)		 Abcam RRID:AB_880266 (1:1000)
Antibody anti-BiP (rabbit
monoclonal)		 Abcam RRID:AB_10859806 (1:5000)
Antibody anti-PDI (rabbit
monoclonal)		 Cell Signaling RRID:AB_2156433 (1:1000)
Antibody HRP-conjugated anti-
FLAG (goat
polyclonal)		 Abcam RRID:AB_299061 (1:10,000)
Antibody HRP-conjugated anti-
mouse secondary (goat
polyclonal)		 BioRad RRID:AB_11125547 (1:5000)
Antibody HRP-conjugated anti-
rabbit IgG (goat
polyclonal)		 BioRad RRID:AB_11125142 (1:5000)
Antibody FITC-conjugated anti-
FLAG (mouse
monoclonal)		 Sigma RRID:AB_439701 (1:500)
Antibody Alexa647-conjugated
anti-rabbit secondary
(donkey polyclonal)
Abcam ab150075 (1:500)
Recombinant DNA reagent pLentiCRISPRv2 Addgene 52961
Recombinant DNA reagent pX459 Addgene 62988
Recombinant DNA reagent pNLF-C1 Promega E1361
Recombinant DNA reagent pDEST-pcDNA5-BirA-FLAG-C-term PMID 24255178
Recombinant DNA reagent pENTR/D-TOPO Invitrogen K2400-20
Chemical compound, drug brefeldin A BioLegend 420601
Chemical compound, drug dithiobis(succinimidyl propionate) Pierce 22586
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Cells and reagents

HEK293T cells were purchased from ATCC (Manassas VA). T-REx-293 cells were purchased from Invitrogen. Cell lines were validated by the AMPFLSTR Identifiler Plus Assay (Applied Biosystems, Foster City CA) and tested by the MycoAlert Mycoplasma Detection Kit (Lonza, Basel Switzerland). Cells were cultured in DMEM (Invitrogen, Carlsbad CA) containing 10% FBS (D10) in a humidified 37°C chamber with 5% CO2. The expression construct for PCSK9-eGFP-2A-A1AT-mCherry was generated by Gibson assembly (Gibson et al., 2009) of vector sequence derived from pNLF-C1 (Promega, Madison WI) and PCSK9 and A1AT cDNA derived by RT-PCR from HepG2 mRNA. Expression constructs for PCSK9, A1AT, SAR1A, and SAR1B fused to BirA* were generated by cDNA ligation into the entry vector pENTR/D-TOPO (Invitrogen) and Gateway cloning into the destination vector pDEST-pcDNA5-BirA-FLAG C-term (Couzens et al., 2013) using LR clonase II (Invitrogen). This vector was also used as a backbone for the Gibson assembly of tetracycline-inducible expression of native PCSK9 and PCSK9-eGFP. For CRISPR experiments, sgRNA sequences were ligated into pLentiCRISPRv2 (Addgene #52961, a gift from Feng Zhang (Sanjana et al., 2014)) or pX459 (Addgene #62988, a gift from Feng Zhang) using BsmBI or BbsI restriction enzyme sites, respectively. Transfections were performed with FugeneHD (Promega) or Lipofectamine 3000 (Invitrogen) per manufacturer’s instructions. Where indicated, clonal cell lines were derived by diluting cell suspensions to a single cell per well and expanding individual wells. Genotyping of clonal cell lines was performed by Sanger sequencing of target site PCR amplicons of genomic DNA isolated by QuickExtract (Epicentre, Madison WI). The pLentiCRISPRv2 whole genome CRISPR library (Addgene #1000000048, a gift from Feng Zhang [Sanjana et al., 2014]) was expanded by eight separate electroporations for each half library into Endura electrocompetent cells (Lucigen, Middleton WI), plated on 24.5 cm bioassay plates, and pooled plasmids isolated by HiSpeed Maxi Prep (Qiagen, Hilden Germany). The pooled lentiviral library was prepared by co-transfecting 120 μg of each half library together with 120 μg of pCMV-VSV-G (Addgene #8454, a gift from Bob Weinberg [Stewart et al., 2003]) and 180 μg psPAX2 (Addgene #12260, a gift from Didier Trono) into a total of 12 T225 tissue culture flasks of ~70% confluent HEK293T cells using FugeneHD per manufacturer’s instructions. Media was replaced at 12 hr post-transfection with D10 supplemented with 1% BSA, which was collected and changed at 24, 36, and 48 hr. Harvested media was centrifuged at 1000 g for 10 min, pooled and filtered through a 0.45 μm filter, aliquoted, snap-frozen with liquid nitrogen and stored at −80°C until the time of use.

Whole genome CRISPR screen

For each of 4 independent biological replicates, a total of ~90 million cells stably expressing PCSK9-eGFP-2A-A1AT-mCherry were transduced at a multiplicity of infection of ~0.3 with the whole genome CRISPR library. Puromycin selection (1 μg/mL) was applied from day 1 to day four post-transduction. Cells were passaged every 2 – 3 days and maintained in logarithmic phase of growth. After 14 days, a total of ~240 million cells were detached with TrypLE Express (Invitrogen), harvested in D10 at 4°C, collected by centrifugation at 500 g for 5 min, the pellet resuspended in 4°C phosphate-buffered saline (PBS) and filtered through a 35 μm nylon mesh into flow cytometry tubes, which were kept on ice until the time of sorting. A BD FacsAriaII was used to isolate cell populations containing ~7 million cells per subpopulation into tubes containing D10. Genomic DNA was isolated using a DNEasy purification kit (Qiagen). Integrated lentiviral sgRNA sequences were then amplified using Herculase II polymerase (Agilent, Santa Clara CA) in a two step PCR reaction as previously described (Sanjana et al., 2014; Shalem et al., 2014) with 20 cycles for round 1 PCR and 14 cycles for round 2. Amplicons were then sequenced on a HiSeq Rapid Run (Illumina, San Diego CA), with 95.2% of clusters passing quality filtering to generate a total of ~210 million reads with a mean quality score of 34.99. Individual sgRNA sequences were mapped with a custom Perl script (Source code 1) that seeded sequences onto a constant 24 nucleotide region upstream of the variable 20mer sgRNA, allowing up to one nucleotide mismatch, and reading the upstream six nucleotide barcode and downstream 20 nucleotide sgRNA sequence, which was then mapped to the library reference database with no mismatches tolerated. Enrichment was assessed using DESeq2 for individual sgRNA sequences (Love et al., 2014) and MAGeCK for gene-level computations (Li et al., 2014).

PCSK9 secretion assays

Clonal cell lines either wild-type for SURF4 or containing frameshift-causing SURF4 indels were isolated on a background of the HEK293T fluorescent reporter cell line or in T-REx-293 cells with a Flp/FRT-integrated PCSK9 cDNA. Cells were seeded at equal density in 10 cm plates and cultured in D10 for non-fluorescence-based assays or Fluorobrite media (ThermoFisher, Waltham MA) supplemented with 10% FBS for fluorescence-based assays. At the time of analysis, conditioned media was removed, clarified by centrifugation for 10 min at 1000 g, and supernatant analyzed immediately or stored at −20°C. Cell monolayers were detached with trypLE express, collected in fresh media, pelleted, washed with PBS, and resuspended in 750 μL RIPA buffer (ThermoFisher) supplemented with protease inhibitors (Roche, Basel Switzerland). RIPA lysates were briefly sonicated, rotated end-over-end for 45 min at 4°C, and centrifuged at ~20,000 g for 30 min. Supernatants were transferred to a new tube and analyzed immediately or stored at −20°C. For fluorescence assays, samples were measured in triplicate in 96 well plates using an EnSpire fluorescence plate reader (PerkinElmer, Waltham MA), with fluorescent intensity zeroed on the autofluorescence of parental cells not expressing fluorescent fusion proteins for lysates or unconditioned media for conditioned media. For immunoblotting, samples were probed with antibodies against PCSK9 (Cayman 10007185, 1:1000), GAPDH (Abcam, Cambridge UK, ab181602, 1:10,000), GFP (Abcam, ab290, 1:5000), β-actin (Santa Cruz, sc-47778, 1:10,000), FLAG (Abcam, ab1238, 1:10,000), mCherry (Abcam, ab167453, 1:1000), LMAN1 (Abcam, ab125006, 1:5000), calnexin (Cell Signaling, 2679, 1:2000), BiP (Abcam, ab108613, 1:5000), PDI (Cell Signaling, 3501, 1:1000). Densitometry was performed with ImageJ software (Schneider et al., 2012).

Endoglycosidase H assays

To test for the N-glycosylation state of PCSK9, approximately 100 μg of RIPA lysate was incubated with denaturation buffer (NEB, Ipswich MA) for 10 min at 95°C, then split in half and incubated with or without 0.5 μL of PNGase (NEB) or EndoH (NEB) for 1 hr at 37°C. Laemmli sample buffer (Laemmli, 1970) was added, samples boiled for 5 min, resolved on a 10% Tris-HCl polyacrylamide gel, and analyzed by immunoblotting as above.

Microscopy

Cells were grown on 35 mm poly-D lysine-coated glass bottom dishes (MatTek, Ashland MA). For live cell microscopy, cells were transiently transfected with an expression plasmid for ERoxBFP (Costantini et al., 2015) (Addgene #68126, a gift from Erik Snapp (Costantini et al., 2015)) and visualized at 24 – 48 hr post-transfection. For immunostaining, cells were washed with PBS, fixed with 2% paraformaldehyde, permeabilized with 0.1% Triton X-100 in PBS, blocked with 1% BSA and 0.1% Tween-20 in PBS, stained with FITC-conjugated anti-FLAG antibody (Sigma, St. Louis MO, F4049) and unconjugated rabbit antibodies against either calnexin (Cell Signaling Technology, Danvers MA, #2679), LMAN1 (Abcam, ab125006), or GM130 (Abcam, ab52649), then stained with Alexa647-conjugated anti-rabbit secondary antibody (Abcam, ab150075). All fluorescent imaging was performed on a Nikon A2 confocal microscope. Colocalization quantification was performed with Nikon Elements software. For all microscopy experiments, the observer was blinded to cell genotype and only unblinded after completion of quantitative analysis.

BioID and mass spectrometry

BioID and mass spectrometry analysis was performed essentially as described (Chapat et al., 2017). Briefly, stable HEK293 Flp-In T-REx cells were grown on 15 cm plates to approximately 75% confluence. Bait expression and proximity labeling were induced by the addition of tetracycline (1 μg/mL) and biotin (50 μM) and proceeded for 24 hr. Cells were collected in PBS and biotinylated proteins purified by streptavidin-agarose affinity purification. Proteins were digested on-bead with sequencing-grade trypsin in 50 mM ammonium bicarbonate pH 8.5. Peptides were acidified by the addition of formic acid (2% (v/v) final) and dried by vacuum centrifugation. Dried peptides were suspended in 5% (v/v) formic acid and analysed on a TripleTOF 6600 mass spectrometer (SCIEX) in-line with a nanoflow electrospray ion source and nano-HPLC system. Raw data were searched and analyzed within ProHits LIMS (Liu et al., 2010) and peptides matched to genes to determine prey spectral counts (Liu et al., 2016). High confidence proximity interactions (BFDR ≤1%) were determined through SAINT analysis (Teo et al., 2014) implemented within ProHits. Bait samples (biological duplicates) were compared against 14 independent negative control samples (2 BirA*-FLAG-GFP only, 6 BirA*-FLAG only, and 6 3xFLAG only expressing cell lines) which were ‘compressed’ to six virtual controls to increase the stringency in scoring (Mellacheruvu et al., 2013). Data has been deposited as a complete submission to the MassIVE repository (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) and assigned the accession number MSV000082222. The ProteomeXchange accession is PXD009368.

Mass spectrometry data analysis

All raw (WIFF and WIFF.SCAN) files were saved in our local interaction proteomics LIMS, ProHits (Liu et al., 2010). mzXML files were generated from raw files using the ProteoWizard (v3.0.4468) and SCIEX converter (v1.3 beta) converters, implemented within ProHits. The searched database contained the human complement of the RefSeq protein database (version 57) complemented with SV40 large T-antigen sequence, protein tags, and common contaminants (72,226 sequences searched including decoy sequences). mzXML files were searched by Mascot (v2.3.02) and Comet (v2016.01 rev. 2) with up to two missed trypsin cleavage sites allowed and methionine oxidation and asparagine/glutamine deamidation set as variable modifications. The fragment mass tolerance was 0.15 Da and the mass window for the precursor was ±30 ppm with charges of 2 + to 4+ (both monoisotopic mass). Search engine results were analyzed using the Trans-Proteomic Pipeline (TPP v4.6 OCCUPY rev three check) (Deutsch et al., 2010) via iProphet (Shteynberg et al., 2011). Peptides with PeptideProphet scores ≥ 0.85 were mapped back to genes (gene IDs were from RefSeq). If peptides were shared between multiple genes, spectral counts were assigned exclusively to those genes with unique peptide assignments proportionally to the evidence for that assignment. If peptides matched only to genes without unique peptide assignments, spectral counts were divided equally between those genes (Liu et al., 2016). SAINTexpress (v3.6.1) (Teo et al., 2014) was used to calculate the probability that identified proteins were significantly enriched above background contaminants using spectral counting (semi-supervised clustering) through comparing bait runs to negative control runs.

Immunoprecipitation

Cells were harvested and resuspended at a density of ~5×106 cells/mL in PBS supplemented with 2 mM CaCl2 and 2 mM dithiobis(succinimidyl propionate) (Pierce, Waltham MA), rotated end-over-end at 4°C, quenched with the addition of Tris-HCl (pH 7.5) to a final concentration of 25 mM, pelleted, and resuspended in IP Lysis Buffer (50 mM Tris-HCl, 150 mM NaCl, 2 mM CaCl2, and 1.0% Triton X-100, supplemented with protease inhibitors (Roche), pH 7.5). Lysates were prepared as described above. Immunoprecipitation from lysates was performed with M2-FLAG affinity gel (Sigma) or GFP-trap magnetic beads (Chromo-Tek, Hauppage NY). To reduce nonspecific binding, M2-FLAG affinity gel was pre-blocked with 1 hr incubation in IP Blocking Buffer (50 mM Tris-HCl, 500 mM NaCl, 2 mM CaCl2, 5% BSA, pH 7.5). Pulldowns were performed with 500 µL of lysate with end-over-end rotation at 4°C overnight. A total of 5 washes were performed with IP Lysis Buffer for GFP-trap beads or IP Blocking Buffer for M2-FLAG affinity gel. Protein elution was performed by incubating beads at room temperature for 15 min with 2X Laemmli sample buffer supplemented with β–mercaptoethanol.

Screen validation

All genes identified by MAGeCK with a FDR < 10% were selected for follow up validation. The top-ranking sgRNA for each gene was individually cloned into BsmBI sites of pLentiCRISPRv2. Individual lentiviral stocks were prepared and used to transduce fluorescent reporter cells at an MOI <0.5, followed by puromycin selection, and passaging for 2 weeks prior to FACS analysis. The mean fluorescence intensity of a total of 20,000 gated events was recorded for each construct and compared to the mean intensity of 3 nontargeting sgRNA constructs. A total of 3 biologic replicates was performed.

Pulse-chase analysis

Cells were seeded into six well plates and induced with 1 µg/mL tetracycline (included in each of the media preparations below) overnight before near-confluent monolayers were washed and incubated with Starvation Media (DMEM lacking cysteine and methionine (Invitrogen) and supplemented with tetracycline with 10% dialyzed FBS (Fisher)) for 20 min at 37°C. Starvation Media was then replaced with Pulse Media (Starvation Media supplemented with 75 µCi/sample EXPRE35S35S Protein Labeling Mix (PerkinElmer)) and cells were incubated for 30 min at 37°C. Cells were then washed and incubated with Chase Media (Starvation Media supplemented with 5 mM each of unlabeled methionine and cysteine) for the indicated time points, after which conditioned media (2 mL per sample) and cellular lysates (collected in 1 mL lysis buffer) were prepared as described above for co-immunoprecipitation experiments. For each immunoprecipitation, 20 µL of GFP-trap beads were used with either 200 µL of cellular lysate of 400 µL of conditioned media. Proteins were eluted in 50 µL of sample buffer, of which 10 µL was analyzed by SDS-PAGE and autoradiography.

Statistical analysis

The statistical significance of differences in quantitative data between control and experimental groups was calculated using the Student’s t-test. CRISPR screen and mass spectrometry data analysis was performed as described above.

Acknowledgments

This research was supported by NIH grants R35-HL135793T (DG) and T32-HL007853 (BTE). D G is a Howard Hughes Medical Institute investigator.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

David Ginsburg, Email: ginsburg@umich.edu.

Randy Schekman, Howard Hughes Medical Institute, University of California, Berkeley, United States.

Randy Schekman, Howard Hughes Medical Institute, University of California, Berkeley, United States.

Funding Information

This paper was supported by the following grants:

  • National Heart, Lung, and Blood Institute R35-HL135793T to David Ginsburg.

  • National Heart, Lung, and Blood Institute T32-HL007853 to Brian Emmer.

Additional information

Competing interests

Reviewing editor, eLife.

No competing interests declared.

Author contributions

Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing.

Data curation, Formal analysis.

Data curation.

Data curation.

Data curation.

Data curation.

Conceptualization, Formal analysis, Writing—review and editing.

Formal analysis.

Conceptualization, Formal analysis, Writing—review and editing.

Additional files

Supplementary file 1. BioID of PCSK9-interacting proteins.

Spectral counts are listed for prey proteins identified from streptavidin-purified lysates of cells expressing fusions of BirA* with GFP, PCSK9, A1AT, SAR1A, and SAR1B. Gene Ontology enrichment analysis (Ashburner et al., 2000; The Gene Ontology Consortium, 2017) is displayed for candidate PCSK9 cargo receptors.

elife-38839-supp1.xlsx (1.2MB, xlsx)
DOI: 10.7554/eLife.38839.014
Supplementary file 2. CRISPR screen sgRNA-level analysis.

DESeq2 output for each sgRNA showing normalized mapped reads in control (GFP-low) populations and log2 fold-change in experiment (GFP-high) populations with significance testing for enrichment or depletion. A copy of the reference library showing the corresponding gene target and target sequence and gene for each sgRNA is included.

elife-38839-supp2.xlsx (16.3MB, xlsx)
DOI: 10.7554/eLife.38839.015
Supplementary file 3. CRISPR screen gene-level analysis.

Enrichment for each sgRNA targeting the same gene was combined to generate a gene-level enrichment score using MAGeCK (Li et al., 2014), which is displayed in the column corresponding to the number of unique sgRNA targeting that same that demonstrated enrichment (p<0.05) by analysis with DESeq2 (Supplementary file 2).

elife-38839-supp3.xlsx (1.6MB, xlsx)
DOI: 10.7554/eLife.38839.016
Supplementary file 4. Oligonucleotide sequences.

Primers used for CRISPR/Cas9 targeting and genotyping are displayed.

elife-38839-supp4.xlsx (12.6KB, xlsx)
DOI: 10.7554/eLife.38839.017
Source code 1. gRNA_mapping.zip.
elife-38839-code1.zip (5.3KB, zip)
DOI: 10.7554/eLife.38839.018
Transparent reporting form
elife-38839-transrepform.docx (246KB, docx)
DOI: 10.7554/eLife.38839.019

Data availability

Proteomic data has been deposited to the MassIVE database under accession MSV000082222. Sequencing data has been uploaded to the Sequence Read Archive under accession SRP149835. Source data files are included in the supplementary information.

The following datasets were generated:

Emmer BT, author; Hesketh GG, author; Kotnik E, author; Tang VT, author; Lascuna PJ, author; Xiang J, author; Gingras A, author; Chen X, author; Ginsburg D, author. The cargo receptor SURF4 promotes the efficient cellular secretion of PCSK9. 2018 ftp://massive.ucsd.edu/MSV000082222 Publicly available at MassIVE (https://massive.ucsd.edu).

Emmer BT, author; Hesketh GG, author; Kotnik E, author; Tang VT, author; Lascuna PJ, author; Xiang J, author; Gingras A, author; Chen X, author; Ginsburg D, author. The cargo receptor SURF4 promotes the efficient cellular secretion of PCSK9. 2018 https://www.ncbi.nlm.nih.gov/sra?term=SRP149835 Publicly available at the NCBI Sequence Read Archive (accession no. SRP149835)

References

  1. Abifadel M, Varret M, Rabès JP, Allard D, Ouguerram K, Devillers M, Cruaud C, Benjannet S, Wickham L, Erlich D, Derré A, Villéger L, Farnier M, Beucler I, Bruckert E, Chambaz J, Chanu B, Lecerf JM, Luc G, Moulin P, Weissenbach J, Prat A, Krempf M, Junien C, Seidah NG, Boileau C. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nature Genetics. 2003;34:154–156. doi: 10.1038/ng1161. [DOI] [PubMed] [Google Scholar]
  2. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. the gene ontology consortium. Nature Genetics. 2000;25:25–29. doi: 10.1038/75556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Barlowe C, Helenius A. Cargo capture and bulk flow in the early secretory pathway. Annual Review of Cell and Developmental Biology. 2016;32:197–222. doi: 10.1146/annurev-cellbio-111315-125016. [DOI] [PubMed] [Google Scholar]
  4. Belden WJ, Barlowe C. Role of Erv29p in collecting soluble secretory proteins into ER-derived transport vesicles. Science. 2001;294:1528–1531. doi: 10.1126/science.1065224. [DOI] [PubMed] [Google Scholar]
  5. Benn M, Nordestgaard BG, Grande P, Schnohr P, Tybjaerg-Hansen A. PCSK9 R46L, low-density lipoprotein cholesterol levels, and risk of ischemic heart disease: 3 independent studies and meta-analyses. Journal of the American College of Cardiology. 2010;55:2833–2842. doi: 10.1016/j.jacc.2010.02.044. [DOI] [PubMed] [Google Scholar]
  6. Butkinaree C, Canuel M, Essalmani R, Poirier S, Benjannet S, Asselin MC, Roubtsova A, Hamelin J, Marcinkiewicz J, Chamberland A, Guillemot J, Mayer G, Sisodia SS, Jacob Y, Prat A, Seidah NG. Amyloid Precursor-like protein 2 and sortilin do not regulate the PCSK9 Convertase-mediated low density lipoprotein receptor degradation but interact with each other. Journal of Biological Chemistry. 2015;290:18609–18620. doi: 10.1074/jbc.M115.647180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chapat C, Jafarnejad SM, Matta-Camacho E, Hesketh GG, Gelbart IA, Attig J, Gkogkas CG, Alain T, Stern-Ginossar N, Fabian MR, Gingras AC, Duchaine TF, Sonenberg N. Cap-binding protein 4EHP effects translation silencing by microRNAs. PNAS. 2017;114:5425–5430. doi: 10.1073/pnas.1701488114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chen XW, Wang H, Bajaj K, Zhang P, Meng ZX, Ma D, Bai Y, Liu HH, Adams E, Baines A, Yu G, Sartor MA, Zhang B, Yi Z, Lin J, Young SG, Schekman R, Ginsburg D. SEC24A deficiency lowers plasma cholesterol through reduced PCSK9 secretion. eLife. 2013;2:e00444. doi: 10.7554/eLife.00444. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cohen JC, Boerwinkle E, Mosley TH, Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. New England Journal of Medicine. 2006;354:1264–1272. doi: 10.1056/NEJMoa054013. [DOI] [PubMed] [Google Scholar]
  10. Costantini LM, Baloban M, Markwardt ML, Rizzo M, Guo F, Verkhusha VV, Snapp EL. A palette of fluorescent proteins optimized for diverse cellular environments. Nature Communications. 2015;6:7670. doi: 10.1038/ncomms8670. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Couzens AL, Knight JD, Kean MJ, Teo G, Weiss A, Dunham WH, Lin ZY, Bagshaw RD, Sicheri F, Pawson T, Wrana JL, Choi H, Gingras AC. Protein interaction network of the mammalian hippo pathway reveals mechanisms of kinase-phosphatase interactions. Science Signaling. 2013;6:rs15. doi: 10.1126/scisignal.2004712. [DOI] [PubMed] [Google Scholar]
  12. Denis N, Palmer-Smith H, Elisma F, Busuttil A, Wright TG, Bou Khalil M, Prat A, Seidah NG, Chrétien M, Mayne J, Figeys D. Quantitative proteomic analysis of PCSK9 gain of function in human hepatic HuH7 cells. Journal of Proteome Research. 2011;10:2011–2026. doi: 10.1021/pr2000072. [DOI] [PubMed] [Google Scholar]
  13. Deutsch EW, Mendoza L, Shteynberg D, Farrah T, Lam H, Tasman N, Sun Z, Nilsson E, Pratt B, Prazen B, Eng JK, Martin DB, Nesvizhskii AI, Aebersold R. A guided tour of the Trans-Proteomic pipeline. Proteomics. 2010;10:1150–1159. doi: 10.1002/pmic.200900375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Exome Aggregation Consortium. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB, Tukiainen T, Birnbaum DP, Kosmicki JA, Duncan LE, Estrada K, Zhao F, Zou J, Pierce-Hoffman E, Berghout J, Cooper DN, Deflaux N, DePristo M, Do R, Flannick J, Fromer M, Gauthier L, Goldstein J, Gupta N, Howrigan D, Kiezun A, Kurki MI, Moonshine AL, Natarajan P, Orozco L, Peloso GM, Poplin R, Rivas MA, Ruano-Rubio V, Rose SA, Ruderfer DM, Shakir K, Stenson PD, Stevens C, Thomas BP, Tiao G, Tusie-Luna MT, Weisburd B, Won HH, Yu D, Altshuler DM, Ardissino D, Boehnke M, Danesh J, Donnelly S, Elosua R, Florez JC, Gabriel SB, Getz G, Glatt SJ, Hultman CM, Kathiresan S, Laakso M, McCarroll S, McCarthy MI, McGovern D, McPherson R, Neale BM, Palotie A, Purcell SM, Saleheen D, Scharf JM, Sklar P, Sullivan PF, Tuomilehto J, Tsuang MT, Watkins HC, Wilson JG, Daly MJ, MacArthur DG. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–291. doi: 10.1038/nature19057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Freeze HH, Kranz C. Endoglycosidase and glycoamidase release of N-linked glycans. Current Protocols in Molecular Biology. 2010;8:im0815s83. doi: 10.1002/0471142727.mb1713as89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Fujii Y, Shiota M, Ohkawa Y, Baba A, Wanibuchi H, Kinashi T, Kurosaki T, Baba Y. Surf4 modulates STIM1-dependent calcium entry. Biochemical and Biophysical Research Communications. 2012;422:615–620. doi: 10.1016/j.bbrc.2012.05.037. [DOI] [PubMed] [Google Scholar]
  17. Gibson DG, Young L, Chuang RY, Venter JC, Hutchison CA, Smith HO. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nature Methods. 2009;6:343–345. doi: 10.1038/nmeth.1318. [DOI] [PubMed] [Google Scholar]
  18. Global Lipids Genetics Consortium. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkilä K, Hyppönen E, Isaacs A, Jackson AU, Johansson Å, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikäinen LP, Magnusson PKE, Mangino M, Mihailov E, Montasser ME, Müller-Nurasyid M, Nolte IM, O'Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney ASF, Döring A, Elliott P, Epstein SE, Ingi Eyjolfsson G, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJP, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimäki T, Lin SY, Lindström J, Loos RJF, Mach F, McArdle WL, Meisinger C, Mitchell BD, Müller G, Nagaraja R, Narisu N, Nieminen TVM, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stančáková A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YI, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrières J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Järvelin MR, Jula A, Kähönen M, Kaprio J, Kesäniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, März W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njølstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PEH, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BHR, Ordovas JM, Boerwinkle E, Palmer CNA, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E, Abecasis GR. Discovery and refinement of loci associated with lipid levels. Nature Genetics. 2013;45:1274–1283. doi: 10.1038/ng.2797. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gustafsen C, Kjolby M, Nyegaard M, Mattheisen M, Lundhede J, Buttenschøn H, Mors O, Bentzon JF, Madsen P, Nykjaer A, Glerup S. The hypercholesterolemia-risk gene SORT1 facilitates PCSK9 secretion. Cell Metabolism. 2014;19:310–318. doi: 10.1016/j.cmet.2013.12.006. [DOI] [PubMed] [Google Scholar]
  20. Herda S, Raczkowski F, Mittrücker HW, Willimsky G, Gerlach K, Kühl AA, Breiderhoff T, Willnow TE, Dörken B, Höpken UE, Rehm A. The sorting receptor Sortilin exhibits a dual function in exocytic trafficking of interferon-γ and granzyme A in T cells. Immunity. 2012;37:854–866. doi: 10.1016/j.immuni.2012.07.012. [DOI] [PubMed] [Google Scholar]
  21. Kim DI, Birendra KC, Zhu W, Motamedchaboki K, Doye V, Roux KJ. Probing nuclear pore complex architecture with proximity-dependent biotinylation. PNAS. 2014;111:E2453–E2461. doi: 10.1073/pnas.1406459111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kjolby M, Andersen OM, Breiderhoff T, Fjorback AW, Pedersen KM, Madsen P, Jansen P, Heeren J, Willnow TE, Nykjaer A. Sort1, encoded by the cardiovascular risk locus 1p13.3, is a regulator of hepatic lipoprotein export. Cell Metabolism. 2010;12:213–223. doi: 10.1016/j.cmet.2010.08.006. [DOI] [PubMed] [Google Scholar]
  23. Laemmli UK. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature. 1970;227:680–685. doi: 10.1038/227680a0. [DOI] [PubMed] [Google Scholar]
  24. Lange LA, Willer CJ, Rich SS. Recent developments in genome and exome-wide analyses of plasma lipids. Current Opinion in Lipidology. 2015;26:96–102. doi: 10.1097/MOL.0000000000000159. [DOI] [PubMed] [Google Scholar]
  25. Li W, Xu H, Xiao T, Cong L, Love MI, Zhang F, Irizarry RA, Liu JS, Brown M, Liu XS. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biology. 2014;15:554. doi: 10.1186/s13059-014-0554-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Liu G, Zhang J, Larsen B, Stark C, Breitkreutz A, Lin ZY, Breitkreutz BJ, Ding Y, Colwill K, Pasculescu A, Pawson T, Wrana JL, Nesvizhskii AI, Raught B, Tyers M, Gingras AC. ProHits: integrated software for mass spectrometry-based interaction proteomics. Nature Biotechnology. 2010;28:1015–1017. doi: 10.1038/nbt1010-1015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Liu G, Knight JD, Zhang JP, Tsou CC, Wang J, Lambert JP, Larsen B, Tyers M, Raught B, Bandeira N, Nesvizhskii AI, Choi H, Gingras AC. Data independent acquisition analysis in ProHits 4.0. Journal of Proteomics. 2016;149:64–68. doi: 10.1016/j.jprot.2016.04.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15:550. doi: 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Ly K, Essalmani R, Desjardins R, Seidah NG, Day R. An unbiased mass spectrometry approach identifies Glypican-3 as an interactor of proprotein convertase subtilisin/Kexin type 9 (PCSK9) and low density lipoprotein receptor (LDLR) in hepatocellular carcinoma cells. Journal of Biological Chemistry. 2016;291:24676–24687. doi: 10.1074/jbc.M116.746883. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Malkus P, Jiang F, Schekman R. Concentrative sorting of secretory cargo proteins into COPII-coated vesicles. The Journal of Cell Biology. 2002;159:915–921. doi: 10.1083/jcb.200208074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Mellacheruvu D, Wright Z, Couzens AL, Lambert JP, St-Denis NA, Li T, Miteva YV, Hauri S, Sardiu ME, Low TY, Halim VA, Bagshaw RD, Hubner NC, Al-Hakim A, Bouchard A, Faubert D, Fermin D, Dunham WH, Goudreault M, Lin ZY, Badillo BG, Pawson T, Durocher D, Coulombe B, Aebersold R, Superti-Furga G, Colinge J, Heck AJ, Choi H, Gstaiger M, Mohammed S, Cristea IM, Bennett KL, Washburn MP, Raught B, Ewing RM, Gingras AC, Nesvizhskii AI. The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nature Methods. 2013;10:730–736. doi: 10.1038/nmeth.2557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Mitrovic S, Ben-Tekaya H, Koegler E, Gruenberg J, Hauri HP. The cargo receptors Surf4, endoplasmic reticulum-Golgi intermediate compartment (ERGIC)-53, and p25 are required to maintain the architecture of ERGIC and golgi. Molecular Biology of the Cell. 2008;19:1976–1990. doi: 10.1091/mbc.e07-10-0989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Nyfeler B, Zhang B, Ginsburg D, Kaufman RJ, Hauri HP. Cargo selectivity of the ERGIC-53/MCFD2 transport receptor complex. Traffic. 2006;7:1473–1481. doi: 10.1111/j.1600-0854.2006.00483.x. [DOI] [PubMed] [Google Scholar]
  34. Nyfeler B, Reiterer V, Wendeler MW, Stefan E, Zhang B, Michnick SW, Hauri HP. Identification of ERGIC-53 as an intracellular transport receptor of alpha1-antitrypsin. The Journal of Cell Biology. 2008;180:705–712. doi: 10.1083/jcb.200709100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. ODYSSEY LONG TERM Investigators. Robinson JG, Farnier M, Krempf M, Bergeron J, Luc G, Averna M, Stroes ES, Langslet G, Raal FJ, El Shahawy M, Koren MJ, Lepor NE, Lorenzato C, Pordy R, Chaudhari U, Kastelein JJ. Efficacy and safety of alirocumab in reducing lipids and cardiovascular events. New England Journal of Medicine. 2015;372:1489–1499. doi: 10.1056/NEJMoa1501031. [DOI] [PubMed] [Google Scholar]
  36. Online Mendelian Inheritance in Man, OMIM 2018. Online mendelian inheritance in man. Johns Hopkins University, Baltimore MD. 185660
  37. Open-Label Study of Long-Term Evaluation against LDL Cholesterol (OSLER) Investigators. Sabatine MS, Giugliano RP, Wiviott SD, Raal FJ, Blom DJ, Robinson J, Ballantyne CM, Somaratne R, Legg J, Wasserman SM, Scott R, Koren MJ, Stein EA. Efficacy and safety of evolocumab in reducing lipids and cardiovascular events. New England Journal of Medicine. 2015;372:1500–1509. doi: 10.1056/NEJMoa1500858. [DOI] [PubMed] [Google Scholar]
  38. Otte S, Belden WJ, Heidtman M, Liu J, Jensen ON, Barlowe C. Erv41p and Erv46p: new components of COPII vesicles involved in transport between the ER and golgi complex. The Journal of Cell Biology. 2001;152:503–518. doi: 10.1083/jcb.152.3.503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Otte S, Barlowe C. Sorting signals can direct receptor-mediated export of soluble proteins into COPII vesicles. Nature Cell Biology. 2004;6:1189–1194. doi: 10.1038/ncb1195. [DOI] [PubMed] [Google Scholar]
  40. Roux KJ, Kim DI, Raida M, Burke B. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. The Journal of Cell Biology. 2012;196:801–810. doi: 10.1083/jcb.201112098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Saegusa K, Sato M, Morooka N, Hara T, Sato K. SFT-4/Surf4 control ER export of soluble cargo proteins and participate in ER exit site organization. The Journal of Cell Biology. 2018;217:2073–2085. doi: 10.1083/jcb.201708115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Sanjana NE, Shalem O, Zhang F. Improved vectors and genome-wide libraries for CRISPR screening. Nature Methods. 2014;11:783–784. doi: 10.1038/nmeth.3047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Schneider CA, Rasband WS, Eliceiri KW. NIH image to ImageJ: 25 years of image analysis. Nature Methods. 2012;9:671–675. doi: 10.1038/nmeth.2089. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Seidah NG, Awan Z, Chrétien M, Mbikay M. PCSK9: a key modulator of cardiovascular health. Circulation Research. 2014;114:1022–1036. doi: 10.1161/CIRCRESAHA.114.301621. [DOI] [PubMed] [Google Scholar]
  45. Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelson T, Heckl D, Ebert BL, Root DE, Doench JG, Zhang F. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science. 2014;343:84–87. doi: 10.1126/science.1247005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Shalem O, Sanjana NE, Zhang F. High-throughput functional genomics using CRISPR-Cas9. Nature Reviews Genetics. 2015;16:299–311. doi: 10.1038/nrg3899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Shteynberg D, Deutsch EW, Lam H, Eng JK, Sun Z, Tasman N, Mendoza L, Moritz RL, Aebersold R, Nesvizhskii AI. iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Molecular & Cellular Proteomics. 2011;10:M111.007690. doi: 10.1074/mcp.M111.007690. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Stewart SA, Dykxhoorn DM, Palliser D, Mizuno H, Yu EY, An DS, Sabatini DM, Chen IS, Hahn WC, Sharp PA, Weinberg RA, Novina CD. Lentivirus-delivered stable gene silencing by RNAi in primary cells. RNA. 2003;9:493–501. doi: 10.1261/rna.2192803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, Schmidt D, O'Keeffe S, Haas S, Vingron M, Lehrach H, Yaspo ML. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science. 2008;321:956–960. doi: 10.1126/science.1160342. [DOI] [PubMed] [Google Scholar]
  50. Teo G, Liu G, Zhang J, Nesvizhskii AI, Gingras AC, Choi H. SAINTexpress: improvements and additional features in significance analysis of INTeractome software. Journal of Proteomics. 2014;100:37–43. doi: 10.1016/j.jprot.2013.10.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. The Gene Ontology Consortium Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Research. 2017;45:D331–D338. doi: 10.1093/nar/gkw1108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Proteomics. Tissue-based map of the human proteome. Science. 2015;347:1260419. doi: 10.1126/science.1260419. [DOI] [PubMed] [Google Scholar]
  53. Wendeler MW, Paccaud JP, Hauri HP. Role of Sec24 isoforms in selective export of membrane proteins from the endoplasmic reticulum. EMBO reports. 2007;8:258–264. doi: 10.1038/sj.embor.7400893. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Xu W, Liu L, Hornby D. c-IAP1 binds and processes PCSK9 protein: linking the c-IAP1 in a TNF-α pathway to PCSK9-mediated LDLR degradation pathway. Molecules. 2012;17:12086–12101. doi: 10.3390/molecules171012086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Zanetti G, Pahuja KB, Studer S, Shim S, Schekman R. COPII and the regulation of protein sorting in mammals. Nature Cell Biology. 2011;14:20–28. doi: 10.1038/ncb2390. [DOI] [PubMed] [Google Scholar]
  56. Zhang B, Kaufman RJ, Ginsburg D. LMAN1 and MCFD2 form a cargo receptor complex and interact with coagulation factor VIII in the early secretory pathway. Journal of Biological Chemistry. 2005;280:25881–25886. doi: 10.1074/jbc.M502160200. [DOI] [PubMed] [Google Scholar]
  57. Zhang B, Zheng C, Zhu M, Tao J, Vasievich MP, Baines A, Kim J, Schekman R, Kaufman RJ, Ginsburg D. Mice deficient in LMAN1 exhibit FV and FVIII deficiencies and liver accumulation of α1-antitrypsin. Blood. 2011;118:3384–3391. doi: 10.1182/blood-2011-05-352815. [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision letter

Editor: Randy Schekman1
Reviewed by: Randy Schekman2, Charles Barlowe3

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "The cargo receptor SURF4 promotes the efficient cellular secretion of PCSK9" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Randy Schekman as the Reviewing/Senior Editor and Reviewer #1. The following individual involved in review of your submission has agreed to reveal their identity: Charles Barlowe (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Emmer et al. have used proximity labeling to identify protein candidates for the putative ER traffic receptor responsible for sorting of PCSK9 that previous work from the Ginsburg lab suggested would respond to SEC24A for sorting into COPII vesicles. Although a number of candidate ER membrane proteins were identified, proximity labeling alone appeared not to offer the spatial or temporal resolution needed to narrow the search to a small number of candidates. They then turned to a CRISPR screen with sgRNAs covering most of the human genome and searched by FACS for cells that accumulated a PCSK9-eGFP reporter in preference to an alpha 1 antitrypsin-mCherry reporter. A fairly exhaustive screen turned up the gene SURF4, a mammalian homolog of the yeast gene ERV29, previously identified as an ER traffic receptor for the precursor of the yeast alpha mating pheromone. SURF4 was also one of the ER membrane proteins identified in their biotin proximity-labeling screen. The identification of SURF4 as important in PCSK9 was confirmed in multiple independent experiments with other tagged and untagged forms of PCSK9 expressed in wt and SURF4 del mutant lines. Consistent with a role in PCSK9 traffic from the ER, SURF4 mutant cells accumulated PCSK9 as detected in lysates of whole cells at the expense of its secretion into the medium and in the ER in intact cells. SURF4 was localized to the ER by immunofluorescence and was found associated with PCSK9 by use of a cleavable crosslinking agent followed by denaturing co-immunoprecipitation.

The work is quite interesting and compelling and adds to the tiny list of such sorting receptors found in mammalian cells. It remains a mystery how the vast majority of secreted proteins are sorted efficiently in the ER, though at least some may be secreted by a receptor-independent bulk flow mechanism. However the importance of the work is diminished to some extent as the yeast equivalent protein, Erv29, was discovered many years ago and its characterization has been reported in several papers over the years. Further, SURF4 was recently reported to facilitate apoB ER traffic in HepG2 cells. Of course, there are many things the authors could have done to further characterize the role of SURF4, such as identifying the sorting signal on PCSK9 responsible for its recognition in the ER, and examining the sorting of SURF4 into COPII vesicles formed in a cell-free reaction. The yeast protein Erv29 is sorted into COPII vesicles but not all ER membrane proteins required for ER traffic accompany their cargo into COPII vesicles (e.g. Shr3). However, as this is an initial report, these more detailed characteristics could perhaps be held for a later installment. Given the limited scope of this work, it may be more appropriately considered as an Advance on the Chen et al., paper of 2013.

Essential revisions:

1) For the cross-linking immunoprecipitation experiment shown in Figure 4D, the experiment would be strengthened by immunoblotting for additional ER/Golgi membrane proteins (e.g. LMAN1, GM130) to demonstrate specificity of the co-immunoprecipitations. At a minimum, immunoblot of actin in the IPs should be shown.

2) The authors conclude that SURF4 depletion or deletion results in only a partial effect on PCSK9 secretion and thus that other, possibly redundant functions may operate. However, the sensitivity of most of their secretion experiments is limited by the long time course of accumulation measured in steady state cells or in one case, in cells induced for PCSK9 secretion over a period of hours (Figure 3F). A clearer case for a kinetic delay dependent on SURF4 would require a pulse-chase experiment conducted over the usual transit time course of secretion (10-60 min) in wt and SURF4 del mutant cells.

3) The data in Figure 5G do not show an obvious effect of SURF4 del on the conversion of endoH sens to resistant mature PCSK9. The quantified data in 5H look good but are hard to reconcile with the appearance of the bands in 5G. For example, the intensity of the endoH resistant band of mature PCSK9 in relation to the minus enzyme control looks pretty much the same for the mutant vs. the rescue sample. Perhaps the authors could show other repetitions of this gel in a figure supplement.

eLife. 2018 Sep 25;7:e38839. doi: 10.7554/eLife.38839.026

Author response

Essential revisions:

1) For the cross-linking immunoprecipitation experiment shown in Figure 4D, the experiment would be strengthened by immunoblotting for additional ER/Golgi membrane proteins (e.g. LMAN1, GM130) to demonstrate specificity of the co-immunoprecipitations. At a minimum, immunoblot of actin in the IPs should be shown.

We agree and have now performed additional immunoblots of immunoprecipitated material with antibodies to LMAN1, calnexin and PDI. A high degree of specificity is seen, with no co-IP seen for either PCSK9 or SURF4 with these control ER/ERGIC markers. These data have now been added to Figure 4D, and are described in the text (subsection “SURF4 localizes to the early secretory pathway where it physically interacts with PCSK9”).

2) The authors conclude that SURF4 depletion or deletion results in only a partial effect on PCSK9 secretion and thus that other, possibly redundant functions may operate. However, the sensitivity of most of their secretion experiments is limited by the long time course of accumulation measured in steady state cells or in one case, in cells induced for PCSK9 secretion over a period of hours (Figure 3F). A clearer case for a kinetic delay dependent on SURF4 would require a pulse-chase experiment conducted over the usual transit time course of secretion (10-60 min) in wt and SURF4 del mutant cells.

To address this important point, we have now performed pulse-chase analysis, which has been added to the manuscript as requested (Figure 5—figure supplement 1 and subsection “Loss of SURF4 results in decreased PCSK9 secretion and ER accumulation of PCSK9”, first paragraph). Indeed, we found that PCSK9 secretion into the conditioned media was detectable at 30 min in SURF4 expressing cells and significantly delayed in SURF4 null cells.

3) The data in Figure 5G do not show an obvious effect of SURF4 del on the conversion of endoH sens to resistant mature PCSK9. The quantified data in 5H look good but are hard to reconcile with the appearance of the bands in 5G. For example, the intensity of the endoH resistant band of mature PCSK9 in relation to the minus enzyme control looks pretty much the same for the mutant vs. the rescue sample. Perhaps the authors could show other repetitions of this gel in a figure supplement.

We thank the reviewers for raising this point. The intensity of the endoH-resistant band is indeed unchanged in the lysate prepared from SURF4 deficient cells. It is only the endoH-sensitive band that increases, consistent with the idea that PCSK9 ER exit is impaired but that once out of the ER, further steps in trafficking proceed with normal kinetics. We have clarified our discussion of these results in the text, and have now included the primary immunoblot data for all 4 replicates in Figure 5—figure supplement 2.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Citations

    1. Online Mendelian Inheritance in Man, OMIM 2018. Online mendelian inheritance in man. Johns Hopkins University, Baltimore MD. 185660

    Supplementary Materials

    Supplementary file 1. BioID of PCSK9-interacting proteins.

    Spectral counts are listed for prey proteins identified from streptavidin-purified lysates of cells expressing fusions of BirA* with GFP, PCSK9, A1AT, SAR1A, and SAR1B. Gene Ontology enrichment analysis (Ashburner et al., 2000; The Gene Ontology Consortium, 2017) is displayed for candidate PCSK9 cargo receptors.

    elife-38839-supp1.xlsx (1.2MB, xlsx)
    DOI: 10.7554/eLife.38839.014
    Supplementary file 2. CRISPR screen sgRNA-level analysis.

    DESeq2 output for each sgRNA showing normalized mapped reads in control (GFP-low) populations and log2 fold-change in experiment (GFP-high) populations with significance testing for enrichment or depletion. A copy of the reference library showing the corresponding gene target and target sequence and gene for each sgRNA is included.

    elife-38839-supp2.xlsx (16.3MB, xlsx)
    DOI: 10.7554/eLife.38839.015
    Supplementary file 3. CRISPR screen gene-level analysis.

    Enrichment for each sgRNA targeting the same gene was combined to generate a gene-level enrichment score using MAGeCK (Li et al., 2014), which is displayed in the column corresponding to the number of unique sgRNA targeting that same that demonstrated enrichment (p<0.05) by analysis with DESeq2 (Supplementary file 2).

    elife-38839-supp3.xlsx (1.6MB, xlsx)
    DOI: 10.7554/eLife.38839.016
    Supplementary file 4. Oligonucleotide sequences.

    Primers used for CRISPR/Cas9 targeting and genotyping are displayed.

    elife-38839-supp4.xlsx (12.6KB, xlsx)
    DOI: 10.7554/eLife.38839.017
    Source code 1. gRNA_mapping.zip.
    elife-38839-code1.zip (5.3KB, zip)
    DOI: 10.7554/eLife.38839.018
    Transparent reporting form
    elife-38839-transrepform.docx (246KB, docx)
    DOI: 10.7554/eLife.38839.019

    Data Availability Statement

    Proteomic data has been deposited to the MassIVE database under accession MSV000082222. Sequencing data has been uploaded to the Sequence Read Archive under accession SRP149835. Source data files are included in the supplementary information.

    The following datasets were generated:

    Emmer BT, author; Hesketh GG, author; Kotnik E, author; Tang VT, author; Lascuna PJ, author; Xiang J, author; Gingras A, author; Chen X, author; Ginsburg D, author. The cargo receptor SURF4 promotes the efficient cellular secretion of PCSK9. 2018 ftp://massive.ucsd.edu/MSV000082222 Publicly available at MassIVE (https://massive.ucsd.edu).

    Emmer BT, author; Hesketh GG, author; Kotnik E, author; Tang VT, author; Lascuna PJ, author; Xiang J, author; Gingras A, author; Chen X, author; Ginsburg D, author. The cargo receptor SURF4 promotes the efficient cellular secretion of PCSK9. 2018 https://www.ncbi.nlm.nih.gov/sra?term=SRP149835 Publicly available at the NCBI Sequence Read Archive (accession no. SRP149835)

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