Systems biology greatly improve activity of secreted therapeutic sulfatase in CHO bioprocess

Niklas Thalén1, Mona Moradi Barzadd1, Magnus Lundqvist1, Johanna Rodhe3, Monica Andersson3, Gholamreza Bidkhori2,4, Dominik Possner3, Chao Su3, Joakim Nilsson3, Peter Eisenhut5,6, Magdalena Malm1, Jeanette Westin3, Johan Forsberg3, Erik Nordling3, Adil Mardinoglu2, Anna-Luisa Volk1, Anna Sandegren3, Johan Rockberg1,*

1 Dept. of science; KTH - Royal Institute of Technology; Stockholm; SE-106 91; Sweden 2 Science for Life Laboratory; KTH - Royal Institute of Technology; Solna; 171 65; Sweden 3 SOBI AB, Tomtebodavägen 23A, Stockholm, Sweden 4 AIVIVO Ltd. Unit 25, Bio-innovation centre, Cambridge Science park, Cambridge, UK. 5 ACIB - Austrian Centre of Industrial Biotechnology, Krenngasse 37, 8010 Graz, Austria 6 BOKU - University of Natural Resources and Life Sciences, Department of Biotechnology, Vienna, 1190, Austria

* To whom correspondence should be addressed: Tel: +46 8 790 99 88; Email: [email protected]

Target journal: Cell Systems

Take home message:

• Transcriptomic comparison of two CHO clones with different productivities showed three relevant for sulfatase activation and secretion • Co-expression of genes with sulfatase led to a 150-fold increase in specific activity • Reduced promoter strength increased specific activity of sulfatase

SUMMARY

Rare diseases are, despite their name, collectively common and millions of people are affected daily of conditions where treatment often is unavailable. Sulfatases are a large family of activating related to several of these diseases. Heritable genetic variations in sulfatases may lead to impaired activity and a reduced macromolecular breakdown within the lysosome, with several severe and lethal conditions as a consequence. While therapeutic options are scarce, treatment for some sulfatase deficiencies by recombinant replacement are available. However, such recombinant production of sulfatases suffers greatly from low product activity and yield, further limiting accessibility for patient groups. Here, we have addressed this problem by defining key- necessary for active sulfatase secretion by comparison of CHO clones with different levels of production of active sulfatase. Quantitative transcriptomic analysis highlighted 14 key genes associated with sulfatase production, and experimental validation by co-expression improved the sulfatase enzyme activity by up to 150-fold. Furthermore, a correlation between product mRNA levels and sulfatase activity were observed and expression with lower activity promoters showed an increased in sulfatase activity. The workflow devised is general and we propose it to be useful for resolving bottlenecks in cellular machineries for improvement of cell factories for other biologics as well.

INTRODUCTION

Rare diseases were for a long time overseen by the pharmaceutical industry. With conditions that only affect a small portion of the public it was seen as not financially feasible to pursuit medical discoveries due to the low impact it would have. However, a shift took place three decades ago when country level initiatives (Gammie, Lu, & Ud-Din Babar, 2015) and rare disease communities (Shore et al., 2006; Zimmer, 2013) pushed for advancements in orphan drug development, ultimately leading to a drastic change within the industry, through regulatory incentives. And today, orphan drugs are a large player on the pharmaceutical market with 906 U.S. Food and Drug Administration (FDA) approved orphan drugs, as of September 2020 (U.S. Food and Drug administration, 2020) and an estimated market share of US $209 billion in 2022, accounting for a 21% of total branded prescription drug sales (Hadjivasiliou, 2017). Lysosomal storage disease (LSD) is a group of 50-60 rare metabolic disorders with a combined prevalence of 1 in every 2000 – 5000 live births. The diseases are characterized by an abnormal build-up of undigested molecules within the lysosome due to deficiency in one or several enzymes involved in the catabolic process (Mehta, Beck, & Sunder- Plassmann, 2006), leading to several severe and lethal conditions. Treatments for these rare conditions are often symptomatic and supportive but for some, the deficient enzyme can be replaced through infusion of a functional replacement. This enzyme replacement therapy (ERT) is strictly dependent on the production of highly purified and functional protein, as well as the effectiveness of their systemic distribution (Martino et al., 2005; Ries, 2017). Due to the importance of functional replacements all available ERT products needs to attain the essential glycans for uptake and transport to the lysosome (Muenzer et al., 2006). Sulfatases belong to a large conserved family of enzymes that are of particular interest for ERT since members from this enzyme family are deficient in seven different LSDs. There are 17 known human sulfatases that all share the same post-translational processing through the Endoplasmic reticulum (ER) and Golgi apparatus were post translational modifications (PTM) and activations takes place for correct function of the enzymes. Within the ER an activation process unique for sulfatases takes place where a C-alpha- formylglycin (FGly) is generated from a in the (Sardiello, Annunziata, Roma, & Ballabio, 2005b). This activation process is enabled through the formylglycine generating enzyme (FGE) that is encoded by the Sulfatase-modifying factor 1 (SUMF1) . This gene is crucial for sulfatase activation and was found through one of the more severe cases of LSD, the multiple sulfatase deficiency, were a mutation in SUMF1 leads to deficiencies in all sulfatases (Cosma et al., 2003b). Currently, three LSD’s, caused by sulfatase deficiencies, have ERT’s available (Idursulfase, Galsulfase, and Elosulfase alfa). Understanding the biology behind sulfatase assembly within the cell could improve not only our understanding of the processes that takes place for an important therapeutic target but it could also improve the production process for some of the most expensive pharmaceuticals on the market (Luzzatto et al., 2018). Chinese hamster ovary (CHO) cells have for a long time been the host of choice for production of various pharmaceuticals, and is today engineered into a sophisticated platform for production of high-quality products (Walsh, 2018). Improvements in CHO platform development has steadily increased its ability to produce high titers of a broad range of pharmaceuticals (Amann, Schmieder, Faustrup Kildegaard, Borth, & Andersen, 2019; Fischer, Handrick, & Otte, 2015; Wurm, 2004). These improvements have mainly been achieved through bioprocess optimization (Jayapal, Wlaschin, Hu, & Yap, 2007), were media composition and bioreactor design modifications have evolved. Selection of new production cell lines has traditionally been done through development and screening of a large number of mutated cells, out of which single clones are selected based on their capabilities to produce correct products, with no further directed development of the per cell yield. However, with the introduction of omics-based approaches to investigate the relationship of gene expressions and productivity levels, new frontiers for precise cell engineering are now available (Kildegaard, Baycin-Hizal, Lewis, & Betenbaugh, 2013; Kuo et al., 2018). Studies exploring gene level expressions and high productivity have found several important factors for increased yield at cell levels, opening up new possibilities for increasing the capabilities of CHO cell production of both new advanced biologics that require complex modifications as well as further titer increase of more traditional monoclonal antibodies (Hong, Lakshmanan, Goudar, & Lee, 2018). Currently, human derived cell lines or CHO cell lines are industry standards for production of enzymes for ERT in LSD due to the importance of correct glycosylation for efficient uptake of the drug (Tian et al., 2019; Whiteman & Kimura, 2017). In this study, sulfatase production is addressed through systems biology in order to find genes and processes involved in the production and secretion of active sulfatases. Two different sulfatases, N-sulphoglucosamine sulphohydrolase (Sulfamidase) and Arylsulfatase A (ASA), were included in order to identify transcriptomic components with impact on productivity regardless of sulfatase to be expressed. Quantitative transcriptomic comparison of two CHO cells with different sulfatase productivity were compared and three different genes related to sulfatase production were identified. Co-transfections of the identified genes yielded an increase from virtually undetectable activity up to a significant amount of sulfatase activity, showing the high impact omics studies can have on improvements of production of important difficult to express proteins. Also, expressing ASA with lower promoter activity through different promoter cassettes increased the specific activity of the enzyme.

RESULTS

Automated bioreactor cultivation of CHO cells with varying sulfatase productivities enables quantitative transcriptomic analyses In order to study differential gene expression related to activation and production of sulfatases, sets of comparable CHO cultures are needed. Here, two CHO production cell clones expressing Sulfamidase were analyzed in a transcriptomics workflow (figure 1). Duplicate samples of the two Sulfamidase producing clones were cultivated for 17 days under three different conditions (standard condition, high cell density condition, and without addition of copper, a cofactor for activation of sulfatases) in an Automated Microscale Bioreactor (Ambr). Samples were collected for observation on titer levels, activity measurements, and cell collection for subsequent RNA sequencing during the cultivation process. Cultivation temperature was lowered to 31°C for maximum productivity of Sulfamidase when the desirable cell density was reached. The specific activity of Sulfamidase from clone A under standard and high cell density conditions were the double of that of clone B under the same conditions on day 17. Without copper addition the specific activity was almost halved for both clones (figure 2A), while the amount of Sulfamidase produced without copper was at the same level as for the standard conditions (figure 2B). Titer levels steadily increased over the cultivation period with up to 5 times higher production in clone B. For both clones, the high cell density condition resulted in the highest Sulfamidase titer of the three different conditions (figure 2B). Sulfamidase specific activity were higher under the first six days as compared to day 12-17 where the activity leveled out at around half of the initial activity for both clones (figure 2A). Due to the clear deviation between titer and activity levels of sulfatases produced by the two CHO clones, a systematic approach through RNA sequencing could be used in order to understand the biology of protein folding, activation, and secretion of given sulfatase. Hence, RNA samples from day 3, 6, 12, 14, and 17 were collected and sequenced for an in-depth transcriptomic analysis of the differences between the clones.

Quantitative transcriptomic comparison of two different sulfatase productivities highlight genes involved in sulfatase activation To study the transcriptomic differences between clone A and clone B, RNA samples from five timepoints during the Ambr cultivation were sequenced on the Illumina HiSeq platform with paired-reads (150 bp insert size) and subsequently analyzed for differentially expressed genes. For the first two collected timepoints, day three and six, clustering of samples based on gene expression were dependent on clone variant and day (figure 3A). Upon the temperature downshift to facilitate optimal protein production, the transcriptome landscape was altered and for the remaining three timepoints there was a clear distinction between the two clones (figure 3A). The shift into hypothermic conditions was also noticeable using principal component analyses in which the late-stage samples showed a much tighter clustering (figure 3B). Furthermore, the separation of the two different clone variants was kept throughout the cultivation (figure 3C). Functional analyses on the differentially expressed genes showed hallmark enrichment for protein secretion when more active Sulfamidase was secreted (clone A) and unfolded protein response when less active Sulfamidase was secreted (clone B) (supplementary data 1). Gene sets for protein secretion and post translational modifications were extracted from Gene Set Enrichment Analysis-Molecular Signature Database (GSEA-MSignDB), giving a set of 83 genes that were differentially expressed between the clones (figure 4A). Out of these 83 genes, 14 genes were found upregulated in clone A compared to clone B over the entire cultivation period. Two of the 14 genes have previously been described to be involved in sulfatase activation and trafficking, SUMF1 and Mannose-6-phosphate receptors (M6PR). SUMF1 is responsible for sulfatase activation within the ER and is also known to be the limiting factor for production of active sulfatases (Cosma et al., 2003). M6PR targets mannose-6- phosphate modified sulfatases for receptor-dependent-transport to the late endosome/lysosome (Braulke & Bonifacino, 2009). It is also reported to recycle secreted SUMF1 back into the ER from the extracellular matrix (Zito et al., 2007). Endoplasmic reticulum-Golgi intermediate compartment protein 3 (ERGIC3) is recognized by the COPI and COPII coats and is presumed to play a role in the early secretory pathway of mammalian cells (Orci, Ravazzola, Mack, Barlowe, & Otte, 2003). ERGIC3 has been described to act in complex with ERGIC2 where they are a retrograde receptor for an unknown class of ER resident proteins (Shibuya, Margulis, Christiano, Walther, & Barlowe, 2015). Four genes that were not shown from the RNA data but are described elsewhere for their role in sulfatase activation are, ERGIC-53, PDIA1, SUMF2, and ERP44 (figure 4B). In order to evaluate potential ways to improve on sulfatase production all seven genes were cloned and co-expressed with ASA in order to evaluate their ability to affect sulfatase activation and secretion.

Co-expression of sulfatases with genes found through transcriptomic comparisons resulted in a 150-fold increase of sulfatase activity Transcriptomic analyses data showed an upregulation of SUMF1, ERGIC3, and M6PR in clone A, when co-expressed with ASA the activity increased significantly for all of these. The highest activation increase was seen when SUMF1 was included, 150-fold for only SUMF1 addition and 35-fold for SUMF1+M6PR addition. Only adding M6PR increased the specific activity of ASA 5-fold and only adding ERGIC3 increased the activity by 3- fold (figure 5A). Since SUMF1 is known to be the key enzyme for sulfatase activation, different folding and transport factors known to be involved in SUMF1 cycling through the cell were also tested for their ability to work as cofactors assisting the endogenous SUMF1 in order to achieve a more active recombinant sulfatase. Co-expressing SUMF2 and PDIA1 resulted in a significant increase in ASA activity of up to 50%. ERGIC53 and ERP44 did however not give rise to a significant increase of ASA activity (figure 5A). Interestingly, when M6PR was added as a co-factor, ASA titers decreased. Since it is known that M6PR is involved in the cellular trafficking of sulfatases it was hypothesized that this could explain the lower titers of secreted ASA. Therefore, western blot analysis on the amount of ASA present in both the supernatant as well as within the cell were analyzed through the co-expression cultivations. No excess build-up of ASA within the cell could be observed for the M6PR co-expression as compared to the other co-expressions. Instead, ASA levels were significantly lower both within the cell and in the secreted media as compared to the other co-expressions (supplementary figure 1).

Reduced promoter strength increased sulfatase activity Titer and activity measurements from the Ambr cultivations with clones A and B showed that the production levels for the recombinant sulfatase correlated negatively with the specific activity of the product (figure 2). Furthermore, mRNA levels of the recombinant Sulfamidase were significant lower for clone A (supplementary figure 2). Since reduction of mRNA synthesis rate can improve expression and folding of proteins (Hou, Liu, Li, & Yang, 2012) an experiment to test transcriptional pressure on sulfatase activity was outlined. For this, the cytomegalovirus immediate-early promoter (CMV), mouse phosphoglycerate kinase 1 promoter (PGK), and human Ubiquitin C promoter (UBC) were cloned into our transient sulfatase expression vector pKTH16 (Eisenhut et al., 2020). These three promoters are known to cover a broad range of promoter strength suitable for correlation studies on sulfatase quality and mRNA pressure (supplementary figure 3). Titer levels for ASA in relation to CMV expression was 58% for PGK expression and 13% for UBC expression (figure 6). Activity measurements showed an increase of specific activity when the two weaker promoters were used. For PGK driven expression, ASA activity increased 4-fold as compared to the CMV. This large improvement of activity compensates for the loss in productivity with more than double the amount of secreted active product as compared to CMV expression. For UBC driven expression, ASA activity increased 3-fold leading to less than half the amount of secreted active product as compared to CMV driven ASA expression (figure 6).

Discussion

Sulfatases are an important family of enzymes that can have direct impact in several different rare diseases were ERT is an option. Investigating genes associated with production of a larger portion of activated product could show co-factors that would enable an increase in host production capabilities. In this study, three different culture conditions were compared (standard, without copper, and high cell number). Excluding addition of copper, a co-factor for sulfatase activation (Knop, Dang, Jeschke, & Seebeck, 2017), limited the amount of activated Sulfamidase produced for both clones and a reduction in the specific activity can be observed from the second measuring timepoint on day 6. Increasing the cell number during cultivation did not negatively affect Sulfamidase activation and the total yield increased (figure 2). These different clones, named A (for high amounts of activated Sulfamidase) and B (for low levels of activated Sulfamidase), enabled our comparative transcriptomic study due to the distinct difference in Sulfamidase activity. Further, Ambr cultivations resulted in highly reproducible cultures reflected in the close clustering between replicates in the transcriptomic landscape (figure 3). During the early days of cultivation when Sulfamidase production was still quite low, a separation on gene level expression is starting and 1429 genes differes between day 3 and 6 (1250 upregulated in day, 179 upregulated in day3 (log2FC > 1 och padj < 0.01)). This differentiation continues between day 6 and day 12 when another 551 genes are differentially expressed (324 upregulated on day 12, 227 upregulated on day 6 (log2FC > 1 och padj < 0.01)). But for the later stage of cultivation, after the temperature downshift and when Sulfamidase production have increased, the gene level expression between days comes to a halt and only one more gene deviated during the remaining days of cultivation. This clustering between day 3, 6, and 12-17 is also visualized in figure 3B. Despite differences in both Sulfamidase titer and specific activity levels, no clear difference within the gene clustering is observed between the different culture conditions (figure 3A). Indicating that increasing the cell density for sulfamidase production does not affect the host cell and higher levels of specific activity is kept with an increase in total output of sulfamidase. Excluding addition of copper severely impairs the activation of sulfamidase, but the different gene expressions levels due not deviate to a larger extent from the normal condition. Possibly, the low levels of genes more abundant in clone A do not deviate even though sumf1 activation cannot occur to the same extent. Due to the known importance of sulfatase folding and activation within the secretary pathway and the upregulated processes of protein secretion of clone A these processes were further analyzed. Extracting protein secretion and post translational modification gene sets from GSEA-MSignDB resulted in a set of 83 genes that were differentially expressed between the clones. The extracted gene set had different expression patterns over the cultivation period. However, out of the 83 genes, 14 were more upregulated in clone A over the entire cultivation period, regardless of culture conditions (figure 4A). Production of activated sulfatases is limited due to post-transcriptional bottlenecks within the secretory pathway. Recently, co-expression with a library of helper proteins were found to improve product titer (H. Hansen et al., 2015). Following this, we here aimed at improving activity using effector genes for co-expression with sulfatase identified by transcriptomics. Out of the 14 upregulated genes, SUMF1, M6PR, and ERGIC3 were selected based on their described or potential linkage to sulfatase activation and secretion. SUMF1 expresses the FGE that converts sulfatases into their active form (Sardiello, Annunziata, Roma, & Ballabio, 2005) and previous co-expression studies have shown high improvements on sulfatase activation (Alméciga-Díaz et al., 2009; Fraldi et al., 2007; Rodríguez-López et al., 2016). Co-expression of SUMF1 with ASA increased the specific activity 150-fold while maintaining a similar total sulfatase yield as the ASA control. When sulfatases are processed through the secretion pathway a mannose-6-phosphate group is covalently added in the cis Golgi compartment, enabling an receptor dependent transport to the lysosome via the M6PR (Coutinho, Prata, & Alves, 2012) and co-expression of M6PR and ASA did effect ASA activity with a 6-fold increase (figure 5A). Surprisingly, the yield decreased by more than 75% when M6PR was co-expressed. Due to M6PR involvement in sulfatase trafficking to the lysosome the amount of ASA within the cell was measured but no additional buildup of ASA, as compared to other co-expressions, within the cell was observed (supplementary figure 1). On the contrary, less ASA was seen when M6PR was co-expressed as compared to other co-expressions. Since both SUMF1 and M6PR are closely linked to sulfatase activation and trafficking, a dual co-expression was tested to see if any synergetic effect on sulfatase activation would occur. However, the specific activity of ASA dropped as compared to only using SUMF1 for co-expression (figure 5A). In this study we have then shown that co-expressing M6PR decreases the yield of ASA and that M6PR is upregulated in a stable Sulfamidase expressing clone. We have also shown that decreasing the promotor activity with different promoter cassettes increases sulfatase activation. The increased activity of ASA when M6PR is co-expressed could then either be from a reduced transcript pace of ASA or an increased activation capability when M6PR is co-expressed. Potentially, the upregulated M6PR in clone A negatively affect secreted sulfatases and therefore reducing M6PR gene expression would be of interest. The third gene identified in the transcriptomic data was ERGIC3, which is involved in the transportation of molecules from the ER to the Golgi apparatus and back again through coat protein (COP)I and COPII vesicles (Orci et al., 2003). To our knowledge no known interactions between sulfatases nor SUMF1 exist for ERGIC3. Here, we show that co-expressing ERGIC3 with ASA increased the specific activity by 300% without hardly affecting titer yields (figure 5A). Suggesting that ERGIC3 is an important factor for sulfatase activation, either via direct transport of sulfatases between ER and Golgi, or transport of cofactors involved in the activation of sulfatases. Since SUMF1 is the gene encoding for the FGE that is the limiting factor for sulfatase activation several genes that did not emerge from the transcriptomic study were tested for co-expression. SUMF2, ERP44, ERGIC53, and PDIA1 have all been described on their link to SUMF1 trafficking and function (figure 4B). Out of these four, two had a beneficial effect on ASA activity, SUMF2 and PDIA1, with an increase of up to 50% as compared to ASA control (figure 5B). Comparison between clone A and clone B showed two distinct properties on their ability to produce secreted Sulfamidase, amount produced and amount active. Increased titer correlated with decreased specific activity. Also, during the cultivation process the specific activity decreased for both clones when more Sulfamidase is produced. Reviewing the transcript levels of recombinant Sulfamidase during the cultivation process displayed a several fold difference of mRNA levels between clone A and B at the later stage of production (supplementary figure 1). Producing activated sulfatases is most important for potential therapeutic use, so reduction on titer levels can be desirable if activation of recombinant product increase. High transcription rates of a recombinant protein can have a negative impact on the secretion pathway (Brown, Gibson, Hatton, Arnall, & James, 2019). Reducing mRNA levels of recombinant N-acetylgalactosamine-6- sulfatase produced in Escherichia coli was previously shown to improve sulfatase activity several folds (Reyes, Cardona, Pimentel, Rodríguez-López, & Alméciga-Díaz, 2017). Here, reducing promoter activity in a CHO expression host had the same effect on improved activity of ASA. Reducing production rate by 50% with a PGK promoter increased activity by 400%, more than doubling the yield of secreted activated ASA. Further reduction of the production rate down to 13%, with an UBC promoter, as compared to CMV promoter did not have additional benefits on activity (figure 6). Possibly, there is an optimal transcription pace for sulfatase activation were the PTM bottlenecks still manage the activation process for as many sulfatases as possible. The observation that clone A produces more activated sulfatases, while having lower sulfatase transcript level, compared to clone B in the Ambr cultivation comparison (figure 2A), further supports this theory. Improving recombinant sulfatase expression is of importance for several different rare diseases were the enzyme is affected through heritable mutations. Previously, low expression of recombinant proteins have been shown to increase through co-expressions of important processing co-factors (H. G. Hansen, Pristovšek, Kildegaard, & Lee, 2017; Le Fourn, Girod, Buceta, Regamey, & Mermod, 2014). In this study, we have investigated transcriptome differences in order to find co-factors that would assist in increasing amount of secreted and activated sulfatases. This selection of co-factors led us to three genes that had a 3 to 150-fold increasing effect on specific sulfatase activity when co-expressed. Furthermore, transcript levels from the study showed a correlation between recombinant mRNA levels and relative activity of secreted sulfatases. When ASA was expressed with a decrease in promoter activity, an increase in specific activity was observed. Taken together, these results demonstrate the high impact that systems biology can have on improving protein production through cell engineering.

MATERIAL AND METHODS

Ambr cultivations Stable expression of Sulfamidase were conducted in an Ambr ®250 (Sartorius, Goettingen, Germany) with two clones derived from a CHO K1 cell line. Fed batch cultivation was started through cell inoculation at a density of 0.3*10^6 cells/ml and harvest took place on day 17 post-inoculation. A temp shift took place on day 3-4 post inoculation for the standard cell densities and on day 4-5 for the high cell density conditions. Samples for RNA analysis and titer were taken at day 3, 6, 12, 14 and 17 post inoculation and the supernatants were kept at -80°C until purification and analysis took place.

RNA sample preparation 1 ml from Ambr cell samples were collected, centrifuged and re-suspended in 200 µl RNAlater Stabilization Solution (Invitrogen, ThemoFisher) to stabilize and preserve the cells. These were then kept at 4°C overnight and then stored at -80°C. Frozen pellets were thawed and RNA were extracted following the Qiagen RNeasy® Plus Universal Mini Kit (Qiagen, Hilden DE) according to manufacturer’s instructions. Subsequently, extracted RNA quality were analyzed on an Agilent Bioanalyzer 2100 system together with the Agilent RNA 6000 Nano kit (Agilent Technologies, Santa Clara, CA, US) and all samples had an RNA Integrity number (RIN) >8. Sequencing was performed on a Illumina HiSeq 2500 High Output Mode, at paired-end 2x150bp (Illumina HiSeq platform via a commercial service of Eurofins MWG GmbH, Ebersberg, Germany).

Transcriptomic data analyses (PMID:26925227, PMID:23444143, PMID:24743996) Paired-end raw sequencing data (FASTQ files) were aligned to reference from ensemble release 92 using “Kallisto” software (PMID: 27043002) to quantify Transcripts Per Million (TPM) and count values of the transcripts. Using R package “tximport” (PMID: 26925227), The gene-level count and TPM values were calculated from the transcript- level abundance and counts. Differential expression analyses were performed based on raw counts using R package “DESeq2” and Wald test p value (FDR < 5%) by partitioning samples regarding condition A and B. The inputs of DESeq2 were gene-level counts for the genes having ID and the median TPM>1 across samples. For the ID mapping the R package “org.Hs.eg.db” was used. The similarity of the samples was calculated based on Spearman correlation regarding the log transformed TPMs (median TPM>1 across samples) and adjusted p value < 0.05. Hierarchical clustering with Ward.D2 and Euclidean distance were used to cluster the samples based on the similarity matrix. Additionally, principal component analysis (PCA) was performed based on the log transformed TPMs and R package “ggplot2” was used for the visualization of the PCA output. Gene set enrichment analysis (GSEA) was performed through R package PIANO (PMID: 23444143) using gene-level Log2 fold changes and FDR obtained from DESeq2, with nPerm = 1000 and gsSizeLim > 2. biological processes were downloaded from MSigDB (PMID:24743996). The directed terms (up/downregulated terms) with FDR <0.05 were considered to compare the conditions A and B. The hallmark enrichment check was performed with Piano (Väremo, Nielsen, & Nookaew, 2013), with defaults for its runGSA function. Fold changes and padj values from DESeq2, and downloaded gene sets from MSigDB (Liberzon et al., 2011; Subramanian et al., 2005).

Transient expression of Arylsulfatase A Transient expression of ASA was conducted in ExpiCHO™ cells (Thermo Fisher Scientific, USA) at a 1:1 plasmid ration of ASA and co-factor and with a total plasmid amount of 20µg ASA plasmid and 20µg of co-factor plasmid, with the exception of M6PR and SUMF1 co-transfections were 10 µg of each was used. Cultivations were performed according to standard protocol with harvest of supernatant at day 8 post transfection. The supernatants were kept on -80°C until purification and activation analyses took place. SUMF1, ERGIC53, SUMF2, PDIA1, ERP44, and ASA were cloned into our own pKTH16-CMV vector with standard restriction cloning. M6PR and ERGIC3 genes were synthesized and cloned into our pKTH16-CMV vector by Geneart (Thermo Fisher Scientific, USA). The promoter regions UBC and PGK were synthesized and cloned into our pKTH16 vector by Geneart (Thermo Fisher Scientific, USA). Transient expression of green fluorescent protein (gfp) was performed in the same way as ASA expression in ExpiCHO™ for 8 days. Subsequently, 5*10^6 viable cells were collected and washed in 200 µl PBSB (1% bovine serum albumin, #A7888, Sigma-Aldrich, USA) 3 times. Next the cells were analyzed for gfp signals in a Gallios™ flow cytometer (Beckman Coulter, USA). Viable cells were gated out based on forward and side scatter and gfp signal was detected through excitation of a blue 488 nm laser and detected with a 525 nm bandpass filter. Results were analyzed was with Kaluza Flow Cytometry Analysis v2.1 software (Beckman Coulter, USA) and the arithmetic mean of each sample was collected and plotted for comparison of expression levels.

Arylsulfatase A purification Purifications of ASA samples were carried out on two different occasions, SUMF1, M6PR, ERGIC3, and ASA control on an ÄKTAxpress (GE Healthcare) and ERIC53, ERP44, PDIA1, SUMF2, and ASA control on an ÄKTApure (GE Healthcare) with an 1 mL Capture SelectTM C-tag pre-packed column (Thermo Fisher Scientific) and 3x5 mL HiTrap desalting columns (GE Healthcare). All supernatants (30 mL) were filtered through a 0.45 µfilter before loading. Buffer A (25 mM Tris, 150 mM NaCl, pH 7,0, sterile filtered) was used for equilibration, sample application and washing. Then buffer B (50 mM HAc, pH 2,5, sterile filtered) was used to isocratically elute ASA. Eluted ASA was directly desalted through the HiTrap desalting columns and eluted in buffer A. The concentration of purified ASA was calculated by measuring the absorbance at 280 nm in an Eppendorf BioPhotometer and using the extinction coefficient of 39350 (1/M) and a molecular weight of 52.677 kDa of ASA.

Arylsulfatase A activity measurements For ASA activity measurement, two separate measurement took place. One for SUMF1, M6PR, ERGIC3, and ASA control and one for ERIC53, ERP44, PDIA1, SUMF2, and ASA control. A stock solution of 5% BSA (RIA Grade, Fraction V, >96%, Sigma #A7888) was prepared using highly purified H2O followed by heat-inactivation at 50 °C for 4 hours. All samples were diluted with BSAp (0,2% inactivated BSA, 0,05% Triton X-100, 3mM NaN3) to contain 10-100 μg/ml ASA. SUMF1 co-expressions had such a large difference in activity so in order to get a valid readout SUMF1 co-expressions were conducted separated from M6PR, ERGIC3, and ASA control with the latter having a 100 µg/ml ASA concentration and SUMF1 samples contained 10µg/ml ASA samples. A stock solution of 4 mM PNC (Para-nitrocatechol, M155.11, 97%, Sigma N15553-1G) in BSAp was prepared. A stock solution of 250 mM PNCS (para-nitrocatechol sulfate dipotassium, M311.3, 97%, Sigma N7251-1G) was prepared in water. The substrate solution was prepared just before the assay by diluting PNCS with Assay buffer (0,1 M NaAc in BSAp) to 50 mM. From the PNC stock solution, a PNC calibration curve was made by diluting PNC at a 4 nmol decrement of PNC from 28 nmol to 4 nmol per well in triplicate. Then 10 μl (ice-chilled) of both calibration curve and the protein samples were added to the well of an incubation plate (PS96U, Greiner #650101) in triplicate. Thereafter the reaction was started by adding 10 μl (ice-chilled) substrate solution to all the wells. The plate was then incubated on ice with gentle shake at 4°C for 1 hour. Thereafter the reaction was terminated by adding 100 μl of Glucosidase stop buffer (NaHCO3/Na2CO3, pH 10,7, 0.025% Triton X-100) to each well. To measure the activity, 100 μl of each well were transferred to a reading plate (PS96F, ½ A, Greiner #675101) and read at 515 nm in a plate reader. By using the calibration curve and plotting a linear curve the activity of ASA could be measured.

Western blot analysis 5*10^6 viable cells from day 8 post transfection were harvested, centrifuged at 2000 rcf for 10 min. Supernatant was transferred to a new tube and cell pellets were lysed with M- PER™ mammalian protein extraction reagent (Thermo Fisher Scientific, USA) according to the manufacturer’s instructions. 10µl of each sample were mixed with 5µl 3x loading buffer (0.1 M Tris-HCl, 45% Glycerol, 0.03% Bromophenol blue, 0.3% SDS) and heated at 95°C for 5 min. Subsequently, samples were separated on a 4–15% Mini-PROTEAN® TGX™ Precast Protein gel (Bio-Rad Laboratories, USA) for 50 min at 200 V. The samples were then transferred to a Trans-Blot® Turbo™ Mini PVDF membrane (Bio-Rad Laboratories, USA) via a Trans-Blot® Turbo™ transfer system (Bio-Rad Laboratories, USA). Afterwards, the membranes were blocked in PBST (0.05% Tween20, pH = 7.4) +5% milk and washed in PBST (0.05% Tween20, pH = 7.4). Primary staining with a goat anti- human SUMF1 IgG (0.5 mg/mL, # PA5-19195, Thermo Fisher Scientific, USA) was conducted at a 1:1000 dilution in blocking solution for 1 h and a rabbit anti-goat IgG (H+L) HRP conjugated antibody (1 mg/mL, # A27014, Thermo Fisher Scientific, USA) was used as secondary antibody at a dilution of 1:3000 in blocking solution. After a final wash, detection of ASA was made with Immobilon western HRP substrate (Merck Millipore, USA) according to the manufacturer’s instructions and imaged on a ChemiDoc XRS+ system (Bio-Rad Laboratories, USA). ASA levels were normalized to an ASA reference in all samples.

Statistical analysis Titer and activity data represent 2 samples where mean value is shown in each graph and the standard deviation is represented by the error bars of each mean. A one-way ANOVA was used to determine statistical differences and a Dunnett´s test was used as post hoc test (α = 0.05) to determine statistical differences to the control. * p-value <0.05, ** p-value <0.01, and *** p-value <0,001.

Acknowledgements The work was funded by Knut and Alice Wallenberg Foundation, SOBI, Swedish Foundation for Strategic Research (SSF), Swedish innovation agency Vinnova through AAVNova, CellNova and AdBIOPRO and the Novo Nordisk Foundation (grant no. NNF10CC1016517).

A. Sulfatase activation B. Stable clones expressing sulfatase at different activity occurs within the ER levels cultivated in Automated Bioreactor High activity clone

Low activity clone

C. dentication of ey genes through RA seuencing

D. Coexpression of ey genes E. mproved specic activity with with sulfatase coexpression of ey genes

Figure 1. Experimental overview – identification of activity related key genes by comparison of CHO clones with different specific activity. Common for all human sulfatases is activation of the enzyme occurring in the ER, including formation of a formyl-glycine (A). Stable CHO clones producing varying amount of active sulfatase are cultivated in an Automated Bioreactor (B) followed by subsequent transcriptomic analysis (C) leading to identification of key-genes linked to activity for confirmatory expression validation in CHO as co-factors (D). Potentially leading to improved specific activity (E).

A Specific activity Titer in media

150 10 clone A standard clone A ig cell nmer 100 clone A itot copper clone standard 5 U/mg clone ig cell nmer 50 clone itot copper Normalized titer in media

0 0 day 3 day 6 day 12 day 14 day 17 day 3 day 6 day 12 day 14 day 17

Figure 2. Titer and activity measurements for Ambr run. Two stable CHO clones producing Sulfamidase were monitored for activity and productivity over a cultivation period of 17 days. Clear differences on activity values enabled a transcriptomic comparative study for gene expression deviations in order to understand sulfatase activation. (A) The specific activity dropped during the cultivation time for both clones. Clone A standard and high cell density had approx. 100% higher specific activity as compared to clone B on day 17. For both clones, the without copper conditions reached the lowest activities. (B) Titer in media increased over the cultivation period. Clone B increased several folds more than clone A for all the different conditions. For both clones the high cell density variant gave the highest titer. Values were normalized against standard condition clone A day 17

Sulfamidase Clone A-highactivitycl B-lactivitycl

Condiion igh cll taa itht c

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● Day_12 8000 Day_14 10000 Day_17 ● A ● ● Day_3 B ●● 4000 Day_6 ●● 5000 ●

0 0 ● ● ● ● PC2 (26.1%) ●●

PC2 (26.1%) ● ● ●●●●●● ●● ●● ● ● ●● ● −5000 ● −4000 ● ● ●● ● ● ●● ●

−15000 −10000 −5000 0 5000 10000 −10000 −5000 0 5000 10000 PC1 (53.1%) PC1 (53.1%) Figure 3. Gene clustering from Ambr cultivation shows distinction between days and clones. (A) Gene expressions are grouped into three different clusters. Day 3 and day 6 for both clones, and after the temp shift and large increase in protein expression day 12-17, one cluster for clone A and one for clone B. (B) Principal component analysis showed that the data can be clustered depending on days. Days 3-6 were closely related, and after an increase in protein expression, days 12-17 were closely related. (C) Principal component analysis based on clone variant shows a clear distinction over all timepoints collected, further visualizing differences on transcriptomic landscape were mainly due to clone variant. A B Sulfamidase Clone A B P TRAF3IP2 1 PTM OLFM2 NOD2 ANXA1 08 SGMS1 CAMK2G RAB11FIP5 EIF2AK3 06 ARFGEF2 OCRL STAM 04 LYSOSOME STT3B GNPTAB CLCN3 ABCA1 02 ATP7A secen RIMS2 AP3B1 0 RAPGEF4 PDIA4 GBF1 AIM2 ILDR2 AP2M1 MON1A NECAB3 LAMP2 GALC TMED10 ARSA ARSK ueulaed SEC22B ADAM10 in lone A YIPF6 SEC31A SEC24D AGT ATG12 STX7 CD36 SCAMP1 GOLPH SH3GL2 SSPN GOLGI TLR2 M6PR S100A13 CD63 M6PR GPAM ERP44 ERGIC3 DSCC1 ESCO2 SUM1 COPII RAB8B ERGIC-3 BET1 LTP2 ATG7 RAB9A GLA SOD1 RAF1 LH1 RAB11B COPI VAMP7 FN3KRP Inactive sulfatase ARCN1 ERGIC-53 RAB2A YKT6 SUMF1 UBE21 VGF Active sulfatase ATP1A1 MYO5A RPS6KA3 RBP GATA3 PDIA1 ESCO1 AP3S1 CAV2 COG2 GNAS RAB13 ARL4D UCLEUS ER FKBP1B Day 3 6 12 14 17 3 6 12 14 17

Figure 4. 14 key genes extracted through functional analyses of gene expression differences. (A) Functional analysis on the differentially expressed genes between clone A and B showed enrichment for protein secretion for clone A and unfolded protein responses for clone B. Extracting protein secretion and post translational modification hallmarks from GSEA-MSignDB, a set of 83 differently expressed genes emerged. The extracted gene set had different expression patterns over the cultivation period. However, out of the 83 genes, 14 are more upregulated in clone A over the entire cultivation period (green). (B) Trafficking of sulfatase and SUMF1 through the cell. Within the ER, SUMF1 is folded in assistance with PDIA1 and transported to the Golgi with ERGIC-53. Retrograde transport of SUMF1 occurs with ERP44 and uptake of secreted SUMF1 is through several Mannose receptors, one being M6PR. Sulfatases are labelled for M6PR recognition within the Golgi and these receptors then transports sulfatase to the lysosome.

A B ✱ ✱✱ 1.5 2.0 1.5 Activity Tite Activity ✱ Tite 150 eaieiter Relative eaieiter Relative 1.5 1.0 1.0 20 6 1.0

4 0.5 0.5

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M6PR M6PR ControlSUMF1 ControlSUMF1 ERGIC3 ERGIC3 PIA1 PIA1 Control ERP44 SUMF2 Control ERP44 SUMF2 ERGIC53 ERGIC53 SUMF1+M6PR SUMF1+M6PR Figure 5. A 150-fold increase in specific activity was achieved for product gene co-expressed with co-factor from transcriptomic analyses. ASA expressed in CHO cells were harvested and purified on a C-tag column. (A) Co-expression performed with genes selected from transcriptomic analyses. All three genes co-expressed with ASA showed an increase in specific enzyme activity. The highest increase measured, 150-fold as compared to control, were with SUMF1 co-expression. Titer levels decreased when M6PR was co-expressed with ASA. One sample for M6PR co- expression was lost during purification. (B) Co-expression with genes selected for their described involvement in sulfatase activation. ERGIC53 and ERP44 showed similar activity as control but PDIA1 and SUMF2 showed an increase in activity of the purified ASA (47% and 56%). Furthermore, ERGIC53 that had a lower production of ASA compared to control (58%) and remaining co-expressions had similar levels of productivity as control. ✱ 5 1.5 Activity ✱✱ ✱✱✱ Tite 4 ✱✱ R 1.0 3

2 0.5

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CMV PGK UBC CMV PGK UBC

Figure 6. A reduction of promoter strength increased the specific activity of ASA by 4-fold. Expressing the recombinant ASA at a lower rate showed different activity patterns. ASA with a PGK promoter produced 4 times more active protein compared to CMV and UBC produced 3 times more active ASA compared to CMV. PGK had 58% sulfatases produced compared to CMV and UBC had 13% produced ASA compared to CMV. The amount of purified active ASA is then 2.4 times higher when an PGK promoter is used compared to CMV.

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