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Published in final edited form as: Glycobiology. 2024 Sep 30;34(11):cwae076. doi: 10.1093/glycob/cwae076

N-glycoproteomic and proteomic alterations in SRD5A3-deficient fibroblasts

Kishore Garapati 1,2,3, Wasantha Ranatunga 4, Neha Joshi 1,2,3, Rohit Budhraja 2, Saniha Sabu 1,2,3, Kristin A Kantautas 5,6, Graeme Preston 7, Ethan O Perlstein 6, Tamas Kozicz 4,7,8,9, Eva Morava 4,7,8,9, Akhilesh Pandey 2,10,*
PMCID: PMC7617596  EMSID: EMS204111  PMID: 39360848

Abstract

SRD5A3-CDG is a congenital disorder of glycosylation (CDG) resulting from pathogenic variants in SRD5A3 and follows an autosomal recessive inheritance pattern. The enzyme encoded by SRD5A3, polyprenal reductase, plays a crucial role in synthesizing lipid precursors essential for N-linked glycosylation. Despite insights from functional studies into its enzymatic function, there remains a gap in understanding global changes in patient cells. We sought to identify N-glycoproteomic and proteomic signatures specific to SRD5A3-CDG, potentially aiding in biomarker discovery and advancing our understanding of disease mechanisms. Using tandem mass tag (TMT)-based relative quantitation, we analyzed fibroblasts derived from five patients along with control fibroblasts. N-glycoproteomics analysis by liquid chromatography–tandem mass spectrometry (LC–MS/MS) identified 3,047 glycopeptides with 544 unique N-glycosylation sites from 276 glycoproteins. Of these, 418 glycopeptides showed statistically significant changes with 379 glycopeptides decreased (P < 0.05) in SRD5A3-CDG patient-derived samples. These included high mannose, complex and hybrid glycan-bearing glycopeptides. High mannose glycopeptides from protocadherin Fat 4 and integrin alpha-11 and complex glycopeptides from CD55 were among the most significantly decreased glycopeptides. Proteomics analysis led to the identification of 5,933 proteins, of which 873 proteins showed statistically significant changes. Decreased proteins included cell surface glycoproteins, various mitochondrial protein populations and proteins involved in the N-glycosylation pathway. Lysosomal proteins such as N-acetylglucosamine-6-sulfatase and procathepsin-L also showed reduced levels of phosphorylated mannose-containing glycopeptides. Our findings point to disruptions in glycosylation pathways as well as energy metabolism and lysosomal functions in SRD5A3-CDG, providing clues to improved understanding and management of patients with this disorder.

Keywords: CDG type I, dolichol, lipid-linked oligosaccharide, N-glycosylation, polyprenol

Introduction

Pathogenic variants in the SRD5A3 gene cause a congenital disorder of glycosylation (CDG), SRD5A3-CDG (Cantagrel et al. 2010), which is characterized by a broad spectrum of clinical manifestations primarily involving ophthalmological and neurological abnormalities with variable dermal, skeletal and gastrointestinal phenotypes (Tuysuz et al. 2016; Kamarus Jaman et al. 2021; Holla et al. 2023). This rare CDG has an estimated prevalence of less than 1 in 1,000,000 with ∼50 cases reported worldwide (Holla et al. 2023). SRD5A3 encodes an endoplasmic reticulum (ER)-resident enzyme in the biosynthetic pathway of dolichols. Dolichols are required for the synthesis of lipid-linked oligosaccharide (LLO) precursors which are essential for protein N-linked glycosylation. Besides, dolichols also have a role in other glycosylation pathways including O-linked glycosylation, C-mannosylation and GPI anchor biosynthesis. Defects in dolichol biosynthesis are thus associated with several different CDG (Cantagrel and Lefeber 2011; Buczkowska et al. 2015). SRD5A3 was first described to likely encode a polyprenol reductase that catalyzes the conversion of polyprenol to dolichol (Cantagrel et al. 2010). Polyprenols have an unsaturated terminal isoprene unit which is reduced to form dolichol. Until recently, this process was believed to be a single step reaction, however a new study demonstrated that the reduction from polyprenol to dolichol is a three-step process which involves oxidation of polyprenol to polyprenal, conversion of polyprenal to dolichal (catalyzed by SRD5A3) and finally, reduction of dolichal to dolichol (Wilson et al. 2024). Consequently, the function of SRD5A3 has been redefined as a polyprenal reductase (Fig. 1).

Fig. 1. SRD5A3 in N-glycosylation.

Fig. 1

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An overview of the role of SRD5A3 in the synthesis of dolichol required for N-glycosylation in the endoplasmic reticulum.

The deficiency of dolichols in SRD5A3-CDG leads to decreased availability of glycosylation precursors, i.e. LLOs, thereby resulting in protein hypoglycosylation. Wilson et al. (2024) demonstrated that defective conversion of polyprenol to dolichol in SRD5A3 knockout cells results in the accumulation of phosphorylated polyprenol species, which replace dolichol as an oligosaccharide acceptor, leading to inefficient glycosylation and the transfer of immature glycans onto proteins. Screening and diagnosis of SRD5A3-CDG involves the analysis of intact transferrin for relative quantitation of various glycoforms of this abundant plasma glycoprotein. In type I CDG such as SRD5A3-CDG, carbohydrate-deficient transferrin (CDT), where one or both major N-linked glycosylation sites of transferrin are hypoglycosylated, is elevated (Francisco et al. 2019; Jaeken et al. 2020). Currently, the treatment of SRD5A3-CDG centers around symptomatic management (Jaeken et al. 2020). There is a need for deeper understanding of the consequences of SRD5A3 deficiency at the molecular level, especially at the level of the proteome and glycoproteome of the cell. Strategies to improve diagnostic outcomes and advanced understanding of disease mechanisms employ a variety of mass spectrometry-based omics analyses. A recent study applied an integrated glycomic and genomic approach to studying a cohort of CDG patients, which helped arrive at a diagnosis for several previously unsolved cases including SRD5A3-CDG (Abu Bakar et al. 2022). Although glycomics studies provide insights into overall glycan changes in a sample, they however do not result in information on how protein glycosylation at specific sites is affected. Glycoproteomics studies which allow identification and quantitation of glycopeptides, i.e. peptides with glycans attached to them along with the identification of the glycosylation site, have emerged as a preferred method of investigating glycosylation alterations in CDG (Budhraja et al. 2024; Garapati et al. 2024). A recent glycoproteomics study focused on brain specific glycoproteins in the cerebrospinal fluid (CSF) of patients with different CDG, leading to the identification of moderate alterations in CSF glycopeptide profiles in SRD5A3-CDG patients (Baerenfaenger et al. 2023). However, the effect of SRD5A3 deficiency on the cellular glycoproteome and proteome has not yet been investigated. Here, we analyzed SRD5A3-CDG patient-derived fibroblasts by liquid chromatography–tandem mass spectrometry (LC– MS/MS)-based N-glycoproteomics and proteomics. We report distinct alterations in all classes of N-glycopeptides as well as in numerous proteins, indicating alteration in cellular glycosylation machinery, autophagy and mitophagy processes as well as mitochondrial and lysosomal proteins.

Results

Affected individuals have early truncating variants in SRD5A3

Fibroblasts from patients with SRD5A3-CDG were analyzed for proteomic and N-glycoproteomic changes in comparison with controls. Five individuals with homozygous or compound heterozygous early truncating variants in SRD5A3, both male (n = 2) and female (n = 3), were included (Table 1). The age range of included subjects was 4–17 y (median = 7 y). All affected individuals had visual manifestations with colobomas as well as cerebellar hypoplasia and developmental delays. Ichthyosis was present in three individuals. Biochemical testing revealed that all affected individuals had elevated liver transaminases and abnormal coagulation profiles. Hematological evaluation showed that two individuals had microcytic anemia (Table 1).

Table 1. Patients and their clinical features. List of included subjects with SRD5A3-CDG along with genetic information and clinical characteristics.

Age (y) Sex Variant in
SRD5A3
(predicted protein-level
change)		 ↑AST/ALT Abnorm.
Coag.		 Microcytic anemia Cerebellar
hypoplasia		 Coloboma
and visual loss		 Ichthyosis Dev
delay/ID
S1 4 F homozygous large deletion in Exon 5 + + + + + + +
S2 7 F p.W21X,
p.W21X		 + + – + + – +
S3 9 M p.R142X,
p.Y163X		 + + – + + + +
S4 6 F p.L98Vfs*121,
p. L98Vfs*121		 + + + + + + +
S5 17 M p. W19X,
p. W19X		 + + – + + – +
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Cellular glycoproteome is significantly altered

To elucidate alterations in site-specific N-glycosylation of cellular proteins in SRD5A3-CDG, we performed quantitative N-glycoproteomics experiments of patient-derived fibroblasts along with control fibroblasts (Fig. 2A). Whole cell lysatederived peptides from 5 patient and 5 control samples were multiplexed using tandem mass tags (TMT) and equal peptide amounts from each sample were combined prior to glycopeptide enrichment by size-exclusion chromatography (SEC) (Saraswat et al. 2021; Saraswat et al. 2022). Liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis of twelve early SEC fractions led to the identification of 3,047 glycopeptides with 544 N-glycosylation sites from 276 proteins. Among these, 606 glycopeptides bore high mannose glycans while 2,417 glycopeptides had either complex or hybrid glycans. The remaining 24 glycopeptides bore only N-glycan core (paucimannose) structures. Low-density lipoprotein receptor-related protein 1 (LRP1) contributed 228 glycopeptides, the highest number of glycopeptides from any protein, with 24 N-glycosylation sites detected. Similarly, the extracellular matrix protein fibronectin (FN1) and membrane proteins aminopeptidase (ANPEP) and integrin beta-1 (ITGB1) were detected with over 100 N-glycopeptides each.

Fig. 2. Cellular glycosylation is profoundly affected in SRD5A3-CDG.

Fig. 2

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A. Experimental strategy for discovery of glycoproteomic and proteomic alterations. Fibroblasts from five affected individuals and five control subjects were processed for generation of peptides followed by multiplexing with tandem mass tags (TMT). Glycopeptides were enriched by size-exclusion chromatography (SEC) and analyzed by liquid chromatography–tandem mass spectrometry (LC–MS/MS). Proteomics analysis was carried out in parallel B. A partial least-squares discriminant analysis (PLS-DA) plot generated using relative quantitation values for glycopeptides across patient and control samples C. A volcano plot showing N-glycopeptide alterations. Selected glycopeptides with significant changes have been labeled and shown with putative glycan structures. The horizontal dashed line corresponds to P = 0.05. D. Box plots of selected N-glycopeptides showing significant changes. Putative glycan structures are shown using SNFG and represent total composition of glycan(s) inferred from mass spectrometry data (Neelamegham et al. 2019). ***: P < 0.001, **: P < 0.01, *: P < 0.05.

Relative quantitation using TMT revealed that 418 N-glycopeptides showed significantly altered abundance (P < 0.05, t-test) in patient samples compared to controls. A partial least squares discriminant analysis (PLS-DA) plot drawn using quantitation values of all identified glycopeptides shows clear separation between patient and control samples (Fig. 2B). As shown in the volcano plot in Fig. 2C, >90% (379 of 418) of the significantly changing N-glycopeptides were decreased in patient samples while only a minority, i.e. 39 glycopeptides, were increased. This indicates the expected global decrease in cellular N-linked glycosylation in SRD5A3-CDG. This pattern of profound alteration is observable across different classes of N-glycopeptides, i.e. high mannose, hybrid/complex, sialylated, non-sialylated, fucosylated and non-fucosylated glycopeptides (Fig. S1).

The levels of some of the most significantly altered glycopeptides are shown as box plots in Fig. 2D. Glycosylation of protein disulfide isomerase A3 (PDIA3), an ER-resident protein at the non-canonical glycosylation site Asn90 by several high mannose glycans (Man6-9GlcNAc2), was detected to be significantly decreased in patients (Fig. 2D). SEL1L, another ER-resident protein involved in protein quality control, also showed altered glycosylation at Asn195. Only one glycopeptide was identified from trans-Golgi network integral membrane protein 2 (TGOLN2, Asn152), with a disialylated glycan and it showed a significant decrease in patients. Glycosylation of the cell membrane protocadherin Fat4 (FAT4) by high mannose glycans at Asn615 was found to be significantly decreased. Further, membrane proteins integrin beta-1 (ITGB1) and decay accelerating factor CD55 also showed significant changes in glycosylation at Asn406 and Asn95, respectively. C-type mannose receptor 2 (MRC2), a membrane lectin receptor involved in endocytosis of glycoconjugates, had lowered levels of glycosylation with a fucosylated complex glycan at Asn69 in SRD5A3-CDG patients (Fig. 2D). A complete list of detected N-glycopeptides and their relative quantitation in patient and control samples is given in Table S1.

Proteomic changes in SRD5A3-CDG

LC–MS/MS analysis of TMT-labeled whole cell-derived peptides led to the identification of 5,933 proteins by 78,266 peptides. Of these, 873 proteins showed significantly different levels in patient and control samples (P < 0.05). While 251 of these proteins were increased, 622 proteins were decreased in SRD5A3-CDG patient-derived fibroblasts. A volcano plot depicting an overview of proteomic alterations is shown in Fig. 3A. A PLS-DA plot drawn using quantitation values of all proteins is shown in Fig. S2. Levels of SRD5A3 were significantly lower in SRD5A3-CDG patient-derived fibroblasts in comparison with controls (P = 0.0001). The other significantly decreased proteins included synaptopodin-2 (SYNPO2) which was detected with a fold-change (SRD5A3-CDG/control) of 0.36 (P = 0.02). The glycosylphosphatidylinositol (GPI)-anchored glycoprotein complement decay accelerating factor, CD55 was lower in patient-derived fibroblasts (fold-change of 0.5, P = 0.00007). Other significantly decreased proteins included LIM domain and actinbinding protein 1 (LIMA1, fold-change of 0.6, P = 0.0007), peroxisomal membrane protein PMP34 (SLC25A17, fold-change of 0.7, P = 0.000005), tripartite motif-containing protein 59 (TRIM59, fold-change of 0.8, P = 0.00006) and the ER-resident guided entry of tail-anchored proteins factor 1 (GET1, fold-change of 0.8, P = 0.0002; Fig. 3B). Cell adhesion-related proteins SHROOM3 (fold-change of 0.65, P = 0003) and teneurin-3 (TENM3, fold-change of 0.65, P = 0.0006). Interestingly, several proteins that are part of the oligosaccharyltransferase complexes OSTA and OSTB were found to be significantly decreased in patient samples. These include the dolichyl-diphosphooligosaccharide-protein glycosyltransferase subunits 1 (RPN1, fold-change of 0.7, P = 0.0002) and the 48 kDa subunit (DDOST, fold-change of 0.75, P = 0.001, Fig. 3B) as well as subunits STT3A (fold-change of 0.67, P = 0.001), STT3B (fold-change of 0.7, P = 0.002), OSTC (fold-change of 0.6, P = 0.02), RPN2 (fold-change of 0.76, P = 0.001) and DAD1 (fold-change of 0.74, P = 0.01), translocon-associated protein subunit delta (SSR4, fold-change of 0.79, P = 0.015), magnesium transporter protein 1 (MAGT1, fold-change of 0.76, P = 0.001) and keratinocyte-associated protein 2 (KRTCAP2, fold-change of 0.7, P = 0.0007) (Fig. S3). Proteins that were detected with higher levels included glutathione S-transferase mu 2 (GSTM2, fold-change of 1.5, P = 0.02), uveal autoantigen with coiled-coil domains and ankyrin repeats (UACA, fold-change of 1.5, P = 0.003), glucose-6-phosphate 1-dehydrogenase (G6PD, fold-change of 1.4, P = 0.02) and EH domain-containing protein 1 (EHD1, fold-change of 1.6, P = 0.02, Fig. 3C). A complete list of detected proteins and their relative quantitation in patient and control samples is given in Table S2.

Fig. 3. Proteomic alterations in SRD5A3-CDG patient-derived fibroblasts.

Fig. 3

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A. A volcano plot showing proteomic alterations. Selected significantly altered proteins are labeled. The horizontal dashed line corresponds to P = 0.05. B. Box plots of selected proteins showing significant decrease in abundance in patient samples C. Box plots of selected proteins showing significant increase in abundance in patient samples. ***: P < 0.001, **: P < 0.01, *: P < 0.05 D. A volcano plot showing the changes in mitochondrial proteins (as annotated in the MitoCarta database) on the background of total proteomic changes. E. Distribution of fold-changes in the relative abundance of various mitochondrial protein populations along with those of total proteome. Statistical significance in variation of the different mitochondrial protein populations as indicated is shown in comparison to the total proteome (####: P < 0.0001, ###: P < 0.001, #: P < 0.05) and also to the entire pool of proteins in the MitoCarta database (****: P < 0.0001).

Mitochondrial proteins are significantly decreased

Because mitochondrial dysfunction has been previously described in other CDG (Ligezka et al. 2023; Budhraja et al. 2024), we were curious to see if mitochondrial proteins are affected in SRD5A3-CDG as well. Data from proteins included in MitoCarta 3.0 (Rath et al. 2021), which were identified in this study were extracted and analyzed. Of the identified 707 proteins with mitochondrial localization, 147 showed statistically significant differences between the two groups (P < 0.05). Further, we analyzed seven mitochondrial protein subpopulations: all MitoCarta proteins, the subunits of oxidative phosphorylation complexes I, II, III, IV and V, mitochondrial ribosomal subunits, and proteins affecting mitochondrial membrane integrity with an associated mitochondrial disease (Messina et al. 2024). SRD5A3-CDG samples displayed a depletion of MitoCarta proteins, with 561 of the 706 MitoCarta proteins displaying FC < 1 (P < 0.0001), and an average fold-change of 0.93 (P < 0.0001) relative to controls (Fig. 3D). SRD5A3-CDG samples also displayed decrease of oxidative phosphorylation complexes I, III, IV and V and ribosomal subunits, as well as membrane integrity proteins relative to controls (Figs 3E and S4A-G). However, these decreases in abundance were all proportionate to the decrease in the MitoCarta protein population as a whole, except in the case of the ribosomal subunit protein population which displayed a decrease relative to controls in excess of the decrease of the MitoCarta protein pool (Fig. S4G).

Proteins involved in autophagy and mitophagy are affected

In earlier studies with other type I CDG such as PMM2-CDG and ALG1-CDG, we observed altered abundance of autophagy- and mitophagy-related proteins (Ligezka et al. 2023; Budhraja et al. 2024). Therefore, we also analyzed the proteomics data from SRD5A3-CDG patient-derived fibroblasts to investigate the levels of proteins associated with autophagy and mitophagy. We observed that multiple proteins which are annotated to be involved these processes were indeed affected in SRD5A3-CDG (Fig. 4A and B). Notably, the expression of pyridoxine-5’-phosphate oxidase (PNPO) was significantly increased showing a 1.4-fold increase in individuals with SRD5A3-CDG (P = 0.03). Other autophagy-associated proteins which were increased included annexin A5 (ANXA5, fold-change of 1.4, P = 0.03), cytoplasmic malate dehydrogenase (MDH1, fold-change of 1.4, P = 0.03) and vacuolar protein sorting-associated protein 4A (VPS4A, fold-change of 1.2, P = 0.02) (Fig. 4A). These proteins are recognized for their role in promoting autophagy within cells. Furthermore, we note that mitophagy-related proteins were also altered in SRD5A3-deficient fibroblasts. The top increased protein among these was ubiquitin-conjugating enzyme E2 L3 (UBE2L3, fold-change of 1.3, P = 0.04), calcium-binding protein 39 (CAB39, fold-change of 1.3, P = 0.04), ubiquitin-conjugating enzyme E2 A (UBE2A, fold-change of 1.3, P = 0.008), and apoptosis regulator BAX (BAX, fold-change of 1.3, P = 0.04). Interestingly, prohibitin (PHB, fold-change of 0.76, P = 0.02) and certain heat shock proteins (HSPA2, fold-change of 0.6, P = 0.03 and 75 TRAP1, fold-change of 0.77, P = 0.01) were significantly lower in SRD5A3-CDG patient-derived samples (Fig. 4B).

Fig. 4. Proteomics data indicating altered cellular processes in SRD5A3-CDG.

Fig. 4

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A. Volcano plot showing altered abundance of autophagy-related proteins. B. Volcano plot showing altered abundance of mitophagy-related proteins. Selected significantly changing proteins are labeled. C. Protein-level and phosphorylated mannose-containing glycopeptide changes in the lysosomal enzyme N-acetylglucosamine-6-sulfatase (GNS). D. Protein-level of procathepsin L (CTSL), a protein that is localized to the lysosome as well as the extracellular space, in patient and control samples along with the levels of glycopeptides with lysosomal targeting glycans. E. Protein-level of palmitoyl-protein thioesterase 1 (PPT1), a protein that is localized to the lysosome as well as the extracellular space, in patient and control samples along with the levels of glycopeptides with lysosomal targeting glycans. Putative glycan structures are shown using SNFG and represent total composition of glycan(s) inferred from mass spectrometry data (Neelamegham et al. 2019). ***: P < 0.001, **: P < 0.01, *: P < 0.05.

Lysosomal protein glycosylation is moderately affected

A number of lysosomal proteins were also affected in SRD5A3-CDG patient-derived fibroblasts. For example, the lysosomal enzymes arylsulfatase B (ARSB, fold-change 0.6, P = 0.004), beta-mannosidase (MANBA, fold-change 0.8, P = 0.003) and N-acetylglucosamine-6-sulfatase (GNS, fold-change 0.7, P = 0.0008) were found to be decreased (Figs 3A and 4C). Many lysosomal proteins are targeted to their destination by the mannose 6-phosphate receptor pathway, which is mediated by a specific glycosylation motif linked to these proteins, i.e. mannose-6-phosphate on the attached N-glycans (Kornfeld 1986). We were curious if lysosomal proteins showed changes in these targeting glycans and carried out additional analysis to identify N-glycans bearing phosphorylated hexose residues. A total of 87 N-glycans were identified with phosphohexose residues from lysosomal proteins, 12 of which were significantly decreased in patient samples. As shown in Fig. 4C, GNS, which was significantly decreased at the protein level was also found to have decreased phosphomannose-bearing glycopeptides. Two other lysosomal proteins which had decreased glycopeptides with lysosomal targeting signatures were procathepsin L (CTSL) and palmitoyl-protein thioesterase 1 (PPT1) (Fig. 4D and E). However, both of these proteins did not show any significant changes at the protein level in these whole cell lysate-derived samples (CTSL, fold-change 1, P = 0.8; PPT1, fold-change 1, P = 1). Interestingly, CTSL and PPT1 are known to be localized to the lysosome as well as other compartments such as the nucleus and extracellular space (Goulet et al. 2004).

Discussion

By using LC–MS/MS-based N-glycoproteomics analysis, we describe site-specific glycopeptide alterations in cells from patients with SRD5A3-CDG. Widespread hypoglycosylation, i.e. decrease in N-glycopeptides belonging to different classes indicates alterations in the underlying cellular N-glycosylation machinery in association with decreased levels of SRD5A3. The paucity of LLOs resulting from suboptimal levels of dolichols is associated with protein hypoglycosylation, and this is reflected in our findings reported here (Cantagrel et al. 2010; Grundahl et al. 2012). These changes are also in keeping with the results of recent studies on other type I CDG (Ligezka et al. 2021; Budhraja et al. 2024) and may be validated in the future by orthogonal methods such as lectin blotting. N-linked glycosylation is essential for the stability and function of many cell membrane and secreted proteins. As such, along with glycopeptide changes, we also report several significant alterations in abundance at the protein level. While many of the significant changes at the protein level are observed in membrane and secreted glycoproteins, it is important to note that proteins in other cellular compartments, especially the ER and Golgi, are also affected. For example, xylosyltransferase 1 (XYLT1) is a Golgi enzyme involved in proteoglycan synthesis, was found to be low in patients (Fig. 3A). Similarly, the E3 ubiquitin ligase TRIM59 was decreased. The mechanisms of these changes are, however, unclear. In addition, GET1 and its binding partner CAMLG, which play an important role in the tail anchoring of proteins to the ER, are decreased (Wilson et al. 2022). Significantly lower levels of the ER-resident OST complex components RPN1, DDOST1, STT3A, STT3B, OSTC, RPN2, DAD1, SSR4, MAGT1 and KRTCAP2 indicate that the cellular N-glycosylation machinery is likely remodeled on the background of reduced availability of glycosylation precursors (Figs 3B and S3). In addition, dolichol is required for the synthesis of mannosylation precursors and its deficiency also affects GPI anchor biosynthesis, which could explain the observed ∼50% decrease in the abundant cell membrane GPI-anchored protein CD55 (Fig. 3B). There is considerable overlap in the phenotypes of SRD5A3-CDG and disorders of GPI biosynthesis such as hypotonia, microcephaly, seizures, developmental delay and visual loss (Buczkowska et al. 2015; Kamarus Jaman et al. 2021). As such, lower levels of CD55 may indicate an overlap in the molecular phenotype as well. The mechanisms of these changes, however, are not clear.

Patients with CDG may have overlapping clinical features with mitochondrial disorders including developmental delay, epilepsy, myopathy and ataxia, often leading to delayed diagnosis (Briones et al. 2001; Gardeitchik et al. 2018). At the molecular level, there is increasing evidence of association between impaired glycosylation in CDG and alterations in mitochondrial energy metabolism, which may in part explain this overlap (Ligezka et al. 2023; Budhraja et al. 2024; Radenkovic et al. 2024). Our findings of decreased levels of mitochondrial protein synthetic machinery add new insights to this association. Our data suggest that SRD5A3-CDG patient samples display a depleted mitochondrial translational machinery, leading to a depletion in mitochondrial mass, and subsequent depletion of the mitochondrial respiratory chain, impaired oxidative phosphorylation and ATP synthesis relative to total protein levels in the cell, though not necessarily in relation to mitochondrial mass. Besides the association of mitochondrial function with CDG, it is important to note that SRD5A3 is described to be part of a family of enzymes (steroid 5-alpha-reductases) that play catalytic roles in steroidogenesis. This process involves both the ER and mitochondria (Doghman-Bouguerra and Lalli 2017). Although SRD5A3 is a polyprenal reductase, its activity as a steroid reductase is well documented (Uemura et al. 2008; Fouad Mansour et al. 2016). This raises the possibility of a more direct link between SRD5A3 deficiency and mitochondrial dysfunction that may be the subject of future studies.

The dysregulation of autophagy and mitophagy-related proteins in SRD5A3-CDG fibroblasts suggest that defects in N-glycosylation biosynthesis machinery could lead to cellular stress and responses involving these pathways. We also report that several lysosomal proteins, which require specific N-glycosylation signatures for lysosomal targeting, are decreased in patient samples. However, it is interesting to note that proteins with dual localization to the lysosome as well as the extracellular space, are not decreased at the protein level even though the levels of their lysosomal targeting glycans are significantly lowered. This indicates that although such proteins are present in whole fibroblasts derived from SRD5A3-CDG patients to the same levels as control samples, the degree of their lysosomal targeting is likely decreased. This might explain increased levels of lysosomal enzyme secretion into circulation that has been noted previously in CDG (Grunewald et al. 2003; Michelakakis et al. 2009; Sabry et al. 2023). Specifically, an important phenotype of SRD5A3-CDG patients is progressive neurological impairment including visual loss and retinal dystrophy (Ichisaka et al. 1998; Taylor et al. 2017). It is likely that these progressive manifestations are, at least partially, due to a secondary lysosomal enzyme trafficking defect. Further functional studies could elucidate the mechanisms of these secondary changes in this disorder.

To conclude, we have taken advantage of mass spectrometry-based quantitative protein site-specific N-glycosylation profiling to describe a global decreasing trend in glycopeptides in SRD5A3-CDG patient-derived fibroblasts. A limitation of our study is the focus on N-linked glycosylation of the cellular proteome which does not address alterations in glycosylation of the secreted proteome. Further, improper synthesis of dolichol-linked glycosylation precursors resulting from SRD5A3 deficiency may affect other forms of glycosylation such as GPI anchor biosynthesis and O-mannosylation which may be addressed by future studies. Our study not only deepens the understanding of N-glycosylation abnormalities in SRD5A3-CDG but also uncovers broader implications for cellular protein processing and trafficking as well as mitochondrial dysfunction. Further research can validate these identified changes and explore strategies aimed at restoring normal glycosylation patterns and addressing cellular dysfunction, thereby advancing the clinical management of SRD5A3-CDG. Future investigations could also elucidate the mechanistic links between altered N-glycosylation and cellular dysfunction that we describe, potentially paving the way for targeted therapeutic interventions in SRD5A3-CDG and related disorders.

Materials and methods

Study approval

All affected individuals included in this work are enrolled in the FCDGC natural history study (Mayo Clinic IRB 19-005187; https://clinicaltrials.gov/ct2/show/NCT04199000?cond=CDG&draw=2&rank=4). Written informed consent was obtained from the legally authorized representatives of the individuals prior to study initiation.

Cell culture

Patient-derived and control fibroblasts (GM5565, GM5381, GM5757, GM5400, GM8399, Coriell Institute) were cultured in Minimum Essential Medium (MEM; Gibco, Carlsbad, CA, USA; 1 g/L glucose) supplemented with 10% fetal bovine serum (FBS; Gibco), 10% antibiotic-antimycotic with gentamicin, and 10% non-essential amino acids and maintained in an incubator at 37 °C, 5% CO2. Cells were cultured to confluence and were harvested by trypsinization with 0.05% Trypsin–EDTA (Gibco). They were washed with phosphate-buffered saline (PBS) and pelleted down by gentle centrifugation (2,000 rpm for 10 min).

Sample preparation

Cell pellets were lysed in 8 M urea, 50 mM TEAB buffer, pH 7.4 and sonicated with a tip sonicator at 40% amplitude for 3 cycles of 10 s each. The samples were spun at 21,000 × g for 10 min and the supernatants were collected as protein extracts. Protein estimation was done by bicinchoninic acid (BCA) assay, and equal amounts of protein were taken. Proteins were reduced with dithiothreitol (DTT, final concentration of 10 mM) and alkylated with iodoacetamide (IAA, final concentration of 40 mM). Digestion was carried out overnight with sequencing grade trypsin (Worthington) added at a ratio of 20:1 (w/w, total protein:trypsin) after diluting the sample 10× to bring the concentration of urea to <1 M. The resulting peptides were cleaned up using C18 tips (GlyGen) and labeled with tandem mass tag (TMT) reagents as per the manufacturer’s protocol (Thermo Fisher Scientific, A44520). A small aliquot was taken from each sample and pooled, and a label check was done by mass spectrometry. Peptide samples were normalized based on the label check and all labeled samples were pooled. The pooled sample (4 mg of peptides) was split into two aliquots for total proteomics (0.5 mg) and glycoproteomics experiments (3.5 mg). The first aliquot was cleaned up by C18 TopTips (Glygen TT200C18.96) and fractionated by basic pH reversed-phase liquid chromatography (bRPLC) on a reversed phase C18 column (4.6 × 100 mm column). Twelve fractions were dried and resuspended in 0.1% formic acid for liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis for total proteomics. The second aliquot of dried peptides was resuspended in 100 μL of 0.1% formic acid and injected onto Superdex peptide 10/300 column (GE Healthcare) for size-exclusion chromatography (SEC) as previously described (Saraswat et al. 2021; Saraswat et al. 2022). Twelve early fractions were collected (total run time of 130 min) and analyzed by LC–MS/MS for glycoproteomics.

Liquid chromatography–tandem mass spectrometry

Previously published LC–MS/MS parameters were used with some modifications (Saraswat et al. 2022; Garapati et al. 2024). Twelve early fractions from SEC and twelve fractions of bRPLC were analyzed by an Orbitrap Exploris480 mass spectrometer (Thermo Fisher Scientific) coupled to an EASY-Spray column (Thermo Fisher Scientific). Every run was 150 min with a flow rate of 400 nl/min. Online liquid chromatography was carried out uising a mobile phase consisting of solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile). The gradient used for separation was as follows: equilibration at 2% solvent B from 0 to 5 min, 2% to 35% solvent B from 5 to 110 min, 35% to 50% solvent B from 110.1 to 125 min, 50% to 90% solvent B from 125.1 to 141 min. All experiments were done in data-dependent acquisition (DDA) mode with top 15 ions isolated at a window of 0.7 m/z and with a default charge state of +2. Charge states ranging from +2 to +7 were considered for MS/MS events. Stepped collision energy was applied to precursors at normalized collision energies of 15%, 25%, and 40%. MS precursor mass range was set to 375 to 2,000 m/z and 100 to 2,000 for MS/MS. Automatic gain control (AGC) for MS and MS/MS were 106 and 1 × 105, respectively, and injection time to reach AGC were 50 ms and 250 ms, respectively. Dynamic exclusion was applied for 60 s. Data acquisition was performed with option of lock mass (441.1200025 m/z) for all data.

Database searching and analysis

The publicly available software pGlyco 3 (Zeng et al. 2021) was used for glycopeptide database searching. The native human glycan database available with the software containing 2,922 entries and the UniProt Human Reviewed protein sequence database (20,432 entries, downloaded 2021 February 1) as the protein sequence fasta file were searched against for identifying glycopeptides. Cleavage specificity was set to fully tryptic with up to 2 missed cleavages and precursor and fragment tolerance were set to 10 and 20 ppm, respectively. Cysteine carbamidomethylation was set as a fixed modification and oxidation of methionine as variable modification. The results were filtered to retain only entries which had <1% false discovery rate (FDR) at the glycopeptide level. Reporter ion quantification was performed in Proteome Discoverer version 2.5 using “reporter ion quantifier” node and IDs were matched with quantitation on a scan-to-scan basis (MS/MS). The total proteomics dataset was searched using Sequest in Proteome Discoverer version 2.5 against the UniProt Human Reviewed protein sequences. Data were analyzed for individual proteins and glycopeptides by comparing the average fold-changes for each (average abundance values in patient samples)/(average abundance values in control samples) and statistical analysis was performed by student t-test. Data from mitochondrial protein quantitation was analyzed by both counted variable statistical analysis, counting the number of protein species with fold-change greater or less than 1 and running Fisher’s exact test, and continuous variable statistical analysis, assessing the average fold-change of the population and performing a t-test.

Supplementary Material

Supplementary Material

Acknowledgments

We thank Seul Kee Byeon for helpful discussions.

Funding

We thank Mayo Clinic DERIVE Office and Mayo Clinic Center for Biomedical Discovery for financial support and a grant from DBT/Wellcome Trust India Alliance entitled “Center for Rare Disease Diagnosis, Research, and Training” (IA/CRC/20/1/600002) to AP. This research was also supported by funding from the Sappani Foundation and Cure SRD5A3.

Footnotes

Author contributions

KG, EM and AP conceptualized and designed the study. KG, WR and NJ performed experiments. KG, RB and GP analyzed the data. KG and SS wrote the initial draft of the manuscript. KAK, EOP, TK, EM and AP critically edited the manuscript. All authors read and reviewed the manuscript.

Kishore Garapati (Conceptualization [equal], Data curation [lead], Formal analysis [lead], Investigation [lead], Methodology [equal], Visualization [lead], Writing—original draft [lead], Writing—review & editing [equal]), Wasantha Ranatunga (Investigation [equal], Methodology [equal]), Neha Joshi (Investigation [equal], Methodology [equal], Software [equal]), Rohit Budhraja (Data curation [equal], Formal analysis [equal], Visualization [equal]), Saniha Sabu (Writing—original draft [equal]), Kristin A. Kantautas (Investigation [supporting], Resources [equal], Writing—original draft [supporting]), Graeme Preston (Data curation [equal], Formal analysis [equal], Writing—original draft [equal]), Ethan O. Perlstein (Formal analysis [equal], Resources [equal]), Tamas Kozicz (Project administration [supporting], Supervision [supporting], Writing—original draft [supporting]), Eva Morava (Funding acquisition [supporting], Resources [equal], Supervision [supporting], Writing—original draft [supporting]), and Pandey Akhilesh (Conceptualization [equal], Funding acquisition [lead], Project administration [lead], Resources [lead], Supervision [lead], Writing—review & editing [equal]).

Conflict of interest statement. All authors declare no conflicts of interest.

Data availability

The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al. 2022) partner repository with the dataset identifier PXD055397.

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Associated Data

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

Supplementary Materials

Supplementary Material
EMS204111-supplement-Supplementary_Material.zip (1.6MB, zip)

Data Availability Statement

The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al. 2022) partner repository with the dataset identifier PXD055397.

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