MGAT1 knockout in human dendritic cells enhance CD8+ T cell activation

Dendritic cells (DCs) are crucial in regulating immune responses, making them a compelling target for immunotherapy. While DC vaccines have demonstrated safety and feasibility, their limited clinical efficacy underscores the need for strategies to enhance DC functionality. Emerging evidence highlights the regulatory roles of sialoglycans in DC biology, yet the structure-function relationships of other glycans remain poorly understood. To aid the understanding of DC glycobiology, we recently developed and validated a human model system based on genetically glycoengineered MUTZ-3-derived DCs and showed that ST6GAL1-mediated α2,6-sialylation specifically modulates CD4⁺ T cell activation. In this study, we knocked-out (KO) mannosyl (α-1,3-)-glycoprotein β-1,2-N-acetylglucosaminyltransferase (MGAT1) to investigate how the shift from complex to oligomannose N-glycans affects DC biology and function. MGAT1 KO completely abolished the synthesis of complex and hybrid N-glycans. Differentiation of MGAT1 KO MUTZ-3 cells into immature DCs (iDCs) induced upregulation of DC markers including CD1a, CD80, CD86, CCR6, and CD209, comparable to the upregulation observed in WT iDCs. Interestingly, MGAT1 KO iDCs displayed an enhanced immunostimulatory profile, marked by increased surface densities of CD40, HLA-ABC, and HLA-DR, in combination with elevated mRNA levels of NFKB1 and IFNB1. Consistent with this profile, MGAT1 KO iDCs highly enhanced the activation and proliferation of allogeneic human CD8⁺ T cells in vitro, resulting in significantly higher levels of secreted proinflammatory cytokines compared to WT iDCs. This enhanced CD8⁺ T cell activation persisted under PD-L1 blockade, underscoring the robustness of the MGAT1 KO–driven effect. Significantly elevated NFKB1 levels in the MGAT1 KO iDCs suggest enhanced NF-κB activity driving HLA and costimulatory molecule upregulation and robust CD8⁺ T cell activation. We further demonstrate that MGAT1 KO promotes accelerated DC differentiation, yielding DCs that after three days of differentiation acquire the capacity to activate T cells. Building on previous research into sialic acids in DC biology, our findings reveal a regulatory role for complex and hybrid N-glycans and specifically demonstrate how sialic acids on N-glycans influence distinct functional outcomes in T cell activation. Our findings support cell-based glycoengineering as an effective strategy to improve DC-based immunotherapies.

1 Introduction

Dendritic cells (DCs) are major antigen-presenting cells essential for directing the response of the immune system toward tolerance or inflammation, through their unique ability to activate and prime naïve T cells. DCs achieve this by processing and presenting antigens through major histocompatibility complexes (MHC) type I and II (HLA-ABC and HLA-DR/DP/DQ, respectively) and by transmitting immunomodulatory signals via direct cell-to-cell interactions and cytokine secretion, thereby priming both CD4⁺ and CD8⁺ T cells (1–3). In addition to the functional changes that promote T cell activation during DC activation, the expression profile of N- and O-linked glycans, which are essential for glycoprotein folding, secretion, and stability, is also reshaped, suggesting a significant role for protein glycosylation in DC biology (4–6). As the most structurally diverse form of post-translational modification, glycosylation is becoming increasingly acknowledged for its essential roles in various processes within the human immune system (7–9).

Given the central role of DCs in coordinating immune responses, they are also key mediators of tumor-directed immunity. As central components of the tumor microenvironment (TME), DCs promote anti-tumor T cell responses, and their potent antigen presenting capacity has positioned them as a prominent target in cancer immunotherapy (1, 10, 11). Despite decades of research, DC-based cancer vaccines have yielded only modest clinical benefit. The first and only FDA-approved DC vaccine, sipuleucel-T (PROVENGE), is an autologous monocyte-derived DC (moDC) product that contains a mixture of various immune cells and has demonstrated inconsistent clinical performance, underscoring the challenges of advancing DC-based therapies to clinical success (12, 13, 14). Although DC vaccine strategies in clinical trials have induced antigen-specific T cell responses in some patients, the magnitude of the immune response, the functionality of activated T cells, and the establishment of long-lasting memory T cells still require significant improvement (11, 13, 15). One explanation for the suboptimal results is that current DC vaccines may lack sufficient immunogenicity to provoke clinically beneficial immune responses and overcome the immunosuppressive TME. Additionally, they may fail to induce the appropriate type of immunity; for instance, DCs might promote a TH₂ response instead of a TH₁ response (3, 12, 15–17). This highlights the need for new strategies to optimize DC function and immunogenicity. By enhancing the immunogenic properties of DCs, such as upregulating costimulatory molecules, boosting cytokine production, and improving antigen presentation capabilities, these DCs could potentially stimulate T cell responses more effectively, particularly stimulation of CD8⁺ T cells, which are crucial for eliminating cancer cells (13, 18). In addition to enhanced immunogenicity, an optimal DC vaccine should be easy to manufacture and ensure consistency in immunological activity, which are two challenges faced by autologous DC vaccines (19). Off-the-shelf allogeneic DC platforms can address these challenges while also reducing costs, production time, and overcoming the limited availability of autologous DC precursors. These advantages make allogeneic DC platforms particularly valuable, especially when combined with partial MHC class match (semi-allogeneic), as they can provide a reliable and reproducible therapeutic option that directly benefits patients (14, 20). Few off-the-shelf allogeneic DC-based platforms have been tested in clinical trials, but there are reports on DC cell lines that induce antigen-specific T cell responses (19–23). One example is the DC vaccine DCP-001, derived from the acute myeloid leukemia (AML) cell line DCOne, which has progressed to clinical trials for treatment of high grade serous ovarian cancer (NCT04739527) and AML (NCT03697707), currently in phase 1 and 2, respectively. The phase 2 study, involving 20 patients, reported findings suggesting that vaccinations promoted the expansion of DC populations, thereby boosting T cell activity against tumors (24). We recently developed and validated a human DC model system based on genetically glycoengineered MUTZ-3-derived DCs (25). The MUTZ-3 cell line originates from an AML patient, and MUTZ-3 progenitor cells (PCs) can be differentiated over a 7-day cytokine driven protocol into immature DCs (iDCs), that closely resemble moDCs in their surface receptors and gene expression profiles after cytokine-induced differentiation. MUTZ-3-derived iDCs are fully functional DCs capable of stimulating antigen-specific CD4⁺ and CD8⁺ T cells by processing and presenting protein antigens as peptides on MHC-I and MHC-II molecules, making this cell line a useful alternative model system for human moDCs (22, 26–32).

Previously, we validated the MUTZ-3 cell line as a suitable model for studying DC glycobiology and demonstrated that ST6GAL1-mediated α2,6-sialylation regulates the expression of various DC-related surface proteins, and selectively enhances CD4⁺ T cell activation, while having no effect on CD8⁺ T cell activation (25). These findings established the MUTZ-3 system a tractable isogenic model for dissecting the role of individual glycosyltransferases in DC biology and suggested that DC glycoengineering could be leveraged to optimize DC activation of T cells for future DC vaccine approaches. Moreover, combining ST6GAL1 KO with enzymatic desialylation further enhanced CD8⁺ T cell activation, indicating the role of other sialic acid linkages in selective T cell activation (25). Collectively, previous studies have expanded our understanding of how sialylation modulates DC:T cell interactions and highlighted the potential of targeted glycan modification to fine-tune DC immunogenicity (4, 6, 25, 33–35).

Building upon our previous work demonstrating the functional relevance of α2,6-linked sialic acids in DC biology, we next sought to investigate the broader consequences of depleting all N-linked sialic acids by targeting the central glycosyltransferase mannosyl (α-1,3-)-glycoprotein β-1,2-N-acetylglucosaminyltransferase (MGAT1). MGAT1 initiates formation of hybrid and complex N-glycans via addition of a GlcNAc residue to the α3-linked mannose of the trimannosyl core of N-linked glycans. MGAT1 KO therefore disrupts the synthesis of complex and hybrid N-glycans, resulting in an enrichment of high-mannose structures, specifically Man₅GlcNAc₂, and a complete loss of terminal sialylation on N-glycans, including both α2,3- and α2,6-linked sialic acids, but not on other glycans such as O-glycans and glycolipids (Figure 1A) (36). This makes MGAT1 KO a powerful model to explore how the absence of broader glycan structures, including N-glycan sialylation and branching affects DC biology and T cell activation. This is to our knowledge, the first study to investigate how MGAT1 KO, impacts human DC biology and function. By leveraging our isogenic DC model, we further elucidate how specific sialic acid linkages contribute to DC function, extending our previous findings on selective α2,6-sialylation (ST6GAL1 KO) to a context where both α2,3 and α2,6 sialylation are absent selectively on N-glycans (25). In this study, we comprehensively characterize the phenotype and allogeneic T cell activation potential of MGAT1-deficient MUTZ-3 DCs.

Figure 1

Figure 1. Successful and stable MGAT1 KO in MUTZ-3 precursor cells (PCs) eliminates cell surface complex and hybrid N-glycans. (A) Simplified overview of MGAT1 function in N-glycan processing. MGAT1 initiates the conversion of high-mannose glycans to hybrid and complex N-glycans by adding a GlcNAc to the core structure. In the absence of MGAT1 (MGAT1 KO), this step is blocked, resulting in accumulation of high-mannose glycans. Created in BioRender. Blomberg, A. (2025) https://BioRender.com/oheioep (B) Chromatograms displaying Sanger sequencing results for genotyping of MUTZ-3 WT and MGAT1 KO pool. The gRNA target cleavage sites are marked by a vertical dashed red line. The initial sequencing (1st seq) and the subsequent sequencing (re-seq) were performed with 7 weeks of routine cell passaging in between. (C) Summary of Indel % and KO score for MGAT1 KO PCs at initial sequencing (1st seq) and the subsequent sequencing (re-seq) performed with 7 weeks of routine cell passaging in between. (D) Illustration of MUTZ-3 PC differentiation protocol. Created in BioRender. Blomberg, A. (2025) https://BioRender.com/v5iyr9n. (E) WT and MGAT1 KO PCs and iDCs were surface-labeled with various biotinylated lectins, followed by staining with AF488-streptavidin to assess the impact of MGAT1 KO on cell surface glycan structures. The histograms display representative data from a single experiment, while the accompanying bar graphs show the mean ± SD of lectin intensities relative to WT PCs from seven independent experiments. Cells from different passages and differentiations were used across the seven experiments. The dotted histograms in each plot serve as the negative control, stained exclusively with AF488-streptavidin. Statistical analysis was conducted using two-way ANOVA on matched datasets, followed by Sidak’s multiple comparisons test to assess differences between WT cells and MGAT1 KO cells. Statistical significance is denoted as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Glycan structures were created in BioRender. Blomberg, A. (2025) https://BioRender.com/h2ia4rl.

2 Materials and methods

2.1 Cell culture and maintenance

The human acute myeloid leukemia-derived cell line MUTZ-3 (DSMZ, #ACC 295) and 5637 (DSMZ, #ACC 35) were cultured as previously described (25). Briefly, the MUTZ-3 cells were cultured in MEMα media (Gibco) supplemented with 20% FBS, 20% conditioned media from human cell line 5637 and 1% penicillin-streptomycin. Cells were passaged twice weekly at a density of 0.5×10⁶ cells/mL and maintained at 37°C in a 5% CO₂ humidified incubator, with regular mycoplasma checks.

2.2 Differentiation of the MUTZ-3 DC

Differentiation of MUTZ-3 precursor cells (PCs) into immature dendritic cells (iDCs) was done as previously described (25, 31). Briefly, PCs were seeded into T75 flasks at a density of 1x10⁵ cells/mL in complete MEMα medium (MEMα+20%FBS+1% penicillin-streptomycin), supplemented with a cytokine cocktail consisting of 100 ng/mL GM-CSF (Peprotech), 10 ng/mL IL-4 (Peprotech), and 2.5 ng/mL TNFα (Peprotech) for 7 days. Fresh medium and cytokines were supplemented on day 3 or 4. Early immature dendritic cells (early iDCs) were generated following the same procedure, with the modification that cells were harvested on day 3 before cytokine replenishment.

2.3 CRISPR-Cas9 gene editing

MGAT1 KO MUTZ-3 cells were generated by CRISPR-Cas9 gene editing as previously described (25). To summarize, validated guide RNA (gRNA) sequences were selected from a previous publication (37) and DNA oligos containing the gRNA sequence were synthesized by Macrogen Europe and ligated into U6GRNA plasmid (Addgene #68370). This plasmid, along with CAS9PBKS (Addgene #68371) containing Cas9-2A-EGFP, was transfected into MUTZ-3 cells. 48 hours post transfection cells were sorted based on GFP fluorescence using fluorescence-activated cell sorting (FACS). The KO was confirmed using Sanger Sequencing and ICE CRISPR Analysis Tool (Synthego), and selective lack of complex N-glycans on the KO cell surface was confirmed by lectin staining periodically as quality control of the cell lines in parallel to functional studies. To mitigate any unintended phenotypic or genetic alterations introduced during the gene editing process, WT cells were treated under identical conditions as MGAT1 KO cells, including freeze/thaw cycles and passage numbers.

2.4 Glycan profiling by lectin staining

The selective lack of complex N-glycans on the MGAT1 KO cell surface was confirmed by lectin staining as previously described (25). Briefly, cells were stained with LIVE/DEAD™ Fixable Yellow Dead Cell Stain to assess viability, before incubation with a selection of biotinylated lectins from Vector Laboratories: Aleuria aurantia lectin (AAL), Maackia amurensis lectin I and II (MAL-I and MAL-II), Sambucus nigra agglutinin (SNA), Phaseolus vulgaris lectin L (PHA-L) and Peanut agglutinin (PNA). Detection was achieved using AF488-conjugated streptavidin (Invitrogen), and both lectins and streptavidin were incubated with the cells on ice for 20 min. Detailed information on the working concentrations and catalogue numbers of the lectins is provided in Supplementary Table S1. The cells were acquired on MACSQuant^® Analyzer 16 (Miltenyi Biotec) and data was analyzed in FlowJo v10.8.1.

2.5 Microfludic high-throughput qPCR

Cells were lysed, and RNA was extracted as previously described (25). The purity, concentration, and quality of the extracted RNA were assessed using a NanoDrop™ One/OneC Microvolume UV–Vis Spectrophotometer (Thermo Fisher) and an Agilent 2100 Bioanalyzer (Agilent Technologies), respectively. Complementary DNA (cDNA) synthesis and the pre-amplification step were carried out analogously to the previously described method (25), with the following exceptions: cDNA was synthesized using 300 ng of total RNA, and amplification was performed by incubation at 95°C for 10 minutes, followed by 20 cycles of 95°C for 10 seconds and 60°C for 4 minutes. The specific primes are listed in Supplementary Table S2.

Microfluidic high-throughput qPCR was performed using a 96.96 Dynamic Array Integrated Fluidic Circuit (IFC) chip (Standard Biotools) on the BioMark real-time PCR instrument (Standard Biotools), as previously described (25). Data were normalized using GAPDH and RPLP0 after evaluation of reference genes with NormFinder (38) and geNorm (39). Technical replicates were averaged, and relative quantities were calculated based on the lowest expressed sample for each assay. The graphs depicted are based on log-transformed data, generated using RStudio.

2.6 Isolation of primary cells and coculture of MUTZ-3 iDCs and T cells

Buffy coats were obtained from nine anonymized healthy donors with informed written consent, following local ethics committee guidelines (Region Hovedstaden, Denmark). Peripheral blood mononuclear cells (PBMCs) were isolated using a two-step density gradient centrifugation (200xg and 460xg) with Histopaque-1077 (Sigma Aldrich). PBMCs were collected from the interphase, washed multiple times with Dulbecco’s PBS (DPBS), and depleted for any leftover red blood cells using red blood cell lysis buffer (eBioscience). T cells were subsequently isolated from the PBMC fraction using Dynabeads Untouched Human T cell kit according to the manufacturer’s protocol (Invitrogen). For proliferation analysis, purified T cells were incubated with 5 µM CellTrace™ Violet Cell Proliferation Kit (Invitrogen) in PBS with 5% FBS for 5 minutes, followed by two washes with PBS and 5% FBS. T cells were cocultured with MUTZ-3-derived iDCs or early iDCs in U-bottom 96-well plates at a T cell to DC ratio of 10:1 in complete RPMI 1640 (Sigma Aldrich) with a seeding density of 1x10⁶ T cells/mL. In indicated experiments, an in-house–produced non-glycosylated IgG1 anti–PD-L1 (aPD-L1) antibody was added at a final concentration of 1 µg/mL (see below). After 5 days of coculture at 37°C in a humidified incubator with 5% CO2, T cell activation was assessed by flow cytometry (detailed in the Materials and Methods section on phenotyping DCs and T cells). Supernatants were collected on day 5 of coculture and stored at -80°C for further analysis.

2.7 Cytokine profiling of supernatants

Supernatants from iDC and T cell cocultures were examined for cytokine content using V-Plex Proinflammatory Panel 1 (human) and V-Plex Cytokine Panel 1 (human) MesoScale Discovery (MSD) multiplex assays. Sample preparation followed the manufacturer’s guidelines, and the analysis was conducted using MSD Sector Imager. Data from these assays were processed and analyzed with Discovery Workbench software v4.

2.8 Phenotyping of DCs and T cells

Cells were stained in a 96-well U-bottom plate at 0.5-1x10⁵ cells/well in FACS buffer (PBS + 2% FBS + 2mM Ethylenediaminetetraacetic acid (EDTA)) and were washed in between incubations. Cells were first incubated for 10 min with LIVE/DEAD™ Fixable Yellow Dead Cell Stain (Invitrogen) followed by 10 min incubating with FcR Blocking Reagent (Miltenyi) and finally 10 min incubation with fluorescently labeled detection antibodies. All incubation steps were performed cold in the dark. The antibodies are listed in Supplementary Table S3. After staining, cells were fixed with Fixation buffer (eBioscience) for later analysis. For surface staining, gating was based on the relevant fluorescence minus one (FMO) control on pooled samples. All samples were analyzed on MACSQuant^® Analyzer 16 (Miltenyi Biotec), and data further compensated and analyzed in FlowJo version 10.8.1.

2.9 aPD-L1 antibody

The antibody directed against PD-L1 (aPD-L1) was generated as previously described (40, 41). Briefly, the variable heavy (VH) and light (VL) domains of atezolizumab were cloned into the constant domains of a human IgG1 using pcDNA3.1-based expression vectors. To generate an Fc-silent variant, a point mutation (N297A) was introduced in the heavy-chain CH2 domain using Q5 site-directed mutagenesis kit (New England Biolabs) to eliminate N-linked glycosylation and Fc-mediated effector functions. Heavy- and light-chain plasmids were co-transfected into CHO-S cells to establish a stable expression pool, and antibodies were purified using Protein A affinity chromatography. Endotoxin levels were <0.2 EU/mg, and antibody purity was verified by SDS–PAGE. Binding affinity (K_D) of aPD-L1 to human PD-L1 (ACROBiosystems, #PD1-H52H3) were measured by biolayer interferometry (BLI) on an Octet RED96e system (FortéBio). Antibodies (1.5 µg/mL in PBS, pH 7.4, 0.02% Tween-20, 0.1% BSA) were immobilized on AHC biosensors (Sartorius), associated with PD-L1 (0–5 nM) for 600 s, and allowed to dissociate for 2400 s. Data were analyzed using ForteBio Data Analysis Software 12.0, aligning to the association step and applying a Savitzky–Golay filter to globally fit sensorgrams to a 1:1 binding model (n = 3). Atezolizumab (Tecentriq^®) was included as a control.

3 Results

3.1 Successful and stable MGAT1 KO in MUTZ-3 precursor cells eliminates cell surface complex and hybrid N-glycans

The MUTZ-3 MGAT1 KO cell line was generated utilizing the CRISPR-Cas9 mediated genetic engineering strategy, previously published by our group (25). The MGAT1 KO cell pool was analyzed for KO percentage using Sanger sequencing 3- and 10 weeks post FACS (Figure 1B). The MGAT1 KO was confirmed with an initial KO score of 87%, followed by a second score of 92% after a 7-week culture period (Figure 1C). The heterogeneous bulk MGAT1 KO cell pool and WT PCs were utilized for subsequent experiments.

To evaluate the functional impact of MGAT1 KO on the cell surface glycoprofile of MUTZ-3 PCs and iDCs, WT and MGAT1 KO PCs were differentiated for 7 days with GM-CSF, IL-4, and TNFα (Figure 1D), and both the PCs and iDCs were stained with a series of lectins and analyzed by flow cytometry (Figure 1E). The glycan specificities of the lectins used are depicted above each set of histograms in Figure 1D (42). Monosaccharide symbols follow the SNFG (Symbol Nomenclature for Glycans) system (43). The successful functional MGAT1 KO was confirmed by PHA-L staining, which recognizes tri– and tetraantennary complex N-glycans and demonstrated complete loss of binding. This was further confirmed by the reduced binding of SNA, recognizing α2,6-sialylated Lac(di)NAc, MAL-I, recognizing α2,3-sialylated Lac(di)NAc, and AAL, recognizing α-fucose. These reductions reflect the depletion of complex N-glycans, leading to diminished terminal sialylation and fucosylation. Because SNA, MAL-I, and AAL also bind O-glycans and are not exclusive to N-glycans, a complete loss of binding was neither observed nor anticipated (42). No difference in O-glycan binding MAL-II was observed between MGAT1 KO and WT cells on either PC or iDC, whereas a significant reduction in PNA binding was observed in MGAT1 KO iDCs compared to WT iDCs.

3.2 MGAT1 KO induces enhanced surface density of HLA-ABC, HLA-DR, and CD40 upon differentiation to iDCs alongside increased mRNA levels of pro-inflammatory regulators

MUTZ-3 PCs are known to comprise three distinct subpopulations based on CD34 and CD14 surface expression (25, 31). The CD34⁺CD14⁻ subpopulation represents a stem cell-like, proliferative fraction that differentiates through a CD34⁻CD14⁻ intermediate stage to form the CD34⁻CD14⁺ subpopulation, which acts as an immediate precursor to MUTZ-3 derived DCs (31). When comparing WT and MGAT1 KO cells, MGAT1 PCs exhibited fewer CD34^-CD14⁺ cells and more CD34⁺CD14^- PCs (Figure 2A). However, as a response to differentiation both WT and MGAT1 iDCs significantly downregulated CD34⁺ expression as expected. DC cell surface markers CD1a, CD80, CD86, CD209, CCR6 and PD-L1 were all detected at very low levels in WT and MGAT1 KO PCs by flow cytometry. Upon exposure to the differentiation cocktail, all markers were significantly upregulated to comparable levels in both WT and MGAT1 KO cell lines. Notably, CD1a and PD-L1 expression was significantly higher on MGAT1 KO iDCs compared to WT iDCs, while CD209 expression was significantly lower on MGAT1 KO iDCs compared to WT iDCs (Figure 2A). CD40, which was already highly expressed at PC level, also increased further after differentiation. Again, a similar response between WT and MGAT1 KO cells was observed regarding regulation of this DC cell surface receptor (Figure 2A). Gene expression-levels of CD14, CD34, CD1A, CD40, CD86, CD209, CD274/PD-L1, HLA-A/B/C, and HLA-DRA/DPA/DQA were consistent with expression patterns observed by flow cytometry (Figures 2A, B). Notably, the differences in CD14, CD34, CD1A, and CD86 surface expression on MGAT1 KO PCs and iDCs compared to WT were also reflected at the transcriptional level, confirming that the observed differences are transcriptionally regulated rather than due to glycosylation-dependent differences in detection antibody binding.

Figure 2

Figure 2. MGAT1 KO PCs effectively differentiate into MGAT1 KO iDCs by regulating expression of various DC related receptors both on protein and mRNA level. (A) MUTZ-3 WT PCs and MUTZ-3 MGAT1 KO PCs were differentiated into iDCs, with phenotypic characterization at each stage performed by flow cytometry for a panel of DC markers. Data is presented as percentage of positive viable cells, illustrating the development of indicated surface markers from PC to iDC stage. Statistical analysis was conducted using a two-way ANOVA on paired datasets (n=3-6), followed by Sidak’s multiple comparisons test to evaluate differences between PCs and iDCs and the difference between WT and MGAT1 KO. Statistical differences for MGAT1 KO PCs versus iDCs are indicated by orange asterisk, for WT by grey asterisk, and for comparisons between WT and MGAT1 KO at both PC and iDC stages by black asterisk. Significance is denoted as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. (B) Bar graphs depict the transcriptional regulation of the same DC markers analyzed by flow cytometry (n=6–7). Bars represent mean mRNA levels ± SEM. Asterisks denote statistically significant differences between samples, determined by Student’s t-test (*P < 0.05, **P < 0.01, ***P < 0.001).

The differentiation into iDCs did not alter the percentage of cells positive for HLA-ABC and HLA-DR in either WT or MGAT1 KO cells, with nearly 100% of both PCs and iDCs expressing HLA-ABC, HLA-DR, and CD40 (Figure 2A). However, significant differences in mean fluorescence intensity (MFI), which reflects the cell surface density of the analyzed receptors, were observed between WT and MGAT1 KO iDCs; notably, MGAT1 KO iDCs displayed significantly higher MFI levels for HLA-ABC, HLA-DR, and CD40 compared to WT iDCs (Figures 3A, B).

Figure 3

Figure 3. MGAT1 KO iDCs display increased MFI of HLA-ABC, HLA-DR and CD40 molecules and elevated mRNA levels of pro-inflammatory regulators. (A) Representative histograms showing mean fluorescence intensities (MFI) for HLA-ABC, HLA-DR, and CD40 in PCs and iDCs for WT cells and MGAT1 KO cells. The dotted line represents FMO control. (B) Bar graphs showing MFI for DC receptors that are expressed by ~100% if the DCs. Statistical analysis was conducted using two-way ANOVA on paired datasets, followed by Sidak’s multiple comparisons test to assess differences between WT cells and MGAT1 KO cells on PCs and iDC state. Bar graphs represent 5–7 independent experiments. (C) Bar graphs depict transcriptional regulation of select genes, highlighting statistically significant differences between WT and MGAT1 KO at the iDC stage (n=6–7), with bars showing mean mRNA levels ± SEM. Statistical significance was assessed by Student’s t-test (*P < 0.05, **P < 0.01, ***P < 0.001). (D) Principal component analysis (PCA) of WT and MGAT1 KO cells at PC and iDC stage based on data from transcript analysis. The PCA analysis is based on 47 genes related to DC phenotype and function. The specific genes are indicated in Figure 3D and Supporting Information Supplementary Table S2. (E) Heatmap of gene expression for 47 selected genes related to DC function and phenotype. Genes in bold represent the genes with differential expression between MGAT1 KO iDCs and WT iDCs.

In addition to assessing phenotypical differences between WT and MGAT1 KO cells, we conducted qPCR analysis on a broader panel of genes associated with DC activation and maturation (Figure 3E, Supplementary Table S2). Principal Component Analysis (PCA) of gene expression profiles revealed that WT and MGAT1 KO groups were not separated at either the PC or iDC stage, indicating similar overall transcriptional patterns between WT and MGAT1 KO within each stage (Figure 3D). This is consistent with flow cytometry data where no large phenotypic differences were observed between the groups at either stage (Figure 2A). However, the PCA revealed a clear separation between the PC and iDC stages, regardless of WT or MGAT1 KO status, reflecting distinct transcriptional differences associated with these developmental stages (Figure 3D). Closer examination at the expression of individual genes at the iDC stage revealed strongly upregulated mRNA levels of NFKB1 and IL23A, moderate upregulation of IFNB1, and upregulation of IRF1 (though not statistically significant) in MGAT1 KO iDCs compared to WT iDCs. In contrast, strong downregulation of SIGLEC7 and CXCR2 expressions were also detected in MGAT1 KO iDCs (Figure 3C).

3.3 MGAT1 KO iDCs strongly enhance activation of CD8⁺ T cells and cytokine production after coculture

Phenotyping of WT and MGAT1 KO PCs and iDCs revealed similar regulatory patterns across various DC markers upon differentiation (Figure 2A). However, HLA-ABC, HLA-DR, and CD40 showed significantly enhanced surface density on MGAT1 KO iDCs compared to WT iDCs (Figures 3A, B). Additionally, elevated expression levels of specific pro-inflammatory genes were also observed in MGAT1 KO iDCs including NFKB1 and IFNB1 (Figure 3C). Given the importance of these receptors and genes in DC maturation and T cell priming, we next examined whether coculturing primary T cells with WT or MGAT1 KO iDCs would result in differences in T cell activation. After 5 days of coculture between iDCs and primary T cells, CD4⁺ and CD8⁺ T cell activation was assessed by examining CD25 surface expression and proliferative capacity (Figures 4A–D). No differences in CD25 expression were initially observed on CD4⁺ T cells after coculture with WT or MGAT1 KO iDCs (Figures 4A, C, D. However, closer analysis revealed a modest but statistically significant increase in the proportion of proliferating CD25⁺ CD4⁺ T cells in MGAT1 KO iDC cocultures compared with WT (Figure 4D). The magnitude of this difference varied between donors, with some showing a clear enhancement in response to MGAT1 KO iDCs and others displaying minimal change. In contrast, the CD8⁺ T cell subset showed a strong, robust, and reproducible increase in both CD25 expression and the frequency of proliferating CD25⁺ cells following coculture with MGAT1 KO iDCs compared with WT, representing a marked enhancement of CD8⁺ T cell activation (Figures 4A–D). The supernatants from the cocultures (day 5) were investigated by Mesoscale to determine the resulting cytokine profile (Figure 4E). The increased CD8⁺ T cell activation was supported by the cytokine profile where the MGAT1 KO coculture had significantly higher levels of IL-2, IFNγ, TNFα and TNFβ. Additionally a significant increase in GM-CSF was observed in MGAT1 KO iDC cocultures together with an increased level of IL-13. No significant differences were observed in the other tested cytokines.

Figure 4

Figure 4. MGAT1 KO in iDCs enhance CD25 expression and proliferation of allogenic CD8⁺ T cells and increase secretion of Type 1 cytokines. WT and MGAT1 KO iDCs were cocultured with allogenic primary T cells for 5 days, after which the activation of the T cells was measured by evaluation of their proliferation via Cell Trace Violet (CTV) staining and cell surface expression of CD25 on CD4⁺ and CD8⁺ T cell subpopulations. (A) Histograms displaying proliferation of CD4⁺ and CD8⁺ T cells after coculture. Green histograms indicate T cells activated with TransAct™ for full activation. Dotted histograms displaying unstimulated T cells. (B) Representative dot plots of CD8⁺ T cells from one donor showing the difference in the CD25⁺ proliferating subpopulation between cells cocultured with WT and MGAT1 KO iDCs. (C, D). (C, D) Bar plots summarize the overall percentage of CD25⁺ cells and the percentage of proliferating CD25⁺ cells within each T cell subpopulation from nine different donors. Statistical analysis was performed on matched donor datasets using repeated-measures (RM) one-way ANOVA with the Geisser–Greenhouse correction, followed by Tukey’s multiple comparisons test. Significance is denoted as p < 0.05, p < 0.01, p < 0.001, and p < 0.0001. (E) Cytokine levels in supernatants after 5 days of coculture between T cells and WT or MGAT1 iDC. (-) shows cytokine levels of unstimulated T cells. Statistical analysis of differences between WT and MGAT1 KO was performed using a paired two-tailed ratio-t test. (*p < 0.05, **p < 0.01, and ***p < 0.001). The functional categorization of cytokines and chemokines are based on (44, 45). ns: not significant.

3.4 MGAT1 KO iDCs maintain enhanced CD8⁺ T cell activation under PD-L1 blockade

To increase the T cell activation in the cocultures, we applied PD-L1 blockade using an anti–PD-L1 antibody (aPD-L1). Both WT and MGAT1 KO iDCs expressed PD-L1 (Figure 2A), providing the rationale for applying PD-L1 blockade to further enhance T cell activation. The aPD-L1 is based on the variable domains of atezolizumab fused to human IgG1 Fc mutated at position 297 in CH2 domain to lack the glycosylation which leads to abrogation of Fc mediated immune effector functions, including lack FcγR (CD16) binding, but retaining full binding affinity and PD1 axis immune blockade. As shown in Figure 5A, our aPD-L1 exhibited binding kinetics comparable to atezolizumab, with similar K_D values determined by BLI. After 5 days of coculture between iDCs and T cells in the presence of aPD-L1, a minor but statistically significant increase in T cell viability was observed in MGAT1 KO iDC cocultures compared to WT cocultures, although overall viability remained high across all conditions (Figure 5B). As expected, aPD-L1 treatment markedly enhanced T cell activation, increasing CD25 expression and proliferation in both CD4⁺ and CD8⁺ populations across WT and MGAT1 KO cocultures (Figures 5C–F). Despite this increase in overall activation, and a tendency for slightly improved CD25 expression and proliferation, no significant differences were observed between WT and MGAT1 KO iDC cocultures within the CD4⁺ T cell subset (Figures 5E, F). In contrast, CD8⁺ T cells exhibited consistently stronger responses in MGAT1 KO iDC cocultures, with significantly higher frequencies of CD25⁺ and proliferating CD25⁺ cells compared to WT, both with and without aPD-L1 treatment (Figures 5E, F). These data show that PD-L1 blockade successfully enhances global T cell activation in our MUTZ-3 DC model, and the stronger CD8⁺ T cell response induced by MGAT1 KO iDCs remains evident under PD-L1–inhibited conditions.

Figure 5

Figure 5. PD-L1 blockade enhances overall T cell activation while maintaining stronger CD8⁺ T cell responses induced by MGAT1 KO iDCs. (A) Bar graphs illustrating the mean PD-L1 K_D values (nM) of in-house produced aPD-L1 and commercial atezolizumab (B) T cell viability after 5 days of coculture between iDCs and primary T cells in the presence or absence of aPD-L1 is shown as the percentage of viable cells for each condition. (C, D) T cell proliferation (CellTrace Violet) and CD25 expression of CD4⁺ and CD8⁺ T cells after 5 days of coculture with WT or MGAT1 KO iDCs in the presence or absence of aPD-L1 are shown as representative histograms (C) and contour plots (D) from one donor, with numbers indicating the percentage of cells within the respective gates. (E, F) Bar plots summarize the overall percentage of CD25⁺ cells (E) and proliferating CD25⁺ cells (F) within each T cell subpopulation after 5 days of coculture with WT or MGAT1 KO iDCs in the presence or absence of aPD-L1. Statistical analysis was performed on matched donor datasets using repeated-measures (RM) one-way ANOVA with the Geisser–Greenhouse correction, followed by Tukey’s multiple comparisons test (n = 3) (*p < 0.05, **p < 0.01, and ***p < 0.001).

3.5 MGAT1 KO accelerates differentiation of MUTZ-3 PCs into functionally competent DCs

Because MGAT1 KO iDCs elicited stronger T cell activation (Figures 5C–F) despite few phenotypic differences after 7 days of differentiation (Figures 2A, 3A), we next asked whether MGAT1 deficiency also exerts its effects at earlier stages of DC differentiation. To explore this, we harvested iDCs after 3 days of differentiation and characterized these early immature DCs (early iDCs) (Figure 6A). Phenotypic analysis revealed that MGAT1 KO cells responded rapidly to the cytokine cocktail and upregulated DC-associated surface markers, including CD1a and CD86, already by day 2 which was not the case with WT cells (Figure 6B). By day 3, MGAT1 KO early iDCs displayed a clear phenotypic shift, with significantly increased expression of CD1a, CD40, CD86, HLA-ABC, and HLA-DR compared with PCs, whereas WT cells showed only minor, non-significant upregulation at this early stage (Figure 6C). In contrast, WT cells required the full 7-day differentiation period to reach a significant upregulation of DC markers compared with their PC level, indicating that MGAT1 deficiency accelerates the transition of PCs toward a DC phenotype. To determine whether these phenotypic differences translated into functional competence, early iDCs were cocultured with primary T cells for 5 days. MGAT1 KO early iDCs induced pronounced proliferation of both CD4⁺ and CD8⁺ T cells, whereas WT early iDCs elicited only minimal proliferation, indicating a markedly much lower capacity to stimulate T cell activation (Figures 6D, E). This low responsiveness in WT cocultures correlates with their limited upregulation of DC surface markers at the early differentiation stage. In contrast, MGAT1 KO early iDCs promoted robust T cell activation, with a significantly higher proportion of proliferating CD25⁺ T cells compared with WT.

Figure 6

Figure 6. MGAT1 KO accelerates MUTZ-3 differentiation toward functionally competent DC. (A) Schematic overview of MUTZ-3 differentiation into immature dendritic cells (iDCs), illustrating the generation of iDCs harvested on day 3 (early iDC) and iDCs harvested on day 7 (iDC). Created in BioRender. Blomberg, A. (2025) https://BioRender.com/cbe7i13 (B) Expression of early DC markers CD1a and CD86 in WT and MGAT1 KO cells after 2 days of cytokine-induced differentiation, presented as the percentage of positive viable cells. (C) Mean fluorescence intensity (MFI) of DC-associated surface markers (CD1a, CD40, CD86, HLA-ABC, and HLA-DR) measured in WT and MGAT1 KO cells at the precursor (PC), early iDC (day 3), and iDC (day 7) stages. (D, E) Activation and proliferation of CD4⁺ and CD8⁺ T cells after 5 days of coculture with either early iDCs or iDCs derived from WT or MGAT1 KO MUTZ-3 cells. Data are presented as (D) representative histograms showing proliferation (CellTrace Violet dilution) and CD25 expression and (E) bar plots summarizing the percentage of CD25⁺ and proliferating CD25⁺ cells within each T cell subpopulation. Statistical analysis was performed on matched donor datasets using repeated-measures (RM) one-way ANOVA with the Geisser–Greenhouse correction, followed by Tukey’s multiple comparisons test (n = 3). (*p < 0.05, **p < 0.01, ***p < 0.001) and ****p<0.0001).

4 Discussion

In this study, we generated a stable and functional MGAT1 KO MUTZ-3 cell line to examine how oligomannose N-glycosylation impacts human DC biology and function. We previously demonstrated that a functional KO of ST6GAL1 in the MUTZ-3 cell line significantly altered the phenotype of MUTZ-3-derived DCs and significantly increased CD4⁺ T cell activation, but not CD8⁺ T cell activation (25). To confirm that these effects were linked specifically to the ST6GAL1 KO and not an artifact of the KO procedure, we created a FUT8 KO MUTZ-3 DC pool, which exhibited T cell activation levels comparable to WT (25). Furthermore, in this study the combination of the ST6GAL1 KO in MUTZ-3 with additional enzymatic desialylation lead to an additional boost of activation of CD8⁺ T cells yielding both enhanced activation of CD4⁺ and CD8⁺ T cell activation indicating the role of other sialic acid linkages in selective T cell activation. These previous findings indicate that glycoengineering of DCs may represent a promising approach to modulate DC phenotype and function, thereby enhancing their therapeutic potential.

The MGAT1 KO MUTZ-3 PCs generated in this study showed abolished PHA-L binding, proving the absence of all complex N-glycans on the cell surface (42). Despite the significantly distinct glycoprofiles between WT and MGAT1 KO PCs (Figure 1E), both cell lines successfully differentiated into iDCs, following a 7 day differentiation protocol (Figure 1D), characterized by the downregulation of CD14 and CD34 and the upregulation of CD1a, CD40, CD80, CD86, CCR6, CD209, and PD-L1 at both protein and transcriptional level (Figures 2A, B). At the iDC stage, nearly 100% of both WT and MGAT1 KO iDCs were positive for HLA-ABC, HLA-DR, and CD40, however, the cell surface density of these molecules was significantly higher in MGAT1 KO compared to WT iDCs (Figures 3A, B). All HLA family members are known to carry N-linked glycans (46, 47) and this glycosylation is essential for proper HLA protein folding and surface expression (48). A study by Silva et al., (49) examined the effects of enzymatic desialylation on both lymphoblastoid cells and moDCs, demonstrating that desialylation resulted in increased surface expression of MHC-I complexes and a twofold extension of the half-life of MHC-I molecules at the cell surface in both cell types. The functional impact of this desialylation of moDCs was further evaluated in a mixed leukocyte reaction (MLR) with autologous CD8⁺ T cells, where desialylation enhanced IFNγ production by the T cells. Our findings complement this work by showing that MGAT1 KO iDCs have enhanced surface MHC-I expression (Figures 3A, B) and allogenic T cells cocultured with MGAT1 KO iDCs exhibit increased IFNγ production. In the MGAT1 KO DCs, all N-glycan sialic acids are depleted, as they do not occur on the remaining high mannose structures, while sialic acids on O-glycans and glycolipids are not directly affected.

The coculture between MGAT1 KO iDCs and allogenic T cells also led to a significantly increased activation of CD8⁺ T cells, marked by upregulated CD25 expression, enhanced proliferation and elevated secretion of proinflammatory cytokines including TNFα, TNFβ, and IL-2, in addition to IFNγ (Figure 4E). These cytokines are all known to be secreted by CD8⁺ T cells, thereby reflecting the increased CD8⁺ T cell activation (44, 50). The increased cell surface density of HLA-ABC could partially explain this. While an increased HLA-DR MFI was also observed, the effect on CD4⁺ T cells was less pronounced and appeared more variable between donors. A modest but statistically significant increase in proliferating CD25⁺ CD4⁺ T cells was detected in MGAT1 KO cocultures compared with WT, whereas total CD25⁺ frequencies did not differ significantly. This variability suggests that CD4⁺ T cell activation is more donor dependent, with some individuals showing stronger responses than others, in contrast to the consistent strong enhancement of CD8⁺ activation observed across donors when cocultured with MGAT1 KO iDCs. Because the baseline level of T cell activation varied between donors, we sought to enhance T cell stimulation and thereby reduce inter-donor variability. PD-L1 blockade disrupts the PD-1/PD-L1 interaction, relieving inhibitory signaling and enhancing T cell activation (51). PD-L1 inhibition increased activation across all donors and partially reduced donor-to-donor variability due to the higher activation baseline (Figures 5E, F). No significant differences between WT and MGAT1 KO iDCs were observed among CD4⁺ T cells following aPD-L1 treatment. In contrast, MGAT1 KO iDCs continued to induce markedly higher frequencies of activated and proliferating CD8⁺ T cells compared with WT, demonstrating that the MGAT1 KO–driven enhancement of CD8⁺ T cell activation persists even under conditions of strongly increased overall T cell activation.

Interestingly, our previous study investigating ST6GAL1 KO showed enhanced HLA-DR MFI, but not HLA-ABC. Additionally, ST6GAL1 KO resulted in increased CD4⁺ T cell activation, but no CD8⁺ T cell activation compared to WT. Upon neuraminidase treatment of the ST6GAL1 KO iDCs, increased CD8⁺ T cell activation was observed as well (25). These data, in combination with the consistent increased CD8⁺ T cell activation observed after stimulation with MGAT1 KO iDC in this study, suggests that α2,3 sialic acids or α2,8 polysialylation (4, 52) on DC N-glycans either inhibit CD8⁺ T cell activation or that their removal results in increased activation. While removal of N-linked α2,6 sialic acids appear to consistently increase CD4⁺ T cell activation, as observed in our previous ST6GAL1 KO study (25), a shift to oligomannose N-glycans and consequently a complete loss of sialylated hybrid and complex N-glycans does not increase the CD4⁺ T cell activation as profoundly. This points to a potential role for desialylated hybrid and complex N-glycans in facilitating enhanced CD4⁺ T cell activation or implies that additional modifications, such as alternative capping of N-glycans may be crucial for modulating this response. Taken together, these findings also demonstrate how the specific type of sialic acid linkages can lead to significantly different functional outcomes.

The increased CD8⁺ T cell activation was accompanied by an elevated mRNA level of NFKB1 in MGAT1 KO iDCs (Figure 3C). NFKB1 encodes a DNA-binding subunit of the NF-κB complex, a master regulator of immune and inflammatory responses (Figure 3C) (53, 54). The NF-κB transcription factor is essential for DC maturation and the activation of NF-κB controls the expression of HLA-ABC/DR and key costimulatory molecules like CD40, along with various proinflammatory cytokines (54). Inhibition of NF-κB activity in DCs has been shown markedly inhibit T cell proliferation and reduce IL-2 and IFNγ production in a MLR (53, 55). The substantial increase of NFKB1 mRNA in MGAT1 KO iDCs strongly supports a direct association between enhanced NF-κB activity and the increased surface expression of CD40, HLA-ABC, and HLA-DR ultimately driving increased T cell proliferation and cytokine secretion (Figures 3A, B, 4B). Additionally, IFNB1 expression was significantly increased in MGAT1 KO iDCs, with IRF1 showing a similar trend, though not statistically significant (Figure 3C). IRF1 is a primary transcription factor regulating IFN-mediated gene expression, including type 1 interferon IFNB1 (56, 57). The NF-κB and IFN pathways were recently described to be highly enriched in a certain functionally mature subpopulation of conventional type 1 dendritic cells (cDC1s) (57). A defining characteristic of cDC1s is their ability to cross-present antigens via MHC-I to CD8⁺ T cells, a process essential for effective antitumor immunity (17, 57). This functional capacity is closely linked to the activity of NF-κB and IRF1, as the inhibition of either transcription factor impairs CD8⁺ T cell activation (57). Consistent with previous studies highlighting the important roles of NF-κB and interferon related genes in DC function, our findings demonstrate that in MGAT1 KO iDCs enhanced NFKB1 expression correlates with increased surface expression of HLA-ABC, HLA-DR, and CD40. In coculture, this phenotype is further linked to enhanced CD8⁺ T cell activation and proliferation, accompanied by elevated production of IL-2 and IFNγ.

We also observed a significantly decreased expression of SIGLEC7 in MGAT1 KO iDCs compared to WT iDCs. SIGLEC7 encodes the sialic acid–binding immunoglobulin-like lectin 7 (Siglec-7) which is an inhibitory glycan-binding immune receptor expressed on various immune cells, including DCs (58–60). Disialyl core-1 O-glycans are the primary immune ligands for Siglec-7 and are particularly abundant on naïve T cells. Blocking of Siglec-7 in DCs has been shown to enhance the activation of both primary T cells and antigen-presenting DCs in vitro (59). The lower SIGLEC7 expression observed in MGAT1 KO iDCs may attenuate tolerogenic signaling, thereby further enhancing T cell activation.

Altogether, the observed changes in genotype and phenotype in MGAT1 KO iDCs provide a plausible explanation for their altered behavior and enhanced ability to prime CD8⁺ T cells, as our data highlights key differences in proinflammatory regulators and antigen-presenting molecules. While these findings offer valuable insights into the molecular and functional impact of MGAT1 KO in human DCs, further studies are needed to fully elucidate the precise mechanisms underlying these effects.

Given the numerous N-glycosylated receptors on the DC surface, including key costimulatory molecules, it is possible that changes in glycosylation patterns impact receptor–ligand binding affinities with T cell receptors, thereby altering T cell activation. In addition, many cytokines and cytokine receptors are N-glycosylated and in MGAT1 KO DCs, the secreted cytokines and receptors will present oligomannose N-glycans (61, 62). Glycosylation is known to influence receptor binding, stability, and the function of cytokines, offering a plausible mechanism for distinct effects in T cell activation observed between WT and MGAT1 KO DCs (63). Additionally, cytokine receptors on MGAT1 KO DCs might exhibit altered affinities for differentiation cytokines such as GM-CSF, TNF-α, and IL-4, thus impacting differentiation responses. All receptors binding these cytokines are N-glycosylated, and intact N-glycosylation sites are essential for high-affinity GM-CSF receptor binding and N-glycosylation has been found vital for TNFR1’s binding to TNFα and supports a TNFα autocrine loop, which amplifies inflammation through NF-κB pathways in microglia (64–66). Consistent with this, our data demonstrates that MGAT1 KO MUTZ-3 precursor cells respond more rapidly to cytokine stimulation and acquire DC-associated surface markers such as CD1a and CD86 earlier during differentiation (Figure 6B). By day 3, MGAT1 KO early iDCs already exhibit significantly elevated expressions of CD40, HLA-ABC, and HLA-DR compared to PC stage, indicating accelerated progression toward a DC-like phenotype (Figure 6C). Functionally, these early MGAT1 KO iDCs were already capable of inducing strong T cell proliferation, whereas WT early iDCs displayed minimal stimulatory capacity (Figures 6D, E). Together, these findings suggest that loss of MGAT1 and the resulting shift to oligomannose N-glycans enhance the responsiveness of DC precursors to cytokine signaling, promoting faster differentiation into immunocompetent DCs and contributing to the increased T cell activation observed in mature MGAT1 KO iDCs.

The primary focus of the present study was to investigate how MGAT1 impacts DC function and T cell activation, using allogeneic cocultures as the central approach. Utilizing this widely used and accepted experimental setup to investigate the overall immunostimulatory capacity of MGAT1 KO DCs, our data demonstrates that a MGAT1 KO accelerates the differentiation into DCs and that MGAT1 KO DCs consistently enhance CD8⁺ T cell activation. However, additional studies are needed to determine whether MGAT1 KO also affects antigen processing and presentation. While previous work has demonstrated that MUTZ-3 DCs successfully can be loaded with antigens and used to prime tumor-specific cytotoxic T cells (28), antigen-specific T cell activation assays and antigen uptake assays will be important to fully evaluate the potential of MGAT1 KO DCs in the context of antigen processing and presentation. Our isogenic DC model provides a strong foundation for future studies investigating how altered glycosylation shapes antigen handling and the induction of antigen-specific immune responses.

Overall, this study reveals how MGAT1 KO in DCs generates a markedly enhanced immunostimulatory profile compared to WT DCs. The KO significantly boosts CD8⁺ T cell activation and cytokine production, thereby offering a promising strategy to improve DC performance in immunotherapy. When considered alongside our previous findings showing that ST6GAL1 KO selectively boosted CD4⁺ T cell activation (25), these results suggest that targeted glycoengineering can fine-tune immune responses and potentially steer activation toward specific T cell subsets. Overall, these findings highlight the potential of glycoengineering and glycooptimization to advance immune modulation for therapeutic applications.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by local ethics committee guidelines (Region Hovedstaden, Denmark). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

AB: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. BH: Data curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – review & editing. WT: Data curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing – review & editing. KS: Supervision, Writing – review & editing, Data curation. SS: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. SG: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing, Formal Analysis, Investigation, Methodology, Validation, Writing – original draft.

Funding

The author(s) declared that financial support was received for this work and/or its publication. We gratefully acknowledge the Novo Nordisk Foundation for funding this work through grants NNF20SA0066621, NNF19SA0056783, NNF21SA0072683, and NNF19SA0035474 awarded to SG. This study was supported by the DigitSTEM Cooperation Agreement on Research with DTU (to SG) funded by Bioneer A/S, a non-for-profit research-based organization. The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Acknowledgments

Thank you to Denisa Jones and Karin Tarp (Department of Biotechnology and Biomedicine, DTU, Denmark) for excellent technical assistance with microfluidic qPCR.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. During the preparation of this manuscript, the author(s) used ChatGPT-3.5 to enhance language and readability. Following its use, the author(s) carefully reviewed and revised the content as necessary and take full responsibility for the accuracy and integrity of the final publication.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2025.1588795/full#supplementary-material

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