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RPN1 at the crossroads of glycosylation, tumor immunity, and disulfidptosis

5 September 2026 at 18:00

Front Pharmacol. 2026 Aug 21;17:1911035. doi: 10.3389/fphar.2026.1911035. eCollection 2026.

ABSTRACT

Ribophorin I (RPN1), a core component of the oligosaccharyltransferase complex, is traditionally known for its role in endoplasmic reticulum-associated N-glycosylation. Recent studies have identified RPN1 as an emerging regulator of tumor progression and immunity. Aberrant RPN1 overexpression has been reported in multiple malignancies, including glioma, hepatocellular carcinoma, sarcoma, and triple-negative breast cancer, where it is frequently associated with aggressive clinicopathological features and poor prognosis. RPN1 promotes tumor immune evasion by promoting N-glycosylation and stabilization of programmed death-ligand 1 (PD-L1), thereby enhancing immune checkpoint signaling and directly inhibiting anti-tumor T-cell responses. Consequently, elevated RPN1 expression is consistently associated with an immunosuppressive tumor microenvironment rich in M2 macrophages and poor in CD8+ T cells. More importantly, multiple omics signature analyses indicate RPN1 is integrated into several disulfidptosis-related risk models; however, direct experimental evidence confirming the causal linkage between RPN1 and disulfidptosis remains limited. Correlative database data also show potential associations between RPN1 upregulation and genomic instability and treatment resistance. Based on tiered classification of existing evidence (biochemical functional validation vs. multi-omics correlation), this review systematically summarizes the biological roles of RPN1 in cancer, its functions in tumor immunity and disulfidptosis-associated pathways and finally evaluates its potential as a therapeutic target in precision oncology.

PMID:42698633 | PMC:PMC13542368 | DOI:10.3389/fphar.2026.1911035

Reconstituting human primitive streak formation through extra-embryonic cell coordination

Tissue patterning events of early human embryo development, including an embryo-disc with primitive streak-like structures, are captured using an in vitro model where embryonic stem cells are cultured with extra-embryonic cell types.

Integrin β3 deficiency unleashes spontaneous pulmonary inflammation by promoting B cell hyperactivation via the CD40-CD40L axis

Front Immunol. 2026 Mar 24;17:1796926. doi: 10.3389/fimmu.2026.1796926. eCollection 2026.

ABSTRACT

BACKGROUND: Pulmonary immune homeostasis requires tight control of adaptive responses. Integrin β3 is a well-known mediator of cell adhesion and platelet function. However, its role in adaptive immunity, especially in B cell responses, remains unclear.

METHODS: We defined the pulmonary phenotype of constitutive β3-deficient (β3-/-) mice by histopathology. We performed integrated transcriptomic and proteomic profiling of lung tissue to map the molecular signature of spontaneous pulmonary inflammation. We further probed the underlying mechanisms with additional histology and functional assays and tested for biological significance using transcriptomics data from auto-immune disease patients.

RESULTS: β3-/- mice developed spontaneous pulmonary inflammation marked by B cell activation and in situ immune-complex deposition within alveoli. Multi-omics integration implicated the CD40-CD40 Ligand (CD40L) axis as a central driver of this pathology. Mechanistically, loss of β3 enhanced CD40L-CD40 engagement on B cells, resulting in NF-κB pathway hyperactivation. Consistent with our murine data, reduced ITGB3 expression in patients with autoimmune disease correlated with transcriptional signatures of B cell activation and inflammation.

CONCLUSIONS: These results reframe integrin β3 as a threshold regulator of B cell activation. The β3-CD40L-CD40 axis therefore represents a potential therapeutic target for B cell-mediated autoimmune diseases.

PMID:41953039 | PMC:PMC13055533 | DOI:10.3389/fimmu.2026.1796926

Integrin β3 deficiency unleashes spontaneous pulmonary inflammation by promoting B cell hyperactivation via the CD40-CD40L axis

Front Immunol. 2026 Mar 24;17:1796926. doi: 10.3389/fimmu.2026.1796926. eCollection 2026.

ABSTRACT

BACKGROUND: Pulmonary immune homeostasis requires tight control of adaptive responses. Integrin β3 is a well-known mediator of cell adhesion and platelet function. However, its role in adaptive immunity, especially in B cell responses, remains unclear.

METHODS: We defined the pulmonary phenotype of constitutive β3-deficient (β3-/-) mice by histopathology. We performed integrated transcriptomic and proteomic profiling of lung tissue to map the molecular signature of spontaneous pulmonary inflammation. We further probed the underlying mechanisms with additional histology and functional assays and tested for biological significance using transcriptomics data from auto-immune disease patients.

RESULTS: β3-/- mice developed spontaneous pulmonary inflammation marked by B cell activation and in situ immune-complex deposition within alveoli. Multi-omics integration implicated the CD40-CD40 Ligand (CD40L) axis as a central driver of this pathology. Mechanistically, loss of β3 enhanced CD40L-CD40 engagement on B cells, resulting in NF-κB pathway hyperactivation. Consistent with our murine data, reduced ITGB3 expression in patients with autoimmune disease correlated with transcriptional signatures of B cell activation and inflammation.

CONCLUSIONS: These results reframe integrin β3 as a threshold regulator of B cell activation. The β3-CD40L-CD40 axis therefore represents a potential therapeutic target for B cell-mediated autoimmune diseases.

PMID:41953039 | PMC:PMC13055533 | DOI:10.3389/fimmu.2026.1796926

Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay

arXiv:2506.05316v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become an effective approach for fine-tuning large language models (LLMs), particularly to enhance their reasoning capabilities. However, RL fine-tuning remains highly resource-intensive, and existing work has largely overlooked the problem of data efficiency. In this paper, we propose two techniques to improve data efficiency in LLM RL fine-tuning: difficulty-targeted online data selection and rollout replay. We introduce the notion of adaptive difficulty to guide online data selection, prioritizing questions of moderate difficulty that are more likely to yield informative learning signals. To estimate adaptive difficulty efficiently, we develop an attention-based framework that requires rollouts for only a small reference set of questions. The adaptive difficulty of the remaining questions is then estimated based on their similarity to this set. To further reduce rollout cost, we introduce a rollout replay mechanism inspired by experience replay in traditional RL. This technique reuses recent rollouts, lowering per-step computation while maintaining stable updates. Experiments across 6 LLM-dataset combinations show that our method reduces RL fine-tuning time by 23% to 62% while reaching the same level of performance as the original GRPO algorithm. Our code is available at https://github.com/ASTRAL-Group/data-efficient-llm-rl.
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