Daily Endocrinology Research Analysis
Analyzed 51 papers and selected 3 impactful papers.
Summary
Three papers span mechanistic endocrinology and precision prevention. Epigenetic co-activators CBP/p300 were identified as central regulators of pancreatic α-cell mass via amino acid sensing, while adipocytes were shown to recruit specific mRNAs from neighboring cells via extracellular vesicles to restore gene expression. A multi-omics risk score substantially improved early, personalized prediction of type 2 diabetes beyond clinical factors.
Research Themes
- Islet epigenetics and amino acid sensing in α-cell biology
- Extracellular vesicle-mediated intercellular RNA transfer in adipose tissue
- Multi-omics models for early and personalized type 2 diabetes risk stratification
Selected Articles
1. CBP/p300 is critical for the expansion and maintenance of functional pancreatic α cell mass.
Using α cell–specific knockout mice, the authors show that CBP/p300 maintains functional α-cell mass by integrating amino acid sensing with mTORC1 signaling, in part via regulation of the amino acid transporter Slc7a2 and H3K27 acetylation. Loss of CBP/p300 causes hypoglucagonemia, hyperaminoacidemia, α-cell dedifferentiation and cell loss, and blocks glucagon receptor antibody-induced α-cell proliferation.
Impact: This work reveals an epigenetic control node (CBP/p300) that links amino acid transport, histone acetylation, and mTORC1 to α-cell identity and proliferation, a mechanistic advance with therapeutic implications for diabetes and glucagon-targeted interventions.
Clinical Implications: While preclinical, these findings suggest that preserving CBP/p300 activity or rescuing Slc7a2-dependent amino acid sensing could modulate α-cell mass and influence responses to glucagon receptor–targeted therapies. They also caution that α-cell proliferative responses to hyperaminoacidemia may depend on intact CBP/p300 signaling.
Key Findings
- α cell–specific CBP/p300 deletion in mice caused hypoglucagonemia, hyperaminoacidemia, and reduced functional α-cell mass via impaired proliferation, dedifferentiation, and cell loss.
- CBP/p300 knockout blocked glucagon receptor antibody–stimulated α-cell proliferation and mTORC1 signaling.
- Single-cell RNA-seq showed upregulated autophagy genes and downregulated α-cell identity genes and amino acid transporters, including Slc7a2.
- Slc7a2 downregulation impaired lysine-facilitated H3K27 acetylation and arginine-stimulated mTORC1, suppressing α-cell proliferation and triggering autophagy.
Methodological Strengths
- α細胞特異的遺伝子改変マウスとin vivo機能評価（mTORC1、増殖）を組み合わせた厳密な機序解析
- 単一細胞RNAシーケンスにより細胞種別の遺伝子ネットワーク変化と輸送体発現を解明
Limitations
- Findings are from murine models; human islet validation is needed.
- The upstream signals triggering Slc7a2 regulation and broader metabolic consequences were not fully delineated.
Future Directions: Validate the CBP/p300–Slc7a2–mTORC1 axis in human α cells, test pharmacologic modulation of CBP/p300 or Slc7a2, and assess how this pathway shapes responses to glucagon receptor–directed therapies and diabetes phenotypes.
Pancreatic α cell senses amino acid availability to adjust secretion function and proliferation, yet the underlying molecular mechanisms remain unclear. Here, α cell-specific deletion of CBP/p300 in mice leads to hypoglucagonemia and hyperaminoacidemia, along with decreased functional pancreatic α cell mass due to impaired cell proliferation, dedifferentiation, and cell loss. The knockout of CBP/p300 blocks glucagon receptor antibody-stimulated α cell proliferation and mTORC1 signaling in mice. In CBP/p300-deficient α cells, single-cell RNA sequencing reveals upregulated autophagy-related genes and downregulated α cell identity genes and amino acid transporters, including Slc7a2. Slc7a2 is involved in regulating α cell identity gene expression through lysine-facilitated H3K27 acetylation. In addition, Slc7a2 downregulation compromises arginine-stimulated mTORC1 signaling, thereby suppressing α cell proliferation and triggering autophagy. Collectively, our findings uncover CBP/p300 as central regulators of functional α cell mass partially by orchestrating Slc7a2-mediated amino acid sensing.
2. Adipocytes signal to recruit specific mRNAs from surrounding cells to restore expression deficits.
In adipocyte-specific SMPD3 loss, neighboring immune-like preadipocytes release EVs carrying SMPD3 mRNA, leading to selective restoration of SMPD3 expression in null adipocytes without altering other ceramide pathway transcripts. This uncovers a compensatory, EV-mediated intercellular mRNA transfer mechanism within adipose tissue.
Impact: This study demonstrates selective intercellular mRNA trafficking that restores gene expression deficits, redefining adipose tissue plasticity and EV biology with potential translational avenues for RNA-based therapeutics.
Clinical Implications: Although early-stage, EV-mediated delivery of specific transcripts to adipocytes could be harnessed to correct metabolic enzyme deficiencies, suggesting a platform for future RNA or EV-based interventions in metabolic disease.
Key Findings
- Adipocyte-specific SMPD3 ablation triggers surrounding immune-like preadipocytes to release EVs carrying SMPD3 mRNA.
- SMPD3-null adipocytes exhibit increased SMPD3 mRNA and protein despite no change in other ceramide pathway transcripts.
- The data support a selective, compensatory mechanism of intercellular mRNA acquisition from the microenvironment to restore lost gene expression.
- Findings highlight an EV-dependent communication axis that can specifically traffic defined mRNAs between adipose-resident cells.
Methodological Strengths
- In vivo adipocyte-specific genetic manipulation with purification of target cells to assess transcript restoration
- Selective transcript analyses demonstrating specificity relative to other ceramide metabolism genes
Limitations
- Demonstrated in male mice; generalizability to females and humans remains to be shown.
- Upstream signals and functional metabolic consequences of restored SMPD3 were not fully explored.
Future Directions: Define the signals driving selective mRNA packaging/release, test EV-mediated mRNA delivery therapeutically, and evaluate the phenomenon in human adipose depots and metabolic disease states.
Extracellular vesicles (EVs) are nano-sized, membrane-delimited, particles released by cells that carry signaling macromolecules. A major pathway of EV production is potentiated by neutral sphingomyelinase 2 (SMPD3/nSMAse2), an enzyme that generates ceramide from sphingomyelin. In our attempt to study this pathway in adipocytes of male mice, we discover that the elimination of SMPD3 from adipocytes in vivo triggers a signal to surrounding immune cell-like preadipocytes to release EVs that carry SMPD3 mRNA. This results in a widespread increase in SMPD3 mRNA in purified null adipocytes without a change in the transcripts of other enzymes involved in ceramide metabolism. These results point to a selective mechanism by which specific mRNA molecules are acquired from the microenvironment to a level that can restore expression of mRNA and protein in a cell that is depleted of the corresponding genetic information.
3. A multi-omics risk score enables early and personalized screening for type 2 diabetes: A population-based cohort study.
In UK Biobank, proteomic (ProS) and combined multi-omics (ComS) risk scores outperformed clinical risk scoring (C-index 0.84 vs 0.76; NRI 0.328), reclassifying 73 additional T2D cases per 1000 without increasing false positives. Metabolomic and proteomic scores identified high-risk individuals missed by clinical factors, supporting earlier screening (before age 40) in top risk quintiles; FGF23 was validated as a key proteomic contributor in an independent cohort.
Impact: By integrating proteomics and metabolomics with genetics and clinical factors, this study delivers a high-performing, actionable risk model that could shift T2D screening toward earlier, personalized strategies.
Clinical Implications: Health systems could incorporate multi-omics risk scores to refine screening initiation age and intensity, prioritizing top-quintile risk individuals for earlier lifestyle or pharmacologic prevention while balancing cost and feasibility.
Key Findings
- Proteomic score (ProS) achieved C-index 0.80; combined multi-omics score (ComS) reached 0.84, outperforming the clinical score (0.76) with NRI 0.328 (p<0.001).
- ComS reclassified 73 additional incident T2D cases per 1000 without increasing false positives and provided superior Kaplan–Meier risk stratification (log-rank p<0.0001).
- Metabolomic and proteomic scores identified high-risk individuals missed by clinical factors; top risk quintiles warranted screening initiation before age 40.
- FGF23 was independently validated as a key proteomic contributor to T2D risk in the Liyang cohort.
Methodological Strengths
- Population-based cohort with rigorous modeling (Ridge Cox), discrimination and reclassification metrics (C-index, NRI)
- Independent external validation of a proteomic marker (FGF23) in a separate cohort
Limitations
- Observational design limits causal inference; generalizability and implementation costs for multi-omics screening require evaluation.
- Event counts, sample sizes per omics subset, and calibration across diverse ancestries were not detailed in the abstract.
Future Directions: Prospective implementation studies comparing multi-omics–guided screening vs. standard care on clinical endpoints and cost-effectiveness; optimization of reduced biomarker panels for scalability.
OBJECTIVE: This study aims to develop a multi-omics model to predict type 2 diabetes onset using multi-omics data, beyond established clinical and genetic factors. METHODS: In the UK Biobank cohort (N = 21,312,893 incident type 2 diabetes cases), Ridge Cox regression models were constructed based on clinical factors defined by the Finnish Diabetes Risk Score (CliS), plasma metabolomics (MetS), proteomics (ProS) and polygenic risk score (PRS). Model performance was evaluated by C-index and net reclassification improvement (NRI). Clinical utility was assessed through net benefit, risk stratification and screening initiation ages. Independent validation of protein markers was performed in the Liyang Cohort (N = 10,056). RESULTS: The ProS was the top-performing single-omics predictor (C-index: 0.80). ComS (Combined risk score) achieved the highest discriminative ability (C-index: 0.84), significantly outperforming the CliS (C-index: 0.76, NRI: 0.328, p < 0.001). ComS reclassified 73 additional type 2 diabetes cases per 1000 participants not receiving intervention, without increasing false positives. Kaplan-Meier analysis confirmed superior risk stratification of ComS (Log-rank p < 0.0001). Critically, both MetS and ProS identified high-risk individuals missed by the conventional CliS. Screening initiation before age 40 was warranted for individuals in the top risk quintile of MetS, ProS, or ComS. In the Liyang Cohort, plasma FGF23 levels were significantly elevated in type 2 diabetes cases (p < 0.05), corroborating its role as a key proteomic contributor to risk prediction. CONCLUSION: The combined multi-omics model enables more precise, earlier type 2 diabetes risk stratification, supporting personalized screening strategies years before clinical onset.