Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD) is closely related to the metabolic syndrome of chronic liver disease and its pathogenesis involves a variety of cell types of metabolic disorders and inflammatory response, however, treatment is limited. Mas is a G protein-coupled receptor highly expressed by myeloid cells. It may play a key role in regulating myeloid-associated inflammation and metabolic homeostasis in the process of MASLD. However, its specific mechanism of action in MASLD remains unclear.

Aims & Methods

This study aims to explore the regulatory mechanism of MAS in myeloid cells in the occurrence and development of MASLD. In this study, liver tissues of patients with MASLD and animal models of MASLD induced by high-fat diet (HFD) were analyzed. In this study, myeloid-specific Mas1 knockout (Lysm^creMas1^f/f) mice were constructed. Single-cell RNA sequencing and multi-omics analysis were used to explore the role and mechanism of myeloid Mas in HFD-induced MASLD mice.

Results

The study found that the expression of MAS protein on myeloid cells in the livers of MASLD patients and MASLD animal models was significantly increased. Mononuclear phagocyte system（MPs）undergo metabolic disorders in the MASLD model, with elevated glycolysis levels, increased lactic acid production, and at the same time, the expression of cellular senescence markers (such as p16 and p21) is upregulated, and the secretion of inflammatory factors is increased. Single-cell multi-omics analysis revealed that myeloid MAS1 knockout activated the FN1⁺CCR2⁺monocytes first. Pseudo-temporal analysis showed that the FN1⁺CCR2⁺ monocytes had spatiotemporal metabolic responses with the macrophages, significantly involving the activation of glycolysis and inflammatory senescence by myeloid MAS. Non-target metabolomics of MASLD patients also revealed elevated glycolytic metabolism and increased lactic acid secretion. Mass spectrometry revealed that Mas specifically bound to the key glycolytic protein pyruvate kinase M2 (PKM2) and activated the lactylation of transcription factor Spi1-K208 site mediated by PKM2. The lactylation of Spi1-K208 promoted the transcription of senescence-associated secretory phenotype (SASP) -related proteins. Myeloid Mas1 knockout inhibited the nuclear translocation of PKM2 and blocked the lactylation of Spi1, inhibiting the transcriptional initiation of SASP. Screening of over 100,000 small molecule compounds through a virtual drug library revealed that Theaflavin 3,3'-digallate (TFDG) might reduce the protein expression of Mas and PKM2, and block the binding of the MAS-PKM2 complex. We synthesized macrophage membrane to encapsulat PLGA nanoparticles loaded with TFDG (MM@NP-TFDG) and found that MM@NP-TFDG effectively reduced the expression level of Mas-PKM2 complex protein expression in the MASLD mouse model, inhibited the glycolytic - inflammatory senescence axis of FN1⁺CCR2⁺ monocytes and macrophages, and alleviated liver inflammation and tissue damage. While the lipid deposition in the liver is reduced and the fatty acid oxidation is enhanced.

Conclusion

This study reveals that myeloid MAS1 protein participates in the occurrence and development of MASLD by regulating PKM2-mediated Spi1 lactylation and reprogramming the metabolic-inflammatory senescence axis of MPs during the process of MASLD. This discovery provides new potential targets for the treatment of MASLD patients and is expected to offer new clinical treatment strategies for MASLD patients.

Disclosure

The authors declare no conflict of interest.

Introduction

Helicobacter pylori (H. pylori) infection is a major cause of gastric diseases, with the virulence factor CagA carried by exosomes playing a crucial role in regulating inflammation. Ferroptosis and M1 polarization are key mechanisms in promoting inflammation, yet the role of CagA in inducing macrophage ferroptosis and M1 polarization to exacerbate gastric mucosal inflammation remains unclear.

Aims & Methods

CagA-positive (CagA+) and CagA-negative (CagA-) H. pylori strains were co-cultured with gastric epithelial cells, and their supernatants were collected. Exosomes were isolated using ultracentrifugation to obtain CagA+Exo and CagA-Exo. Changes in macrophage M1/M2 polarization markers and ferroptosis markers were analyzed under CagA+Exo and CagA-Exo treatment. The activation of the JAK/STAT1 signaling pathway was assessed by Western blot, and STAT1 expression in the nucleus was evaluated using a nuclear protein extraction kit. Chromatin immunoprecipitation (ChIP) was used to identify the binding site of STAT1 on the SLC7A11 promoter. Biopsy samples from the gastric mucosa of gastritis patients infected with CagA+ or CagA- H. pylori were analyzed for macrophage polarization and ferroptosis markers.

Results

Exosomal CagA was found to induce macrophage M1 polarization and ferroptosis, with the ferroptosis inhibitor SLC7A11 playing a key role in CagA-induced ferroptosis. Exosomal CagA activated the JAK/STAT1 signaling pathway, leading to M1 polarization and ferroptosis. Additionally, CagA+Exo increased nuclear STAT1 expression, which subsequently bound to the SLC7A11 promoter to regulate its transcription. In gastric mucosa biopsies from CagA+ H. pylori-infected gastritis patients, increased levels of iNOS (a marker of M1 polarization) and decreased expression of SLC7A11 were observed.

Conclusion

Our findings indicate that exosomal CagA promotes macrophage M1 polarization and ferroptosis through the JAK/STAT1 signaling pathway. This study provides new insights and potential therapeutic targets for H. pylori-associated gastritis.

Introduction

Prospective evidence of the long-term efficacy of endoscopic submucosal dissection (ESD) for T1 colorectal cancer (CRC) is insufficient.

Aims & Methods

We aimed to evaluate long-term ESD outcomes for T1 CRC, focusing on the risk stratification criteria and necessity of additional surgery.
This large-scale, multicenter, prospective cohort study was conducted by Hiroshima GI Endoscopy Research Group. All consecutive patients who underwent colorectal ESD between January 2014 and December 2018 were included, and those with pathologically confirmed T1 CRC were analyzed. Patients were classified into low-risk and high-risk groups based on histopathological risk factors for lymph node metastasis in JSCCR guidelines. High-risk patients were categorized into those who underwent additional surgery (surgery group) and those who were followed up without surgery (follow-up group).

Results

Among 2357 patients who underwent ESD for 2478 early colorectal neoplasms, 431 were diagnosed with T1 CRC. Ten recurrences (2.6%) and five T1 CRC-associated deaths (1.3%) occurred in the high-risk group, with none in the low-risk group. Five-year cumulative overall and local recurrence rates were significantly higher in the follow-up versus surgery group (7.7% and 1.9%; P=0.019 versus 6.4% and 0%; P<0.001), with no local recurrence in the surgery group. Five-year overall survival was significantly worse in the follow-up group (79.5% and 95.2%; P<0.001), while no significant difference was found in 5-y disease-specific survival (98.5% and 98.5%; P=0.447).

Conclusion

No recurrence or mortality occurred in the low-risk group, suggesting that intensive surveillance may be unnecessary for these patients after curative ESD. Additional surgery could prevent local recurrence for high-risk T1 CRC, supporting a risk-based treatment approach.

Introduction

Inflammatory Bowel Disease (IBD)^1,2 requires close monitoring to detect flare ups early and guide treatment. While endoscopy is the gold standard, it is invasive, costly, and uncomfortable^1,2. Intestinal Ultrasound (IUS) is a non-invasive alternative with strong correlation to endoscopic findings—especially bowel wall thickness (BWT), a marker of disease activity³. Nevertheless, IUS remains limited due to operator dependence and a steep learning curve⁴.
AI has improved consistency in other domains, e.g. endoscopic evaluation, but its application to IUS in IBD remains underexplored. Prior models lack interpretability and require manual image cropping, or are trained on ideal, selected data, limiting clinical use^5,6.

Aims & Methods

We aim to develop a deep-learning model that automatically identifies and paints the bowel wall and measures BWT directly from raw clinical IUS images.
A training dataset of 570 images from 144 IUS videos and a testing dataset of 55 images from 55 separate exams were created. All images were extracted from previously performed IUS examinations reflecting real-world variation, including e.g. unclear boundaries and artefacts.
All images were annotated by International Bowel Ultrasound Group (IBUS) certified experts, including outline-paintings of the inner and outer bowel wall and two BWT measurements.
The AI consists of a combination of Convolutional neural networks and other image processing algorithms.
Evaluation included BWT error against the expert mean, classification accuracy using the standard IBUS 3 mm threshold³, and a leave-one-out comparison with individual doctors.

Results

The model produced predictions on 54 of 55 test images.
The model deviated from the gold standard mean by 0.98 mm (SD 1.1 mm) per image on the regression task. The average distance to expert-defined bounds was 0.44 mm (SD 0.89 mm), with 59% of predictions inside this range.
For classification (using a 3 mm threshold), the model reached an accuracy of 0.77, sensitivity of 0.69, specificity of 0.94, and Cohen’s Kappa of 0.56.
In the leave-one-out analysis, expert performance ranged from 0.89–0.93 accuracy and 0.79–0.83 Kappa. Depending on which expert was excluded from the gold standard, the model achieved 0.74–0.80 accuracy and 0.49–0.60 Kappa. Experts stayed within expert-defined bounds in 72–81% of cases, while the model did so in 46–55%.

Conclusion

Clinically, the model performed well. An error of 0.5–1 mm is negligible in practice and matches typical variation in manual measurements. Many real-world test images had BWT values near the 3 mm threshold, so small deviations led to misclassifications. In practice, a 2.5 mm reading could still raise concerns based on symptoms³.
While the system alone cannot provide expert-level BWT measurements, it could provide assistance to experts as well as non-expert and junior doctors, especially in locating the bowel, which is an essential part of IUS.
The model works on unprocessed clinical data, with the only selection criteria being that there is an identifiable bowel segment, measurable by an IBUS-certified doctor. Allowing the images to reflect realistic conditions
In summary, we developed an AI model that identifies the bowel and measures BWT with acceptable accuracy. We have already started the processes of collecting video data, to extend the AI’s functionality to a fully clinical setting.

References

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