Introduction

Micronutrient deficiency is an underestimated problem in the westernized population with unclear effects on the manifestation of acute or chronic disease. Westernized lifestyle instigates disrupted gut-microbial crosstalk and chronic low-grade inflammation¹. Tryptophan (Trp) is an essential amino acid used for de novo synthesis of cellular nicotinamide adenine dinucleotide (NAD). We have previously shown increased Trp degradation in several chronic inflammatory disorders, including IBD^2,3. Whether Trp deficiency is a widespread phenomenon in the western population is unknown. Here, we tested the hypothesis that Trp deficiency is frequent in the western population and associates with increased overall disease severity in internal-medicine and especially gastrointestinal (GI) diseases.

Aims & Methods

To screen the micronutrient status of the healthy population, we assessed quantitative amino acid levels in n=15.188 healthy controls (HC) with a commercially available home-care dry blood spot test for targeted metabolomics (cohort 1). Self-reported symptoms were assessed with a digital questionnaire. To validate Trp deficiency (defined as the lowest Trp quartile) in acute internal-medicine diseases (cohort 2), we measured serum Trp by HPLC in HC (n=291) and patients presenting in the emergency ward of the University Hospital Schleswig-Holstein, Kiel (n=4349, categorized by ICD10; thereof GI diseases n=516 (K00-K93: diseases of the digestive system); 10/2023-09/2024). Next to laboratory and clinical assessment at presentation, disease courses were followed up digitally for at least 6 months. Statistical analyses were performed with linear mixed models (LMM) and logistic regression correcting for age and sex at birth; predictive performance was evaluated via area under the receiver-operating-characteristic (AUROC; R version 4.4.1).

Results

In cohort 1, 39.3% of HC presented with Trp deficiency. Trp deficiency significantly inversely correlated with the amount of self-reported symptoms, such as e.g. GI symptoms. Validating these findings in internal-medicine and a subgroup of GI diseases in cohort 2, we found significantly reduced serum Trp compared to HC (mean Trp 56.58±10.84µM vs. 45.06±16.65 (all disease groups), 45.21±20.69 (GI diseases)), especially when the serum inflammation marker C-reactive protein was not elevated (<5 mg/l). Here, Trp deficiency was observed in 1.4% of HC, while it was strongly increased in all disease groups (26.6%) and GI diseases (30.2%). Using LMM, we found that patients that were hospitalized, admitted to the ICU or died within the follow-up period had a significantly lower Trp compared to patients with favourable outcomes (outpatient/no ICU/alive). Patients with Trp at the lowest compared to the highest quartile underwent longer hospitalisation and ICU treatment (all diseases: 3.17/0.37 days (d) longer; GI diseases: 3.91/0.48d longer) as well as a higher number of in-hospital procedures (all diseases: 1.47 more; GI diseases: 3.3 more). The predictive value of Trp for complicated outcomes (hospitalisation, hospitalisation >7d, ICU treatment, death) as assessed by AUROC was low-moderate (all diseases: AUC 0.69, 0.67, 0.61, 0.72; GI diseases: AUC 0.61, 0.6, 0.54 and 0.62, respectively).

Conclusion

We report that Trp deficiency is frequent in the westernized population and associates with more severe disease courses. Our data suggests Trp degradation along the de novo NAD synthesis pathway as an overarching metabolic hallmark of chronic low-grade inflammation and a risk factor for severe disease courses of internal-medicine and GI diseases.

References

1. Christ, A. & Latz, E. The Western lifestyle has lasting effects on metaflammation. Nature Reviews Immunology vol. 19 267–268 Preprint at https://doi.org/10.1038/s41577-019-0156-1 (2019).
2. Harris, D. M. M. et al. Tryptophan degradation as a systems phenomenon in inflammation – an analysis across 13 chronic inflammatory diseases. EBioMedicine 102, 105056 (2024).
3. Nikolaus, S. et al. Increased Tryptophan Metabolism Is Associated With Activity of Inflammatory Bowel Diseases. Gastroenterology 153, (2017).

Introduction

Artificial Intelligence (AI)- enabled endoscopy and histology offer accurate, objective, and rapid assessment of disease activity in Ulcerative Colitis (UC). Emerging multi-source AI models integrating diverse datasets may enhance standardised disease evaluation and outcome prediction.

Aims & Methods

This study aimed to develop a novel AI model fusing endoscopic and histological findings to improve the assessment of disease remission and predict early response to therapy in UC clinical trials. A novel multimodal AI fusion algorithm was developed by integrating paired endoscopic videos and histological whole-slide images (WSIs) from the phase 2 clinical trial of Mirikizumab in UC (NCT02589665). The endoscopy branch of the model was trained with 291 white-light videos, using a convolutional neural network to select informative frames and the BioMedCLIP foundational model to extract features. The histology branch utilised the CONCH foundational model and was fine-tuned on 291 WSIs to obtain patch-level features. Features from both modalities were aggregated and fused using multi-head self-attention. The model’s ability to assess histological remission and response to therapy at weeks 12 and 52 was evaluated. Histological remission was defined as Geboes ≤2B.0, while a response to therapy at different time points was based on histological remission or improvement (Geboes <3.1).

Results

The fusion model outperformed single-modality assessments for histological remission, achieving a sensitivity of 89.72% (95% CI: 82.35–94.76), specificity of 89.67% (95% CI: 84.34–93.67), and accuracy of 89.69% (95% CI: 85.61–92.94). It demonstrated remarkable performance in assessing response to therapy at 12 and 52 weeks, with sensitivity of 97.96% (95% CI: 89.15–99.95), specificity of 86.84% (95% CI: 71.91–95.59) and accuracy of 93.10% (95% CI 85.59 – 97.43) for histological remission at week 52. Substantial agreement was observed between the AI fusion model and central readout.

Conclusion

This innovative multimodal fusion AI model enhances the assessment of histological remission and accurately predicts response to therapy. By potentially standardising central readouts and enabling automated disease assessment, this novel tool marks a significant advancement towards precision medicine in clinical trials.

Disclosure

Marietta Iacucci received research grants and consultant fees from Olympus, Pentax, Eli Lilly, and Janssen. All other authors declare no competing interests.

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

Esophageal squamous cell carcinoma (ESCC) is associated with poor prognosis despite advances in neoadjuvant immunochemotherapy (NICT). With highly variable treatment responses, identifying reliable predictive biomarkers remains crucial. While gut microbiome influences on immunotherapy efficacy are increasingly recognized, the specific role of the fungal microbiome (mycobiome) in NICT outcomes for ESCC remains unexplored, representing a significant gap in developing personalized treatment strategies.

Aims & Methods

This study aimed to characterize gut mycobiome signatures in ESCC patients and develop fungal biomarker-based predictive models for NICT response. We conducted ITS2 sequencing on 136 fecal samples collected from 68 ESCC patients (pre- and post-NICT) and 19 healthy controls (HC). Patients were stratified as responders (R, n=26) or non-responders (NR, n=42) based on tumor regression grade (TRG) scores. We performed comprehensive analyses of mycobiome diversity, composition, co-occurrence networks, and functional pathways across groups. The Boruta algorithm was employed for feature selection to identify fungal biomarkers, followed by multilayer perceptron (MLP) machine learning with five-fold cross-validation to develop predictive models. Additionally, ESCC xenograft models were established to investigate gut mycobiome-immune interactions through fecal microbiota transplantation (FMT) and targeted fungal manipulation experiments.

Results

ESCC patients demonstrated significant mycobiome dysbiosis compared to healthy controls, characterized by reduced fungal alpha diversity and distinct beta diversity patterns. At baseline, ESCC patients showed enrichment of Rhodotorula minuta, Actinomucor elegans, and several Candida species. Responders exhibited significantly higher baseline fungal diversity than non-responders, with enrichment of Meyerozyma, Candida boidinii, Trichosporon dermatis, and Cryptococcus species, whereas non-responders showed predominance of Saccharomyces, Nakaseomyces, and pathogenic Candida species (C. glabrata, C. parapsilosis, C. albicans). Responders also displayed more complex, stable fungal co-occurrence networks with higher density and clustering coefficients. Post-NICT, both groups showed partial recovery of mycobiome diversity, with significant increases in beneficial fungal taxa. Functional pathway analysis revealed distinct metabolic profiles between responders and non-responders. Using the Boruta algorithm, we identified 23 genus-level and 22 species-level fungal biomarkers that demonstrated high predictive value. The MLP model based on genus-level markers achieved AUCs of 90.2% (training) and 91.9% (test), while the species-level model showed comparable performance. In animal experiments, mice receiving R-FMT exhibited reduced tumor growth and enhanced CD8+ T cell infiltration compared to NR-FMT recipients. Notably, Candida boidinii supplementation synergized with anti-PD-1 therapy, while Saccharomyces depletion enhanced treatment sensitivity.

Conclusion

Our findings establish the gut mycobiome as a powerful predictor of NICT response in ESCC, with machine learning models achieving >90% accuracy using fungal biomarkers. Experimental validation not only confirms mechanistic links between specific fungi and antitumor immunity but also demonstrates that targeted mycobiome modulation can enhance immunotherapy efficacy. This dual approach of prediction and intervention offers a novel framework for precision oncology in ESCC, potentially transforming clinical decision-making and treatment outcomes in this challenging malignancy.

References

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