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M. Charrout

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6 records found

Journal article (2026) - Sandrine Nugteren, Beatriz Calado, Ytje Simons-Oosterhuis, Daniëlle H. Hulleman-van Haaften, Willem K. Smits, Renz Cw Klomberg, Bastiaan Tuk, Mohammed Charrout, Dicky J. Lindenbergh-Kortleve, More Authors
Heterogeneity in disease severity and treatment response in inflammatory bowel disease (IBD) likely evolves from individual differences in host-microbiota-immune interactions. Histological evaluation of intestinal biopsies is central to diagnosis, but histological parameters that define underlying immune mechanisms are limited. We investigated histological features that distinguish individual patient immune profiles in therapy-naive pediatric IBD patients (age 6-18 years) using biopsy immunohistochemistry and transcriptomics and plasma proteomics across two cohorts. High colonic epithelial expression of secretory leukocyte protease inhibitor (SLPI), a microbiota-induced regulator of epithelial function, occurred in IBD patients with high clinical disease activity and more severe endoscopic and microscopic disease activity. SLPI expression was related to increased neutrophil infiltration, transcriptomic signatures of activation, and genes known to associate with therapeutic resistance. High SLPI colocalized with high densities of IL-17-secreting cells and was associated with high plasma concentrations of Th17-related immune proteins. Additionally, patients with high intestinal SLPI had an intrinsically different immunotype, in which circulating neutrophils exhibited altered transcription of genes involved in neutrophil granule formation, phagocytosis, oxidative phosphorylation, and interferon signaling. Thus, high colonic SLPI expression at diagnosis associates with severe IBD, increased IL-17A-neutrophil pathway responses, and altered transcriptomic wiring of circulating neutrophils. ...

Real-world Data from the International Prospective PIBD-SETQuality Inception Cohort Study

Journal article (2024) - Renz C.W. Klomberg, Hella C. van der Wal, Martine A. Aardoom, Polychronis Kemos, Dimitris Rizopoulos, Frank M. Ruemmele, Mohammed Charrout, Hankje C. Escher, Lissy de Ridder, More authors...
Background and Aims
Treatment guidelines for paediatric Crohn’s disease [CD] suggest early use of anti-tumour necrosis factor alpha [anti-TNFα] in high-risk individuals. The aim is to evaluate the effect of early anti-TNF in a real-world cohort.

Methods
Children with newly diagnosed CD were prospectively recruited at 28 participating sites of the international observational PIBD-SETQuality study. Outcomes were compared at 3 months, 1 and 2 years between patients receiving early anti-TNF [<90 days after diagnosis] and those not receiving early anti-TNF. Outcomes included sustained steroid-free remission [SSFR] without treatment intensification [specified as SSFR*] and sustained steroid-free mild/inactive disease without treatment intensification [specified as SSFMI*]. Penalised logistic regression model-based standardisation was applied to estimate the relative risks [RR] of early therapy on outcomes. RRs were estimated for high-risk and low-risk patients, based on presence of predictors of poor outcome [POPOs] and disease activity at diagnosis.

Results
In total, 331 children (median age 13.9 years [IQR 12.2–15.3]) were enrolled, with 135 [41%] receiving early anti-TNF. At 1 year, patients on early anti-TNF had higher rates of SSFR* [30% vs 14%, p <0.001] and SSFMI* [69% vs 33%, p <0.001], with RRs of 2.95 [95% CI 1.63-5.36] and 4.67 [95% CI 2.46-8.87], respectively. At 1 year, the RRs for SSFMI* were higher, and statistically significant in high-risk patients, i.e. those with moderate/severe disease compared with mild/inactive disease at diagnosis (5.50 [95% CI 2.51-12.05] vs 2.91 [95% CI 0.92-9.11]), and those with any POPO compared with no POPO (5.05 [95% CI 2.45-10.43] vs 3.41 [95% CI 0.54-21.7]).

Conclusion
In this cohort of children with newly-diagnosed CD, early anti-TNF demonstrated superior effectiveness in high-risk patients. ...
Journal article (2024) - Maud Heredia, Mohammed Charrout, Renz C.W. Klomberg, Martine A. Aardoom, Maria M.E. Jongsma, Polychronis Kemos, Danielle H. Hulleman-van Haaften, Ahmed Mahfouz, Marcel J.T. Reinders, More authors...
Inflammatory bowel disease (IBD) chronicity results from memory T helper cell (Tmem) reactivation. Identifying patient-specific immunotypes is crucial for tailored treatment. We conducted a comprehensive study integrating circulating immune proteins and circulating Tmem, with intestinal tissue histology and mRNA analysis, in therapy-naïve pediatric IBD (Crohn's disease, CD: n = 62; ulcerative colitis, UC: n = 20; age-matched controls n = 43), and after 10–12 weeks’ induction therapy. At diagnosis, plasma protein profiles unveiled two UC and three CD clusters with distinct disease courses. UC patients displayed unchanged circulating Tmem, while CD exhibited increased frequencies of gut-homing ex-Th17, known for high IFN-γ production. UC#2 had elevated Th17/neutrophil-pathway-related proteins and severe disease, with higher endoscopic and histological damage and Th17/neutrophil infiltration. Although both UC#1 and UC#2 responded to therapy, UC#2 required earlier immunomodulation. CD#3 had lower plasma protein concentrations, especially IFN-γ pathway proteins, fewer gut-homing ex-Th17 and clinically milder disease, confirmed by intestinal gene expression. CD#1 and CD#2 had comparably high Th1-related immune profiles, but CD#1 exhibited higher concentrations of proteins previously associated with poorer prognosis. Both CD clusters responded to induction therapy, with similar one-year outcomes. This study highlights feasibility of discriminating patient-specific immunotypes in IBD, advancing our understanding of immune pathogenesis, needed for tailored treatment strategies. ...
Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer. However, these models are difficult to train due to the large number of hyperparameters that need to be tuned. To get a better understanding of the importance of the different hyperparameters, we examined six different VAE models when trained on TCGA transcriptomics data and evaluated on the downstream tasks of cluster agreement with cancer subtypes and survival analysis. We studied the effect of the latent space dimensionality, learning rate, optimizer, initialization and activation function on the quality of subsequent downstream tasks on the TCGA samples. We found β-TCVAE and DIP-VAE to have a good performance, on average, despite being more sensitive to hyperparameters selection. Based on these experiments, we derived recommendations for selecting the different hyperparameters settings. To ensure generalization, we tested all hyperparameter configurations on the GTEx dataset. We found a significant correlation (ρ = 0.7) between the hyperparameter effects on clustering performance in the TCGA and GTEx datasets. This highlights the robustness and generalizability of our recommendations. In addition, we examined whether the learned latent spaces capture biologically relevant information. Hereto, we measured the correlation and mutual information of the different representations with various data characteristics such as gender, age, days to metastasis, immune infiltration, and mutation signatures. We found that for all models the latent factors, in general, do not uniquely correlate with one of the data characteristics nor capture separable information in the latent factors even for models specifically designed for disentanglement. ...
Journal article (2022) - Arno R. Bourgonje, Shixian Hu, Lieke M. Spekhorst, Daria V. Zhernakova, Arnau Vich Vila, Yanni Li, Mohammed Charrout, Ahmed Mahfouz, Marcel J.T. Reinders, More authors...
Background and Aims: Protein profiling in patients with inflammatory bowel diseases [IBD] for diagnostic and therapeutic purposes is underexplored. This study analysed the association between phenotype, genotype, and the plasma proteome in IBD. Methods: A total of 92 inflammation-related proteins were quantified in plasma of 1028 patients with IBD (567 Crohn's disease [CD]; 461 ulcerative colitis [UC]) and 148 healthy individuals to assess protein-phenotype associations. Corresponding whole-exome sequencing and global screening array data of 919 patients with IBD were included to analyse the effect of genetics on protein levels (protein quantitative trait loci [pQTL] analysis). Intestinal mucosal RNA sequencing and faecal metagenomic data were used for complementary analyses. Results: Thirty-two proteins were differentially abundant between IBD and healthy individuals, of which 22 proteins were independent of active inflammation; 69 proteins were associated with 15 demographic and clinical factors. Fibroblast growth factor-19 levels were decreased in CD patients with ileal disease or a history of ileocecal resection. Thirteen novel cis-pQTLs were identified and 10 replicated from previous studies. One trans-pQTL of the fucosyltransferase 2 [FUT2] gene [rs602662] and two independent cis-pQTLs of C-C motif chemokine 25 [CCL25] affected plasma CCL25 levels. Intestinal gene expression data revealed an overlapping cis-expression [e]QTL-variant [rs3745387] of the CCL25 gene. The FUT2 rs602662 trans-pQTL was associated with reduced abundances of faecal butyrate-producing bacteria. Conclusions: This study shows that genotype and multiple disease phenotypes strongly associate with the plasma inflammatory proteome in IBD, and identifies disease-associated pathways that may help to improve disease management in the future. ...
Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity toward the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unraveling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajectory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell variation. ...