Biology subjects

Cozzetto, D.

Publications and source records attributed to Cozzetto, D..

7 recordsLinked to original sources

bioRxiv2026

A microbiota-responsive polyfunctional cytotoxic CD4⁺ T-cell state promotes mucosal inflammation in ulcerative colitis

Ulcerative colitis (UC) is characterised by chronic colonic inflammation with marked heterogeneity in disease severity and therapeutic outcomes. Here, we define a spatially organised, polyfunctional cytotoxic CD4 T-cell state associated with mucosal inflammation and adverse therapeutic outcomes in UC. Integrating ex vivo T-cell receptor stimulation with multi-cohort bulk and single-cell transcriptomics and multiparameter flow cytometry, we show that GZMB CD4 T cells are preferentially enriched in inflamed UC mucosa, but not peripheral blood, and co-express cytotoxic molecules, Th1- and Th17-associated cytokines and chemokines, and immunoregulatory receptors. Single-cell analyses implicate inflammatory cytokine and antigen-presentation signals in the acquisition or maintenance of this state. High-resolution spatial profiling localised this programme predominantly to Th17 cells, which were preferentially enriched within multicellular inflammatory and tertiary lymphoid structure-associated niches. Across independent patient cohorts, a transcriptional signature derived from this state increased with endoscopic disease severity and was associated with reduced response to anti-TNF and anti-IL-12/23p40 therapies. Adoptive transfer of Gzma/Gzmb-deficient rather than wild-type CD4 T cells into Rag2-deficient recipient mice markedly attenuated experimental colitis and abrogated the polyfunctional cytokine phenotype, demonstrating that granzyme-dependent effector activity is a key mechanism driving CD4+ T-cell-mediated intestinal inflammation. Finally, human host-microbiome analysis linked this programme to intestinal dysbiosis, while transfer of dysbiotic microbiota promoted the emergence of a corresponding state in vivo. Collectively, these findings define a microbiota-responsive, spatially organised polyfunctional cytotoxic CD4 T-cell programme that contributes to intestinal inflammation and is associated with disease severity and treatment resistance in UC.

immunology↗

bioRxiv2026

A dysregulated stromal remodelling programme characterises prior anti-TNF failure in ulcerative colitis

Prior anti-tumour necrosis factor (TNF) failure is associated with reduced efficacy of subsequent advanced therapies in ulcerative colitis (UC), but the biological basis of this treatment-refractory state remains unclear. We integrated clinical outcomes and baseline colonic transcriptomic data from UC patients in the UNIFI phase III trial programme with regulatory and signalling network inference, connectivity mapping, and single-cell-resolution spatial transcriptomics. Colonic transcriptomic analyses identified coordinated enrichment of extracellular matrix organisation, collagen remodelling and integrin-associated programmes, increased stromal cell representation and elevated inferred MAPK/EGFR activity in UC patients with prior anti-TNF failure. Causal network inference prioritised MAPK3 as a candidate regulator of this state, while connectivity mapping identified MEK/EGFR inhibitors as candidate perturbagens. MEK inhibition suppressed stromal pathways and reduced inferred MAPK/EGFR activity ex vivo. Spatial profiling of active UC and non-IBD colonic tissues localised these programmes to UC-enriched stromal niches. Ligand-receptor inference further identified reciprocal stromal-myeloid communication within these niches. Collectively, these findings define a stromal remodelling programme associated with prior anti-TNF failure and nominate MAPK/EGFR signalling as a potentially tractable component of treatment-refractory UC.

systems biology↗

bioRxiv2026

Redox-activated induction of Warburg-type metabolism in the adult heart

Proliferating cells rewire glucose metabolism away from oxidative ATP production towards glycolysis and anabolic branch pathways (the Warburg effect). In contrast, the contractile function of terminally differentiated cardiomyocytes in the heart critically depends upon mitochondrial ATP generation. Adult cardiomyocytes are largely non-proliferative but undergo hypertrophy in response to increased heart workload. A fundamental question in the field is how cardiac metabolism is modified to balance competing energetic and anabolic demands. We previously reported that the redox-signalling H2O2-generating enzyme, NADPH oxidase 4 (NOX4), facilitates compensated cardiac function in hearts undergoing hypertrophic remodelling. Here, we show that NOX4 induces Warburg-type reprogramming of glucose metabolism in the healthy heart, with increased flux into the pentose phosphate, serine and nucleotide biosynthetic pathways. Using an integrated multiomics approach, we uncover a NOX4-regulated network involving interplay between direct transcriptional activation of metabolic genes via NRF2 [aka NFE2L2] and ATF4 and a broader epigenetic regulation. This reprogramming of glucose intermediary metabolism along with previously reported NOX4-mediated enhancement of fatty acid oxidation may serve to optimally support the dual requirements of increased energy demand and remodelling in the heart. Our findings identify a novel paradigm for redox-regulated Warburg-type metabolic reprogramming in the terminally differentiated heart.

physiology↗

bioRxiv2023

Invariant natural killer T-cell and CD4+ T-cell derived IL22 is a regulator of epithelial-to-mesenchymal transition and extracellular matrix remodelling in perianal fistulas

Background and aimsPerianal fistulization is a challenging phenotype of Crohns disease (CD). Unravelling the cytokine networks and cellular mediators driving tissue injury in perianal CD (pCD) will help inform much needed novel treatment strategies. MethodsThe phenotype of different T-cell subsets, including unconventional lineages, such as {gamma}{delta} T-cells, MAIT and iNKT-cells in fistula tract tissue and blood samples of patients with pCD or cryptoglandular perianal fistulas was determined using multiparameter flow cytometry. Transcriptomic profiling of fistula tract tissue was performed by RNA-sequencing. ResultsCD161+ CD4+ T-cells and CD161+ CD4- CD8- iNKT-cells significantly accumulated in fistula tissue and produced interleukin-(IL)22 and IL13. Transcripts associated with epithelial-to-mesenchymal transition (EMT), extracellular matrix (ECM) remodelling, interferon-gamma, JAK-STAT and lymphocyte signalling were enriched in pCD, as well as inhibition of pathways associated with wound closure. IL22-responsive transcripts were significantly enriched in fistulas and network analysis identified IL22-mediated regulation of EMT, ECM and other inflammatory pathways. ConclusionThis study provides novel molecular and cellular insights into fistula pathogenesis, identifying IL22 producing lymphocytes as novel immune regulators of EMT and ECM dysregulation in perianal fistulas. Targeting the regulatory networks controlling IL22 producing lymphocytes may offer novel therapeutic strategies in pCD.

immunology↗

bioRxiv2020

Cancer-associated hypersialylated MUC1 drives the differentiation of monocytes into macrophages with a pathogenic phenotype

The tumour microenvironment plays a crucial role in the growth and progression of cancer and the presence of tumour-associated macrophages (TAMs) is associated with poor prognosis. Recent studies show that TAMs show transcriptomic, phenotypic, functional and geographical diversity. Here we show that a sialylated tumour-associated glycoform of the mucin MUC1, MUC1-ST, through the engagement of Siglec-9 can specifically and independently induce the differentiation of monocytes into TAMs with a unique phenotype. These TAMs can recruit and maintain neutrophils, inhibit the function of T cells, degrade basement membrane allowing for invasion, are inefficient at phagocytosis, and can induce plasma clotting. This novel macrophage phenotype is enriched in the stroma at the edge of breast cancer nests and their presence is associated with poor prognosis in breast cancer patients.

cancer biology↗

bioRxiv2018

Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks

Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other network embedding-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.

bioinformatics↗

bioRxiv2018

Predicting Human Protein Function with Multi-task Deep Neural Networks

Machine learning methods for protein function prediction are urgently needed, especially now that a substantial fraction of known sequences remains unannotated despite the extensive use of functional assignments based on sequence similarity. One major bottleneck supervised learning faces in protein function prediction is the structured, multi-label nature of the problem, because biological roles are represented by lists of terms from hierarchically organised controlled vocabularies such as the Gene Ontology. In this work, we build on recent developments in the area of deep learning and investigate the usefulness of multi-task deep neural networks (MTDNN), which consist of upstream shared layers upon which are stacked in parallel as many independent modules (additional hidden layers with their own output units) as the number of output GO terms (the tasks). MTDNN learns individual tasks partially using shared representations and partially from task-specific characteristics. When no close homologues with experimentally validated functions can be identified, MTDNN gives more accurate predictions than baseline methods based on annotation frequencies in public databases or homology transfers. More importantly, the results show that MTDNN binary classification accuracy is higher than alternative machine learning-based methods that do not exploit commonalities and differences among prediction tasks. Interestingly, compared with a single-task predictor, the performance improvement is not linearly correlated with the number of tasks in MTDNN, but medium size models provide more improvement in our case. One of advantages of MTDNN is that given a set of features, there is no requirement for MTDNN to have a bootstrap feature selection procedure as what traditional machine learning algorithms do. Overall, the results indicate that the proposed MTDNN algorithm improves the performance of protein function prediction. On the other hand, there is still large room for deep learning techniques to further enhance prediction ability.

bioinformatics↗