Biology subjects

Gagliani, N.

Publications and source records attributed to Gagliani, N..

8 recordsLinked to original sources

bioRxiv2026

Ryanodine receptor 1 is dispensable for CD4+ T-cell differentiation and effector function in intestinal inflammation models

T-cell receptor signaling is necessary for the activation and differentiation of CD4 T cells. Calcium (Ca2+) signaling is essential for this process, and the complexity of Ca2+ channels presents a potential therapeutic target for modulating the strength of T-cell receptor signaling and further differentiation of CD4 T cells. Nicotinic acid adenine dinucleotide phosphate (NAADP) is a potent Ca2+-mobilizing second messenger that triggers Ca2+ release through ryanodine receptor 1 (RYR1) in T cells. While the molecular and biophysical properties of NAADP-induced Ca2+ microdomains in T cells have been thoroughly investigated, and the function of the NAADP-HN1L/JPT2-RYR1 axis has been proven in T-cell activation and proliferation, its role in intestinal inflammation in vivo remains to be elucidated. In this study, we generated a conditional knockout mouse with Ryr1 deleted in {beta} T cells to investigate the functional relevance of RYR1 signaling in CD4 T cells. Ryr1 deletion in CD4+ T cells decreased TCR-induced Ca2+ microdomain formation, reduced peak Ca2+ amplitude and delayed initial velocity of global Ca2+ signaling in vitro. However, Ryr1 expression in CD4 T cells was dispensable for their pathogenicity in murine models of intestinal inflammation. Thus, Ryr1 expression in CD4+ T cells plays a redundant role in intestinal inflammation.

immunology↗

bioRxiv2024

The integrated stress response/eIF2α pathway controls cytokine production in tissue-resident memory CD4+ T cells

Tissue-resident memory T (Trm) cells are a specialized T cell population that resides in tissues and can play both a protective and pathogenic role. The mechanism that enables Trm cells to provide a rapid protective response while restricting their function in homeostasis remains unclear. Here, we show that human and mouse CD4+ Trm cells exist in a poised state, characterized by storage of proinflammatory type-1 and type-3 cytokine mRNAs without protein production. In steady-state conditions, cytokine mRNA translation in Trm cells is suppressed by the integrated stress response (ISR)/eIF2 pathway, whereas Trm-cell activation under inflammatory conditions results in eIF2 dephosphorylation, leading to derepression and rapid translation of the cytokine mRNAs stored in stress granules. Pharmacological inhibition of eIF2 dephosphorylation resulted in reduced cytokine production from Trm cells, and ameliorated autoimmune kidney disease in mice. Consistent with these results, the ISR pathway in Trm cells was downregulated in patients with immune-mediated diseases of the kidney and the intestine. Our results identify ISR/eIF2-mediated control of cytokine mRNA translation as an underlying mechanism that restricts Trm cell activity in homeostasis but also promotes rapid response upon local infection or autoimmune reaction.

immunology↗

bioRxiv2023

Emergence and suppressive function of Tr1 cells in glomerulonephritis

T regulatory type 1 (Tr1) cells, which are defined by their regulatory function, lack of Foxp3, high expression of IL-10, CD49b, and LAG3, are known to be able to suppress Th1 and Th17 in the intestine. Th1 and Th17 cells are also the main drivers of crescentic glomerulonephritis, the most severe form of renal autoimmune disease. However, whether Tr1 cells emerge in renal inflammation and moreover, whether they exhibit regulatory function during glomerulonephritis has not been thoroughly investigated yet. To address these questions, we used a mouse model of experimental crescentic glomerulonephritis and double Foxp3mRFP IL-10eGFP reporter mice. We found that Foxp3neg IL-10-producing CD4+ T cells infiltrate the kidneys during glomerulonephritis progression. Using single-cell RNA- sequencing, we could show that these cells express the core transcriptional factors characteristic of Tr1 cells. In line with this, Tr1 cells showed a strong suppressive activity ex vivo and were protective in experimental crescentic glomerulonephritis in vivo. Finally, we could also identify Tr1 cells in the kidneys of patients with anti-neutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis and define their transcriptional profile. Tr1 cells are currently used in several immune-mediated inflammatory diseases, e.g. as T- cell therapy. Thus, our study provides proof of concept for Tr1 cell-based therapies in experimental glomerulonephritis.

immunology↗

bioRxiv2022

GOAT: Deep learning-enhanced Generalized Organoid Annotation Tool

Organoids have emerged as a powerful technology to investigate human development, model diseases and for drug discovery. However, analysis tools to rapidly and reproducibly quantify organoid parameters from microscopy images are lacking. We developed a deep-learning based generalized organoid annotation tool (GOAT) using instance segmentation with pixel-level identification of organoids to quantify advanced organoid features. Using a multicentric dataset, including multiple organoid systems (e.g. liver, intestine, tumor, lung), we demonstrate generalization of the tool to annotate a diverse range of organoids generated in different laboratories and high performance in comparison to previously published methods. In sum, GOAT provides fast and unbiased quantification of organoid experiments to accelerate organoid research and facilitates novel high-throughput applications.

bioinformatics↗

bioRxiv2022

DiSCERN - Deep Single Cell Expression ReconstructioN for improved cell clustering and cell subtype and state detection

Single cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification. Here we present DISCERN, a novel deep generative network that reconstructs missing single cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We used DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilized T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 81% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single cell sequencing workflows and readily adapted to enhance various other biomedical data types.

bioinformatics↗

bioRxiv2021

The spatial transcriptomic landscape of the healing intestine following damage

The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics (ST) to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we used our resource to map transcriptomics modules associated with human diseases demonstrating that ST can be used to inform clinical practice. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses.

systems biology↗

bioRxiv2019

Conserved transcriptomic profile between mouse and human colitis allows temporal dynamic visualization of IBD-risk genes and unsupervised patient stratification

AbstrasctDespite the fact that ulcerative colitis (UC) patients show heterogeneous clinical manifestation and diverse response to biological therapies, all UC patients are classified as one group. Therefore, there is a lack of tailored therapies. In order to design these, an unsupervised molecular re-classification of UC patients is evoked. Classical clustering approaches based on tissue transcriptomic data were not able to classify UC patients into subgroups, likely due to associated covariates. In addition, while genome wide association studies (GWAS) have identified potential new target genes, their temporal dynamic revealing the optimal therapeutic window of time remains to be elucidated. To overcome the limitations, we generated time-series transcriptome data from a mouse model of colitis, which was then cross-compared with human datasets. This allowed us to visualize IBD-risk gene expression kinetics and reveal that the expression of the majority of IBD-risk genes peak during the inflammatory phase, and not the recovery phase. Moreover, by restricting the analysis to the most differentially expressed genes shared between mouse and human, we were able to cluster UC patients into two subgroups, termed UC1 and UC2. We found that UC1 patients expressed higher copy of genes involved in neutrophil recruitment, activation and degranulation compared to UC2. Of note, we found that over 87% of UC1 patients failed to respond to two of the most widely-used biological therapies for UC.\n\nThis study serves as a proof of concept that cross-species comparison of gene expression profiles enables the temporal annotation of disease-associated gene expression and the stratification of patients as of yet considered molecularly undistinguishable.

immunology↗

bioRxiv2018

Skin inflammation driven by differentiation of quiescent tissue-resident ILCs into a spectrum of pathogenic effectors

Psoriasis pathology is driven by the type 3 cytokines IL-17 and Il-22, but little is understood about the dynamics that initiate alterations in tissue homeostasis. Here, we use mouse models, single-cell RNA-seq (scRNA-seq), computational inference and cell lineage mapping to show that psoriasis induction reconfigures the functionality of skin-resident ILCs to initiate disease. Tissue-resident ILCs amplified an initial IL-23 trigger and were sufficient, without circulatory ILCs, to drive pathology, indicating that ILC tissue remodeling initiates psoriasis. Skin ILCs expressed type 2 cytokines IL-5 and IL-13 in steady state, but were epigenetically poised to become ILC3-like cells. ScRNA-seq profiles of ILCs from psoriatic and naive skin of wild type (WT) and Rag1-/- mice form a dense continuum, consistent with this model of fluid ILC states. We inferred biological \"topics\" underlying these states and their relative importance in each cell with a generative model of latent Dirichlet allocation, showing that ILCs from untreated skin span a spectrum of states, including a naive/quiescent-like state and one expressing the Cd74 and Il13 but little Il5. Upon disease induction, this spectrum shifts, giving rise to a greater proportion of classical Il5- and Il13- expressing \"ILC2s\" and a new, mixed ILC2/ILC3-like subset, expressing Il13, Il17, and Il22. Using these key topics, we related the cells through transitions, revealing a quiescence-ILC2-ILC3s state trajectory. We demonstrated this plasticity in vivo, combining an IL-5 fate mouse with IL-17A and IL-22 reporters, validating the transition of IL-5-producing ILC2s to IL-22- and IL-17A-producing cells during disease initiation. Thus, steady-state skin ILCs are actively repressed and cued for a plastic, type 2 response, which, upon induction, morphs into a type 3 response that drives psoriasis. This suggests a general model where specific immune activities are primed in healthy tissue, dynamically adapt to provocations, and left unchecked, drive pathological remodeling.

immunology↗