Biology subjects

Hegazy, A. N.

Publications and source records attributed to Hegazy, A. N..

7 recordsLinked to original sources

bioRxiv2024

Integrated multi-model analysis of intestinal inflammation exposes key molecular features of preclinical and clinical IBD

BackgroundInflammatory bowel disease (IBD) is a chronic inflammatory condition of the intestine with a complex and multifaceted pathogenesis. While various animal models exist to study specific disease mechanisms relevant to human IBD, a comprehensive comparative framework linking these to IBD pathophysiology is lacking. ObjectiveIn our study, we aimed at providing a framework that delineates common and unique features encountered in 13 widely used mouse models comparing them with human IBD to identify translatable pathways in model-cohort pairs. Another aim of our study was to provide an explorable resource for looking up gene and pathway level changes in mouse models assisting in hypothesis testing and minimizing animal burden abiding by the 3R principals. DesignWe employed comparative transcriptomic analyses with curated and a priori statistical correlative methods between mouse models versus established as well as own patient datasets at both bulk and single cell levels. ResultsWe identify IBD-related pathways, ontologies, and cellular processes that are translatable between mouse models and patient cohorts. Moreover, we identify, known and novel IBD-associated subcellular mechanisms and how they are recapitulated in specific mouse models. ConclusionOur findings provide a valuable resource for selecting the most appropriate experimental paradigm to model unique features of IBD pathomechanisms, allowing analysis at the tissue, cellular, and subcellular levels. What is already known on this topicPreclinical modelling of IBD is key to the discovery of pathomechanisms and the evaluation of therapeutic approaches. However, individual models do not recapitulate the complexity of the disease and comprehensive studies comparing modelling paradigms with human IBD are lacking. What this study addsOur study provides a comparative analysis of thirteen commonly used intestinal inflammation models, identifying core-conserved pathways between mouse models and IBD patient cohorts. In addition, our study shows how specific pathways involved in IBD are recapitulated in specific mouse models and introduces a web tool to analyse the models. How this study might affect research, practice or policyBy identifying conserved and discrepant pathways between specific mouse models and IBD patient cohorts, our analysis platform provides an invaluable resource for translational IBD research.

systems biology↗

bioRxiv2023

Multimodal profiling of peripheral blood identifies proliferating circulating effector CD4+ T cells as predictors for response to integrin α4β7-blocking therapy in patients with inflammatory bowel disease

Despite the success of biological therapies in inflammatory bowel disease (IBD), patient management remains challenging due to a lack of therapy response predictors. Here we prospectively sampled two cohorts of IBD patient cohorts receiving the anti-integrin 4{beta}7 antibody vedolizumab. Samples were subjected to mass cytometry, single-cell RNA sequencing, single-cell V(D)J sequencing, serum proteomics, and multidimensional flow cytometry to comprehensively assess vedolizumab-induced immunological changes in the peripheral blood and their potential associations with treatment response. Vedolizumab induced changes in the abundance of both circulating innate and adaptive immune cell compartments and modified the T cell receptor diversity of circulating gut-homing CD4+ memory T cells. Through integration of multimodal parameters and machine learning, we identify that pretreatment activated proliferating CD4+ memory T cell abundance is associated with treatment failure, independent of clinical variables, thereby providing a reliable predictive classifier with significant implications for the personalized management of IBD patients.

immunology↗

bioRxiv2022

Statistical Inference of Enhancer-Gene Networks Reveals Pivotal Role of T-bet Expression Intensity for T Helper Cell Fate

Mammalian genomes harbor many more enhancers than genes, which greatly complicates the elucidation of cell-state-specific regulatory networks. Here, we developed a computational framework for learning enhancer-based gene networks from joint data on enhancer activity and transcript abundance. Dissecting the developmental plasticity of T helper (Th) cells with this approach, we uncovered a highly connected enhancer-gene network that supports graded Th-cell differentiation states, rather than mutual exclusivity of type-1 and type-2 immunity. Machine learning identifies a small number of regulatory enhancer types as network hubs. Hub enhancers in Th1 cells integrate as inputs the expression level of the master-regulator transcription factor, T-bet, and STAT signals governed by the cytokine environment. The quantitative balance between cell-intrinsic T-bet, driving phenotypic stability, and environmental cues enabling plasticity explains the heterogeneous reprogramming capacities of individual Th1 cells differentiating during natural infections in vivo. Moreover, we provide a framework for elucidating genome-scale regulatory networks based on enhancer activity.

systems biology↗

bioRxiv2022

Plasticity and lineage commitment of individual Th1 cells are determined by stable T-bet expression quantities

T helper 1 (Th1) cell identity is defined by the expression of the lineage-defining transcription factor T-bet. Here, we examine the influence of T-bet expression heterogeneity on subset plasticity by leveraging cell sorting of distinct in vivo-differentiated Th1 cells based on their quantitative expression of T-bet and interferon-{gamma}. Heterogeneous T-bet expression states were regulated by virus-induced type-I interferons and were stably maintained even after secondary viral infection. Exposed to Th2-polarizing conditions, the sorted subpopulations exhibited graded levels of plasticity: T-bet quantities were inversely correlated with the ability to express the Th2 lineage-specifying transcription factor GATA-3 and Th2 cytokines. Reprogramed Th1 cells acquired graded, but stable mixed Th1+2 phenotypes with a hybrid epigenetic landscape. Continuous presence of T-bet in differentiated Th1 cells was essential to ensure Th1 cell stability. Thus, innate cytokine signals regulate Th1 cell plasticity via an individual cell-intrinsic rheostat to enable T cell subset adaptation to subsequent challenges. HIGHLIGHTSO_LIType-I interferons triggered by infection determine T-bet expression states in Th1 cells C_LIO_LIT-bet and IFN-{gamma} expression states indicate the plasticity of individual Th1 cells C_LIO_LIIndividual T-bet expression states and plasticity persist after secondary infection C_LIO_LIReprogramming yields stable Th1+2 phenotypes and a mixed epigenetic landscape C_LI

immunology↗

bioRxiv2022

High-resolution kinetic gene expression analysis of T helper cell differentiation reveals a STAT-dependent, unique transcriptional program in Th1/2 hybrid cells

Selective differentiation of CD4+ T helper (Th) cells into specialized subsets such as Th1 and Th2 cells is a key element of the adaptive immune system driving appropriate immune responses. Besides those canonical Th cell lineages, hybrid phenotypes such as Th1/2 cells arise in vivo, and their generation could be reproduced in vitro. While master-regulator transcription factors like T-bet for Th1 and GATA-3 for Th2 cells drive and maintain differentiation into the canonical lineages, the transcriptional architecture of hybrid phenotypes is less well understood. In particular, it has remained unclear whether a hybrid phenotype implies a mixture of the effects of several canonical lineages for each gene, or rather a bimodal behavior across genes. Th cell differentiation is a dynamic process in which the regulatory factors are modulated over time, but longitudinal studies of Th cell differentiation are sparse. Here, we present a dynamic transcriptome analysis following Th cell differentiation into Th1, Th2 and Th1/2 hybrid cells. We identified an early bifurcation point in gene expression programs, and we found that only a minority of [~]20% of Th cell-specific genes showed mixed effects from both Th1 and Th2 cells on Th1/2 hybrid cells. While most genes followed either Th1 or Th2 cell gene expression, another fraction of [~]20% of genes followed a Th1 and Th2 cell-independent transcriptional program under control of the transcription factors STAT1 and STAT4. Overall, our results emphasize the key role of high-resolution longitudinal data for the characterization of cellular phenotypes.

immunology↗

bioRxiv2020

Accurate identification and quantification of commensal microbiota bound by host immunoglobulins

BackgroundIdentifying which taxa are targeted by immunoglobulins can uncover important host-microbe interactions. Immunoglobulin binding of commensal taxa can be assayed by sorting bound bacteria from samples and using amplicon sequencing to determine their taxonomy, a technique most widely applied to study Immunoglobulin A (IgA-Seq). Previous experiments have scored taxon binding in IgA-Seq datasets by comparing abundances in the IgA bound and unbound sorted fractions. However, as these are relative abundances, such scores are influenced by the levels of the other taxa present and represent an abstract combination of these effects. Diversity in the practical approaches of prior studies also warrants benchmarking of the individual stages involved. Here, we provide a detailed description of the design strategy for an optimised IgA-Seq protocol. Combined with a novel scoring method for IgA-Seq datasets that accounts for the aforementioned effects, this platform enables accurate identification and quantification of commensal gut microbiota targeted by host immunoglobulins. ResultsUsing germ-free and Rag1-/- mice as negative controls, and a strain-specific IgA antibody as a positive control, we determine optimal reagents and fluorescence activated cell sorting (FACS) parameters for IgA-Seq. Using simulated IgA-Seq data, we show that existing IgA-Seq scoring methods are influenced by pre-sort relative abundances. This has consequences for the interpretation of case-control studies where there are inherent differences in microbiota composition between groups. We show that these effects can be addressed using a novel scoring approach based on posterior probabilities. Finally, we demonstrate the utility of both the IgA-Seq protocol and probability-based scores by examining both novel and published data from in vivo disease models. ConclusionsWe provide a detailed IgA-Seq protocol to accurately isolate IgA-bound taxa from intestinal samples. Using simulated and experimental data, we demonstrate novel probability-based scores that adjust for the compositional nature of relative abundance data to accurately quantify taxon-level IgA binding. All scoring approaches are made available in the IgAScores R package. These methods should improve the generation and interpretation of IgA-Seq datasets and could be applied to study other immunoglobulins and sample types.

bioinformatics↗

bioRxiv2017

Single cell transcriptomics of regulatory T cells reveals trajectories of tissue adaptation

Non-lymphoid tissues (NLTs) harbour a pool of adaptive immune cells, the development and phenotype of which remains largely unexplored. Here, we used single-cell RNA-seq to characterise CD4+ regulatory (Treg) and memory (Tmem) T cells in mouse skin and colon, the respective draining lymph nodes and spleen. From this data, we modelled a continuous lymphoid-to-NLT trajectory for Treg, and reconstructed the mechanisms of cell migration and NLT adaption. This revealed a shared transcriptional programme of NLT priming in both skin and colon-associated lymph nodes, followed by tissue-specific adaptation. Predicted migration kinetics were validated using a melanoma-induction model, emphasizing the relevance of key regulators and receptors, including Batf, Rora, Ccr8, Samsn1. Finally, we profiled human blood and NLT Treg and Tmem cells, identifying cross-mammalian conserved tissue signatures. In summary, we have identified molecular signals mediating NLT Treg recruitment and tissue adaptation through the combined use of computational prediction and in vivo validation.

immunology↗