Biology subjects

Lewis, M. J.

Publications and source records attributed to Lewis, M. J..

13 recordsLinked to original sources

bioRxiv2026

Characterisation of Peripheral Blood B Cell Receptor Repertoire in Severe Eosinophilic Asthma and EGPA

BackgroundSevere eosinophilic asthma (SEA), eosinophilic granulomatosis with polyangiitis (EGPA) and nasal polyposis (NP) are immune-mediated diseases characterised by eosinophilic inflammation. However, there is also increasing interest in the potential pathological roles of autoantibodies in these diseases. Understanding their B cell receptor (BCR) repertoires may provide valuable insights into disease mechanisms, and potential role of B cells in their pathology. MethodsWe conducted BCR repertoire sequencing using peripheral blood from 43 patients, comprising SEA with nasal polyps (SEA+NP), SEA without nasal polyps (SEA-NP), and EGPA, along with 16 healthy controls (HCs). ResultsCompared to HCs, patients with EGPA exhibited increased relative proportions of IgA1, IgG1, IgG2, and IgG4 subclasses. Similarly, SEA-NP patients demonstrated significantly high proportion of IgG2 sequences. Notably, the IgG4 subclass was significantly elevated across all patient groups compared to HCs. Patients receiving anti-IL-5/5R biologic treatments showed increased relative proportions of IgA2 and IgG2 subclasses compared to untreated patients. Some variation across participant groups in mean somatic hypermutation and mutation frequency was evident. 1,508 clones shared across patients, but not healthy controls, were evident though the majority showed low clonal expansion. Nevertheless, a few shared clones did show either high prevalence across patients and/or higher clonal expansion. ConclusionChanges in BCR repertoires in SEA/EGPA are consistent with a pattern of a more mature B cell component in the periphery and with the T2 inflammatory response observed in SEA and EGPA. BCR clonotypes shared across patients were evident, however, whether such clonotypes are pathological in SEA/EGPA requires further investigation.

immunology↗

bioRxiv2026

cellGeometry: ultra-fast single-cell deconvolution of bulk RNA-Seq using a geometric solution

Single-cell analysis has rapidly expanded to produce cell atlases encompassing all human tissues. However, computational methods to deconvolute bulk samples using single-cell reference data have failed to keep pace with the increasing data size. Here we present cellGeometry, which uses non-negative geometric deconvolution (NGD), an intuitive vector projection method featuring non-negative matrix regularisation. Using matrix operations, cellGeometry scales to massive datasets and is ultrafast. Benchmarked using simulations from single-cell RNA-Seq datasets with >3 million cells, cellGeometry is more accurate than existing methods and more robust against noise simulating different sequencing chemistries. It identifies outlying residual genes which may unveil pathogenic changes in gene expression and the presence of cell types absent from the reference. cellGeometrys flexible architecture allows merging of single-cell reference signatures to expand the range of cell types being deconvoluted. Validated against real bulk RNA blood and tissue samples, cellGeometry produces more accurate and realistic results.

bioinformatics↗

bioRxiv2026

Molecular and spatial profiling identifies immune endotypes for the stratification of OA patients

Osteoarthritis (OA) is a prevalent and heterogeneous joint disease in which synovial inflammation drives structural progression and pain. Despite the recognized heterogeneity of OA, the cellular and molecular organization of synovial tissue remains poorly characterized and defining distinct histological and immune endotypes could guide precision medicine and therapeutic targeting. We show that histologically defined synovial pathotypes are conserved across independent cohorts and correspond to distinct molecular immune endotypes. Integration of bulk and spatial transcriptomics with proteomics revealed niche-specific gene and protein signatures, reflecting the anatomical and functional diversity of OA synovium. The lympho-myeloid pathotype was characterized by mature ectopic lymphoid structures containing CD21+CD23+ follicular dendritic cells, spatially organized T and B cell zones, and clonally expanded T and B cells with shared immune cell receptor motifs, consistent with local adaptive immune activity correlating with radiological joint damage. These findings highlight how immune organization and cellular composition shape OA pathogenesis and provide a framework for endotype-guided stratification and therapeutic targeting.

immunology↗

bioRxiv2025

Discrete cytokine signaling networks instruct distinctsynovial pathotypes in inflammatory arthritis

Patients with rheumatoid arthritis (RA) display distinct patterns of synovitis. To define the inflammatory mechanisms driving this heterogeneity, we analyzed the inflamed synovium of wild-type (WT), Il6ra-/-, and Il27ra-/- mice with antigen-induced arthritis (AIA). Remarkably, each strain developed a joint pathology mirroring a major RA synovial pathotype: myeloid-rich (WT), fibroblast-rich/pauci-immune (Il6ra-/-), and lymphoid-rich (Il27ra-/-) synovitis. Histology confirmed minimal immune infiltration in Il6ra-/- joints, while WT and Il27ra-/- mice exhibited prominent immune involvement, including organized synovial lymphoid-like aggregates in Il27ra-/- mice. Transcriptomic and epigenomic profiling revealed both shared and distinct regulatory programs among genotypes. Il6ra-/- mice showed increased WNT, DKK, and AMPK signaling associated with fibroblast, chondrocyte, and osteoclast activation (e.g., Adamts19, Dkk1, Ecm1). Consistent with synovial ectopic lymphoid-like structures, Il27ra-/- mice showed enrichment of lymphocyte activation (e.g., Il17a, Il22, Bhlhe40). WT mice exhibited hallmarks of MAP kinase activation. These molecular signatures parallel those of fibroblast-, lymphoid-, and myeloid-rich synovitis in RA. Defining a STAT1-STAT3 regulatory interplay influencing transcriptional decisions in WT and Il27ra-/- mice, our findings offer insights into cytokine-driven disease heterogeneity. Together, these results establish a framework for mechanism-based classification of synovitis and introduce new mouse models to study the molecular drivers of synovial pathotypes and treatment response.

pathology↗

bioRxiv2025

Interleukin-36 upregulates type-I interferon responses in systemic lupus erythematosus by promoting the accumulation of self-nucleic acids

IntroductionSeveral studies have reported an up-regulation of interleukin (IL)-36 in the serum of patients with systemic lupus erythematosus (SLE). Here, we sought to define the mechanisms whereby IL-36 may contribute to the over-activation of type I Interferon (IFN) responses observed in SLE. MethodsWe carried out single-cell (sc)RNA-seq in healthy peripheral blood mononuclear cells treated with IL-36 (n=5 donors). We compared the genes and transcriptional networks that were induced by IL-36 with those that were upregulated in a published SLE scRNA-seq dataset (n=33 cases and 11 controls). In follow-up studies, we validated the effects of IL-36 on monocytes by real-time PCR (n=9 donors) and flow-cytometry (n=6). ResultsClassical monocytes were the immune population most affected by IL-36 treatment (n=203 Differentially Expressed Genes). In these cells, IL-36 upregulated transcriptional networks (regulons) driven by IRF7, a key activator of type I IFN responses. A similar upregulation of IRF7 regulons was observed in the monocytes of SLE cases, where measurements of IL-36 and IRF7 activity were significantly correlated (r=0.35, P=0.02). Experimental follow-up studies in human monocytes showed that IL-36 downregulates multiple RNAse genes (RNASE1, RNASE6, RNASET2). IL-36 treatment of monocytes also increased the percentage of apoptotic cells (45% vs 37% in untreated cells; P=0.001), which are a critical source of self-nucleic acids. ConclusionWe find that IL-36 promotes monocyte apoptosis while downregulating self-nucleic acid clearance. Thus, IL-36 contributes to the accumulation of self-nucleic acids, a key driver of type I IFN responses in SLE.

immunology↗

bioRxiv2025

multiDEGGs: a multi-omic differential network analysis package for biomarker discovery and predictive modeling

Modern clinical trials increasingly leverage high-throughput omic data for patient stratification and biomarker discovery. While traditional differential gene expression analysis disregards the networked nature of molecular entities and produces extensive gene lists with limited interpretability, differential network analysis has emerged as a crucial complementary analysis for comparative studies. Here we present multiDEGGs, a CRAN R package that enables differential network analysis in multi-omic scenarios. multiDEGGs uses a multi-layer graph framework to model omic data by leveraging an internal network of over 10 000 literature-validated biological interactions. For each data type, differential networks are generated, and the statistical significance of each link (p-values or adjusted p-values) is evaluated through robust linear regression with interaction terms. These networks are then integrated into a comprehensive visualisation that allows interactive exploration of cross-omic patterns. Beyond network visualization and exploration, multiDEGGs extends its utility to predictive modelling applications. The package facilitates seamless integration into cross-validation machine learning pipelines, serving as feature selection and augmentation tool. We validated multiDEGGs using two cohorts of rheumatoid arthritis patients who underwent tocilizumab and rituximab therapy, respectively. For each treatment group, multi-layer differential interactions were identified, and seven machine learning models were trained to predict treatment resistance using synovial RNA-seq data. We systematically compared multiDEGGs against five traditional feature selection methods. On average, AUC values obtained with multiDEGGs showed an improvement of 0.10 compared to conventional filters. KEY POINTSO_LITraditional gene expression analysis leaves researchers with hundreds of significant genes but no clear biological story. The multiDEGGs CRAN package shifts the focus: instead of asking which genes change, it asks which gene relationships change. C_LIO_LIIt can be used with single or multi-omic data: differential networks are calculated separately for each data type, with results integrated into a comprehensive, interactive view. C_LIO_LImultiDEGGs can be combined with the nestedcv CRAN package (nested cross-validation) to serve as feature selection and augmentation tool. C_LIO_LIIn comparative evaluations, machine learning models trained with multiDEGGs-selected features showed AUC improvements of 0.10 compared to other feature selection methods. C_LI

bioinformatics↗

bioRxiv2025

Spatial patterning of fibroblast TGFβ signaling underlies treatment resistance in rheumatoid arthritis

Treatment-refractory rheumatoid arthritis (RA) is a major unmet need, and the mechanisms driving treatment resistance are poorly understood. To identify molecular determinants of RA non-remission, we performed spatial transcriptomic profiling on pre- and post-treatment synovial tissue biopsies from treatment naive patients who received conventional DMARDs or adalimumab for 6 months. In the baseline biopsies of non-remission patients, we identified significant expansion of fibrogenic fibroblasts marked by high expression of COMP, a fibrosis-associated extracellular matrix protein. COMPhi fibroblasts localized to perivascular niches that, unexpectedly, served as transcriptional hubs for TGF{beta} activity. We identified endothelial-derived Notch signaling as an upstream regulator of fibroblast TGF{beta} signaling via its dual role in driving TGF{beta} isoform expression and suppressing TGF{beta} receptors, generating a proximal-distal gradient of TGF{beta} activity. Further, disruption of steady-state Notch signaling in vitro enabled fibrogenic fibroblast activation. Analysis of post-treatment biopsies revealed marked expansion of COMPhi fibroblasts in non-remission RA patients, despite evidence of successful immune cell depletion, suggesting a spatiotemporal process of fibrogenic remodeling linked to treatment resistance. Collectively, our data implicates targeting of TGF{beta} signaling to prevent exuberant synovial tissue fibrosis as a potential therapeutic strategy for refractory RA.

immunology↗

bioRxiv2025

The acidic N-terminus of HHARI and neddylation are essential for the activation and maintenance of RIG-I-mediated type I interferon response

Human homolog of Ariadne (HHARI) is a RING-between-RING ubiquitin E3 ligase which interacts with cullin-RING E3 ligase (CRL) complexes. HHARI has been implicated in the type-I interferon anti-viral response. However, how HHARI drives interferon signalling is not fully understood and the function of the unique, highly conserved acidic N-terminal domain of the protein is unknown. Here, we show that HHARI stimulates interferon-{beta} secretion and autocrine type-I interferon signalling by directly targeting the viral RNA sensor RIG-I (Retinoic Acid-Inducible Gene I) in a neddylation-dependent manner. This suggests that neddylation inhibition could be used to treat interferonopathies and related diseases. Truncated HHARI containing only the N-terminal acidic/UBA-like domains retained the ability to induce interferon signalling in a neddylation-dependent mechanism. HHARI-mediated interferon-{beta} secretion was enhanced by overexpression of cullins 1-5. The N-terminal acidic/UBA-like domain of HHARI is critical for RIG-I activation and interferon signalling, as removal of these domains inactivated the pro-interferon phenotype. We propose a mechanism by which the N-terminus of HHARI interacts with all neddylated cullins leading to endogenous HHARI activation. This suggests a model in which the N-terminus of HHARI unlocks and activates neddylated cullins, which in turn are required for activation of HHARI itself. As cullins typically form modular cullin-RING ligase super-assemblies our findings imply that the HHARI N-terminus domain is a critical regulator of the versatile CRL system, which, through widespread protein ubiquitylation, controls many eukaryotic cell functions.

immunology↗

bioRxiv2024

IL-6 and IL-27 negatively regulate CRTAM-expressing CD4+ T-cells associated with lymphoid-driven synovitis.

ABSTRACT-Joint pathology in rheumatoid arthritis is heterogeneous, with histology providing evidence of fibroblast-driven, myeloid-driven, and lymphoid-driven synovitis. However, the immuno-modulatory pathways underlying their development remain unclear. Profiling synovial tissues from rheumatoid arthritis patients and mice with antigen-induced arthritis, we identified a subset of synovial infiltrating CD4+ T-cells expressing CRTAM (class-I MHC-restricted T-cell-associated molecule). In human synovial biopsies, CRTAM correlated with the expression of effector cytokines (IL21, IFNG), chemokine receptors (CXCR3, CXCR4, CCR5), granzymes (GZMA, GZMB, GZMK), and regulatory factors (TIGIT, EOMES, BATF) linked with T-cell-mediated immunity. Studies of antigen-induced arthritis showed that CRTAM+CD4+ T-cells accumulate in the inflamed synovium following disease onset. CRTAM+CD4+ T-cells were particularly abundant in synovial tissue from Il27ra-/- mice displaying ectopic lymphoid-like structures. CADM1 (cell adhesion molecule-1), the endogenous ligand for CRTAM, was also expressed in human synovitis and synovial tissues from wild-type, Il6ra-/-, and Il27ra-/- mice with antigen-induced arthritis. Cells expressing human CADM1 included synovial fibroblasts and subsets of monocytic and CD19+ cells. Considering the ex vivo regulation of CRTAM, we identified that activation of naive CD4+ T-cell increased CRTAM expression. This induction was blocked by IL-6 and IL-27, with further studies identifying a role for STAT3 in controlling the CRTAM transcriptional repressor, ZEB1. These results provide insights into the cytokine control of CRTAM on CD4+ T-cells and support the involvement of CRTAM+CD4+ T-cells in lymphoid-driven synovitis.

immunology↗

bioRxiv2024

Analgesic targets identified in mouse sensory neuron somata and terminal pain translatomes

The relationship between transcription and protein expression is complex. We identified polysome-associated RNA transcripts in the somata and central terminals of mouse sensory neurons in control, painful (+ Nerve Growth Factor (NGF)) and pain-free conditions (Nav1.7 null mice). The majority (98%) of translated transcripts are shared between male and female mice in both the somata and terminals. Some transcripts are highly enriched in the somata or terminals. Changes in the translatome in painful and pain-free conditions include novel and known regulators of pain pathways. Antisense knockdown of selected somatic and terminal polysome-associated transcripts that correlate with pain states diminished pain behaviour. Terminal-enriched transcripts encoding synaptic proteins (e.g. Synaptotagmin), non-coding RNAs, transcription factors (e.g. Znf431), proteins associated with trans-synaptic trafficking (HoxC9), GABA generating enzymes (Gad1 and Gad2) and neuropeptides (Penk). Thus, central terminal translation may well be a significant regulatory locus for peripheral input from sensory neurons.

neuroscience↗

bioRxiv2022

BBmix: a Bayesian Beta-Binomial mixture model for accurate genotyping from RNA-sequencing

MotivationWhile many pipelines have been developed for calling genotypes using RNA-sequencing data, they all have adapted DNA genotype callers that do not model biases specific to RNA-sequencing such as reference panel bias or allele specific expression. ResultsHere, we present BBmix, a Bayesian Beta-Binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than one hour is sufficient to call genotypes in a large number of samples. AvailabilityWe implemented BBmix as an R package that is available for free under a GPL-2 licence at https://gitlab.com/evigorito/bbmix and accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.

genomics↗

bioRxiv2021

Harnessing gene-gene interactions via an RNA-Sequencing network analysis framework improves precision medicine prediction in rheumatoid arthritis

The study of gene-gene interactions in RNA-Sequencing (RNA-Seq) data has traditionally been hard owing the large number of genes detectable by Next-Generation Sequencing (NGS). However, differential gene-gene pairs can inform our understanding of biological processes and yield improved prediction models. Here, we utilised four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We then extracted specific gene-gene interaction networks in synovial RNA-Seq to characterise histologically-defined pathotypes in early rheumatoid arthritis patients. Specific gene-gene networks were also leveraged to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). We statistically evaluated the differential interactions identified within each network using robust linear regression models, and the ability to predict response was evaluated by receiver operating characteristic (ROC) curve analysis. The analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. In conclusions, we demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

genomics↗

bioRxiv2017

De Novo Mutations Implicate Novel Genes With Burden Of Rare Variants In Systemic Lupus Erythematosus

The omnigenic model of complex diseases stipulates that the majority of the heritability will be explained by the effects of common variation on genes in the periphery of core disease pathways. Rare variant associations, expected to explain far less of the heritability, may be enriched in core disease genes and thus will be instrumental in the understanding of complex disease pathogenesis and their potential therapeutic targets. Here, using complementary whole-exome sequencing (WES), high-density imputation, and in vitro cellular assays, we identify three candidate core genes in the pathogenesis of Systemic Lupus Erythematosus (SLE). Using extreme-phenotype sampling, we sequenced the exomes of 30 SLE parent-affected-offspring trios and identified 14 genes with missense de novo mutations (DNM), none of which are within the >80 SLE susceptibility loci implicated through genome-wide association studies (GWAS). In a follow-up cohort of 10,995 individuals of matched European ancestry, we imputed genotype data to the density of the combined UK10K-1000 genomes Phase III reference panel across the 14 candidate genes. We identify a burden of rare variants across PRKCD associated with SLE risk (P=0.0028), and across DNMT3A associated with two severe disease prognosis sub-phenotypes (P=0.0005 and P=0.0033). Both genes are functional candidates and significantly constrained against missense mutations in gene-level analyses, along with C1QTNF4. We further characterise the TNF-dependent functions of candidate gene C1QTNF4 on NF-{kappa}B activation and apoptosis, which are inhibited by the p.His198Gln DNM. Our results support extreme-phenotype sampling and DNM gene discovery to aid the search for core disease genes implicated through rare variation.\n\nSignificance StatementRare variants, present in <1% in population, are expected to explain little of the heritability of complex diseases, such as Systemic Lupus Erythematosus (SLE), yet are likely to identify core genes crucial to disease mechanisms. Their rarity, however, limits the power to show their statistical association with disease. Through sequencing the exomes of SLE patients and their parents, we identified non-inherited de novo mutations in 14 genes and hypothesised that these are prime candidates for harbouring additional disease-associated rare variants. We demonstrate that two of these genes also carry a significant excess of rare variants in an independent, large cohort of SLE patients. Our findings will influence future study designs in the search for the missing heritability of complex diseases.

genetics↗