The regulation and differentiation of regulatory T cells and their dysfunction in autoimmune diseases.

The discovery of FOXP3+ regulatory T (Treg) cells as a distinct cell lineage with a central role in regulating immune responses provided a deeper understanding of self-tolerance. The transcription factor FOXP3 serves a key role in Treg cell lineage determination and maintenance, but is not sufficient to enable the full potential of Treg cell suppression, indicating that other factors orchestrate the fine-tuning of Treg cell function. Moreover, FOXP3-independent mechanisms have recently been shown to contribute to Treg cell dysfunction. FOXP3 mutations in humans cause lethal fulminant systemic autoinflammation (IPEX syndrome). However, it remains unclear to what degree Treg cell dysfunction is contributing to the pathophysiology of common autoimmune diseases. In this Review, we discuss the origins of Treg cells in the periphery and the multilayered mechanisms by which Treg cells are induced, as well as the FOXP3-dependent and FOXP3-independent cellular programmes that maintain the suppressive function of Treg cells in humans and mice. Further, we examine evidence for Treg cell dysfunction in the context of common autoimmune diseases such as multiple sclerosis, inflammatory bowel disease, systemic lupus erythematosus and rheumatoid arthritis.

Single-Cell Transcriptomic Analyses of Brain Parenchyma in Patients With New-Onset Refractory Status Epilepticus (NORSE).

BACKGROUND AND OBJECTIVES: New-onset refractory status epilepticus (NORSE) occurs in previously healthy children or adults, often followed by refractory epilepsy and poor outcomes. The mechanisms that transform a normal brain into an epileptic one capable of seizing for prolonged periods despite treatment remain unclear. Nonetheless, several pieces of evidence suggest that immune dysregulation could contribute to hyperexcitability and modulate NORSE sequelae. METHODS: We used single-nucleus RNA sequencing to delineate the composition and phenotypic states of the CNS of 4 patients with NORSE, to better understand the relationship between hyperexcitability and immune disturbances. We compared them with 4 patients with chronic temporal lobe epilepsy (TLE) and 2 controls with no known neurologic disorder. RESULTS: Patients with NORSE and TLE exhibited a significantly higher proportion of excitatory neurons compared with controls, with no discernible difference in inhibitory GABAergic neurons. When examining the ratio between excitatory neurons and GABAergic neurons for each patient individually, we observed a higher ratio in patients with acute NORSE or TLE compared with controls. Furthermore, a negative correlation was found between the ratio of excitatory to GABAergic neurons and the proportion of GABAergic neurons. The ratio between excitatory neurons and GABAergic neurons correlated with the proportion of resident or infiltrating macrophages, suggesting the influence of microglial reactivity on neuronal excitability. Both patients with NORSE and TLE exhibited increased expression of genes associated with microglia activation, phagocytic activity, and NLRP3 inflammasome activation. However, patients with NORSE had decreased expression of genes related to the downregulation of the inflammatory response, potentially explaining the severity of their presentation. Microglial activation in patients with NORSE also correlated with astrocyte reactivity, possibly leading to higher degrees of demyelination. DISCUSSION: Our study sheds light on the complex cellular dynamics in NORSE, revealing the potential roles of microglia, infiltrating macrophages, and astrocytes in hyperexcitability and demyelination, offering potential avenues for future research targeting the identified pathways.

Neutrophils insert elastase into hepatocytes to regulate calcium signaling in alcohol-associated hepatitis.

Neutrophil infiltration occurs in a variety of liver diseases, but it is unclear how neutrophils and hepatocytes interact. Neutrophils generally use granule proteases to digest phagocytosed bacteria and foreign substances or neutralize them in neutrophil extracellular traps. In certain pathological states, granule proteases play a destructive role against the host as well. More recently, nondestructive actions of neutrophil granule proteins have been reported, such as modulation of tissue remodeling and metabolism. Here, we report a completely different mechanism by which neutrophils act nondestructively, by inserting granules directly into hepatocytes. Specifically, elastase-containing granules were transferred to hepatocytes where elastase selectively degraded intracellular calcium channels to reduce cell proliferation without cytotoxicity. In response, hepatocytes increased expression of Serpin E2 and A3, which inhibited elastase activity. Elastase insertion was seen in patient specimens of alcohol-associated hepatitis, and the relationship between elastase-mediated ITPR2 degradation and reduced cell proliferation was confirmed in mouse models. Moreover, neutrophils from patients with alcohol-associated hepatitis were more prone to degranulation and more potent in reducing calcium channel expression than neutrophils from healthy individuals. This nondestructive and reversible action on hepatocytes defines a previously unrecognized role for neutrophils in the transient regulation of epithelial calcium signaling mechanisms.

Meta-analysis identifies common gut microbiota associated with multiple sclerosis.

BACKGROUND: Previous studies have identified a diverse group of microbial taxa that differ between patients with multiple sclerosis (MS) and the healthy population. However, interpreting findings on MS-associated microbiota is challenging, as there is no true consensus. It is unclear whether there is gut microbiota commonly altered in MS across studies. METHODS: To answer this, we performed a meta-analysis based on the 16S rRNA gene sequencing data from seven geographically and technically diverse studies comprising a total of 524 adult subjects (257 MS and 267 healthy controls). Analysis was conducted for each individual study after reprocessing the data and also by combining all data together. The blocked Wilcoxon rank-sum test and linear mixed-effects regression were used to identify differences in microbial composition and diversity between MS and healthy controls. Network analysis was conducted to identify bacterial correlations. A leave-one-out sensitivity analysis was performed to ensure the robustness of the findings. RESULTS: The microbiome community structure was significantly different between studies. Re-analysis of data from individual studies revealed a lower relative abundance of Prevotella in MS across studies, compared to controls. Meta-analysis found that although alpha and beta diversity did not differ between MS and controls, a higher abundance of Actinomyces and a lower abundance of Faecalibacterium were reproducibly associated with MS. Additionally, network analysis revealed that the recognized negative Bacteroides-Prevotella correlation in controls was disrupted in patients with MS. CONCLUSIONS: Our meta-analysis identified common gut microbiota associated with MS across geographically and technically diverse studies.

Genetic mapping across autoimmune diseases reveals shared associations and mechanisms.

Autoimmune and inflammatory diseases are polygenic disorders of the immune system. Many genomic loci harbor risk alleles for several diseases, but the limited resolution of genetic mapping prevents determining whether the same allele is responsible, indicating a shared underlying mechanism. Here, using a collection of 129,058 cases and controls across 6 diseases, we show that ~40% of overlapping associations are due to the same allele. We improve fine-mapping resolution for shared alleles twofold by combining cases and controls across diseases, allowing us to identify more expression quantitative trait loci driven by the shared alleles. The patterns indicate widespread sharing of pathogenic mechanisms but not a single global autoimmune mechanism. Our approach can be applied to any set of traits and is particularly valuable as sample collections become depleted.

Systems Analysis of Immune Changes after B-cell Depletion in Autoimmune Multiple Sclerosis.

Multiple sclerosis (MS) is a complex genetically mediated autoimmune disease of the central nervous system where anti-CD20-mediated B cell depletion is remarkably effective in the treatment of early disease. While previous studies investigated the effect of B cell depletion on select immune cell subsets using flow cytometry-based methods, the therapeutic impact on patient immune landscape is unknown. In this study, we explored how a therapy-driven " in vivo perturbation " modulates the diverse immune landscape by measuring transcriptomic granularity with single-cell RNA sequencing (scRNAseq). We demonstrate that B cell depletion leads to cell type-specific changes in the abundance and function of CSF macrophages and peripheral blood monocytes. Specifically, a CSF-specific macrophage population with an anti-inflammatory transcriptomic signature and peripheral CD16 + monocytes increased in frequency post-B cell depletion. In addition, we observed increases in TNFα messenger RNA and protein in monocytes post-B cell depletion, consistent with the finding that anti-TNFα treatment exacerbates autoimmune activity in MS. In parallel, B cell depletion also induced changes in peripheral CD4 + T cell populations, including increases in the frequency of TIGIT + regulatory T cells and marked decreases in the frequency of myelin peptide loaded-tetramer binding CD4 + T cells. Collectively, this study provides an exhaustive transcriptomic map of immunological changes, revealing different mechanisms of action contributing to the high efficacy in B cell depletion treatment of MS.

Emerging Cerebrospinal Fluid Biomarkers of Disease Activity and Progression in Multiple Sclerosis.

Importance: Biomarkers distinguishing nonrelapsing progressive disease biology from relapsing biology in multiple sclerosis (MS) are lacking. Cerebrospinal fluid (CSF) is an accessible fluid that most closely reflects central nervous system biology. Objective: To identify CSF biological measures associated with progressive MS pathobiology. Design, Setting, and Participants: This cohort study assessed data from 2 prospective MS cohorts: a test cohort provided serial CSF, clinical, and imaging assessments in a multicenter study of patients with relapsing MS (RMS) or primary progressive MS (PPMS) who were initiating anti-CD20 treatment (recruitment: 2016-2018; analysis: 2020-2023). A single-site confirmation cohort was used to assess CSF at baseline and long-term (>10 year) clinical follow-up (analysis: 2022-2023). Exposures: Test-cohort participants initiated standard-of-care ocrelizumab treatment. Confirmation-cohort participants were untreated or received standard-of-care disease-modifying MS therapies. Main Outcomes and Measures: Twenty-five CSF markers, including neurofilament light chain, neurofilament heavy chain, and glial fibrillary acid protein (GFAP); 24-week confirmed disability progression (CDP24); and brain magnetic resonance imaging measures reflecting focal injury, tissue loss, and progressive biology (slowly expanding lesions [SELs]). Results: The test cohort (n = 131) included 100 patients with RMS (mean [SD] age, 36.6 [10.4] years; 68 [68%] female and 32 [32%] male; Expanded Disability Status Scale [EDSS] score, 0-5.5), and 31 patients with PPMS (mean [SD] age, 44.9 [7.4] years; 15 [48%] female and 16 [52%] male; EDSS score, 3.0-6.5). The confirmation cohort (n = 68) included 41 patients with RMS and 27 with PPMS enrolled at diagnosis (age, 40 years [range, 20-61 years]; 47 [69%] female and 21 [31%] male). In the test cohort, GFAP was correlated with SEL count (r = 0.33), greater proportion of T2 lesion volume from SELs (r = 0.24), and lower T1-weighted intensity within SELs (r = -0.33) but not with acute inflammatory measures. Neurofilament heavy chain was correlated with SEL count (r = 0.25) and lower T1-weighted intensity within SELs (r = -0.28). Immune markers correlated with measures of acute inflammation and, unlike GFAP, were impacted by anti-CD20. In the confirmation cohort, higher baseline CSF GFAP levels were associated with long-term CDP24 (hazard ratio, 2.1; 95% CI, 1.3-3.4; P = .002). Conclusions and Relevance: In this study, activated glial markers (in particular GFAP) and neurofilament heavy chain were associated specifically with nonrelapsing progressive disease outcomes (independent of acute inflammatory activity). Elevated CSF GFAP was associated with long-term MS disease progression.

Features of acute COVID-19 associated with post-acute sequelae of SARS-CoV-2 phenotypes: results from the IMPACC study.

Post-acute sequelae of SARS-CoV-2 (PASC) is a significant public health concern. We describe Patient Reported Outcomes (PROs) on 590 participants prospectively assessed from hospital admission for COVID-19 through one year after discharge. Modeling identified 4 PRO clusters based on reported deficits (minimal, physical, mental/cognitive, and multidomain), supporting heterogenous clinical presentations in PASC, with sub-phenotypes associated with female sex and distinctive comorbidities. During the acute phase of disease, a higher respiratory SARS-CoV-2 viral burden and lower Receptor Binding Domain and Spike antibody titers were associated with both the physical predominant and the multidomain deficit clusters. A lower frequency of circulating B lymphocytes by mass cytometry (CyTOF) was observed in the multidomain deficit cluster. Circulating fibroblast growth factor 21 (FGF21) was significantly elevated in the mental/cognitive predominant and the multidomain clusters. Future efforts to link PASC to acute anti-viral host responses may help to better target treatment and prevention of PASC.

scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles.

Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.