Optimal Selection of IFN-α-Inducible Genes to Determine Type I Interferon Signature Improves the Diagnosis of Systemic Lupus Erythematosus

Profiling type I and II interferon responses reveals distinct subgroups of pediatric patients with autoinflammatory disorders

Background

Elevation of type I interferon (IFN-I) is characteristic of a group of diseases known as type I interferonopathies. Several technologies are available to monitor IFN-I, but there is no consensus on their routine use in medical laboratories.

Objective

We aimed to compare the performance of two technologies for this purpose: NanoString, which monitors messenger RNA expression of interferon-stimulated genes (ISGs), and Simoa, which quantifies IFN-α2 protein in an ultrasensitive way. We also designed a NanoString assay to monitor type II ISGs and tested its value to discriminate clinical conditions.

Methods

A total of 196 samples from patients with diseases associated or not with IFN-I pathway activation were analyzed by NanoString and Simoa.

Results

The comparison between NanoString IFN-I score and IFN-α2 Simoa revealed a r 2 coefficient of 0.55. We identified IFI27, IFI44L, and SIGLEC1 as the ISGs most closely related to IFN-α2 concentration. Nineteen samples had a positive IFN-I score but undetectable IFN-α2. These samples were also positive according to IFN-II score, pointing to IFN-II as the primary ISG inducer in corresponding patients. By measuring IFN-I and IFN-II scores in a subset of patients with systemic lupus erythematosus and systemic juvenile idiopathic arthritis, we identified two subgroups of patients in whom IFN-I and IFN-II were dominant.

Conclusion

Both IFN-α2 quantification and NanoString reliably distinguish type I interferonopathies from other diseases. Type I and II interferons induce different transcriptomic signatures in vitro and in vivo, and our results highlight the value of monitoring both IFN-I and IFN-II in interferon-related diseases.

Integrative, high-resolution analysis of single cell gene expression across experimental conditions with PARAFAC2-RISE

Effective and scalable exploration and analysis tools are vital for the extraction of insights from large-scale single-cell data. However, current techniques for modeling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that Reduction and Insight in Single-cell Exploration (RISE), an adaptation of the tensor decomposition method PARAFAC2, enables the dimensionality reduction and analysis of single-cell data across conditions. We demonstrate the benefits of RISE across two distinct examples of single-cell RNA-sequencing experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. RISE enables straightforward associations of gene variation patterns with specific patients or perturbations, while connecting each coordinated change to single cells without requiring cell type annotations. The theoretical grounding of RISE suggests a unified framework for many single-cell data modeling tasks. Thus, RISE provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

Transcriptomic Biomarkers Associated With Microbiological Etiology and Disease Severity in Childhood Pneumonia

BACKGROUND

Challenges remain in discerning microbiologic etiology and disease severity in childhood pneumonia. Defining host transcriptomic profiles during illness may facilitate improved diagnostic and prognostic approaches.

METHODS

Using whole blood RNA sequencing from 222 hospitalized children with radiographic pneumonia and 45 age-matched controls, we identified differentially expressed (DE) genes that best identified children according to detected microbial pathogens (viral only vs bacterial only and typical vs atypical bacterial [with or without [±] viral co-detection]) and an ordinal measure of phenotypic severity (moderate, severe, very severe).

RESULTS

Overall, 135 (61%) children had viral-only detections, 15 (7%) had typical bacterial detections (± viral co-detections), and 26 (12%) had atypical bacterial detections (± viral co-detections). Eleven DE genes distinguished between viral-only and bacterial-only detections. Sixteen DE genes distinguished between atypical and typical bacterial detections (± viral co-detections). Nineteen DE genes distinguished between levels of pneumonia severity, including 4 genes also identified in the viral-only versus bacterial-only model (IGHGP, PI3, CD177, RAP1GAP1) and 4 genes from the typical versus atypical bacterial model (PRSS23, IFI27, OLFM4, ABO).

CONCLUSIONS

We identified transcriptomic biomarkers associated with microbial detections and phenotypic severity in children hospitalized with pneumonia. These DE genes are promising candidates for validation and translation into diagnostic and prognostic tools.

Update on hereditary C1q deficiency: pathophysiology, clinical presentation, genotype and management

PURPOSE OF REVIEW

C1q deficiency is a rare inborn error of immunity characterized by susceptibility to severe infections and profound immune dysregulation, with a systemic lupus erythematosus-like phenotype. The management of patients with C1q deficiency is challenged by the rarity of this condition and the wide clinical variability. This review aims to emphasize the importance of a thorough immunological and clinical characterization to help guide a personalized and comprehensive approach to patients.

RECENT FINDINGS

We focus on the concept of C1q deficiency as a bridge between the monogenic form of systemic lupus erythematosus and the Mendelian type I interferonopathies. Moreover, we explore the role of new treatment strategies such as Janus-associated kinase (JAK) inhibitors and allogeneic stem cell transplantation.

SUMMARY

In this narrative review, we provide a systematic overview of C1q deficiency, starting with the description of the pathophysiological background and the variable clinical phenotype, and then exploring the different prognoses, the consequent treatment strategies and future directions.

Comparison of B-Cell Lupus and Lymphoma Using a Novel Immune Imbalance Transcriptomics Algorithm Reveals Potential Therapeutic Targets

BACKGROUND/OBJECTIVES

Systemic lupus erythematosus (lupus) and B-cell lymphoma (lymphoma) co-occur at higher-than-expected rates and primarily depend on B cells for their pathology. These observations implicate shared inflammation-related B cell molecular mechanisms as a potential cause of co-occurrence.

METHODS

We consequently implemented a novel Immune Imbalance Transcriptomics (IIT) algorithm and applied IIT to lupus, lymphoma, and healthy B cell RNA-sequencing (RNA-seq) data to find shared and contrasting mechanisms that are potential therapeutic targets.

RESULTS

We observed 7143 significantly dysregulated genes in both lupus and lymphoma. Of those genes, we found 5137 to have a significant immune imbalance, defined as a significant dysregulation by both diseases, as analyzed by IIT. Gene Ontology (GO) term and pathway enrichment of the IIT genes yielded immune-related "Neutrophil Degranulation" and "Adaptive Immune System", which validates that the IIT algorithm isolates biologically relevant genes in immunity and inflammation. We found that 344 IIT gene products are known targets for established and/or repurposed drugs. Among our results, we found 48 known and 296 novel lupus targets, along with 151 known and 193 novel lymphoma targets. Known disease drug targets in our IIT results further validate that IIT isolates genes with disease-relevant mechanisms.

CONCLUSIONS

We anticipate the IIT algorithm, together with the shared and contrasting gene mechanisms uncovered here, will contribute to the development of immune-related therapeutic options for lupus and lymphoma patients.

Evaluation of immune profiles associated with control of mycobacterial growth in systemic lupus erythematosus (SLE) patients

Tuberculosis (TB) is an infectious disease with the burden concentrated in low- and middle-income countries. Systemic lupus erythematosus (SLE) is an autoimmune disease associated with widespread inflammation that is prevalent in some TB endemic areas including East Africa and parts of Southeast Asia. SLE patients are known to be at higher risk of becoming infected with M. tb, developing TB disease. However, the immune mechanisms underlying this susceptibility are not well understood, particularly in the absence of immunosuppressive drugs. We present a pilot study in which we have evaluated intracellular cytokine responses and ex vivo ability to control mycobacterial growth using peripheral blood mononuclear cells (PBMC) collected from SLE patients before and during SLE treatment. After six months of treatment, SLE patients had the highest frequencies of CD8+ T cells, NK cells and NKT cells producing IFN-γ and/or TNF-α. This group also showed superior control of mycobacterial growth, and proinflammatory cytokine-producing NK and NKT cells correlated with mycobacterial growth inhibition at the individual patient level. These findings contribute to a better understanding of autoimmune profiles associated with control of mycobacterial growth in SLE patients, which may inform intervention strategies to reduce risk of TB disease in this population.

Multi-Omics Analysis Reveals the IFI6 Gene as a Prognostic Indicator and Therapeutic Target in Esophageal Cancer

The role of the IFI6 gene has been described in several cancers, but its involvement in esophageal cancer (ESCA) remains unclear. This study aimed to identify novel prognostic indicators for ESCA-targeted therapy by investigating IFI6's expression, epigenetic mechanisms, and signaling activities. We utilized public data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) to analyze IFI6's expression, clinical characteristics, gene function, pathways, and correlation with different immune cells in ESCA. The TIMER2.0 database was employed to assess the pan-cancer expression of IFI6, while UALCAN was used to examine its expression across tumor stages and histology subtypes. Additionally, the KEGG database helped identify related pathways. Our findings revealed 95 genes positively correlated and 15 genes negatively correlated with IFI6 in ESCA. IFI6 was over-expressed in ESCA and other cancers, impacting patient survival and showing higher expression in tumor tissues than normal tissues. IFI6 was also correlated with CD4+ T cells and B cell receptors (BCRs), both essential in immune response. GO Biological Process (GO BP) enrichment analysis indicated that IFI6 was primarily associated with the Type I interferon signaling pathway and the defense response to viruses. Intriguingly, KEGG pathway analysis demonstrated that IFI6 and its positively correlated genes in ESCA were mostly linked to the Cytosolic DNA-sensing pathway, which plays a crucial role in innate immunity and viral defense, and the RIG-I-like receptor (RLR) signaling pathway, which detects viral infections and activates immune responses. Pathways related to various viral infections were also identified. It is important to note that our study relied on online databases. Given that ESCA consists of two distinct subgroups (ESCC and EAC), most databases combine them into a single category. Future research should focus on evaluating IFI6 expression and its impact on each subgroup to gain more specific insights. In conclusion, inhibiting IFI6 using targeted therapy could be an effective strategy for treating ESCA considering its potential as a biomarker and correlation with immune cell factors.

Translational implications of newly characterized pathogenic pathways in systemic lupus erythematosus

Improved characterization of relevant pathogenic pathways in systemic lupus erythematosus (SLE) has been further delineated over the last decades. This led to the development of targeted treatments including belimumab and anifrolumab, which recently became available in clinics. Therapeutic targets in SLE encompass interferon (IFN) signaling, B-T costimulation including immune checkpoints, and increasing modalities of B lineage targeting, such as chimeric antigen receptor (CAR) T cells directed against CD19 or sequential anti-B cell targeting. Patient profiling based on characterization of underlying molecular abnormalities, often performed through comprehensive omics analyses, has recently been shown to better predict patients' treatment responses and also holds promise to unravel key molecular mechanisms driving SLE. SLE carries two key signatures, namely the IFN and B lineage/plasma cell signatures. Recent advances in SLE treatments clearly indicate that targeting innate and adaptive immunity is successful in such a complex autoimmune disease. Although those signatures may interact at the molecular level and provide the basis for the first selective treatments in SLE, it remains to be clarified whether these distinct treatments show different treatment responses among certain patient subsets. In fact, notwithstanding the remarkable amount of novel clues for innovative SLE treatment, harmonization of big data within tailored treatment strategies will be instrumental to better understand and treat this challenging autoimmune disorder. This review will provide an overview of recent improvements in SLE pathogenesis, related insights by analyses of big data and machine learning as well as technical improvements in conducting clinical trials with the ultimate goal that translational research results in improved patient outcomes.

The role and application of three IFN-related reactions in psoriasis

The pathophysiology of psoriasis is a highly complicated one. Due to the disease's specificity, it not only affects the patient's skin negatively but also manifests systemic pathological changes. These clinical symptoms seriously harm the patient's physical and mental health. IFN, a common immunomodulatory factor, has been increasingly demonstrated to have a significant role in the development of psoriatic skin disease. Psoriasis is connected with a variety of immunological responses. New targets for the therapy of autoimmune skin diseases may emerge from further research on the mechanics of the associated IFN upstream and downstream pathways. Different forms of IFNs do not behave in the same manner in psoriasis, and understanding how different types of IFNs are involved in psoriasis may provide a better notion for future research. This review focuses on the involvement of three types of IFNs in psoriasis and related therapeutic investigations, briefly describing the three IFNs' production and signaling, as well as the dual effects of IFNs on the skin. It is intended that it would serve as a model for future research.

Gene signature from cutaneous autoimmune diseases provides potential immunotherapy-relevant biomarkers in melanoma

Immune checkpoint inhibitors (ICIs) are promising agents for treating melanoma. Given that autoimmune skin diseases exhibit hyper immune reaction, investigation of immune cells from autoimmune skin disease is crucial to validate the effectiveness of ICIs in melanoma treatment. We employed multipanel markers to predict the response to immune checkpoint inhibitors by characterizing the gene expression signatures of skin immune cells in systemic lupus erythematosus (SLE), atopic dermatitis (AD), and psoriasis (PS). By analyzing single-cell RNA sequencing data from each dataset, T cell gene signatures from autoimmune skin diseases exhibit a complex immune response in tumors that responded to immunotherapy. Based on that CD86 and CD80 provide essential costimulatory signals for T cell activation, we observed that interaction of CD86 signaling has been enhanced in the T cells of patients with SLE, AD, and PS. Our analysis revealed a common increase in CD86 signals from dendritic cells (DCs) to T cells in patients with SLE, AD, and PS, confirming that dendritic cells produce pro-inflammatory cytokines to activate T cells. Thus, we hypothesize that T cell gene signatures from autoimmune skin diseases exhibit a pro-inflammatory response and have the potential to predict cancer immunotherapy. Our study demonstrated that T cell gene signatures derived from inflammatory skin diseases, particularly SLE and PS, hold promise as potential biomarkers for predicting the response to immune checkpoint blockade therapy in patients with melanoma. Our data provide an understanding of the immune-related characteristics and differential gene expression patterns in autoimmune skin diseases, which may represent promising targets for melanoma immunotherapy.

Interferonopathies masquerading as non-Mendelian autoimmune diseases: pattern recognition for early diagnosis

Type I interferonopathies are a broad category of conditions associated with increased type I interferon gene expression and include monogenic autoinflammatory diseases and non-Mendelian autoimmune diseases such as dermatomyositis and systemic lupus erythematosus. While a wide range of clinical presentations among type I interferonopathies exists, these conditions often share several clinical manifestations and implications for treatment. Presenting symptoms may mimic non-Mendelian autoimmune diseases, including vasculitis and systemic lupus erythematosus, leading to delayed or missed diagnosis. This review aims to raise awareness about the varied presentations of monogenic interferonopathies to provide early recognition and appropriate treatment to prevent irreversible damage and improve quality of life and outcomes in this unique patient population.

Identification and Clinical Correlation Analysis of IFI44 in Systemic Lupus Erythematosus Combined with Bioinformatics and Immune Infiltration Analysis

Purpose

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can cause systemic damage to multiple organs. This study aims to analyze the value and function of IFI44 in the diagnosis and pathology of SLE by bioinformatics and immune infiltration analysis.

Patients and Methods

GSE49454 and GSE65391 of SLE were obtained from the GEO dataset, and R software was employed to identify DEGs and investigate their functions. The PPI network was utilized to identify hub genes associated with SLE. CIBERSORT was used to assess differences in immune cell infiltration in SLE patients and controls. ROC curve analysis was performed to evaluate the diagnostic value of IFI44 in SLE. The expression of IFI44 in PBMCs was detected by RT-qPCR, and the correlation between IFI44 expression and SLE-related clinical indicators was analyzed.

Results

A total of 65 DEGs were identified from the GSE49454 and GSE65391 databases. Through PPI analysis, IFI44 and RSAD2 were identified as significantly aberrantly expressed in SLE patients. SLE patients and controls showed a significant difference in the proportion of immune cell infiltration. IFI44 expression was positively correlated with activated DCs, monocytes, PCs, neutrophils, and activated memory CD4+T cells, while negatively correlated with M0 and CD8+T cells. The expression of IFI44 was significantly higher in SLE patients (P<0.01), especially in male patients (P=0.0376). ROC curve analysis demonstrated that IFI44 had a high diagnostic value for SLE. Correlation analysis indicated that IFI44 expression was correlated with levels of RBC, HGB, HCT, IgA, ESR, UPRO, C3, C4, and ENA in SLE patients.

Conclusion

IFI44 may play a role in the pathogenesis of SLE by influencing the immune microenvironment of SLE patients, and thus has the potential to serve as a diagnostic marker and therapeutic target for SLE.