Higher interferon score and normal complement levels may identify a distinct clinical subset in children with systemic lupus erythematosus

Tesser, Alessandra; Carvalho, Luciana; Sandrin-Garcia, Paula; Pin, Alessia; Pastore, Serena; Taddio, Andrea; Roberti, L; Queiróz, Rosane Gomes de Paula; Ferriani, Virgínia Paes Leme; Crovella, Sérgio; Tommasini, Alberto

doi:10.1186/s13075-020-02161-8

“…This could translate to an immune complex driven disease state in C4, where the type I interferon process was active (low lymphocyte percent and increased neutrophil involvement). In studies using independent patient groups, both changes in complement ratio (C3/C4) 21 and the categorisation of neutrophil to lymphocyte ratio (NLR) 22 , have been suggested as ways to distinguish SLE patient groups. Here, network analysis and unsupervised clustering combined both C3/C4 and NLR biomarker sets and resulted in three separate groups spanning these factors.…”

Section: Discussionmentioning

confidence: 99%

Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data

Yones

¹

,

Annett

²

,

Stoll

³

et al. 2021

Preprint

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Transcriptomic analyses are commonly used to identify differentially expressed genes between patients and controls, or within individuals across disease courses. These methods, whilst effective, cannot encompass the combinatorial effects of genes driving disease. We applied rule-based machine learning (RBML) models and rule networks (RN) to an existing paediatric Systemic Lupus Erythematosus (SLE) blood expression dataset, with the goal of developing gene networks to separate low and high disease activity (DA1 and DA3). The resultant model had an 81% accuracy to distinguish between DA1 and DA3, with unsupervised hierarchical clustering revealing additional subgroups indicative of the immune axis involved or state of disease flare. These subgroups correlated with clinical variables, suggesting that the gene sets identified may further the understanding of gene networks that act in concert to drive disease progression. This included roles for genes i) induced by interferons (IFI35 and OTOF), ii) key to SLE cell types (KLRB1 encoding CD161), or iii) with roles in autophagy and NF-κB pathway responses (CKAP4). As demonstrated here, RBML approaches have the potential to reveal novel gene patterns from within a heterogeneous disease, facilitating patient clinical and therapeutic stratification.

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“…This could translate to an immune complex driven disease state in C5, where the type I interferon process was active (low lymphocyte percent and increased neutrophil involvement). In studies using independent patient groups, both changes in complement ratio (C3/C4) 21 and the categorisation of neutrophil to lymphocyte ratio (NLR) 22 , have been suggested as ways to distinguish SLE patient groups.…”

Section: Discussionmentioning

confidence: 99%

Interpretable Machine Learning Identifies Paediatric Systemic Lupus Erythematosus Subtypes Based On Gene Expression Data

Yones

¹

,

Annett

²

,

Stoll

³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

Transcriptomic analyses are commonly used to identify differentially expressed genes between patients and controls, or within individuals across disease courses. These methods, whilst effective, cannot encompass the combinatorial effects of genes driving disease. We applied rule-based machine learning (RBML) models and rule networks (RN) to an existing paediatric Systemic Lupus Erythematosus (SLE) blood expression dataset, with the goal of developing gene networks to separate low and high disease activity (DA1 and DA3). The resultant model had an 81% accuracy to distinguish between DA1 and DA3, with unsupervised hierarchical clustering revealing additional subgroups indicative of the immune axis involved or state of disease flare. These subgroups correlated with clinical variables, suggesting that the gene sets identified may further the understanding of gene networks that act in concert to drive disease progression. This included roles for genes i) induced by interferons (IFI35 and OTOF), ii) key to SLE cell types (KLRB1 encoding CD161), or iii) with roles in autophagy and NF-κB pathway responses (CKAP4). As demonstrated here, RBML approaches have the potential to reveal novel gene patterns from within a heterogeneous disease, facilitating patient clinical and therapeutic stratification.

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“…This finding offers a very promising target for therapy and even prevention of such autoimmune diseases. Moreover, the integration between IFN signature analysis and other laboratory indices, such as complement levels, seems to help to stratify paediatric SLE patients into two groups, in which the autoimmune or autoinflammatory component of the disease are prevalent, with different response to treatment [255].…”

Section: Ifnopathiesmentioning

confidence: 99%

Monogenic Autoinflammatory Diseases: State of the Art and Future Perspectives

Donato¹,

d’Angelo²,

Breda³

et al. 2021

IJMS

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Systemic autoinflammatory diseases are a heterogeneous family of disorders characterized by a dysregulation of the innate immune system, in which sterile inflammation primarily develops through antigen-independent hyperactivation of immune pathways. In most cases, they have a strong genetic background, with mutations in single genes involved in inflammation. Therefore, they can derive from different pathogenic mechanisms at any level, such as dysregulated inflammasome-mediated production of cytokines, intracellular stress, defective regulatory pathways, altered protein folding, enhanced NF-kappaB signalling, ubiquitination disorders, interferon pathway upregulation and complement activation. Since the discover of pathogenic mutations of the pyrin-encoding gene MEFV in Familial Mediterranean Fever, more than 50 monogenic autoinflammatory diseases have been discovered thanks to the advances in genetic sequencing: the advent of new genetic analysis techniques and the discovery of genes involved in autoinflammatory diseases have allowed a better understanding of the underlying innate immunologic pathways and pathogenetic mechanisms, thus opening new perspectives in targeted therapies. Moreover, this field of research has become of great interest, since more than a hundred clinical trials for autoinflammatory diseases are currently active or recently concluded, allowing us to hope for considerable acquisitions for the next few years. General paediatricians need to be aware of the importance of this group of diseases and they should consider autoinflammatory diseases in patients with clinical hallmarks, in order to guide further examinations and refer the patient to a specialist rheumatologist. Here we resume the pathogenesis, clinical aspects and diagnosis of the most important autoinflammatory diseases in children.

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Higher interferon score and normal complement levels may identify a distinct clinical subset in children with systemic lupus erythematosus

Cited by 27 publications

References 49 publications

Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data

Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data

Interpretable Machine Learning Identifies Paediatric Systemic Lupus Erythematosus Subtypes Based On Gene Expression Data

Monogenic Autoinflammatory Diseases: State of the Art and Future Perspectives

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