AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Blood molecular subtypes to guide precision treatment strategies in systemic juvenile idiopathic arthritis

BACKGROUND

Systemic juvenile idiopathic arthritis (sJIA) is the most severe subtype of JIA, with a combination of diverse clinical manifestations and a variable clinical course. A comprehensive understanding of molecular signatures at the systems level and the discovery of molecular subtypes are the initial steps toward personalized medicine in sJIA.

METHODS

A blood transcriptomic dataset was collected from patients with systemic JIA (sJIA) (n = 168), polyarticular JIA (n = 254), oligoarticular JIA (n = 96), enthesitis-related arthritis (n = 40), and healthy controls (n = 220). Gene expression profiles were filtered for differentially expressed genes and unsupervised clustering, gene set enrichment, and network-based centrality analyses. The molecular signatures of three novel sJIA subgroups (designated as C1, C2, and C3) were investigated, focusing on their distinct features and treatment responses.

RESULTS

Neutrophil degranulation and the IL-1 signaling pathway were the shared key processes for the three subgroups. Proinflammatory signals, including TNF, IL-6, TLR, and G-CSF signaling pathways, were identified with variation across the subgroups. C1 was the most inflammatory subset with a high-risk profile for macrophage activation syndrome. The C2 subset had the most activated IL-1 and IL-18 signaling pathways. C2 and C3 have higher levels of interferon-stimulated signatures. In a canakinumab-treated dataset, treatment response was correlated with IL1B expression and NF-κB signaling pathway, and neutrophil activation-associated processes were effectively suppressed in a good responder group. GSK3B and p38 MAPK inhibitors showed a significant counteracting effect on the perturbed gene expression of sJIA.

CONCLUSIONS

Neutrophil activation was the key feature in active sJIA. The three molecular subtype scheme enables the formulation of precision medicine strategies in sJIA.

Connecting the dots: Computational network analysis for disease insight and drug repurposing

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

Advancements in risk stratification and management strategies in primary cardiovascular prevention

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide, highlighting the urgent need for advancements in risk assessment and management strategies. Although significant progress has been made recently, identifying and managing apparently healthy individuals at a higher risk of developing atherosclerosis and those with subclinical atherosclerosis still poses significant challenges. Traditional risk assessment tools have limitations in accurately predicting future events and fail to encompass the complexity of the atherosclerosis trajectory. In this review, we describe novel approaches in biomarkers, genetics, advanced imaging techniques, and artificial intelligence that have emerged to address this gap. Moreover, polygenic risk scores and imaging modalities such as coronary artery calcium scoring, and coronary computed tomography angiography offer promising avenues for enhancing primary cardiovascular risk stratification and personalised intervention strategies. On the other hand, interventions aiming against atherosclerosis development or promoting plaque regression have gained attention in primary ASCVD prevention. Therefore, the potential role of drugs like statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, omega-3 fatty acids, antihypertensive agents, as well as glucose-lowering and anti-inflammatory drugs are also discussed. Since findings regarding the efficacy of these interventions vary, further research is still required to elucidate their mechanisms of action, optimize treatment regimens, and determine their long-term effects on ASCVD outcomes. In conclusion, advancements in strategies addressing atherosclerosis prevention and plaque regression present promising avenues for enhancing primary ASCVD prevention through personalised approaches tailored to individual risk profiles. Nevertheless, ongoing research efforts are imperative to refine these strategies further and maximise their effectiveness in safeguarding cardiovascular health.

Dissecting the genetic overlap between three complex phenotypes with trivariate MiXeR

Comorbidities are an increasing global health challenge. Accumulating evidence suggests overlapping genetic architectures underlying comorbid complex human traits and disorders. The bivariate causal mixture model (MiXeR) can quantify the polygenic overlap between complex phenotypes beyond global genetic correlation. Still, the pattern of genetic overlap between three distinct phenotypes, which is important to better characterize multimorbidities, has previously not been possible to quantify. Here, we present and validate the trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three phenotypes using summary statistics from genome-wide association studies (GWAS). Our simulations show that the trivariate MiXeR can reliably reconstruct different patterns of genetic overlap. We further demonstrate how the tool can be used to estimate the proportions of genetic overlap between three phenotypes using real GWAS data, providing examples of complex patterns of genetic overlap between diverse human traits and diseases that could not be deduced from bivariate analyses. This contributes to a better understanding of the etiology of complex phenotypes and the nature of their relationship, which may aid in dissecting comorbidity patterns and their biological underpinnings.