Identification of Target Genes at Juvenile Idiopathic Arthritis GWAS Loci in Human Neutrophils

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear.

METHODS

To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk.

RESULTS

We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks.

CONCLUSION

Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

In a large Juvenile Idiopathic Arthritis (JIA) cohort, concomitant celiac disease is associated with family history of autoimmunity and a more severe JIA course: a retrospective study

BACKGROUND

A higher prevalence of celiac disease (CD) has been reported in patients with juvenile idiopathic arthritis (JIA) compared to the general population. Factors related to the increased risk of co-occurrence and associated disease course have not been fully elucidated. Aims of this study were to determine the prevalence of CD in a large Southern Italian cohort of children with JIA, describe their clinical features and disease course and investigate risk factors associated with their co-occurrence.

FINDINGS

Demographic, clinical and laboratory data of all patients with JIA admitted to our Pediatric Rheumatology Unit from January 2001 to June 2019, who underwent CD screening, were retrospectively extracted from clinical charts and analyzed. Eight of 329 JIA patients were diagnosed with CD, resulting in a prevalence higher than the general Italian population (2.4% vs 0.93%, p < 0.05). Familiarity for autoimmunity was reported by 87.5% patients with JIA and CD compared to 45.8% of those without CD (p < 0.05). 87.5% patients with JIA and CD required both a conventional Disease Modifying Anti-Rheumatic Drug (DMARD) and a biological DMARD over time compared to 36.4% of those without CD (p < 0.05).

CONCLUSION

A higher CD prevalence was found in a large JIA cohort, supporting the need for CD screening in all JIA children, especially those with a family history of autoimmunity, found to be associated with the co-occurrence of the two diseases. This is clinically relevant since patients with CD and JIA more often required a step-up therapy, suggesting a more severe JIA clinical course.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

In search of pathobiological endotypes: a systems approach to early rheumatoid arthritis

INTRODUCTION

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease. Early referral and treatment are key to the effective management of the disease. This makes imperative the identification of biomarkers and of pathobiological endotypes.

AREAS COVERED

This review describes recent efforts to integrate large-scale datasets for the identification of disease endotypes for precision medicine in early, seropositive RA. We conducted a search for systems and multi-omics papers in early RA patients through to 1 January 2020. We reviewed investigations of multiple technologies such as transcriptomic, proteomic and metabolomic platforms as well as extensive clinical datasets. We outline progress made and describe some of the advantages and limitations of current computational and statistical methods.

EXPERT OPINION

The search for pathobiological endotypes in early RA is rapidly developing. While currently, studies tend to be small, reliant upon new technologies and unproven analytical tools, as the technology becomes cheaper and more reliable, and the properties of analytical tools for the integration of cross-platform biology become better understood, it seems likely that better biomarkers of disease, remission and response to individual therapies will emerge.