Contribution of HLA and KIR Alleles to Systemic Sclerosis Susceptibility and Immunological and Clinical Disease Subtypes

HLA Association among Thai Patients with Diffuse and Limited Cutaneous Systemic Sclerosis

This study aimed to clarify the association of HLA Class I and II with dcSSc and lcSSc in Thais. HLA typing for 11 gene loci (Class I: HLA-A, B and C, and Class II [HLA-DR, DP and DQ]) was carried out using the Next Generation DNA Sequencing method (three fields) in 92 Thai patients with systemic sclerosis (55 dcSSc, 37 lcSSc) and 135 healthy controls (HCs). The distribution of HLA alleles in patients with dcSSc and lcSSc was compared. When compared with HCs, the AF of A*24:02:01, A*24:07:01, B*27:04:01 and B*27:06 showed an increasing trend in lcSSc patients without statistical significance. DRB1*15:02:01, DRB5*01:02:01, DQA1*01:01:01, DQB1*05:01:24, DPA1*02:01:01 and DPB1*13:01:01 increased significantly in dcSSc patients. DQB1*05:01:24 and DPB1*13:01:01 also increased significantly in lcSSc patients, but less significantly than in dcSSc patients. The association of DPB1*05:01:01 with lcSSc was significantly protective. HLA-A*24:02:01, B*27:06 and C*03:04:01 formed a three-locus haplotype that also constituted an eight-locus haplotype with DRB1*15:02:01, DQA1*01:01:01, DQB1*05:01:24, DPA1*02:01:01 and DPB1*13:01:01. There was a possibility that HLA Class I would play a role in the pathogenesis of lcSSc, while Class II played more of a role in the dcSSc in Thai patients.

Immunogenetics of Systemic Sclerosis

Systemic sclerosis (SSc) is a rare autoimmune connective tissue disorder characterized by massive fibrosis, vascular damage, and immune imbalance. Advances in rheumatology and immunology over the past two decades have led to a redefinition of systemic sclerosis, shifting from its initial perception as primarily a "hyperfibrotic" state towards a recognition of systemic sclerosis as an immune-mediated disease. Consequently, the search for genetic markers has transitioned from focusing on fibrotic mechanisms to exploring immune regulatory pathways. Immunogenetics, an emerging field at the intersection of immunology, molecular biology, and genetics has provided valuable insights into inherited factors that influence immunity. Data from genetic studies conducted thus far indicate that alterations in genetic messages can significantly impact disease risk and progression. While certain genetic variations may confer protective effects, others may exacerbate disease susceptibility. This paper presents a comprehensive review of the most relevant genetic changes that influence both the risk and course of systemic sclerosis. Special emphasis is placed on factors regulating the immune response, recognizing their pivotal role in the pathogenesis of the disease.

Biomarkers in the Pathogenesis, Diagnosis, and Treatment of Systemic Sclerosis

Systemic sclerosis (SSc) is a complex autoimmune disease characterized by vascular damage, vasoinstability, and decreased perfusion with ischemia, inflammation, and exuberant fibrosis of the skin and internal organs. Biomarkers are analytic indicators of the biological and disease processes within an individual that can be accurately and reproducibly measured. The field of biomarkers in SSc is complex as recent studies have implicated at least 240 pathways and dysregulated proteins in SSc pathogenesis. Anti-nuclear antibodies (ANA) are classical biomarkers with well-described clinical classifications and are present in more than 90% of SSc patients and include anti-centromere, anti-Th/To, anti-RNA polymerase III, and anti-topoisomerase I antibodies. Transforming growth factor-β (TGF-β) is central to the fibrotic process of SSc and is intimately intertwined with other biomarkers. Tyrosine kinases, interferon-1 signaling, IL-6 signaling, endogenous thrombin, peroxisome proliferator-activated receptors (PPARs), lysophosphatidic acid receptors, and amino acid metabolites are new biomarkers with the potential for developing new therapeutic agents. Other biomarkers implicated in SSc-ILD include signal transducer and activator of transcription 4 (STAT4), CD226 (DNAX accessory molecule 1), interferon regulatory factor 5 (IRF5), interleukin-1 receptor-associated kinase-1 (IRAK1), connective tissue growth factor (CTGF), pyrin domain containing 1 (NLRP1), T-cell surface glycoprotein zeta chain (CD3ζ) or CD247, the NLR family, SP-D (surfactant protein), KL-6, leucine-rich α2-glycoprotein-1 (LRG1), CCL19, genetic factors including DRB1 alleles, the interleukins (IL-1, IL-4, IL-6, IL-8, IL-10 IL-13, IL-16, IL-17, IL-18, IL-22, IL-32, and IL-35), the chemokines CCL (2,3,5,13,20,21,23), CXC (8,9,10,11,16), CX3CL1 (fractalkine), and GDF15. Adiponectin (an indicator of PPAR activation) and maresin 1 are reduced in SSc patients. A new trend has been the use of biomarker panels with combined complex multifactor analysis, machine learning, and artificial intelligence to determine disease activity and response to therapy. The present review is an update of the various biomarker molecules, pathways, and receptors involved in the pathology of SSc.

Evaluation of KIR3DL1/KIR3DS1 allelic polymorphisms in Kenyan children with endemic Burkitt lymphoma

Endemic Burkitt lymphoma (eBL) is a fast-growing germinal center B cell lymphoma, affecting 5-10 per 100,000 children annually, in the equatorial belt of Africa. We hypothesize that co-infections with Plasmodium falciparum (Pf) malaria and Epstein-Barr virus (EBV) impair host natural killer (NK) and T cell responses to tumor cells, and thus increase the risk of eBL pathogenesis. NK cell education is partially controlled by killer immunoglobulin-like receptors and variable expression of KIR3DL1 has been associated with other malignancies. Here, we investigated whether KIR3D-mediated mechanisms contribute to eBL, by testing for an association of KIR3DL1/KIR3DS1 genotypes with the disease in 108 eBL patients and 99 healthy Kenyan children. KIR3DL1 allelic typing and EBV loads were assessed by PCR. We inferred previously observed phenotypes from the genotypes. The frequencies of KIR3DL1/KIR3DL1 and KIR3DL1/KIR3DS1 did not differ significantly between cases and controls. Additionally, none of the study participants was homozygous for KIR3DS1 alleles. EBV loads did not differ by the KIR3DL1 genotypes nor were they different between eBL survivors and non-survivors. Our results suggest that eBL pathogenesis may not simply involve variations in KIR3DL1 and KIR3DS1 genotypes. However, considering the complexity of the KIR3DL1 locus, this study could not exclude a role for copy number variation in eBL pathogenesis.

Decision trees to evaluate the risk of developing multiple sclerosis

Introduction

Multiple sclerosis (MS) is a persistent neurological condition impacting the central nervous system (CNS). The precise cause of multiple sclerosis is still uncertain; however, it is thought to arise from a blend of genetic and environmental factors. MS diagnosis includes assessing medical history, conducting neurological exams, performing magnetic resonance imaging (MRI) scans, and analyzing cerebrospinal fluid. While there is currently no cure for MS, numerous treatments exist to address symptoms, decelerate disease progression, and enhance the quality of life for individuals with MS.

Methods

This paper introduces a novel machine learning (ML) algorithm utilizing decision trees to address a key objective: creating a predictive tool for assessing the likelihood of MS development. It achieves this by combining prevalent demographic risk factors, specifically gender, with crucial immunogenetic risk markers, such as the alleles responsible for human leukocyte antigen (HLA) class I molecules and the killer immunoglobulin-like receptors (KIR) genes responsible for natural killer lymphocyte receptors.

Results

The study included 619 healthy controls and 299 patients affected by MS, all of whom originated from Sardinia. The gender feature has been disregarded due to its substantial bias in influencing the classification outcomes. By solely considering immunogenetic risk markers, the algorithm demonstrates an ability to accurately identify 73.24% of MS patients and 66.07% of individuals without the disease.

Discussion

Given its notable performance, this system has the potential to support clinicians in monitoring the relatives of MS patients and identifying individuals who are at an increased risk of developing the disease.

Association of class II HLA alleles with susceptibility to develop immune-mediated diseases in Paraguayan patients

Genetic and nongenetic factors are involved in the pathogenesis of immune-mediated inflammatory diseases (IMIDs). The best-known genetic factor for susceptibility to IMIDs is the human leukocyte antigen (HLA). The aim of the present study was to evaluate the association of HLA class II genes with the risk of systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and systemic sclerosis (SSc) in the Paraguayan population. We included 254 patients with IMIDs (101 SLE, 103 RA, and 50 SSc) and 50 healthy controls. The haplotypes of five genes corresponding to HLA class II genes and their relationship to the IMIDs studied were determined. Note that 84.6% were women, with a mean age of 43.4 ± 14 years. Among the associated HLA alleles, we found the previously identified risk factors in other populations like HLA-DRB1*03:01 and HLA-DRB1*14:02 for RA, as well as new ones not previously identified, such as DPA1*02:01 for SLE and, DB1*02:01 for RA and SSc. In the genetic association analysis, already known associations have been replicated, and unpublished associations have been identified in Paraguayan patients with IMIDs. This is the first genetic association study in Paraguayan patients with IMIDs.

Autoantibodies as Biomarker and Therapeutic Target in Systemic Sclerosis

Systemic sclerosis (SSc) is a rare connective tissue disorder characterized by immune dysregulation evoking the pathophysiological triad of inflammation, fibrosis and vasculopathy. In SSc, several alterations in the B-cell compartment have been described, leading to polyclonal B-cell hyperreactivity, hypergammaglobulinemia and autoantibody production. Autoreactive B cells and autoantibodies promote and maintain pathologic mechanisms. In addition, autoantibodies in SSc are important biomarkers for predicting clinical phenotype and disease progression. Autoreactive B cells and autoantibodies represent potentially promising targets for therapeutic approaches including B-cell-targeting therapies, as well as strategies for unselective and selective removal of autoantibodies. In this review, we present mechanisms of the innate immune system leading to the generation of autoantibodies, alterations of the B-cell compartment in SSc, autoantibodies as biomarkers and autoantibody-mediated pathologies in SSc as well as potential therapeutic approaches to target these.

Recent advances in elucidating the genetic basis of systemic sclerosis

PURPOSE OF REVIEW

Systemic sclerosis (SSc) is a complex autoimmune disorder that affects the connective tissue and causes severe vascular damage and fibrosis of the skin and internal organs. There are recent advances in the field that apply novel methods to high throughput genotype information of thousands of patients with SSc and provide promising results towards the use of genomic data to help SSc diagnosis and clinical care.

RECENT FINDINGS

This review addresses the development of the first SSc genomic risk score, which can contribute to differentiating SSc patients from healthy controls and other immune-mediated diseases. Moreover, we explore the implementation of data mining strategies on the results of genome-wide association studies to highlight subtype-specific HLA class II associations and a strong association of the HLA class I locus with SSc for the first time. Finally, the combination of genomic data with transcriptomics informed drug repurposing and genetic association studies in well characterized SSc patient cohorts identified markers of severe complications of the disease.

SUMMARY

Early diagnosis and clinical management of SSc and SSc-related complications are still challenging for rheumatologists. The development of predictive models and tools using genotype data may help to finally deliver personalized clinical care and treatment for patients with SSc in the near future.