Detecting pathogenic variants in autoimmune diseases using high-throughput sequencing

A new genetic diagnosis strategy for paroxysmal kinesigenic dyskinesia: Targeted high-throughput detection of PRRT2 gene c.649 locus

BACKGROUND

Paroxysmal kinesigenic dyskinesia (PKD) is the most prevalent kind type of paroxysmal Dyskinesia, characterized by recurrent and transient episodes of involuntary movements. Most PKD cases were attributed to the proline-rich transmembrane protein 2 (PRRT2) gene, in which the c.649 region is a hotspot for known mutations. Even though some patients with PKD have been genetically diagnosed using whole-exome sequencing (WES) and Sanger sequencing, there are still cases of missed diagnoses due to the limitations of sequencing technology and analytic methods on throughput.

METHODS

Patients meeting the diagnosis criteria of PKD with negative results of PRRT2-Sanger sequencing and WES were included in this study. Mutation screening and targeted high-throughput sequencing were performed to analyze and verify the sequencing results of the potential mutations.

RESULTS

Six patients with PKD with high mutation ratios of c.649dupC were screened using our targeted high-throughput sequencing from 26 PKD patients with negative results of PRRT2-Sanger sequencing and WES (frequency = 23.1%), which compensated for the comparatively shallow sequencing depth and statistical flaws in this region. Compared with the local normal population and other patients with PKD, the mutation ratios of c.649dupC of these six patients with PKD were much higher and also had truncated protein structures and differentially altered mRNA expression.

CONCLUSION

Based on the above studies, we emphasize the routine targeted high-throughput sequencing of the c.649 site in the PRRT2 gene in so-called genetic-testing-negative patients with PKD, and manually calculate the deletion and duplication mutations depth and ratios to lower the rate of clinical misdiagnosis.

Genetic interrogation for sequence and copy number variants in systemic lupus erythematosus

Early-onset systemic lupus erythematosus presents with a more severe disease and is associated with a greater genetic burden, especially in patients from Black, Asian or Hispanic ancestries. Next-generation sequencing techniques, notably whole exome sequencing, have been extensively used in genomic interrogation studies to identify causal disease variants that are increasingly implicated in the development of autoimmunity. This Review discusses the known casual variants of polygenic and monogenic systemic lupus erythematosus and its implications under certain genetic disparities while suggesting an age-based sequencing strategy to aid in clinical diagnostics and patient management for improved patient care.

Artificial intelligence and database for NGS-based diagnosis in rare disease

Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases.

OWL2: a molecular beacon-based nanostructure for highly selective detection of single-nucleotide variations in folded nucleic acids

Hybridization probes have been used in the detection of specific nucleic acids for the last 50 years. Despite the extensive efforts and the great significance, the challenges of the commonly used probes include (1) low selectivity in detecting single nucleotide variations (SNV) at low (e.g. room or 37 °C) temperatures; (2) low affinity in binding folded nucleic acids, and (3) the cost of fluorescent probes. Here we introduce a multicomponent hybridization probe, called OWL2 sensor, which addresses all three issues. The OWL2 sensor uses two analyte binding arms to tightly bind and unwind folded analytes, and two sequence-specific strands that bind both the analyte and a universal molecular beacon (UMB) probe to form fluorescent 'OWL' structure. The OWL2 sensor was able to differentiate single base mismatches in folded analytes in the temperature range of 5-38 °C. The design is cost-efficient since the same UMB probe can be used for detecting any analyte sequence.

Bioinformatics analysis of gene expression profile and functional analysis in periodontitis and Parkinson’s disease

Periodontitis is a chronic inflammatory disease inextricably linked to both the innate and acquired immune systems of the body. Parkinson’s disease (PD) is a neurodegenerative disease caused by immune system dysfunction. Although recent studies suggest that a clinical relationship exists between PD and periodontitis, the pathogenesis of this relationship is unclear. Therefore, in the present study, we obtained datasets of periodontitis and PD from the Gene Expression Omnibus (GEO) database and extracted 785 differentially expressed genes (DEGs), including 15 common upregulated genes and four common downregulated genes. We performed enrichment analyses of these DEGs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses. We found that the genes were mainly enriched in keratinocyte differentiation, neuronal cell bodies, and structural constituents of epidermis terms, and pathways such as immune response and synaptic pathways. In addition, we screened matching hub genes by constructing a protein–protein interaction (PPI) network map and a Molecular Complex Detection (MCODE) map using the Cytoscape software. The hub genes were then subjected to GO enrichment analysis, which revealed that the dopamine biosynthetic process, dopaminergic synapse and dopamine-binding terms, and dopaminergic synapse and serotonergic synapse pathways were primarily where they were expressed. Finally, we selected four of these genes for validation in the periodontitis and PD datasets, and we confirmed that these hub genes were highly sensitive and specific for diagnosing and monitoring PD and periodontitis. In conclusion, the above experimental results indicate that periodontitis is a high-risk factor for PD, and the association between these two conditions is mainly manifested in immune and dopamine-related pathways. Hub genes, such as the CDSN, TH, DDC, and SLC6A3 genes, may serve as potential biomarkers for diagnosing or detecting PD.

Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs

Precision medicine programs to identify clinically relevant genetic variation have been revolutionized by access to increasingly affordable high-throughput sequencing technologies. A decade of continual drops in per-base sequencing costs means it is now feasible to sequence an individual patient genome and interrogate all classes of genetic variation for < $1,000 USD. However, while advances in these technologies have greatly simplified the ability to obtain patient sequence information, the timely analysis and interpretation of variant information remains a challenge for the rollout of large-scale precision medicine programs. This review will examine the challenges and potential solutions that exist in identifying predictive genetic biomarkers and pharmacogenetic variants in a patient and discuss the larger bioinformatic challenges likely to emerge in the future. It will examine how both software and hardware development are aiming to overcome issues in short read mapping, variant detection and variant interpretation. It will discuss the current state of the art for genetic disease and the remaining challenges to overcome for complex disease. Success across all types of disease will require novel statistical models and software in order to ensure precision medicine programs realize their full potential now and into the future.

Recent Advances of Human Leukocyte Antigen (HLA) Typing Technology Based on High-Throughput Sequencing

The major histocompatibility complex (MHC) in humans is a genetic region consisting of cell surface proteins located on the short arm of chromosome 6. This is also known as the human leukocyte antigen (HLA) region. The HLA region consists of genes that exhibit complex genetic polymorphisms, and are extensively involved in immune responses. Each individual has a unique set of HLAs. Donor-recipient HLA allele matching is an important factor for organ transplantation. Therefore, an established rapid and accurate HLA typing technology is instrumental to preventing graft-verses-host disease (GVHD) in organ recipients. As of recent, high-throughput sequencing has allowed for an increase read length and higher accuracy and throughput, thus achieving complete and high-resolution full-length typing. With more advanced nanotechnology used in high-throughput sequencing, HLA typing is more widely used in third-generation single-molecule sequencing. This review article summarizes some of the most widely used sequencing typing platforms and evaluates the latest developments in HLA typing kits and their clinical applications.

The Prevalence of Selective and Partial Immunoglobulin A Deficiency in Patients with Autoimmune Polyendocrinopathy

Background: Autoimmune disorders are reported as presenting signs in patients with immunoglobulin A (IgA) deficiency. Herein, we aim to evaluate serum IgA among patients with autoimmune polyendocrinopathy.Methods: Patients with two or more autoimmune endocrinopathies were selected and the serum IgA levels were measured. Patients with an isolated low serum IgA (<7 mg/dL) after exclusion of other causes of hypogammaglobulinemia were considered as selective IgA deficiency (SIgAD), while partial IgA deficiency (PIgAD) was defined as IgA levels below lower limits of IgA normal range for age but higher than 7 mg/dL.Results: Fifty-three patients (19 [35.8%] male and 34 [64.2%] female) with autoimmune polyendocrinopathy enrolled in the study. Parental consanguinity and positive family history of autoimmunity were reported in 38.0% and 52.9% of patients, respectively. Overall, IgA deficiency was observed in 5 (9.4%) patients including PIgAD in 3 (5.7%) and SIgAD in 2 (3.8%) patients. Among IgA deficient patients, the first autoimmune disorder was developed at earlier ages (p = .002), and the prevalence of infection (p = .002), lymphoproliferation (p = .021), and overlap between insulin-dependent diabetes mellitus and autoimmune thyroiditis (p = .032) were significantly higher than patients with normal IgA. Also, the number of autoimmune comorbidities was closely correlated with the occurrence of IgA deficiency (p = .008).Conclusion: The prevalence of IgA deficiency in patients with autoimmune polyendocrinopathy is higher than that in the general population. In these patients, immunologic workup may lead to early diagnosis of inborn error of immunity, which can positively impact the evolution of complications and even management of the autoimmune disorders.