An integrated multiomic approach as an excellent tool for the diagnosis of metabolic diseases: our first 3720 patients

Focused Exome Sequencing Gives a High Diagnostic Yield in the Indian Subcontinent

The genetically isolated yet heterogeneous and highly consanguineous Indian population has shown a higher prevalence of rare genetic disorders. However, there is a significant socioeconomic burden for genetic testing to be accessible to the general population. In the current study, we analyzed next-generation sequencing data generated through focused exome sequencing from individuals with different phenotypic manifestations referred for genetic testing to achieve a molecular diagnosis. Pathogenic or likely pathogenic variants are reported in 280 of 833 cases with a diagnostic yield of 33.6%. Homozygous sequence and copy number variants were found as positive diagnostic findings in 131 cases (15.7%) because of the high consanguinity in the Indian population. No relevant findings related to reported phenotype were identified in 6.2% of the cases. Patients referred for testing due to metabolic disorder and neuromuscular disorder had higher diagnostic yields. Carrier testing of asymptomatic individuals with a family history of the disease, through focused exome sequencing, achieved positive diagnosis in 54 of 118 cases tested. Copy number variants were also found in trans with single-nucleotide variants and mitochondrial variants in a few of the cases. The diagnostic yield and the findings from this study signify that a focused exome test is a good lower-cost alternative for whole-exome and whole-genome sequencing and as a first-tier approach to genetic testing.

Clinical utility of comprehensive gene panel testing for common and rare causes of skeletal dysplasia and other skeletal disorders: Results from the largest cohort to date

Molecular genetics enables more precise diagnoses of skeletal dysplasia and other skeletal disorders (SDs). We investigated the clinical utility of multigene panel testing for 5011 unrelated individuals with SD in the United States (December 2019-April 2022). Median (range) age was 8 (0-90) years, 70.5% had short stature and/or disproportionate growth, 27.4% had a positive molecular diagnosis (MDx), and 30 individuals received two MDx. Genes most commonly contributing to MDx were FGFR3 (16.9%), ALPL (13.0%), and COL1A1 (10.3%). Most of the 112 genes associated with ≥1 MDx were primarily involved in signal transduction (n = 35), metabolism (n = 23), or extracellular matrix organization (n = 17). There were implications associated with specific care/treatment options for 84.4% (1158/1372) of MDx-positive individuals; >50% were linked to conditions with targeted therapy approved or in clinical development, including osteogenesis imperfecta, achondroplasia, hypophosphatasia, and mucopolysaccharidosis. Forty individuals with initially inconclusive results became MDx-positive following family testing. Follow-up mucopolysaccharidosis enzyme activity testing was positive in 14 individuals (10 of these were not MDx-positive). Our findings showed that inclusion of metabolic genes associated with SD increased the clinical utility of a gene panel and confirmed that integrated use of comprehensive gene panel testing with orthogonal testing reduced the burden of inconclusive results.

Further evidence supporting the role of GTDC1 in glycine metabolism and neurodevelopmental disorders

Copy number variants (CNVs) represent the genetic cause of about 15-20% of neurodevelopmental disorders (NDDs). We identified a ~67 kb de novo intragenic deletion on chromosome 2q22.3 in a female individual showing a developmental encephalopathy characterised by epilepsy, severe intellectual disability, speech delay, microcephaly, and thin corpus callosum with facial dysmorphisms. The microdeletion involved exons 5-6 of GTDC1, encoding a putative glycosyltransferase, whose expression is particularly enriched in the nervous system. In a previous study, a balanced de novo translocation encompassing GTDC1 was reported in a male child with global developmental delay and delayed speech and language development. Based on these premises, we explored the transcriptomic profile of our proband to evaluate the functional consequences of the novel GTDC1 de novo intragenic deletion in relation to the observed neurodevelopmental phenotype. RNA-seq on the proband's lymphoblastoid cell line (LCL) showed expression changes of glycine/serine and cytokine/chemokine signalling pathways, which are related to neurodevelopment and epileptogenesis. Subsequent analysis by ELISA (enzyme-linked immunosorbent assay) and HPLC (high-performance liquid chromatography) revealed increased levels of glycine in the proband's LCL and serum compared to matched controls. Given that an increased level of glycine has been observed in the plasma samples of individuals with Rett syndrome, a condition sharing epilepsy, microcephaly, and intellectual disability with our proband, we proposed that the GTDC1 downregulation is implicated in neurodevelopmental impairment by altering glycine metabolism. Furthermore, our findings expanded the phenotypic spectrum of the novel GTDC1-related condition, including microcephaly and epilepsy among relevant clinical features.

Large-Scale Screening: Phenotypic and Mutational Spectrum in Isolated and Combined Dystonia Genes

BACKGROUND

Pathogenic variants in several genes have been linked to genetic forms of isolated or combined dystonia. The phenotypic and genetic spectrum and the frequency of pathogenic variants in these genes have not yet been fully elucidated, neither in patients with dystonia nor with other, sometimes co-occurring movement disorders such as Parkinson's disease (PD).

OBJECTIVES

To screen >2000 patients with dystonia or PD for rare variants in known dystonia-causing genes.

METHODS

We screened 1207 dystonia patients from Germany (DysTract consortium), Spain, and South Korea, and 1036 PD patients from Germany for pathogenic variants using a next-generation sequencing gene panel. The impact on DNA methylation of KMT2B variants was evaluated by analyzing the gene's characteristic episignature.

RESULTS

We identified 171 carriers (109 with dystonia [9.0%]; 62 with PD [6.0%]) of 131 rare variants (minor allele frequency <0.005). A total of 52 patients (48 dystonia [4.0%]; four PD [0.4%, all with GCH1 variants]) carried 33 different (likely) pathogenic variants, of which 17 were not previously reported. Pathogenic biallelic variants in PRKRA were not found. Episignature analysis of 48 KMT2B variants revealed that only two of these should be considered (likely) pathogenic.

CONCLUSION

This study confirms pathogenic variants in GCH1, GNAL, KMT2B, SGCE, THAP1, and TOR1A as relevant causes in dystonia and expands the mutational spectrum. Of note, likely pathogenic variants only in GCH1 were also found among PD patients. For DYT-KMT2B, the recently described episignature served as a reliable readout to determine the functional effect of newly identified variants. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Unveiling the future of metabolic medicine: omics technologies driving personalized solutions for precision treatment of metabolic disorders

Metabolic disorders are increasingly prevalent worldwide, leading to high rates of morbidity and mortality. The variety of metabolic illnesses can be addressed through personalized medicine. The goal of personalized medicine is to give doctors the ability to anticipate the best course of treatment for patients with metabolic problems. By analyzing a patient's metabolomic, proteomic, genetic profile, and clinical data, physicians can identify relevant diagnostic, and predictive biomarkers and develop treatment plans and therapy for acute and chronic metabolic diseases. To achieve this goal, real-time modeling of clinical data and multiple omics is essential to pinpoint underlying biological mechanisms, risk factors, and possibly useful data to promote early diagnosis and prevention of complex diseases. Incorporating cutting-edge technologies like artificial intelligence and machine learning is crucial for consolidating diverse forms of data, examining multiple variables, establishing databases of clinical indicators to aid decision-making, and formulating ethical protocols to address concerns. This review article aims to explore the potential of personalized medicine utilizing omics approaches for the treatment of metabolic disorders. It focuses on the recent advancements in genomics, epigenomics, proteomics, metabolomics, and nutrigenomics, emphasizing their role in revolutionizing personalized medicine.

Challenges in the Definitive Diagnosis of Niemann-Pick Type C-Leaky Variants and Alternative Transcripts

Niemann-Pick type C (NPC, ORPHA: 646) is a neuro-visceral, psychiatric disease caused predominantly by pathogenic variants in the NPC1 gene or seldom in NPC2. The rarity of the disease, and its wide range of clinical phenotypes and ages of onset, turn the diagnosis into a significant challenge. Other than the detailed clinical history, the typical diagnostic work-up for NPC includes the quantification of pathognomonic metabolites. However, the molecular basis diagnosis is still of utmost importance to fully characterize the disorder. Here, the authors provide an overview of splicing variants in the NPC1 and NPC2 genes and propose a new workflow for NPC diagnosis. Splicing variants cover a significant part of the disease-causing variants in NPC. The authors used cDNA analysis to study the impact of such variants, including the collection of data to classify them as leaky or non-leaky pathogenic variants. However, the presence of naturally occurring spliced transcripts can misdiagnose or mask a pathogenic variant and make the analysis even more difficult. Analysis of the NPC1 cDNA in NPC patients in parallel with controls is vital to assess and detect alternatively spliced forms. Moreover, nonsense-mediated mRNA decay (NMD) analysis plays an essential role in evaluating the naturally occurring transcripts during cDNA analysis and distinguishing them from other pathogenic variants' associated transcripts.

At a glance: the largest Niemann-Pick type C1 cohort with 602 patients diagnosed over 15 years

Niemann-Pick type C1 disease (NPC1 [OMIM 257220]) is a rare and severe autosomal recessive disorder, characterized by a multitude of neurovisceral clinical manifestations and a fatal outcome with no effective treatment to date. Aiming to gain insights into the genetic aspects of the disease, clinical, genetic, and biomarker PPCS data from 602 patients referred from 47 countries and diagnosed with NPC1 in our laboratory were analyzed. Patients' clinical data were dissected using Human Phenotype Ontology (HPO) terms, and genotype-phenotype analysis was performed. The median age at diagnosis was 10.6 years (range 0-64.5 years), with 287 unique pathogenic/likely pathogenic (P/LP) variants identified, expanding NPC1 allelic heterogeneity. Importantly, 73 P/LP variants were previously unpublished. The most frequent variants detected were: c.3019C > G, p.(P1007A), c.3104C > T, p.(A1035V), and c.2861C > T, p.(S954L). Loss of function (LoF) variants were significantly associated with earlier age at diagnosis, highly increased biomarker levels, and a visceral phenotype (abnormal abdomen and liver morphology). On the other hand, the variants p.(P1007A) and p.(S954L) were significantly associated with later age at diagnosis (p < 0.001) and mildly elevated biomarker levels (p ≤ 0.002), consistent with the juvenile/adult form of NPC1. In addition, p.(I1061T), p.(S954L), and p.(A1035V) were associated with abnormality of eye movements (vertical supranuclear gaze palsy, p ≤ 0.05). We describe the largest and most heterogenous cohort of NPC1 patients published to date. Our results suggest that besides its utility in variant classification, the biomarker PPCS might serve to indicate disease severity/progression. In addition, we establish new genotype-phenotype relationships for "frequent" NPC1 variants.

Integrative omics approaches to advance rare disease diagnostics

Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.

Beyond the exome: What's next in diagnostic testing for Mendelian conditions

Despite advances in clinical genetic testing, including the introduction of exome sequencing (ES), more than 50% of individuals with a suspected Mendelian condition lack a precise molecular diagnosis. Clinical evaluation is increasingly undertaken by specialists outside of clinical genetics, often occurring in a tiered fashion and typically ending after ES. The current diagnostic rate reflects multiple factors, including technical limitations, incomplete understanding of variant pathogenicity, missing genotype-phenotype associations, complex gene-environment interactions, and reporting differences between clinical labs. Maintaining a clear understanding of the rapidly evolving landscape of diagnostic tests beyond ES, and their limitations, presents a challenge for non-genetics professionals. Newer tests, such as short-read genome or RNA sequencing, can be challenging to order, and emerging technologies, such as optical genome mapping and long-read DNA sequencing, are not available clinically. Furthermore, there is no clear guidance on the next best steps after inconclusive evaluation. Here, we review why a clinical genetic evaluation may be negative, discuss questions to be asked in this setting, and provide a framework for further investigation, including the advantages and disadvantages of new approaches that are nascent in the clinical sphere. We present a guide for the next best steps after inconclusive molecular testing based upon phenotype and prior evaluation, including when to consider referral to research consortia focused on elucidating the underlying cause of rare unsolved genetic disorders.

Proteomics in Inherited Metabolic Disorders

Inherited metabolic disorders (IMD) are rare medical conditions caused by genetic defects that interfere with the body's metabolism. The clinical phenotype is highly variable and can present at any age, although it more often manifests in childhood. The number of treatable IMDs has increased in recent years, making early diagnosis and a better understanding of the natural history of the disease more important than ever. In this review, we discuss the main challenges faced in applying proteomics to the study of IMDs, and the key advances achieved in this field using tandem mass spectrometry (MS/MS). This technology enables the analysis of large numbers of proteins in different body fluids (serum, plasma, urine, saliva, tears) with a single analysis of each sample, and can even be applied to dried samples. MS/MS has thus emerged as the tool of choice for proteome characterization and has provided new insights into many diseases and biological systems. In the last 10 years, sequential window acquisition of all theoretical fragmentation spectra mass spectrometry (SWATH-MS) has emerged as an accurate, high-resolution technique for the identification and quantification of proteins differentially expressed between healthy controls and IMD patients. Proteomics is a particularly promising approach to help obtain more information on rare genetic diseases, including identification of biomarkers to aid early diagnosis and better understanding of the underlying pathophysiology to guide the development of new therapies. Here, we summarize new and emerging proteomic technologies and discuss current uses and limitations of this approach to identify and quantify proteins. Moreover, we describe the use of proteomics to identify the mechanisms regulating complex IMD phenotypes; an area of research essential to better understand these rare disorders and many other human diseases.