Complex Diagnostics of Non-Specific Intellectual Developmental Disorder

Intellectual development disorder (IDD) is characterized by a general deficit in intellectual and adaptive functioning. In recent years, there has been a growing interest in studying the genetic structure of IDD. Of particular difficulty are patients with non-specific IDD, for whom it is impossible to establish a clinical diagnosis without complex genetic diagnostics. We examined 198 patients with non-specific IDD from 171 families using whole-exome sequencing and chromosome microarray analysis. Hereditary forms of IDD account for at least 35.7% of non-specific IDD, of which 26.9% are monogenic forms. Variants in the genes associated with the BAF (SWI/SNF) complex were the most frequently identified. We were unable to identify phenotypic features that would allow differential diagnosis of monogenic and microstructural chromosomal rearrangements in non-specific IDD at the stage of clinical examination, but due to its higher efficiency, exome sequencing should be the diagnostic method of the highest priority study after the standard examination of patients with NIDD in Russia.     

Molecular Modelling Hurdle in the Next-Generation Sequencing Era

There are challenges in the genetic diagnosis of rare diseases, and pursuing an optimal strategy to identify the cause of the disease is one of the main objectives of any clinical genomics unit. A range of techniques are currently used to characterize the genomic variability within the human genome to detect causative variants of specific disorders. With the introduction of next-generation sequencing (NGS) in the clinical setting, geneticists can study single-nucleotide variants (SNVs) throughout the entire exome/genome. In turn, the number of variants to be evaluated per patient has increased significantly, and more information has to be processed and analyzed to determine a proper diagnosis. Roughly 50% of patients with a Mendelian genetic disorder are diagnosed using NGS, but a fair number of patients still suffer a diagnostic odyssey. Due to the inherent diversity of the human population, as more exomes or genomes are sequenced, variants of uncertain significance (VUSs) will increase exponentially. Thus, assigning relevance to a VUS (non-synonymous as well as synonymous) in an undiagnosed patient becomes crucial to assess the proper diagnosis. Multiple algorithms have been used to predict how a specific mutation might affect the protein’s function, but they are far from accurate enough to be conclusive. In this work, we highlight the difficulties of genomic variability determined by NGS that have arisen in diagnosing rare genetic diseases, and how molecular modelling has to be a key component to elucidate the relevance of a specific mutation in the protein’s loss of function or malfunction. We suggest that the creation of a multi-omics data model should improve the classification of pathogenicity for a significant amount of the detected genomic variability. Moreover, we argue how it should be incorporated systematically in the process of variant evaluation to be useful in the clinical setting and the diagnostic pipeline.     

The Impact of Modern Technologies on Molecular Diagnostic Success Rates,with a Focus on Inherited Retinal Dystrophy and Hearing Loss

The identification of pathogenic variants in monogenic diseases has been of interest to researchers and clinicians for several decades. However, for inherited diseases with extremely high genetic heterogeneity, such as hearing loss and retinal dystrophies, establishing a molecular diagnosis requires an enormous effort. In this review, we use these two genetic conditions as examples to describe the initial molecular genetic identification approaches, as performed since the early 90s, and subsequent improvements and refinements introduced over the years. Next, the history of DNA sequencing from conventional Sanger sequencing to high-throughput massive parallel sequencing, a.k.a. next-generation sequencing, is outlined, including their advantages and limitations and their impact on identifying the remaining genetic defects. Moreover, the development of recent technologies, also coined “third-generation” sequencing, is reviewed, which holds the promise to overcome these limitations. Furthermore, we outline the importance and complexity of variant interpretation in clinical diagnostic settings concerning the massive number of different variants identified by these methods. Finally, we briefly mention the development of novel approaches such as optical mapping and multiomics, which can help to further identify genetic defects in the near future.     

Whole Genome Sequencing,Focused Assays and Functional Studies Increasing Understanding in Cryptic Inherited Retinal Dystrophies

The inherited retinal dystrophies (IRDs) are a clinically and genetically complex group of disorders primarily affecting the rod and cone photoreceptors or other retinal neuronal layers, with emerging therapies heralding the need for accurate molecular diagnosis. Targeted capture and panel-based strategies examining the partial or full exome deliver molecular diagnoses in many IRD families tested. However, approximately one in three families remain unsolved and unable to obtain personalised recurrence risk or access to new clinical trials or therapy. In this study, we investigated whole genome sequencing (WGS), focused assays and functional studies to assist with unsolved IRD cases and facilitate integration of these approaches to a broad molecular diagnostic clinical service. The WGS approach identified variants not covered or underinvestigated by targeted capture panel-based clinical testing strategies in six families. This included structural variants, with notable benefit of the WGS approach in repetitive regions demonstrated by a family with a hybrid gene and hemizygous missense variant involving the opsin genes, OPN1LW and OPN1MW. There was also benefit in investigation of the repetitive GC-rich ORF15 region of RPGR. Further molecular investigations were facilitated by focused assays in these regions. Deep intronic variants were identified in IQCB1 and ABCA4, with functional RNA based studies of the IQCB1 variant revealing activation of a cryptic splice acceptor site. While targeted capture panel-based methods are successful in achieving an efficient molecular diagnosis in a proportion of cases, this study highlights the additional benefit and clinical value that may be derived from WGS, focused assays and functional genomics in the highly heterogeneous IRDs.     

Integrative Genomic Tests in Clinical Oncology

Many clinical decisions in oncology practice rely on the presence or absence of an alteration in a single genetic locus, be it a pathogenic variant in a hereditary cancer gene or activating mutation in a drug target. In addition, there are integrative tests that produce continuous variables and evaluate complex characteristics of the entire tumor genome. Microsatellite instability (MSI) analysis identifies tumors with the accumulation of mutations in short repetitive nucleotide sequences. This procedure is utilized in Lynch syndrome diagnostic pipelines and for the selection of patients for immunotherapy. MSI analysis is well-established for colorectal malignancies, but its applications in other cancer types lack standardization and require additional research. Homologous repair deficiency (HRD) indicates tumor sensitivity to PARP inhibitors and some cytotoxic drugs. HRD-related “genomic scars” are manifested by a characteristic pattern of allelic imbalances, accumulation of deletions with flanking homology, and specific mutation signatures. The detection of the genetic consequences of HRD is particularly sophisticated and expensive, as it involves either whole genome sequencing (WGS) or the utilization of large next-generation sequencing (NGS) panels. Tumor mutation burden (TMB) can be determined by whole exome sequencing (WES) or middle-throughput NGS multigene testing. Although TMB is regarded as an agnostic indicator of tumor sensitivity to immunotherapy, the clinical utility of this test is proven only for a few cancer types.     

Clinical Advances in Molecular Biomarkers for Cancer Diagnosis and Therapy

Cancer diagnosis is currently undergoing a paradigm shift with the incorporation of molecular biomarkers as part of routine diagnostic panel. The molecular alteration ranges from those involving the DNA, RNA, microRNAs (miRNAs) and proteins. The miRNAs are recently discovered small non-coding endogenous single-stranded RNAs that critically regulates the development, invasion and metastasis of cancers. They are altered in cancers and have the potential to serve as diagnostic markers for cancer. Moreover, deregulating their activity offers novel cancer therapeutic approaches. The availability of high throughput techniques for the identification of altered cellular molecules allowed their use in cancer diagnosis. Their application to a variety of body specimens from blood to tissues has been helpful for appreciating their use in the clinical context. The development of innovative antibodies for immunohistochemical detection of proteins also assists in diagnosis and risk stratification. Overall, the novel cancer diagnostic tools have extended their application as prognostic risk factors and can be used as targets for personalized medicine.     

Combined Genome,Transcriptome and Metabolome Analysis in the Diagnosis of Childhood Cerebellar Ataxia

Ataxia in children is a common clinical sign of numerous neurological disorders consisting of impaired coordination of voluntary muscle movement. Its most common form, cerebellar ataxia, describes a heterogeneous array of neurologic conditions with uncountable causes broadly divided as acquired or genetic. Numerous genetic disorders are associated with chronic progressive ataxia, which complicates clinical management, particularly on the diagnostic stage. Advances in omics technologies enable improvements in clinical practice and research, so we proposed a multi-omics approach to aid in the genetic diagnosis and molecular elucidation of an undiagnosed infantile condition of chronic progressive cerebellar ataxia. Using whole-exome sequencing, RNA-seq, and untargeted metabolomics, we identified three clinically relevant mutations (rs141471029, rs191582628 and rs398124292) and an altered metabolic profile in our patient. Two POLR1C diagnostic variants already classified as pathogenic were found, and a diagnosis of hypomyelinating leukodystrophy was achieved. A mutation on the MMACHC gene, known to be associated with methylmalonic aciduria and homocystinuria cblC type, was also found. Additionally, preliminary metabolome analysis revealed alterations in our patient’s amino acid, fatty acid and carbohydrate metabolism. Our findings provided a definitive genetic diagnosis reinforcing the association between POLR1C mutations and hypomyelinating leukodystrophy and highlighted the relevance of multi-omics approaches to the disease.     

NGS in Hereditary Ataxia: When Rare Becomes Frequent

The term hereditary ataxia (HA) refers to a heterogeneous group of neurological disorders with multiple genetic etiologies and a wide spectrum of ataxia-dominated phenotypes. Massive gene analysis in next-generation sequencing has entered the HA scenario, broadening our genetic and clinical knowledge of these conditions. In this study, we employed a targeted resequencing panel (TRP) in a large and highly heterogeneous cohort of 377 patients with a clinical diagnosis of HA, but no molecular diagnosis on routine genetic tests. We obtained a positive result (genetic diagnosis) in 33.2% of the patients, a rate significantly higher than those reported in similar studies employing TRP (average 19.4%), and in line with those performed using exome sequencing (ES, average 34.6%). Moreover, 15.6% of the patients had an uncertain molecular diagnosis. STUB1, PRKCG, and SPG7 were the most common causative genes. A comparison with published literature data showed that our panel would have identified 97% of the positive cases reported in previous TRP-based studies and 92% of those diagnosed by ES. Proper use of multigene panels, when combined with detailed phenotypic data, seems to be even more efficient than ES in clinical practice.     

Multiple Variant Calling Pipelines in Wheat Whole Exome Sequencing

The highly challenging hexaploid wheat (Triticum aestivum) genome is becoming ever more accessible due to the continued development of multiple reference genomes, a factor which aids in the plight to better understand variation in important traits. Although the process of variant calling is relatively straightforward, selection of the best combination of the computational tools for read alignment and variant calling stages of the analysis and efficient filtering of the false variant calls are not always easy tasks. Previous studies have analyzed the impact of methods on the quality metrics in diploid organisms. Given that variant identification in wheat largely relies on accurate mining of exome data, there is a critical need to better understand how different methods affect the analysis of whole exome sequencing (WES) data in polyploid species. This study aims to address this by performing whole exome sequencing of 48 wheat cultivars and assessing the performance of various variant calling pipelines at their suggested settings. The results show that all the pipelines require filtering to eliminate false-positive calls. The high consensus among the reference SNPs called by the best-performing pipelines suggests that filtering provides accurate and reproducible results. This study also provides detailed comparisons for high sensitivity and precision at individual and population levels for the raw and filtered SNP calls.     

Molecular Methodologies for Improved Polymicrobial Sepsis Diagnosis

Polymicrobial sepsis is associated with worse patient outcomes than monomicrobial sepsis. Routinely used culture-dependent microbiological diagnostic techniques have low sensitivity, often leading to missed identification of all causative organisms. To overcome these limitations, culture-independent methods incorporating advanced molecular technologies have recently been explored. However, contamination, assay inhibition and interference from host DNA are issues that must be addressed before these methods can be relied on for routine clinical use. While the host component of the complex sepsis host–pathogen interplay is well described, less is known about the pathogen’s role, including pathogen–pathogen interactions in polymicrobial sepsis. This review highlights the clinical significance of polymicrobial sepsis and addresses how promising alternative molecular microbiology methods can be improved to detect polymicrobial infections. It also discusses how the application of shotgun metagenomics can be used to uncover pathogen/pathogen interactions in polymicrobial sepsis cases and their potential role in the clinical course of this condition.     

New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches

Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools and availability of high throughput sequencing data have enabled the reanalysis of previously undiagnosed patients. In this review, we have systematically compiled the latest developments in the discovery of the genetic causes of rare diseases using machine learning methods. Importantly, we have detailed methods available to reanalyze existing whole exome sequencing data of unsolved rare diseases. We have identified different reanalysis methodologies to solve problems associated with sequence alterations/mutations, variation re-annotation, protein stability, splice isoform malfunctions and oligogenic analysis. In addition, we give an overview of new developments in the field of rare disease research using whole genome sequencing data and other omics.     

The Emerging Roles of Long Non-Coding RNAs in Intellectual Disability and Related Neurodevelopmental Disorders

In the human brain, long non-coding RNAs (lncRNAs) are widely expressed in an exquisitely temporally and spatially regulated manner, thus suggesting their contribution to normal brain development and their probable involvement in the molecular pathology of neurodevelopmental disorders (NDD). Bypassing the classic protein-centric conception of disease mechanisms, some studies have been conducted to identify and characterize the putative roles of non-coding sequences in the genetic pathogenesis and diagnosis of complex diseases. However, their involvement in NDD, and more specifically in intellectual disability (ID), is still poorly documented and only a few genomic alterations affecting the lncRNAs function and/or expression have been causally linked to the disease endophenotype. Considering that a significant fraction of patients still lacks a genetic or molecular explanation, we expect that a deeper investigation of the non-coding genome will unravel novel pathogenic mechanisms, opening new translational opportunities. Here, we present evidence of the possible involvement of many lncRNAs in the etiology of different forms of ID and NDD, grouping the candidate disease-genes in the most frequently affected cellular processes in which ID-risk genes were previously collected. We also illustrate new approaches for the identification and prioritization of NDD-risk lncRNAs, together with the current strategies to exploit them in diagnosis.     

A Schematic Approach to Defining the Prevalence of COL VI Variants in Five Years of Next-Generation Sequencing

Objective: To define the prevalence of variants in collagen VI genes through a next-generation sequencing (NGS) approach in undiagnosed patients with suspected neuromuscular disease and to propose a diagnostic flowchart to assess the real pathogenicity of those variants. Methods: In the past five years, we have collected clinical and molecular information on 512 patients with neuromuscular symptoms referred to our center. To pinpoint variants in COLVI genes and corroborate their real pathogenicity, we sketched a multistep flowchart, taking into consideration the bioinformatic weight of the gene variants, their correlation with clinical manifestations and possible effects on protein stability and expression. Results: In Step I, we identified variants in COLVI-related genes in 48 patients, of which three were homozygous variants (Group 1). Then, we sorted variants according to their CADD score, clinical data and complementary studies (such as muscle and skin biopsy, study of expression of COLVI on fibroblast or muscle and muscle magnetic resonance). We finally assessed how potentially pathogenic variants (two biallelic and 12 monoallelic) destabilize COL6A1-A2-A3 subunits. Overall, 15 out of 512 patients were prioritized according to this pipeline. In seven of them, we confirmed reduced or absent immunocytochemical expression of collagen VI in cultured skin fibroblasts or in muscle tissue. Conclusions: In a real-world diagnostic scenario applied to heterogeneous neuromuscular conditions, a multistep integration of clinical and molecular data allowed the identification of about 3% of those patients harboring pathogenetic collagen VI variants.     

Cerebrospinal Fluid Metabolome in Parkinson’s Disease and Multiple System Atrophy

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.     

Identification of a Novel Mutation of β-Spectrin in Hereditary Spherocytosis Using Whole Exome Sequencing

Hereditary spherocytosis (HS), the most commonly inherited hemolytic anemia in northern Europeans, comprises a group of diseases whose heterogeneous genetic basis results in a variable clinical presentation. High-throughput genome sequencing methods have made a leading contribution to the recent progress in research on and diagnostics of inherited diseases and inspired us to apply whole exome sequencing (WES) to identify potential mutations in HS. The data presented here reveal a novel mutation probably responsible for HS in a single Polish family. Patients with clinical evidence of HS (clinical symptoms, hematological data, and EMA test) were enrolled in the study. The examination of the resulting WES data showed a number of polymorphisms in 71 genes associated with known erythrocyte pathologies (including membranopathies, enzymopathies, and hemoglobinopathies). Only a single SPTB gene variant indicated the possible molecular mechanism of the disease in the studied family. The new missense mutation p.C183Y was identified using WES in the SPTB gene, which is most likely the cause of clinical symptoms typical of hereditary spherocytosis (membranopathy) due to structural and functional impairments of human β-spectrin. This mutation allows for a better understanding of the molecular mechanism(s) of one of the membranopathies, hereditary spherocytosis.     

Germline Whole-Gene Deletion of FH Diagnosed from Tumor Profiling

Hereditary leiomyomatosis and renal cell carcinoma (HL (RCC)) entails cutaneous and uterine leiomyomatosis with aggressive type 2 papillary RCC-like histology. HLRCC is caused by pathogenic variants in the FH gene, which encodes fumarate hydratase (FH). Here, we describe an episode of young-onset RCC caused by a genomic FH deletion that was diagnosed via clinical sequencing. A 35-year-old woman was diagnosed with RCC and multiple metastases: histopathological analyses supported a diagnosis of FH-deficient RCC. Although the patient had neither skin tumors nor a family history of HLRCC, an aggressive clinical course at her age and pathological diagnosis of FH-deficient RCC suggested a germline FH variant. After counseling, the patient provided written informed consent for germline genetic testing. She was simultaneously subjected to paired tumor profiling tests targeting the exome to identify a therapeutic target. Although conventional germline sequencing did not detect FH variants, exome sequencing revealed a heterozygous germline FH deletion. As such, paired tumor profiling, not conventional sequencing, was required to identify this genetic deletion. RCC caused by a germline FH deletion has hitherto not been described in Japan, and the FH deletion detected in this patient was presumed to be of maternal European origin. Although the genotype-phenotype correlation in HLRCC-related tumors is unclear, the patient’s family was advised to undergo genetic counseling to consider additional RCC screening.