"Transcriptomics": molecular diagnosis of inborn errors of metabolism via RNA-sequencing

Mitochondrial disorder diagnosis and management- what the pediatric neurologist wants to know

Childhood-onset mitochondrial disorders are rare genetic diseases that often manifest with neurological impairment due to altered mitochondrial structure or function. To date, pathogenic variants in 373 genes across the nuclear and mitochondrial genomes have been linked to mitochondrial disease, but the ensuing genetic and clinical complexity of these disorders poses considerable challenges to their diagnosis and management. Nevertheless, despite the current lack of curative treatment, recent advances in next generation sequencing and -omics technologies have laid the foundation for precision mitochondrial medicine through enhanced diagnostic accuracy and greater insight into pathomechanisms. This holds promise for the development of targeted treatments in this group of patients. Against a backdrop of inherent challenges and recent technological advances in mitochondrial medicine, this review discusses the current diagnostic approach to a child with suspected mitochondrial disease and outlines management considerations of particular relevance to paediatric neurologists. We highlight the importance of mitochondrial expertise centres in providing the laboratory infrastructure needed to supplement uninformative first line genomic testing with focused and/or further unbiased investigations where needed, as well as coordinating an integrated multidisciplinary model of care that is paramount to the management of patients affected by these conditions.

LNC-ing Genetics in Mitochondrial Disease

Primary mitochondrial disease (MD) is a group of rare genetic diseases reported to have a prevalence of 1:5000 and is currently without a cure. This group of diseases includes mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), maternally inherited diabetes and deafness (MIDD), Leber's hereditary optic neuropathy (LHON), Leigh syndrome (LS), Kearns-Sayre syndrome (KSS), and myoclonic epilepsy and ragged-red fiber disease (MERRF). Additionally, secondary mitochondrial dysfunction has been implicated in the most common current causes of mortality and morbidity, including cardiovascular disease (CVD) and cancer. Identifying key genetic contributors to both MD and secondary mitochondrial dysfunction may guide clinicians to assess the most effective treatment course and prognosis, as well as informing family members of any hereditary risk of disease transmission. Identifying underlying genetic causes of primary and secondary MD involves either genome sequencing (GS) or small targeted panel analysis of known disease-causing nuclear- or mitochondrial genes coding for mitochondria-related proteins. Due to advances in GS, the importance of long non-coding RNA (lncRNA) as functional contributors to the pathophysiology of MD is being unveiled. A limited number of studies have thus far reported the importance of lncRNAs in relation to MD causation and progression, and we are entering a new area of attention for clinical geneticists in specific rare malignancies. This commentary provides an overview of what is known about the role of lncRNAs as genetic and molecular contributors to disease pathophysiology and highlights an unmet need for a deeper understanding of mitochondrial dysfunction in serious human disease burdens.

Quantitative proteomics of patient fibroblasts reveal biomarkers and diagnostic signatures of mitochondrial disease

BACKGROUNDMitochondrial diseases belong to the group of inborn errors of metabolism (IEM), with a prevalence of 1 in 2,000-5,000 individuals. They are the most common form of IEM, but, despite advances in next-generation sequencing technologies, almost half of the patients are left genetically undiagnosed.METHODSWe investigated a cohort of 61 patients with defined mitochondrial disease to improve diagnostics, identify biomarkers, and correlate metabolic pathways to specific disease groups. Clinical presentations were structured using human phenotype ontology terms, and mass spectrometry-based proteomics was performed on primary fibroblasts. Additionally, we integrated 6 patients carrying variants of uncertain significance (VUS) to test proteomics as a diagnostic expansion.RESULTSProteomic profiles from patient samples could be classified according to their biochemical and genetic characteristics, with the expression of 5 proteins (GPX4, MORF4L1, MOXD1, MSRA, and TMED9) correlating with the disease cohort, thus acting as putative biomarkers. Pathway analysis showed a deregulation of inflammatory and mitochondrial stress responses. This included the upregulation of glycosphingolipid metabolism and mitochondrial protein import, as well as the downregulation of arachidonic acid metabolism. Furthermore, we could assign pathogenicity to a VUS in MRPS23 by demonstrating the loss of associated mitochondrial ribosome subunits.CONCLUSIONWe established mass spectrometry-based proteomics on patient fibroblasts as a viable and versatile tool for diagnosing patients with mitochondrial disease.FUNDINGThe NovoNordisk Foundation, Knut and Alice Wallenberg Foundation, Wellcome Centre for Mitochondrial Research, UK Medical Research Council, and the UK NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and Children.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network and Genomics Research to Elucidate the Genetics of Rare Disease Consortium. Across six routinely collected biospecimens, 61% of quantified genes were not influenced by genome build. However, we identified 1,492 genes with build-dependent quantification, 3,377 genes with build-exclusive expression, and 9,077 genes with annotation-specific expression across six routinely collected biospecimens, including 566 clinically relevant and 512 known OMIM genes. Further, we demonstrate that between builds for a given gene, a larger difference in quantification is well correlated with a larger change in expression outlier calling. Combined, we provide a database of genes impacted by build choice and recommend that transcriptomics-guided analyses and diagnoses are cross referenced with these data for robustness.

Impact of genome build on RNA-seq interpretation and diagnostics

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network (UDN) and Genomics Research to Elucidate the Genetics of Rare Disease (GREGoR) Consortium. We identified 2,800 genes with build-dependent quantification across six routinely-collected biospecimens, including 1,391 protein-coding genes and 341 known rare disease genes. We further observed multiple genes that only have detectable expression in a subset of genome builds. Finally, we characterized how genome build impacts the detection of outlier transcriptomic events. Combined, we provide a database of genes impacted by build choice, and recommend that transcriptomics-guided analyses and diagnoses are cross-referenced with these data for robustness.

Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications

Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.

Leigh Syndrome Spectrum: A Portuguese Population Cohort in an Evolutionary Genetic Era

Mitochondrial diseases are the most common inherited inborn error of metabolism resulting in deficient ATP generation, due to failure in homeostasis and proper bioenergetics. The most frequent mitochondrial disease manifestation in children is Leigh syndrome (LS), encompassing clinical, neuroradiological, biochemical, and molecular features. It typically affects infants but occurs anytime in life. Considering recent updates, LS clinical presentation has been stretched, and is now named LS spectrum (LSS), including classical LS and Leigh-like presentations. Apart from clinical diagnosis challenges, the molecular characterization also progressed from Sanger techniques to NGS (next-generation sequencing), encompassing analysis of nuclear (nDNA) and mitochondrial DNA (mtDNA). This upgrade resumed steps and favored diagnosis. Hereby, our paper presents molecular and clinical data on a Portuguese cohort of 40 positive cases of LSS. A total of 28 patients presented mutation in mtDNA and 12 in nDNA, with novel mutations identified in a heterogeneous group of genes. The present results contribute to the better knowledge of the molecular basis of LS and expand the clinical spectrum associated with this syndrome.

Stepwise use of genomics and transcriptomics technologies increases diagnostic yield in Mendelian disorders

Purpose: Multi-omics offer worthwhile and increasingly accessible technologies to diagnostic laboratories seeking potential second-tier strategies to help patients with unresolved rare diseases, especially patients clinically diagnosed with a rare OMIM (Online Mendelian Inheritance in Man) disease. However, no consensus exists regarding the optimal diagnostic care pathway to adopt after negative results with standard approaches. Methods: In 15 unsolved individuals clinically diagnosed with recognizable OMIM diseases but with negative or inconclusive first-line genetic results, we explored the utility of a multi-step approach using several novel omics technologies to establish a molecular diagnosis. Inclusion criteria included a clinical autosomal recessive disease diagnosis and single heterozygous pathogenic variant in the gene of interest identified by first-line analysis (60%-9/15) or a clinical diagnosis of an X-linked recessive or autosomal dominant disease with no causative variant identified (40%-6/15). We performed a multi-step analysis involving short-read genome sequencing (srGS) and complementary approaches such as mRNA sequencing (mRNA-seq), long-read genome sequencing (lrG), or optical genome mapping (oGM) selected according to the outcome of the GS analysis. Results: SrGS alone or in combination with additional genomic and/or transcriptomic technologies allowed us to resolve 87% of individuals by identifying single nucleotide variants/indels missed by first-line targeted tests, identifying variants affecting transcription, or structural variants sometimes requiring lrGS or oGM for their characterization. Conclusion: Hypothesis-driven implementation of combined omics technologies is particularly effective in identifying molecular etiologies. In this study, we detail our experience of the implementation of genomics and transcriptomics technologies in a pilot cohort of previously investigated patients with a typical clinical diagnosis without molecular etiology.

Deep intronic NIPBL de novo mutations and differential diagnoses revealed by whole genome and RNA sequencing in Cornelia de Lange syndrome patients

Cornelia de Lange syndrome (CdLS; MIM# 122470) is a rare developmental disorder. Pathogenic variants in 5 genes explain approximately 50% cases, leaving the other 50% unsolved. We performed whole genome sequencing (WGS) ± RNA sequencing (RNA-seq) in 5 unsolved trios fulfilling the following criteria: (i) clinical diagnosis of classic CdLS, (ii) negative gene panel sequencing from blood and saliva-isolated DNA, (iii) unaffected parents' DNA samples available and (iv) proband's blood-isolated RNA available. A pathogenic de novo mutation (DNM) was observed in a CdLS differential diagnosis gene in 3/5 patients, namely POU3F3, SPEN, and TAF1. In the other two, we identified two distinct deep intronic DNM in NIPBL predicted to create a novel splice site. RT-PCRs and RNA-Seq showed aberrant transcripts leading to the creation of a novel frameshift exon. Our findings suggest the relevance of WGS in unsolved suspected CdLS cases and that deep intronic variants may account for a proportion of them.

Transcriptome analysis provides critical answers to the "variants of uncertain significance" conundrum

While whole-genome and exome sequencing have transformed our collective understanding of genetics' role in disease pathogenesis, there are certain conditions and populations for whom DNA-level data fails to identify the underlying genetic etiology. Specifically, patients of non-White race and non-European ancestry are disproportionately affected by "variants of unknown/uncertain significance" (VUS), limiting the scope of precision medicine for minority patients and perpetuating health disparities. VUS often include deep intronic and splicing variants which are difficult to interpret from DNA data alone. RNA analysis can illuminate the consequences of VUS, thereby allowing for their reclassification as pathogenic versus benign. Here we review the critical role transcriptome analysis plays in clarifying VUS in both neoplastic and non-neoplastic diseases.

OMIXCARE: OMICS technologies solved about 33% of the patients with heterogeneous rare neuro-developmental disorders and negative exome sequencing results and identified 13% additional candidate variants

Purpose: Patients with rare or ultra-rare genetic diseases, which affect 350 million people worldwide, may experience a diagnostic odyssey. High-throughput sequencing leads to an etiological diagnosis in up to 50% of individuals with heterogeneous neurodevelopmental or malformation disorders. There is a growing interest in additional omics technologies in translational research settings to examine the remaining unsolved cases. Methods: We gathered 30 individuals with malformation syndromes and/or severe neurodevelopmental disorders with negative trio exome sequencing and array comparative genomic hybridization results through a multicenter project. We applied short-read genome sequencing, total RNA sequencing, and DNA methylation analysis, in that order, as complementary translational research tools for a molecular diagnosis. Results: The cohort was mainly composed of pediatric individuals with a median age of 13.7 years (4 years and 6 months to 35 years and 1 month). Genome sequencing alone identified at least one variant with a high level of evidence of pathogenicity in 8/30 individuals (26.7%) and at least a candidate disease-causing variant in 7/30 other individuals (23.3%). RNA-seq data in 23 individuals allowed two additional individuals (8.7%) to be diagnosed, confirming the implication of two pathogenic variants (8.7%), and excluding one candidate variant (4.3%). Finally, DNA methylation analysis confirmed one diagnosis identified by genome sequencing (Kabuki syndrome) and identified an episignature compatible with a BAFopathy in a patient with a clinical diagnosis of Coffin-Siris with negative genome and RNA-seq results in blood. Conclusion: Overall, our integrated genome, transcriptome, and DNA methylation analysis solved 10/30 (33.3%) cases and identified a strong candidate gene in 4/30 (13.3%) of the patients with rare neurodevelopmental disorders and negative exome sequencing results.

Identification of Clinical Variants beyond the Exome in Inborn Errors of Metabolism

Inborn errors of metabolism (IEM) constitute a huge group of rare diseases affecting 1 in every 1000 newborns. Next-generation sequencing has transformed the diagnosis of IEM, leading to its proposed use as a second-tier technology for confirming cases detected by clinical/biochemical studies or newborn screening. The diagnosis rate is, however, still not 100%. This paper reports the use of a personalized multi-omics (metabolomic, genomic and transcriptomic) pipeline plus functional genomics to aid in the genetic diagnosis of six unsolved cases, with a clinical and/or biochemical diagnosis of galactosemia, mucopolysaccharidosis type I (MPS I), maple syrup urine disease (MSUD), hyperphenylalaninemia (HPA), citrullinemia, or urea cycle deficiency. Eight novel variants in six genes were identified: six (four of them deep intronic) located in GALE, IDUA, PTS, ASS1 and OTC, all affecting the splicing process, and two located in the promoters of IDUA and PTS, thus affecting these genes’ expression. All the new variants were subjected to functional analysis to verify their pathogenic effects. This work underscores how the combination of different omics technologies and functional analysis can solve elusive cases in clinical practice.

How to proceed after "negative" exome: A review on genetic diagnostics, limitations, challenges, and emerging new multiomics techniques

Exome sequencing (ES) in the clinical setting of inborn metabolic diseases (IMDs) has created tremendous improvement in achieving an accurate and timely molecular diagnosis for a greater number of patients, but it still leaves the majority of patients without a diagnosis. In parallel, (personalized) treatment strategies are increasingly available, but this requires the availability of a molecular diagnosis. IMDs comprise an expanding field with the ongoing identification of novel disease genes and the recognition of multiple inheritance patterns, mosaicism, variable penetrance, and expressivity for known disease genes. The analysis of trio ES is preferred over singleton ES as information on the allelic origin (paternal, maternal, "de novo") reduces the number of variants that require interpretation. All ES data and interpretation strategies should be exploited including CNV and mitochondrial DNA analysis. The constant advancements in available techniques and knowledge necessitate the close exchange of clinicians and molecular geneticists about genotypes and phenotypes, as well as knowledge of the challenges and pitfalls of ES to initiate proper further diagnostic steps. Functional analyses (transcriptomics, proteomics, and metabolomics) can be applied to characterize and validate the impact of identified variants, or to guide the genomic search for a diagnosis in unsolved cases. Future diagnostic techniques (genome sequencing [GS], optical genome mapping, long-read sequencing, and epigenetic profiling) will further enhance the diagnostic yield. We provide an overview of the challenges and limitations inherent to ES followed by an outline of solutions and a clinical checklist, focused on establishing a diagnosis to eventually achieve (personalized) treatment.

The clinical and biochemical hallmarks generally associated with GLUT1DS may be caused by defects in genes other than SLC2A1

Glucose transporter 1 deficiency syndrome (GLUT1DS) is a neurometabolic disorder caused by haploinsufficiency of the GLUT1 glucose transporter (encoded by SLC2A1) leading to defective glucose transport across the blood–brain barrier. This work describes the genetic analysis of 56 patients with clinical or biochemical GLUT1DS hallmarks. 55.4% of these patients had a pathogenic variant of SLC2A1, and 23.2% had a variant in one of 13 different genes. No pathogenic variant was identified for the remaining patients. Expression analysis of SLC2A1 indicated a reduction in SLC2A1 mRNA in patients with pathogenic variants of this gene, as well as in one patient with a pathogenic variant in SLC9A6, and in three for whom no candidate variant was identified. Thus, the clinical and biochemical hallmarks generally associated with GLUT1DS may be caused by defects in genes other than SLC2A1.

Mitochondrial Disorders

BACKGROUND

Mitochondrial disorders are among the most common heritable diseases, with an overall lifetime risk of approximately one in 1500. Nonetheless, their diagnosis is often missed because of their extreme phenotypic and genotypic heterogeneity.

METHODS

This review is based on publications retrieved by a selective literature search on the clinical features, genetics, pathogenesis, diagnosis, and treatment of mitochondrial diseases.

RESULTS

Pathogenic defects of energy metabolism have been described to date in over 400 genes. Only a small number of these genes lie in the mitochondrial DNA; the corresponding diseases are either maternally inherited or of sporadic distribution. The remaining diseaseassociated genes are coded in nuclear DNA and cause diseases that are inherited according to Mendelian rules, mostly autosomal recessive. The most severely involved organs are generally those with the highest energy requirements, including the brain, the sensory epithelia, and the extraocular, cardiac, and skeletal musculature. Typical manifestations include epileptic seizures, stroke-like episodes, hearing loss, retinopathy, external ophthalmoparesis, exercise intolerance, and diabetes mellitus. More than two manifestations of these types should arouse suspicion of a disease of energy metabolism. The severity of mitochondrial disorders ranges from very severe disease, already evident in childhood, to relatively mild disease arising in late adulthood. The diagnosis is usually confirmed with molecular-genetic methods. Symptomatic treatment can improve patients' quality of life. The only disease-modifying treatment that has been approved to date is idebenone for the treatment of Leber hereditary optic neuropathy. Intravitreal gene therapy has also been developed for the treatment of this disease; its approval by the European Medicines Agency is pending.

CONCLUSION

Patients with mitochondrial diseases have highly varied manifestations and can thus present to physicians in practically any branch of medicine. A correct diagnosis is the prerequisite for genetic counseling and for the initiation of personalized treatment.

Technological Improvements in the Genetic Diagnosis of Rett Syndrome Spectrum Disorders

Rett syndrome (RTT) is a severe neurodevelopmental disorder that constitutes the second most common cause of intellectual disability in females worldwide. In the past few years, the advancements in genetic diagnosis brought by next generation sequencing (NGS), have made it possible to identify more than 90 causative genes for RTT and significantly overlapping phenotypes (RTT spectrum disorders). Therefore, the clinical entity known as RTT is evolving towards a spectrum of overlapping phenotypes with great genetic heterogeneity. Hence, simultaneous multiple gene testing and thorough phenotypic characterization are mandatory to achieve a fast and accurate genetic diagnosis. In this review, we revise the evolution of the diagnostic process of RTT spectrum disorders in the past decades, and we discuss the effectiveness of state-of-the-art genetic testing options, such as clinical exome sequencing and whole exome sequencing. Moreover, we introduce recent technological advancements that will very soon contribute to the increase in diagnostic yield in patients with RTT spectrum disorders. Techniques such as whole genome sequencing, integration of data from several “omics”, and mosaicism assessment will provide the tools for the detection and interpretation of genomic variants that will not only increase the diagnostic yield but also widen knowledge about the pathophysiology of these disorders.

The evolving genetic landscape of congenital disorders of glycosylation

Congenital Disorders of Glycosylation (CDG) are an expanding and complex group of rare genetic disorders caused by defects in the glycosylation of proteins and lipids. The genetic spectrum of CDG is extremely broad with mutations in over 140 genes leading to a wide variety of symptoms ranging from mild to severe and life-threatening. There has been an expansion in the genetic complexity of CDG in recent years. More specifically several examples of alternate phenotypes in recessive forms of CDG and new types of CDG following an autosomal dominant inheritance pattern have been identified. In addition, novel genetic mechanisms such as expansion repeats have been reported and several already known disorders have been classified as CDG as their pathophysiology was better elucidated. Furthermore, we consider the future and outlook of CDG genetics, with a focus on exploration of the non-coding genome using whole genome sequencing, RNA-seq and multi-omics technology.

Circulating Cell-Free Mitochondrial DNA in Cerebrospinal Fluid as a Biomarker for Mitochondrial Diseases

BACKGROUND

Mitochondrial diseases (MD) are genetic metabolic disorders that impair normal mitochondrial structure or function. The aim of this study was to investigate the status of circulating cell-free mitochondrial DNA (ccfmtDNA) in cerebrospinal fluid (CSF), together with other biomarkers (growth differentiation factor-15 [GDF-15], alanine, and lactate), in a cohort of 25 patients with a molecular diagnosis of MD.

METHODS

Measurement of ccfmtDNA was performed by using droplet digital PCR.

RESULTS

The mean copy number of ccfmtDNA was approximately 6 times higher in the MD cohort compared to the control group; patients with mitochondrial deletion and depletion syndromes (MDD) had the higher levels. We also detected the presence of both wild-type mtDNA and mtDNA deletions in CSF samples of patients with single deletions. Patients with MDD with single deletions had significantly higher concentrations of GDF-15 in CSF than controls, whereas patients with point mutations in mitochondrial DNA presented no statistically significant differences. Additionally, we found a significant positive correlation between ccfmtDNA levels and GDF-15 concentrations (r = 0.59, P = 0.016).

CONCLUSION

CSF ccfmtDNA levels are significantly higher in patients with MD in comparison to controls and, thus, they can be used as a novel biomarker for MD research. Our results could also be valuable to support the clinical outcome assessment of MD patients.

Gene expression analysis method integration and co-expression module detection applied to rare glucide metabolism disorders using ExpHunterSuite

High-throughput gene expression analysis is widely used. However, analysis is not straightforward. Multiple approaches should be applied and methods to combine their results implemented and investigated. We present methodology for the comprehensive analysis of expression data, including co-expression module detection and result integration via data-fusion, threshold based methods, and a Naïve Bayes classifier trained on simulated data. Application to rare-disease model datasets confirms existing knowledge related to immune cell infiltration and suggest novel hypotheses including the role of calcium channels. Application to simulated and spike-in experiments shows that combining multiple methods using consensus and classifiers leads to optimal results. ExpHunter Suite is implemented as an R/Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite. It can be applied to model and non-model organisms and can be run modularly in R; it can also be run from the command line, allowing scalability with large datasets. Code and reports for the studies are available from https://github.com/fmjabato/ExpHunterSuiteExamples.

Patient derived stem cells for discovery and validation of novel pathogenic variants in inherited retinal disease

Our understanding of inherited retinal disease has benefited immensely from molecular genetic analysis over the past several decades. New technologies that allow for increasingly detailed examination of a patient's DNA have expanded the catalog of genes and specific variants that cause retinal disease. In turn, the identification of pathogenic variants has allowed the development of gene therapies and low-cost, clinically focused genetic testing. Despite this progress, a relatively large fraction (at least 20%) of patients with clinical features suggestive of an inherited retinal disease still do not have a molecular diagnosis today. Variants that are not obviously disruptive to the codon sequence of exons can be difficult to distinguish from the background of benign human genetic variations. Some of these variants exert their pathogenic effect not by altering the primary amino acid sequence, but by modulating gene expression, isoform splicing, or other transcript-level mechanisms. While not discoverable by DNA sequencing methods alone, these variants are excellent targets for studies of the retinal transcriptome. In this review, we present an overview of the current state of pathogenic variant discovery in retinal disease and identify some of the remaining barriers. We also explore the utility of new technologies, specifically patient-derived induced pluripotent stem cell (iPSC)-based modeling, in further expanding the catalog of disease-causing variants using transcriptome-focused methods. Finally, we outline bioinformatic analysis techniques that will allow this new method of variant discovery in retinal disease. As the knowledge gleaned from previous technologies is informing targets for therapies today, we believe that integrating new technologies, such as iPSC-based modeling, into the molecular diagnosis pipeline will enable a new wave of variant discovery and expanded treatment of inherited retinal disease.

The Role of RNA-Sequencing as a New Genetic Diagnosis Tool

Purpose of Review

Whole exome sequencing (WES) and whole-genome sequencing (WGS) are frontline approaches for the genetic diagnosis of rare diseases. However, WES/WGS fails in up to 75% of cases. Transcriptomics via RNA-sequencing (RNA-Seq) is a novel approach that aims to increase the diagnostic yield in rare diseases.

Recent Findings

Recent publications focus on the success of RNA-Seq for increasing diagnosis rates in WES/WGS-negative patients in up to 36% of cases, across a range of different diseases, sample sizes, and tissue types.

Summary

RNA-Seq is beneficial for aiding prioritisation of causative variants currently not detected or often overlooked by WES/WGS alone. An improvement in diagnostic yields has been demonstrated using multiple source tissues, with muscle and fibroblasts being the most representative, but the more accessible blood still demonstrating diagnostic success, particularly in neuromuscular disorders. The introduction of RNA-Seq to the genetic diagnosis toolbox promises to be a useful complementary tool to WES/WGS for improving genetic diagnosis in patients with rare disease.

Facilitations and Hurdles of Genetic Testing in Neuromuscular Disorders

Neuromuscular disorders (NMDs) comprise a heterogeneous group of disorders that affect about one in every thousand individuals worldwide. The vast majority of NMDs has a genetic cause, with about 600 genes already identified. Application of genetic testing in NMDs can be useful for several reasons: correct diagnostic definition of a proband, extensive familial counselling to identify subjects at risk, and prenatal diagnosis to prevent the recurrence of the disease; furthermore, identification of specific genetic mutations still remains mandatory in some cases for clinical trial enrollment where new gene therapies are now approaching. Even though genetic analysis is catching on in the neuromuscular field, pitfalls and hurdles still remain and they should be taken into account by clinicians, as for example the use of next generation sequencing (NGS) where many single nucleotide variants of “unknown significance” can emerge, complicating the correct interpretation of genotype-phenotype relationship. Finally, when all efforts in terms of molecular analysis have been carried on, a portion of patients affected by NMDs still remain “not genetically defined”. In the present review we analyze the evolution of genetic techniques, from Sanger sequencing to NGS, and we discuss “facilitations and hurdles” of genetic testing which must always be balanced by clinicians, in order to ensure a correct diagnostic definition, but taking always into account the benefit that the patient could obtain especially in terms of “therapeutic offer”.

Facilitations and Hurdles of Genetic Testing in Neuromuscular Disorders

Neuromuscular disorders (NMDs) comprise a heterogeneous group of disorders that affect about one in every thousand individuals worldwide. The vast majority of NMDs has a genetic cause, with about 600 genes already identified. Application of genetic testing in NMDs can be useful for several reasons: correct diagnostic definition of a proband, extensive familial counselling to identify subjects at risk, and prenatal diagnosis to prevent the recurrence of the disease; furthermore, identification of specific genetic mutations still remains mandatory in some cases for clinical trial enrollment where new gene therapies are now approaching. Even though genetic analysis is catching on in the neuromuscular field, pitfalls and hurdles still remain and they should be taken into account by clinicians, as for example the use of next generation sequencing (NGS) where many single nucleotide variants of "unknown significance" can emerge, complicating the correct interpretation of genotype-phenotype relationship. Finally, when all efforts in terms of molecular analysis have been carried on, a portion of patients affected by NMDs still remain "not genetically defined". In the present review we analyze the evolution of genetic techniques, from Sanger sequencing to NGS, and we discuss "facilitations and hurdles" of genetic testing which must always be balanced by clinicians, in order to ensure a correct diagnostic definition, but taking always into account the benefit that the patient could obtain especially in terms of "therapeutic offer".

Treatable inherited metabolic disorders causing intellectual disability: 2021 review and digital app

BACKGROUND

The Treatable ID App was created in 2012 as digital tool to improve early recognition and intervention for treatable inherited metabolic disorders (IMDs) presenting with global developmental delay and intellectual disability (collectively 'treatable IDs'). Our aim is to update the 2012 review on treatable IDs and App to capture the advances made in the identification of new IMDs along with increased pathophysiological insights catalyzing therapeutic development and implementation.

METHODS

Two independent reviewers queried PubMed, OMIM and Orphanet databases to reassess all previously included disorders and therapies and to identify all reports on Treatable IDs published between 2012 and 2021. These were included if listed in the International Classification of IMDs (ICIMD) and presenting with ID as a major feature, and if published evidence for a therapeutic intervention improving ID primary and/or secondary outcomes is available. Data on clinical symptoms, diagnostic testing, treatment strategies, effects on outcomes, and evidence levels were extracted and evaluated by the reviewers and external experts. The generated knowledge was translated into a diagnostic algorithm and updated version of the App with novel features.

RESULTS

Our review identified 116 treatable IDs (139 genes), of which 44 newly identified, belonging to 17 ICIMD categories. The most frequent therapeutic interventions were nutritional, pharmacological and vitamin and trace element supplementation. Evidence level varied from 1 to 3 (trials, cohort studies, case-control studies) for 19% and 4-5 (case-report, expert opinion) for 81% of treatments. Reported effects included improvement of clinical deterioration in 62%, neurological manifestations in 47% and development in 37%.

CONCLUSION

The number of treatable IDs identified by our literature review increased by more than one-third in eight years. Although there has been much attention to gene-based and enzyme replacement therapy, the majority of effective treatments are nutritional, which are relatively affordable, widely available and (often) surprisingly effective. We present a diagnostic algorithm (adjustable to local resources and expertise) and the updated App to facilitate a swift and accurate workup, prioritizing treatable IDs. Our digital tool is freely available as Native and Web App (www.treatable-id.org) with several novel features. Our Treatable ID endeavor contributes to the Treatabolome and International Rare Diseases Research Consortium goals, enabling clinicians to deliver rapid evidence-based interventions to our rare disease patients.

Genetic basis of mitochondrial diseases

Mitochondrial disorders are monogenic disorders characterized by a defect in oxidative phosphorylation and caused by pathogenic variants in one of over 340 different genes. The implementation of whole-exome sequencing has led to a revolution in their diagnosis, duplicated the number of associated disease genes, and significantly increased the diagnosed fraction. However, the genetic etiology of a substantial fraction of patients exhibiting mitochondrial disorders remains unknown, highlighting limitations in variant detection and interpretation, which calls for improved computational and DNA sequencing methods, as well as the addition of OMICS tools. More intriguingly, this also suggests that some pathogenic variants lie outside of the protein-coding genes and that the mechanisms beyond the Mendelian inheritance and the mtDNA are of relevance. This review covers the current status of the genetic basis of mitochondrial diseases, discusses current challenges and perspectives, and explores the contribution of factors beyond the protein-coding regions and monogenic inheritance in the expansion of the genetic spectrum of disease.

Utility of RNA Sequencing Analysis in the Context of Genetic Testing

Purpose of Review

RNA analysis is beginning to be integrated into clinical laboratory genomics, and a review of its current uses and limitations is warranted. Here, we summarize the scope and utility of RNA analysis in the context of clinical genetic testing, including considerations for genetic counseling.

Recent Findings

RNA analysis is a powerful approach for interpreting some variants of uncertain significance, for analyzing splicing alterations, for providing additional functional evidence for sequence and structural variants, and for discovering novel variants. However, a review of RNA sequencing methods has noted variability in both laboratory processes and findings. Genetic counseling related to RNA analysis has to take into account nonstandardized laboratory processes, sample-type limitations, and differences in variant-interpretation outcomes.

Summary

RNA analysis is an important complement to DNA testing, although limitations still exist. Maximizing the utility of RNA analysis will require appropriate patient referrals and standardization of laboratory processes as the practice continues to expand the ability to identify and resolve molecular diagnoses.

[Application of RNA sequencing in clinical diagnosis of Mendelian disease]

Gene panel and whole exome sequencing are now commonly used to detect Mendelian disease, but the current molecular diagnostic rate of DNA sequencing is only 35%-50%. In recent years, RNA sequencing emerges as a promising diagnostic method. It can detect new pathogenic mutations, and analyze allele-specific expression. This will be helpful to understand the relationship between disease genotype and phenotype, and can complement genome sequencing in order to expand the traditional genomic diagnostic methods of Mendelian disease. RNA sequencing is expected to become a routine tool for diagnosing Mendelian diseases. This article reviews the application of RNA sequencing in the clinical diagnosis of Mendelian disease.

Transcriptome sequencing identifies a noncoding, deep intronic variant in CLCN7 causing autosomal recessive osteopetrosis

Background

Over half of children with rare genetic diseases remain undiagnosed despite maximal clinical evaluation and DNA‐based genetic testing. As part of an Undiagnosed Diseases Program applying transcriptome (RNA) sequencing to identify the causes of these unsolved cases, we studied a child with severe infantile osteopetrosis leading to cranial nerve palsies, bone deformities, and bone marrow failure, for whom whole‐genome sequencing was nondiagnostic.

Methods

We performed transcriptome (RNA) sequencing of whole blood followed by analysis of aberrant transcript isoforms and osteoclast functional studies.

Results

We identified a pathogenic deep intronic variant in CLCN7 creating an unexpected, frameshifting pseudoexon causing complete loss of function. Functional studies, including osteoclastogenesis and bone resorption assays, confirmed normal osteoclast differentiation but loss of osteoclast function.

Conclusion

This is the first report of a pathogenic deep intronic variant in CLCN7, and our approach provides a model for systematic identification of noncoding variants causing osteopetrosis—a disease for which molecular‐genetic diagnosis can be pivotal for potentially curative hematopoietic stem cell transplantation. Our work illustrates that cryptic splice variants may elude DNA‐only sequencing and supports broad first‐line use of transcriptome sequencing for children with undiagnosed diseases.

From old to new - Repurposing drugs to target mitochondrial energy metabolism in cancer

Although we have entered the era of personalized medicine and tailored therapies, drugs that target a large variety of cancers regardless of individual patient differences would be a major advance nonetheless. This review article summarizes current concepts and therapeutic opportunities in the area of targeting aerobic mitochondrial energy metabolism in cancer. Old drugs previously used for diseases other than cancer, such as antibiotics and antidiabetics, have the potential to inhibit the growth of various tumor entities. Many drugs are reported to influence mitochondrial metabolism. However, here we consider only those drugs which predominantly inhibit oxidative phosphorylation.

Molecular basis of Leigh syndrome: a current look

Leigh Syndrome (OMIM 256000) is a heterogeneous neurologic disorder due to damage in mitochondrial energy production that usually starts in early childhood. The first description given by Leigh pointed out neurological symptoms in children under 2 years and premature death. Following cases brought some hypothesis to explain the cause due to similarity to other neurological diseases and led to further investigation for metabolic diseases. Biochemical evaluation and specific metabolic profile suggested impairment in energy production (OXPHOS) in mitochondria. As direct approach to involved tissues is not always possible or safe, molecular analysis is a great cost-effective option and, besides biochemical results, is required to confirm the underlying cause of this syndrome face to clinical suspicion. The Next Generation Sequencing (NGS) advance represented a breakthrough in molecular biology allowing simultaneous gene analysis giving short-time results and increasing the variants underlying this syndrome, counting over 75 monogenic causes related so far. NGS provided confirmation of emerging cases and brought up diagnosis in atypical presentations as late-onset cases, which turned Leigh into a heterogeneous syndrome with variable outcomes. This review highlights clinical presentation in both classic and atypical phenotypes, the investigation pathway throughout confirmation emphasizing the underlying genetic heterogeneity and increasing number of genes assigned to this syndrome as well as available treatment.

Exploring the RNA Gap for Improving Diagnostic Yield in Primary Immunodeficiencies

Challenges in diagnosing primary immunodeficiency are numerous and diverse, with current whole-exome and whole-genome sequencing approaches only able to reach a molecular diagnosis in 25–60% of cases. We assess these problems and discuss how RNA-focused analysis has expanded and improved in recent years and may now be utilized to gain an unparalleled insight into cellular immunology. We review how investigation into RNA biology can give information regarding the differential expression, monoallelic expression, and alternative splicing—which have important roles in immune regulation and function. We show how this information can inform bioinformatic analysis pipelines and aid in the variant filtering process, expediting the identification of causal variants—especially those affecting splicing—and enhance overall diagnostic ability. We also demonstrate the challenges, which remain in the design of this type of investigation, regarding technological limitation and biological considerations and suggest potential directions for the clinical applications.

A tailored approach to fusion transcript identification increases diagnosis of rare inherited disease

BACKGROUND

RNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5-35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data.

METHODS

We describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization.

RESULTS

We demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance.

CONCLUSIONS

The approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis.

RNA-Seq detects a SAMD12-EXT1 fusion transcript and leads to the discovery of an EXT1 deletion in a child with multiple osteochondromas

Background

We describe a patient presenting with pachygyria, epilepsy, developmental delay, short stature, failure to thrive, facial dysmorphisms, and multiple osteochondromas.

Methods

The patient underwent extensive genetic testing and analysis in an attempt to diagnose the cause of his condition. Clinical testing included metaphase karyotyping, array comparative genomic hybridization, direct sequencing and multiplex ligation‐dependent probe amplification and trio‐based exome sequencing. Subsequently, research‐based whole transcriptome sequencing was conducted to determine whether it might shed light on the undiagnosed phenotype.

Results

Clinical exome sequencing of patient and parent samples revealed a maternally inherited splice‐site variant in the doublecortin (DCX) gene that was classified as likely pathogenic and diagnostic of the patient's neurological phenotype. Clinical array comparative genome hybridization analysis revealed a 16p13.3 deletion that could not be linked to the patient phenotype based on affected genes. Further clinical testing to determine the cause of the patient's multiple osteochondromas was unrevealing despite extensive profiling of the most likely causative genes, EXT1 and EXT2, including mutation screening by direct sequence analysis and multiplex ligation‐dependent probe amplification. Whole transcriptome sequencing identified a SAMD12‐EXT1 fusion transcript that could have resulted from a chromosomal deletion, leading to the loss of EXT1 function. Re‐review of the clinical array comparative genomic hybridization results indicated a possible unreported mosaic deletion affecting the SAMD12 and EXT1 genes that corresponded precisely to the introns predicted to be affected by a fusion‐causing deletion. The existence of the mosaic deletion was subsequently confirmed clinically by an increased density copy number array and orthogonal methodologies

Conclusions

While mosaic mutations and deletions of EXT1 and EXT2 have been reported in the context of multiple osteochondromas, to our knowledge, this is the first time that transcriptomics technologies have been used to diagnose a patient via fusion transcript analysis in the congenital disease setting.

Advancing genomic approaches to the molecular diagnosis of mitochondrial disease

Mitochondrial diseases present a diagnostic challenge due to their clinical and genetic heterogeneity. Achieving comprehensive molecular diagnosis via a conventional candidate-gene approach is likely, therefore, to be labour- and cost-intensive given the expanding number of mitochondrial disease genes. The advent of whole exome sequencing (WES) and whole genome sequencing (WGS) hold the potential of higher diagnostic yields due to the universality and unbiased nature of the methods. However, these approaches are subject to the escalating challenge of variant interpretation. Thus, integration of functional ‘multi-omics’ data, such as transcriptomics, is emerging as a powerful complementary tool in the diagnosis of mitochondrial disease patients for whom extensive prior analysis of DNA sequencing has failed to return a genetic diagnosis.