Increasing diagnostic yield by RNA-Sequencing in rare disease-bypass hurdles of interpreting intronic or splice-altering variants

A guide for the diagnosis of rare and undiagnosed disease: beyond the exome

Rare diseases affect 30 million people in the USA and more than 300-400 million worldwide, often causing chronic illness, disability, and premature death. Traditional diagnostic techniques rely heavily on heuristic approaches, coupling clinical experience from prior rare disease presentations with the medical literature. A large number of rare disease patients remain undiagnosed for years and many even die without an accurate diagnosis. In recent years, gene panels, microarrays, and exome sequencing have helped to identify the molecular cause of such rare and undiagnosed diseases. These technologies have allowed diagnoses for a sizable proportion (25-35%) of undiagnosed patients, often with actionable findings. However, a large proportion of these patients remain undiagnosed. In this review, we focus on technologies that can be adopted if exome sequencing is unrevealing. We discuss the benefits of sequencing the whole genome and the additional benefit that may be offered by long-read technology, pan-genome reference, transcriptomics, metabolomics, proteomics, and methyl profiling. We highlight computational methods to help identify regionally distant patients with similar phenotypes or similar genetic mutations. Finally, we describe approaches to automate and accelerate genomic analysis. The strategies discussed here are intended to serve as a guide for clinicians and researchers in the next steps when encountering patients with non-diagnostic exomes.

Feasibility of predicting allele specific expression from DNA sequencing using machine learning

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.

Transcriptomic and bioinformatic analysis of Clcn7-dependent Autosomal Dominant Osteopetrosis type 2. Preclinical and clinical implications

Autosomal Dominant Osteopetrosis type 2 (ADO2) is a rare genetic disease characterized by dense yet fragile bones. To date, the radiological approach remains the gold standard for ADO2 diagnosis. However, recent observations unveiled that ADO2 is a systemic disease affecting various organs beyond bone, including lung, kidney, muscle, and brain. Monitoring disease status and progression would greatly benefit from specific biomarkers shared by the affected organs. In this work, data derived from RNA deep sequencing (RNA dSeq) of bone, lung, kidney, muscle, brain, and osteoclasts isolated from wildtype (WT) and Clcn7^G213R ADO2 mice were subjected to gene ontology and pathway analyses. Results showed the presence of alterations in gene ontology terms and pathways associated with bone metabolism and osteoclast biology, including JAK-STAT, cytokine-cytokine receptor, and hematopoietic cell lineage. Furthermore, in line with the multiorgan alterations caused by ADO2, the analysis of soft organs showed an enrichment of PPAR and neuroactive ligand-receptor interaction pathways known to be involved in the onset of tissue fibrosis and behavioral alterations, respectively. Finally, we observed the modulations of potential ADO2 biomarkers in organs and cells of ADO2 mice and in the peripheral blood mononuclear cells of patients, using conventional methods. Of note, some of these biomarkers could be possibly responsive to an effective experimental therapy based on a mutation-specific siRNA. Overall, the identified gene signature and the soluble forms of the encoded proteins could potentially represent reliable disease biomarkers that could improve the ADO2 diagnosis, the monitoring of both the skeletal and non-skeletal dysfunctions, and the assessment of the response to therapy.

Research Techniques Made Simple: Whole-Transcriptome Sequencing by RNA-Seq for Diagnosis of Monogenic Disorders

Mendelian disorders with cutaneous manifestations comprise a genotypically heterogeneous group of over 1,000 diseases, and in most of them mutant genes have been identified. Mutation detection approaches in these diseases have largely focused on DNA analysis by next-generation sequencing techniques, including gene-targeted sequencing panels as well as whole-exome and whole-genome sequencing. Genome-wide homozygosity mapping (HM), based on DNA polymorphism, has also assisted in the identification of candidate genes in families with consanguinity. However, specific pathogenic variants have not been disclosed in many individual patients when analyzed by next-generation sequencing, and in particular, DNA-based analysis failed to identify many of the mutations impacting on splicing or gene expression. Whole-transcriptome sequencing by RNA sequencing (RNA-Seq), with appropriate bioinformatics, provides a robust tool to identify additional mutations to facilitate genetic diagnosis in genodermatoses. RNA-Seq can be used for variant calling and HM similar to DNA-based approaches, but it also allows for the identification of mutations that result in aberrant transcriptome expression, as displayed by heatmap analysis, and altered splicing patterns of RNA, as visualized by Sashimi plots. Thus, clinical RNA-Seq extends molecular diagnostics of rare genodermatoses, and it could provide a reliable first-tier diagnostic approach to extend mutation databases in patients with heritable skin diseases.

New Strategies for Clinical Trials in Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that poses several challenges in terms of clinical diagnosis and investigation of molecular etiology. The lack of knowledge on the pathogenic mechanisms underlying ASD has hampered the clinical trials that so far have tried to target ASD behavioral symptoms. In order to improve our understanding of the molecular abnormalities associated with ASD, a deeper and more extensive genetic profiling of targeted individuals with ASD was needed.

METHODS

The recent availability of new and more powerful sequencing technologies (third-generation sequencing) has allowed to develop novel strategies for the characterization of comprehensive genetic profiles of individuals with ASD. In particular, this review will describe integrated approaches based on the combination of various omics technologies that will lead to a better stratification of targeted cohorts for the design of clinical trials in ASD.

RESULTS

In order to analyze the big data collected by assays such as the whole genome, epigenome, transcriptome, and proteome, it is critical to develop an efficient computational infrastructure. Machine learning models are instrumental to identify non-linear relationships between the omics technologies and, therefore, establish a functional informative network among the different data sources.

CONCLUSION

The potential advantage provided by these new integrated omics-based strategies is better characterization of the genetic background of ASD cohorts, to identify novel molecular targets for drug development, and ultimately offer a more personalized approach in the design of clinical trials for ASD.

Review: Understanding Rare Genetic Diseases in Low Resource Regions Like Jammu and Kashmir - India

Rare diseases (RDs) are the clinical conditions affecting a few percentage of individuals in a general population compared to other diseases. Limited clinical information and a lack of reliable epidemiological data make their timely diagnosis and therapeutic management difficult. Emerging Next-Generation DNA Sequencing technologies have enhanced our horizons on patho-physiological understanding of many of the RDs and ushered us into an era of diagnostic and therapeutic research related to this ignored health challenge. Unfortunately, relevant research is meager in developing countries which lack a reliable estimate of the exact burden of most of the RDs. India is to be considered as the "Pandora's Box of genetic disorders." Owing to its huge population heterogeneity and high inbreeding or endogamy rates, a higher burden of rare recessive genetic diseases is expected and supported by the literature findings that endogamy is highly detrimental to health as it enhances the degree of homozygosity of recessive alleles in the general population. The population of a low resource region Jammu and Kashmir (J&K) - India, is highly inbred. Some of its population groups variably practice consanguinity. In context with the region's typical geographical topography, highly inbred population structure and unique but heterogeneous gene pool, a huge burden of known and uncharacterized genetic disorders is expected. Unfortunately, many suspected cases of genetic disorders remain undiagnosed or misdiagnosed due to lack of appropriate clinical as well as diagnostic resources in the region, causing patients to face a huge psycho-socio-economic crisis and many a time suffer life-long with their ailment. In this review, the major challenges associated with RDs are highlighted in general and an account on the methods that can be adopted for conducting fruitful molecular genetic studies in genetically vulnerable and low resource regions is also provided, with an example of a region like J&K - India.