Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

Wide diagnostic and genotypic spectrum in patients with suspected mitochondrial disease

BACKGROUND

Mitochondrial Diseases (MDs) are a diverse group of neurometabolic disorders characterized by impaired mitochondrial oxidative phosphorylation and caused by pathogenic variants in more than 400 genes. The implementation of next-generation sequencing (NGS) technologies helps to increase the understanding of molecular basis and diagnostic yield of these conditions. The purpose of the study was to investigate diagnostic and genotypic spectrum in patients with suspected MD. The comprehensive analysis of mtDNA variants using Sanger sequencing was performed in the group of 83 unrelated individuals with clinically suspected mitochondrial disease. Additionally, targeted next generation sequencing or whole exome sequencing (WES) was performed for 30 patients of the study group.

RESULTS

The overall diagnostic rate was 21.7% for the patients with suspected MD, increasing to 36.7% in the group of patients where NGS methods were applied. Mitochondrial disease was confirmed in 11 patients (13.3%), including few classical mitochondrial syndromes (MELAS, MERRF, Leigh and Kearns-Sayre syndrome) caused by pathogenic mtDNA variants (8.4%) and MDs caused by pathogenic variants in five nDNA genes. Other neuromuscular diseases caused by pathogenic variants in seven nDNA genes, were confirmed in seven patients (23.3%).

CONCLUSION

The wide spectrum of identified rare mitochondrial or neurodevelopmental diseases proves that MD suspected patients would mostly benefit from an extensive genetic profiling allowing rapid diagnostics and improving the care of these patients.

Prospective on Imaging Mass Spectrometry in Clinical Diagnostics

Imaging mass spectrometry (IMS) is a molecular technology utilized for spatially driven research, providing molecular maps from tissue sections. This article reviews matrix-assisted laser desorption ionization (MALDI) IMS and its progress as a primary tool in the clinical laboratory. MALDI mass spectrometry has been used to classify bacteria and perform other bulk analyses for plate-based assays for many years. However, the clinical application of spatial data within a tissue biopsy for diagnoses and prognoses is still an emerging opportunity in molecular diagnostics. This work considers spatially driven mass spectrometry approaches for clinical diagnostics and addresses aspects of new imaging-based assays that include analyte selection, quality control/assurance metrics, data reproducibility, data classification, and data scoring. It is necessary to implement these tasks for the rigorous translation of IMS to the clinical laboratory; however, this requires detailed standardized protocols for introducing IMS into the clinical laboratory to deliver reliable and reproducible results that inform and guide patient care.

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.

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 or RNA 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 a consortium such as GREGoR, which is focused on elucidating the underlying cause of rare unsolved genetic disorders.

A Formative Study of the Implementation of Whole Genome Sequencing in Northern Ireland

Background: The UK 100,000 Genomes Project was a transformational research project which facilitated whole genome sequencing (WGS) diagnostics for rare diseases. We evaluated experiences of introducing WGS in Northern Ireland, providing recommendations for future projects. Methods: This formative evaluation included (1) an appraisal of the logistics of implementing and delivering WGS, (2) a survey of participant self-reported views and experiences, (3) semi-structured interviews with healthcare staff as key informants who were involved in the delivery of WGS and (4) a workshop discussion about interprofessional collaboration with respect to molecular diagnostics. Results: We engaged with >400 participants, with detailed reflections obtained from 74 participants including patients, caregivers, key National Health Service (NHS) informants, and researchers (patient survey n = 42; semi-structured interviews n = 19; attendees of the discussion workshop n = 13). Overarching themes included the need to improve rare disease awareness, education, and support services, as well as interprofessional collaboration being central to an effective, mainstreamed molecular diagnostic service. Conclusions: Recommendations for streamlining precision medicine for patients with rare diseases include administrative improvements (e.g., streamlining of the consent process), educational improvements (e.g., rare disease training provided from undergraduate to postgraduate education alongside genomics training for non-genetic specialists) and analytical improvements (e.g., multidisciplinary collaboration and improved computational infrastructure).

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.

Integrating serum proteomics and metabolomics to compare the common and distinct features between acute aggressive ischemic stroke (APIS) and acute non-aggressive ischemic stroke (ANPIS)

Understanding common and distinct pathophysiological features between acute progressive ischemic stroke (APIS) and acute non-progressive ischemic stroke (ANPIS) is a prerequisite to making clear the mechanism to determine the prognosis of acute ischemic stroke (AIS). Here, we recruited three independent sets of subjects, all of which included the APIS, ANPIS, and control groups. They were used for serum proteomic and metabolomic analyses, and validation of the critical pathophysiological processes and potential biomarkers of APIS, respectively. Results showed that there were both common and distinct metabolome and proteome between APIS and ANPIS. APIS and ANPIS shared basic processes of AIS in inflammation and oxidative stress response. Coagulation and lipid metabolism disorder, activation of the complement system, and inflammation may enhance with each other in the symptom worsening of APIS. The contents of serum amyloid A1 (SAA1) and S100 calcium-binding protein A9 (S100-A9) in the validation set confirmed the key pathophysiological processes indicated by omics data; they also jointly conferred a moderate value to distinguish APIS from ANPIS. Collectively, disturbance in coagulation and lipid metabolism, complement activation, and inflammation may be synergistically involved in symptom deterioration in APIS. SAA1 and S100-A9 serve as a potential biomarker panel to distinguish APIS from ANPIS. THE SIGNIFICANCE: In this study, we integrated serum proteomics and metabolomics to explore the similarities and differences in pathophysiological processes between APIS and ANPIS. The global metabolic networks have been constructed, and the crucial common pathophysiological processes and the key distinct pathophysiological features between APIS and ANPIS were investigated based on the differentially expressed proteins and metabolites (DEPs/DEMs). Furthermore, pivotal serum proteins (SAA1 and S100A9) were detected in a dependent set to validate the key pathophysiological characteristics, as well as to assess the possibility of them being used as a biomarker panel. Taken together, the multi-omics integration strategy used in this clinical study shows potential to comprehensively interpret and compare the pathophysiological processes of AIS in various conditions, as well as to screen a reliable new biomarker panel.

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.

Tackling Dysfunction of Mitochondrial Bioenergetics in the Brain

Oxidative phosphorylation (OxPhos) is the basic function of mitochondria, although the landscape of mitochondrial functions is continuously growing to include more aspects of cellular homeostasis. Thanks to the application of -omics technologies to the study of the OxPhos system, novel features emerge from the cataloging of novel proteins as mitochondrial thus adding details to the mitochondrial proteome and defining novel metabolic cellular interrelations, especially in the human brain. We focussed on the diversity of bioenergetics demand and different aspects of mitochondrial structure, functions, and dysfunction in the brain. Definition such as 'mitoexome', 'mitoproteome' and 'mitointeractome' have entered the field of 'mitochondrial medicine'. In this context, we reviewed several genetic defects that hamper the last step of aerobic metabolism, mostly involving the nervous tissue as one of the most prominent energy-dependent tissues and, as consequence, as a primary target of mitochondrial dysfunction. The dual genetic origin of the OxPhos complexes is one of the reasons for the complexity of the genotype-phenotype correlation when facing human diseases associated with mitochondrial defects. Such complexity clinically manifests with extremely heterogeneous symptoms, ranging from organ-specific to multisystemic dysfunction with different clinical courses. Finally, we briefly discuss the future directions of the multi-omics study of human brain disorders.

Integration strategies of multi-omics data for machine learning analysis

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems.

Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.

The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives

Hereditary spinocerebellar degeneration (SCD) encompasses an expanding list of rare diseases with a broad clinical and genetic heterogeneity, complicating their diagnosis and management in daily clinical practice. Correct diagnosis is a pillar for precision medicine, a branch of medicine that promises to flourish with the progressive improvements in studying the human genome. Discovering the genes causing novel Mendelian phenotypes contributes to precision medicine by diagnosing subsets of patients with previously undiagnosed conditions, guiding the management of these patients and their families, and enabling the discovery of more causes of Mendelian diseases. This new knowledge provides insight into the biological processes involved in health and disease, including the more common complex disorders. This review discusses the evolution of the clinical and genetic approaches used to diagnose hereditary SCD and the potential of new tools for future discoveries.

Mitochondrial Syndromes Revisited

In the last ten years, the knowledge of the genetic basis of mitochondrial diseases has significantly advanced. However, the vast phenotypic variability linked to mitochondrial disorders and the peculiar characteristics of their genetics make mitochondrial disorders a complex group of disorders. Although specific genetic alterations have been associated with some syndromic presentations, the genotype–phenotype relationship in mitochondrial disorders is complex (a single mutation can cause several clinical syndromes, while different genetic alterations can cause similar phenotypes). This review will revisit the most common syndromic pictures of mitochondrial disorders, from a clinical rather than a molecular perspective. We believe that the new phenotype definitions implemented by recent large multicenter studies, and revised here, may contribute to a more homogeneous patient categorization, which will be useful in future studies on natural history and clinical trials.