Multiomics tools for the diagnosis and treatment of rare neurological disease

Big data and transformative bioinformatics in genomic diagnostics and beyond

The current era of high-throughput analysis-driven research offers invaluable insights into disease etiologies, accurate diagnostics, pathogenesis, and personalized therapy. In the field of movement disorders, investigators are facing an increasing growth in the volume of produced patient-derived datasets, providing substantial opportunities for precision medicine approaches based on extensive information accessibility and advanced annotation practices. Integrating data from multiple sources, including phenomics, genomics, and multi-omics, is crucial for comprehensively understanding different types of movement disorders. Here, we explore formats and analytics of big data generated for patients with movement disorders, including strategies to meaningfully share the data for optimized patient benefit. We review computational methods that are essential to accelerate the process of evaluating the increasing amounts of specialized data collected. Based on concrete examples, we highlight how bioinformatic approaches facilitate the translation of multidimensional biological information into clinically relevant knowledge. Moreover, we outline the feasibility of computer-aided therapeutic target evaluation, and we discuss the importance of expanding the focus of big data research to understudied phenotypes such as dystonia.

Drug Discovery and Development for Heart Failure Using Multi-Omics Approaches

Heart failure (HF) is a complex, heterogeneous syndrome with rising prevalence and high morbidity and mortality. The pathophysiology and diverse etiologies of HF present significant challenges for developing effective therapies. Omics technologies-including genomics, proteomics, transcriptomics, metabolomics, and epigenomics-have reshaped our understanding of HF at the molecular level, uncovering new biomarkers and potential therapeutic targets. Omics also enable insights into individualized treatment responses, the risks of adverse drug effects, and patient stratification for clinical trials. This review explores how multi-omics can enhance heart failure drug discovery and development across all stages of the therapeutic pipeline: (1) target selection and lead identification, (2) preclinical studies, and (3) clinical trials. By integrating omics approaches throughout the drug development process, we can accelerate the discovery of more effective and personalized therapies for heart failure.

Omics and rare diseases: challenges, applications, and future perspectives

INTRODUCTION

Rare diseases (RDs) are a heterogeneous group of diseases recognized as a relevant global health priority but posing aspects of complexity, such as geographical scattering of affected individuals, improper/late diagnosis, limited awareness, difficult surveillance and monitoring, limited understanding of natural history, and lack of treatment. Usually, RDs have a pediatric onset and are life-long, multisystemic, and associated with a poor prognosis.

AREAS COVERED

In this work, we review how high-throughput omics technologies such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and other well-established omics, which are increasingly more affordable and efficient, can be applied to the study of RDs promoting diagnosis, understanding of pathological mechanisms, biomarker discovery, and identification of treatments.

EXPERT OPINION

RDs, despite their challenges, offer a niche where collaborative efforts and personalized treatment strategies might be feasible using omics technologies. Specialized consortia fostering multidisciplinary collaboration, data sharing, and the development of biobanks and registries can be built; multi-omics approaches, including so far less exploited omics technologies, along with the implementation of AI tools can be undertaken to deepen our understanding of RDs, driving biomarker discovery and clinical interventions. Nevertheless, technical, ethical, legal, and societal issues must be clearly defined and addressed.

Polymerase Chain Reaction Chips for Biomarker Discovery and Validation in Drug Development

Polymerase chain reaction (PCR) chips are advanced, microfluidic platforms that have revolutionized biomarker discovery and validation because of their high sensitivity, specificity, and throughput levels. These chips miniaturize traditional PCR processes for the speed and precision of nucleic acid biomarker detection relevant to advancing drug development. Biomarkers, which are useful in helping to explain disease mechanisms, patient stratification, and therapeutic monitoring, are hard to identify and validate due to the complexity of biological systems and the limitations of traditional techniques. The challenges to which PCR chips respond include high-throughput capabilities coupled with real-time quantitative analysis, enabling researchers to identify novel biomarkers with greater accuracy and reproducibility. More recent design improvements of PCR chips have further expanded their functionality to also include digital and multiplex PCR technologies. Digital PCR chips are ideal for quantifying rare biomarkers, which is essential in oncology and infectious disease research. In contrast, multiplex PCR chips enable simultaneous analysis of multiple targets, therefore simplifying biomarker validation. Furthermore, single-cell PCR chips have made it possible to detect biomarkers at unprecedented resolution, hence revealing heterogeneity within cell populations. PCR chips are transforming drug development, enabling target identification, patient stratification, and therapeutic efficacy assessment. They play a major role in the development of companion diagnostics and, therefore, pave the way for personalized medicine, ensuring that the right patient receives the right treatment. While this tremendously promising technology has exhibited many challenges regarding its scalability, integration with other omics technologies, and conformity with regulatory requirements, many still prevail. Future breakthroughs in chip manufacturing, the integration of artificial intelligence, and multi-omics applications will further expand PCR chip capabilities. PCR chips will not only be important for the acceleration of drug discovery and development but also in raising the bar in improving patient outcomes and, hence, global health care as these technologies continue to mature.

Targeting Ferroptosis in Rare Neurological Disorders Including Pediatric Conditions: Innovations and Therapeutic Challenges

Ferroptosis, characterized by iron dependency and lipid peroxidation, has emerged as a key mechanism underlying neurodegeneration in rare neurological disorders. These conditions, often marked by significant therapeutic gaps and high unmet medical needs, present unique challenges for intervention development. This review examines the involvement of ferroptosis in rare neurological disease pathogenesis, focusing on its role in oxidative damage and neuronal dysfunction. We explore recent pharmacological advancements, including iron chelators, lipid peroxidation blockers, and antioxidant-based strategies, designed to target ferroptosis. While these approaches show promise, challenges such as disease heterogeneity, limited diagnostic tools, and small patient cohorts hinder progress. Furthermore, we discuss the translational and regulatory barriers to implementing ferroptosis-based therapies in clinical practice. By addressing these obstacles and fostering innovative solutions, this review underscores the potential of ferroptosis-targeting strategies to revolutionize treatment paradigms for rare neurological disorders.

Genes to therapy: a comprehensive literature review of whole-exome sequencing in neurology and neurosurgery

Whole-exome sequencing (WES), a ground-breaking technology, has emerged as a linchpin in neurology and neurosurgery, offering a comprehensive elucidation of the genetic landscape of various neurological disorders. This transformative methodology concentrates on the exonic portions of DNA, which constitute approximately 1% of the human genome, thus facilitating an expedited and efficient sequencing process. WES has been instrumental in advancing our understanding of neurodegenerative diseases, neuro-oncology, cerebrovascular disorders, and epilepsy by revealing rare variants and novel mutations and providing intricate insights into their genetic complexities. This has been achieved while maintaining a substantial diagnostic yield, thereby offering novel perspectives on the pathophysiology and personalized management of these conditions. The utilization of WES boasts several advantages over alternative genetic sequencing methodologies, including cost-effectiveness, reduced incidental findings, simplified analysis and interpretation process, and reduced computational demands. However, despite its benefits, there are challenges, such as the interpretation of variants of unknown significance, cost considerations, and limited accessibility in resource-constrained settings. Additionally, ethical, legal, and social concerns are raised, particularly in the context of incidental findings and patient consent. As we look to the future, the integration of WES with other omics-based approaches could help revolutionize the field of personalized medicine through its implications in predictive models and the development of targeted therapeutic strategies, marking a significant stride toward more effective and clinically oriented solutions.

Succinic semialdehyde dehydrogenase deficiency: a metabolic and genomic approach to diagnosis

Genomic sequencing offers an untargeted, data-driven approach to genetic diagnosis; however, variants of uncertain significance often hinder the diagnostic process. The discovery of rare genomic variants without previously known functional evidence of pathogenicity often results in variants being overlooked as potentially causative, particularly in individuals with undifferentiated phenotypes. Consequently, many neurometabolic conditions, including those in the GABA (gamma-aminobutyric acid) catabolism pathway, are underdiagnosed. Succinic semialdehyde dehydrogenase deficiency (SSADHD, OMIM #271980) is a neurometabolic disorder in the GABA catabolism pathway. The disorder is due to bi-allelic pathogenic variants in ALDH5A1 and is usually characterized by moderate-to-severe developmental delays, hypotonia, intellectual disability, ataxia, seizures, hyperkinetic behavior, aggression, psychiatric disorders, and sleep disturbances. In this study, we utilized an integrated approach to diagnosis of SSADHD by examining molecular, clinical, and metabolomic data from a single large commercial laboratory. Our analysis led to the identification of 16 patients with likely SSADHD along with three novel variants. We also showed that patients with this disorder have a clear metabolomic signature that, along with molecular and clinical findings, may allow for more rapid and efficient diagnosis. We further surveyed all available pathogenic/likely pathogenic variants and used this information to estimate the global prevalence of this disease. Taken together, our comprehensive analysis allows for a global approach to the diagnosis of SSADHD and provides a pathway to improved diagnosis and potential incorporation into newborn screening programs. Furthermore, early diagnosis facilitates referral to genetic counseling, family support, and access to targeted treatments-taken together, these provide the best outcomes for individuals living with either GABA-TD or SSADHD, as well as other rare conditions.

Next-generation sequencing and bioinformatics in rare movement disorders

The ability to sequence entire exomes and genomes has revolutionized molecular testing in rare movement disorders, and genomic sequencing is becoming an integral part of routine diagnostic workflows for these heterogeneous conditions. However, interpretation of the extensive genomic variant information that is being generated presents substantial challenges. In this Perspective, we outline multidimensional strategies for genetic diagnosis in patients with rare movement disorders. We examine bioinformatics tools and computational metrics that have been developed to facilitate accurate prioritization of disease-causing variants. Additionally, we highlight community-driven data-sharing and case-matchmaking platforms, which are designed to foster the discovery of new genotype-phenotype relationships. Finally, we consider how multiomic data integration might optimize diagnostic success by combining genomic, epigenetic, transcriptomic and/or proteomic profiling to enable a more holistic evaluation of variant effects. Together, the approaches that we discuss offer pathways to the improved understanding of the genetic basis of rare movement disorders.

Proteomics in Inherited Metabolic Disorders

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

Changes in the Cerebrospinal Fluid and Plasma Lipidome in Patients with Rett Syndrome

Rett syndrome (RTT) is defined as a rare disease caused by mutations of the methyl-CpG binding protein 2 (MECP2). It is one of the most common causes of genetic mental retardation in girls, characterized by normal early psychomotor development, followed by severe neurologic regression. Hitherto, RTT lacks a specific biomarker, but altered lipid homeostasis has been found in RTT model mice as well as in RTT patients. We performed LC-MS/MS lipidomics analysis to investigate the cerebrospinal fluid (CSF) and plasma composition of patients with RTT for biochemical variations compared to healthy controls. In all seven RTT patients, we found decreased CSF cholesterol levels compared to age-matched controls (n = 13), whereas plasma cholesterol levels were within the normal range in all 13 RTT patients compared to 18 controls. Levels of phospholipid (PL) and sphingomyelin (SM) species were decreased in CSF of RTT patients, whereas the lipidomics profile of plasma samples was unaltered in RTT patients compared to healthy controls. This study shows that the CSF lipidomics profile is altered in RTT, which is the basis for future (functional) studies to validate selected lipid species as CSF biomarkers for RTT.

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.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

Lessons learnt from multifaceted diagnostic approaches to the first 150 families in Victoria's Undiagnosed Diseases Program

BACKGROUND

Clinical exome sequencing typically achieves diagnostic yields of 30%-57.5% in individuals with monogenic rare diseases. Undiagnosed diseases programmes implement strategies to improve diagnostic outcomes for these individuals.

AIM

We share the lessons learnt from the first 3 years of the Undiagnosed Diseases Program-Victoria, an Australian programme embedded within a clinical genetics service in the state of Victoria with a focus on paediatric rare diseases.

METHODS

We enrolled families who remained without a diagnosis after clinical genomic (panel, exome or genome) sequencing between 2016 and 2018. We used family-based exome sequencing (family ES), family-based genome sequencing (family GS), RNA sequencing (RNA-seq) and high-resolution chromosomal microarray (CMA) with research-based analysis.

RESULTS

In 150 families, we achieved a diagnosis or strong candidate in 64 (42.7%) (37 in known genes with a consistent phenotype, 3 in known genes with a novel phenotype and 24 in novel disease genes). Fifty-four diagnoses or strong candidates were made by family ES, six by family GS with RNA-seq, two by high-resolution CMA and two by data reanalysis.

CONCLUSION

We share our lessons learnt from the programme. Flexible implementation of multiple strategies allowed for scalability and response to the availability of new technologies. Broad implementation of family ES with research-based analysis showed promising yields post a negative clinical singleton ES. RNA-seq offered multiple benefits in family ES-negative populations. International data sharing strategies were critical in facilitating collaborations to establish novel disease-gene associations. Finally, the integrated approach of a multiskilled, multidisciplinary team was fundamental to having diverse perspectives and strategic decision-making.

Current progress in metabolomics of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and can cause short- and long-term adverse effects in both pregnant women and their offspring. However, the etiology and pathogenesis of GDM are still unclear. As a metabolic disease, GDM is well suited to metabolomics study, which can monitor the changes in small molecular metabolites induced by maternal stimuli or perturbations in real time. The application of metabolomics in GDM can be used to discover diagnostic biomarkers, evaluate the prognosis of the disease, guide the application of diet or drugs, evaluate the curative effect, and explore the mechanism. This review provides comprehensive documentation of metabolomics research methods and techniques as well as the current progress in GDM research. We anticipate that the review will contribute to identifying gaps in the current knowledge or metabolomics technology, provide evidence-based information, and inform future research directions in GDM.

Leveraging proteomics in orphan disease research: pitfalls and potential

Introduction: The term 'orphan diseases' includes conditions meeting prevalence-based or commercial viability criteria: they affect a small number of individuals and are considered an unviable market for drug development. Proteomics is an important technology to study them, providing information on mechanisms and evolution, biomarkers, and effects of therapeutic interventions.Areas covered: Herein, we review how proteomics and bioinformatic tools could be applied to the study of rare diseases and discuss pitfalls and potential.Expert opinion: Research in the field of rare diseases has to face many challenges, and implementation plans should foresee highly specialized collaborative consortia to create multidisciplinary frameworks for data sharing, advancing research, supporting clinical studies, and accelerating drug development. The integration of different technologies will allow better knowledge of disease pathophysiology, and the inclusion of proteomics and other omics technologies in this context will be pivotal to this aim.Several aspects of rare diseases, often perceived as limiting factors, might actually be advantages for a precision medicine approach: the limited number of patients, the collaboration with patient societies, and the availability of curated clinical registries could allow the development of homogeneous clinical databases and ultimately a better control over the data to be analyzed.

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.

New Diagnostic Approaches for Undiagnosed Rare Genetic Diseases

Accurate diagnosis is the cornerstone of medicine; it is essential for informed care and promoting patient and family well-being. However, families with a rare genetic disease (RGD) often spend more than five years on a diagnostic odyssey of specialist visits and invasive testing that is lengthy, costly, and often futile, as 50% of patients do not receive a molecular diagnosis. The current diagnostic paradigm is not well designed for RGDs, especially for patients who remain undiagnosed after the initial set of investigations, and thus requires an expansion of approaches in the clinic. Leveraging opportunities to participate in research programs that utilize new technologies to understand RGDs is an important path forward for patients seeking a diagnosis. Given recent advancements in such technologies and international initiatives, the prospect of identifying a molecular diagnosis for all patients with RGDs has never been so attainable, but achieving this goal will require global cooperation at an unprecedented scale.

Metabolomic Analysis Identifies Glycometabolism Pathways as Potential Targets of Qianggan Extract in Hyperglycemia Rats

Qianggan formula, a designed prescription according to the Traditional Chinese Medicine (TCM) theory, is widely used in treating chronic liver diseases, and indicated to prevent blood glucose increase in patients via unknown mechanisms. To unravel the effects and underlying mechanisms of Qianggan formula on hyperglycemia, we administrated Qianggan extract to high fat and high sucrose (HFHS) diet rats. Results showed that four-week Qianggan extract intervention significantly decreased serum fasting blood glucose, hemoglobin A1c, and liver glycogen levels. Gas chromatography-mass spectrometry (GC-MS) approach was employed to explore metabolomic profiles in liver and fecal samples. By multivariate and univariate statistical analysis (variable importance of projection value > 1 and p value < 0.05), 44 metabolites (18 in liver and 30 in feces) were identified as significantly different. Hierarchical cluster analysis revealed that most differential metabolites had opposite patterns between pair-wise groups. Qianggan extract restored the diet induced metabolite perturbations. Metabolite sets enrichment and pathway enrichment analysis revealed that the affected metabolites were mainly enriched in glycometabolism pathways such as glycolysis/gluconeogenesis, pentose phosphate pathway, fructose, and mannose metabolism. By compound-reaction-enzyme-gene network analysis, batches of genes (e.g. Hk1, Gck, Rpia, etc) or enzymes (e.g. hexokinase and glucokinase) related to metabolites in enriched pathways were obtained. Our findings demonstrated that Qianggan extract alleviated hyperglycemia, and the effects might be partially due to the regulation of glycometabolism related pathways.

A scoping review and proposed workflow for multi-omic rare disease research

BACKGROUND

Patients with rare diseases face unique challenges in obtaining a diagnosis, appropriate medical care and access to support services. Whole genome and exome sequencing have increased identification of causal variants compared to single gene testing alone, with diagnostic rates of approximately 50% for inherited diseases, however integrated multi-omic analysis may further increase diagnostic yield. Additionally, multi-omic analysis can aid the explanation of genotypic and phenotypic heterogeneity, which may not be evident from single omic analyses.

MAIN BODY

This scoping review took a systematic approach to comprehensively search the electronic databases MEDLINE, EMBASE, PubMed, Web of Science, Scopus, Google Scholar, and the grey literature databases OpenGrey / GreyLit for journal articles pertaining to multi-omics and rare disease, written in English and published prior to the 30th December 2018. Additionally, The Cancer Genome Atlas publications were searched for relevant studies and forward citation searching / screening of reference lists was performed to identify further eligible articles. Following title, abstract and full text screening, 66 articles were found to be eligible for inclusion in this review. Of these 42 (64%) were studies of multi-omics and rare cancer, two (3%) were studies of multi-omics and a pre-cancerous condition, and 22 (33.3%) were studies of non-cancerous rare diseases. The average age of participants (where known) across studies was 39.4 years. There has been a significant increase in the number of multi-omic studies in recent years, with 66.7% of included studies conducted since 2016 and 33% since 2018. Fourteen combinations of multi-omic analyses for rare disease research were returned spanning genomics, epigenomics, transcriptomics, proteomics, phenomics and metabolomics.

CONCLUSIONS

This scoping review emphasises the value of multi-omic analysis for rare disease research in several ways compared to single omic analysis, ranging from the provision of a diagnosis, identification of prognostic biomarkers, distinct molecular subtypes (particularly for rare cancers), and identification of novel therapeutic targets. Moving forward there is a critical need for collaboration of multi-omic rare disease studies to increase the potential to generate robust outcomes and development of standardised biorepository collection and reporting structures for multi-omic studies.

The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia

The complex and heterogeneous pathophysiology of schizophrenia can be deconstructed by integration of large-scale datasets encompassing genes through behavioral phenotypes. Genome-wide datasets are now available for genetic, epigenetic and transcriptomic variations in schizophrenia, which are then analyzed by newly devised systems biology algorithms. A missing piece, however, is the inclusion of information on the proteome and its dynamics in schizophrenia. Proteomics has lagged behind omics of the genome, transcriptome and epigenome since analytic platforms were relatively less robust for proteins. There has been remarkable progress, however, in the instrumentation of liquid chromatography (LC) and mass spectrometry (MS) (LCMS), experimental paradigms and bioinformatics of the proteome. Here, we present a summary of methodological innovations of recent years in MS based proteomics and the power of new generation proteomics, review proteomics studies that have been conducted in schizophrenia to date, and propose how such data can be analyzed and integrated with other omics results. The function of a protein is determined by multiple molecular properties, i.e., subcellular localization, posttranslational modification (PTMs) and protein-protein interactions (PPIs). Incorporation of these properties poses additional challenges in proteomics and their integration with other omics; yet is a critical next step to close the loop of multi-omics integration. In sum, the recent advent of high-throughput proteome characterization technologies and novel mathematical approaches enable us to incorporate functional properties of the proteome to offer a comprehensive multi-omics based understanding of schizophrenia pathophysiology.

Metabolomics for Animal Models of Rare Human Diseases: An Expert Review and Lessons Learned

Rare diseases occur with a frequency ≤1:1500-1:2500 depending on the location and applicable definitions across countries. Although individually rare, they collectively affect as much as 4-8% of population imposing a large burden on public health. Rarity in prevalence means prolonged path to accurate diagnosis, lack of treatment options, and also limited chances for preclinical studies of pathogenesis. I discuss in this expert review (1) what metabolomics, as a high throughput systems sciences technology, offers for rare disease studies, (2) why animal models are important for the study of rare human diseases and what should be kept in mind while using animal models, and finally, (3) provide examples of recent research to highlight how metabolomics on animal models of rare diseases perform, and how these results can lead to the knowhow, which raises genome, metabolome, and phenotype integration to a whole new level. In sum, metabolomics has been for years in clinical use for diagnosis of certain types of rare diseases. Determination of pathogenesis of more complex diseases and testing of treatment strategies is where animal models and systems biology analytical approaches are necessary. From gathered data, it is possible to go back to diagnostic and prognostic markers for rare diseases, which so far lack reliable and robust diagnosis and therapeutic options. In the future, a major challenge is to reveal the links between genotype, metabolism, and phenotype. Rare diseases could be the key in that process.

Protocol for a scoping review of multi-omic analysis for rare diseases

INTRODUCTION

The development of next generation sequencing technology has enabled cost-efficient, large scale, multiple 'omic' analysis, including epigenomic, genomic, metabolomic, phenomic, proteomic and transcriptomic research. These integrated approaches hold significant promise for rare disease research, with the potential to aid biomarker discovery, improve our understanding of disease pathogenesis and identify novel therapeutic targets. In this paper we outline a systematic approach for a scoping review designed to evaluate what primary research has been performed to date on multi-omics and rare disease.

METHODS AND ANALYSIS

This protocol was designed using the Joanna Briggs Institute methodology for scoping reviews. Databases to be searched will include: MEDLINE, EMBASE, PubMed, Web of Science, Scopus and Google Scholar for primary studies relevant to the key terms 'multi-omics' and 'rare disease', published prior to 30th December 2018. Grey literature databases GreyLit and OpenGrey will also be searched, as well as reverse citation screening of relevant articles and forward citation searching using Web of Science Cited Reference Search Tool. Data extraction will be performed using customised forms and a narrative synthesis of the results will be presented.

ETHICS AND DISSEMINATION

As a secondary analysis study with no primary data generated, this scoping review does not require ethical approval. We anticipate this review will highlight a gap in rare disease research and provide direction for novel research. The completed review will be submitted for publication in peer-reviewed journals and presented at relevant conferences discussing rare disease research and/or molecular strategies.

Metabolic Evaluation of Epilepsy: A Diagnostic Algorithm With Focus on Treatable Conditions

Although inborn errors of metabolism do not represent the most common cause of seizures, their early identification is of utmost importance, since many will require therapeutic measures beyond that of common anti-epileptic drugs, either in order to control seizures, or to decrease the risk of neurodegeneration. We translate the currently-known literature on metabolic etiologies of epilepsy (268 inborn errors of metabolism belonging to 21 categories, with 74 treatable errors), into a 2-tiered diagnostic algorithm, with the first-tier comprising accessible, affordable, and less invasive screening tests in urine and blood, with the potential to identify the majority of treatable conditions, while the second-tier tests are ordered based on individual clinical signs and symptoms. This resource aims to support the pediatrician, neurologist, biochemical, and clinical geneticists in early identification of treatable inborn errors of metabolism in a child with seizures, allowing for timely initiation of targeted therapy with the potential to improve outcomes.

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome-all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n-of-1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.