Targeted versus untargeted omics - the CAFSA story

Cellular and Molecular Responses to Mitochondrial DNA Deletions in Kearns-Sayre Syndrome: Some Underlying Mechanisms

Kearns-Sayre syndrome (KSS) is a rare multisystem mitochondrial disorder. It is caused by mitochondrial DNA (mtDNA) rearrangements, mostly large-scale deletions of 1.1-10 kb. These deletions primarily affect energy supply through impaired oxidative phosphorylation and reduced ATP production. This impairment gives rise to dysfunction of several tissues, in particular those with high energy demand like brain and muscles. Over the past decades, changes in respiratory chain complexes and energy metabolism have been emphasized, whereas little attention has been paid to other reports on ROS overproduction, protein synthesis inhibition, myelin vacuolation, demyelination, autophagy, apoptosis, and involvement of lipid raft and oligodendrocytes in KSS. Therefore, this paper draws attention towards these relatively underemphasized findings that might further clarify the pathologic cascades following deletions in the mtDNA.

Tritium: Its relevance, sources and impacts on non-human biota

Tritium (³H) is a radioactive isotope of hydrogen that is abundantly released from nuclear industries. It is extremely mobile in the environment and in all biological systems, representing an increasing concern for the health of both humans and non-human biota (NHB). The present review examines the sources and characteristics of tritium in the environment, and evaluates available information pertaining to its biological effects at different levels of biological organisation in NHB. Despite an increasing number of publications in the tritium radiobiology field, there exists a significant disparity between data available for the different taxonomic groups and species, and observations are heavily biased towards marine bivalves, fish and mammals (rodents). Further limitations relate to the scarcity of information in the field relative to the laboratory, and lack of studies that employ forms of tritium other than tritiated water (HTO). Within these constraints, different responses to HTO exposure, from molecular to behavioural, have been reported during early life stages, but the potential transgenerational effects are unclear. The application of rapidly developing "omics" techniques could help to fill these knowledge gaps and further elucidate the relationships between molecular and organismal level responses through the development of radiation specific adverse outcome pathways (AOPs). The use of a greater diversity of keystone species and exposures to multiple stressors, elucidating other novel effects (e.g., by-stander, germ-line, transgenerational and epigenetic effects) offers opportunities to improve environmental risk assessments for the radionuclide. These could be combined with artificial intelligence (AI) including machine learning (ML) and ecosystem-based approaches.

Increased Sphingomyelin and Free Sialic Acid in Cerebrospinal Fluid of Kearns-Sayre Syndrome: New Findings Using Untargeted Metabolomics

BACKGROUND

Kearns-Sayre syndrome (KSS) is caused by duplications and/or deletions of mitochondrial DNA (mtDNA) and is typically diagnosed based on a classic triad of symptoms with chronic progressive external ophthalmoplegia (CPEO), retinitis pigmentosa, and onset before age 20 years. The present study aimed to diagnose two patients, on suspicion of KSS.

METHODS

One of the patients went through a diagnostic odyssey, with normal results from several mtDNA analyses, both in blood and muscle, before the diagnosis was confirmed genetically.

RESULTS

Two patients presented increased tau protein and low 5-methyltetrahydrofolate (5-MTHF) levels in the cerebrospinal fluid (CSF). Untargeted metabolomics on CSF samples also showed an increase in the levels of free sialic acid and sphingomyelin C16:0 (d18:1/C16:0), compared with four control groups (patients with mitochondrial disorders, nonmitochondrial disorders, low 5-MTHF, or increased tau proteins).

CONCLUSIONS

It is the first time that elevated sphingomyelin C16:0 (d18:1/C16:0) and tau protein in KSS are reported. Using an untargeted metabolomics approach and standard laboratory methods, the study could shed new light on metabolism in KSS to better understand its complexity. In addition, the findings may suggest the combination of elevated free sialic acid, sphingomyelin C16:0 (d18:1/C16:0), and tau protein as well as low 5-MTHF as new biomarkers in the diagnostics of KSS.

Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation

Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as many metabolites as possible at once, identify unexpected metabolic changes, and characterize novel metabolites in biological samples. However, the identification of metabolites and the biological interpretation of such large and complex datasets remain challenging. One approach to address these challenges is considering that metabolites are connected through informative relationships. Such relationships can be formalized as networks, where the nodes correspond to the metabolites or features (when there is no or only partial identification), and edges connect nodes if the corresponding metabolites are related. Several networks can be built from a single dataset (or a list of metabolites), where each network represents different relationships, such as statistical (correlated metabolites), biochemical (known or putative substrates and products of reactions), or chemical (structural similarities, ontological relations). Once these networks are built, they can subsequently be mined using algorithms from network (or graph) theory to gain insights into metabolism. For instance, we can connect metabolites based on prior knowledge on enzymatic reactions, then provide suggestions for potential metabolite identifications, or detect clusters of co-regulated metabolites. In this review, we first aim at settling a nomenclature and formalism to avoid confusion when referring to different networks used in the field of metabolomics. Then, we present the state of the art of network-based methods for mass spectrometry-based metabolomics data analysis, as well as future developments expected in this area. We cover the use of networks applications using biochemical reactions, mass spectrometry features, chemical structural similarities, and correlations between metabolites. We also describe the application of knowledge networks such as metabolic reaction networks. Finally, we discuss the possibility of combining different networks to analyze and interpret them simultaneously.

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.

Metabolomics for Investigating Physiological and Pathophysiological Processes

Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics is leading to a renewed interest in metabolism and the role that small molecule metabolites play in many biological processes. As a result, traditional views of metabolites as being simply the "bricks and mortar" of cells or just the fuel for cellular energetics are being upended. Indeed, metabolites appear to have much more varied and far more important roles as signaling molecules, immune modulators, endogenous toxins, and environmental sensors. This review explores how metabolomics is yielding important new insights into a number of important biological and physiological processes. In particular, a major focus is on illustrating how metabolomics and discoveries made through metabolomics are improving our understanding of both normal physiology and the pathophysiology of many diseases. These discoveries are yielding new insights into how metabolites influence organ function, immune function, nutrient sensing, and gut physiology. Collectively, this work is leading to a much more unified and system-wide perspective of biology wherein metabolites, proteins, and genes are understood to interact synergistically to modify the actions and functions of organelles, organs, and organisms.

Value of genetic analysis for confirming inborn errors of metabolism detected through the Spanish neonatal screening program

The present work describes the value of genetic analysis as a confirmatory measure following the detection of suspected inborn errors of metabolism in the Spanish newborn mass spectrometry screening program. One hundred and forty-one consecutive DNA samples were analyzed by next-generation sequencing using a customized exome sequencing panel. When required, the Illumina extended clinical exome panel was used, as was Sanger sequencing or transcriptional profiling. Biochemical tests were used to confirm the results of the genetic analysis. Using the customized panel, the metabolic disease suspected in 83 newborns (59%) was confirmed. In three further cases, two monoallelic variants were detected for two genes involved in the same biochemical pathway. In the remainder, either a single variant or no variant was identified. Given the persistent absence of biochemical alterations, carrier status was assigned in 39 cases. False positives were recorded for 11. In five cases in which the biochemical pattern was persistently altered, further genetic analysis allowed the detection of two variants affecting the function of BCAT2, ACSF3, and DNAJC12, as well as a second, deep intronic variant in ETFDH or PTS. The present results suggest that genetic analysis using extended next-generation sequencing panels can be used as a confirmatory test for suspected inborn errors of metabolism detected in newborn screening programs. Biochemical tests can be very helpful when a diagnosis is unclear. In summary, simultaneous genomic and metabolomic analyses can increase the number of inborn errors of metabolism that can be confirmed following suggestive newborn screening results.

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.