Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations.

BACKGROUND: Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs. METHODS: Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis. RESULTS: The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis. CONCLUSION: The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.

It takes two to tango: The essential role of ER-mitochondrial contact sites in mitochondrial dynamics.

Mitochondria change their shape, size and number, in response to cellular demand, through mitochondrial dynamics. The interaction between mitochondria and the ER, through ER-mitochondrial contact sites, is crucial in mitochondrial dynamics. Several protein complexes tethering mitochondria to the ER include proteins involved in fission or fusion but also proteins involved in calcium homeostasis, which is known to affect mitochondrial dynamics. The formation of these contact sites are especially important for mitochondrial fission as these contact sites induce both outer and inner membrane constriction, prior to recruitment of Drp1. While the exact molecular mechanisms behind these constrictions remain uncertain, several hypotheses have been proposed. In this review, we discuss the involvement of tethering complexes in mitochondrial dynamics and provide an overview of the current knowledge and hypotheses on the constriction of the outer and inner mitochondrial membrane at ER-mitochondrial contact sites.

Pathogenic SLIRP variants as a novel cause of autosomal recessive mitochondrial encephalomyopathy with complex I and IV deficiency.

In a Dutch non-consanguineous patient having mitochondrial encephalomyopathy with complex I and complex IV deficiency, whole exome sequencing revealed two compound heterozygous variants in SLIRP. SLIRP gene encodes a stem-loop RNA-binding protein that regulates mitochondrial RNA expression and oxidative phosphorylation (OXPHOS). A frameshift and a deep-intronic splicing variant reduced the amount of functional wild-type SLIRP RNA to 5%. Consequently, in patient fibroblasts, MT-ND1, MT-ND6, and MT-CO1 expression was reduced. Lentiviral transduction of wild-type SLIRP cDNA in patient fibroblasts increased MT-ND1, MT-ND6, and MT-CO1 expression (2.5-7.2-fold), whereas mutant cDNAs did not. A fourfold decrease of citrate synthase versus total protein ratio in patient fibroblasts indicated that the resulting reduced mitochondrial mass caused the OXPHOS deficiency. Transduction with wild-type SLIRP cDNA led to a 2.4-fold increase of this ratio and partly restored OXPHOS activity. This confirmed causality of the SLIRP variants. In conclusion, we report SLIRP variants as a novel cause of mitochondrial encephalomyopathy with OXPHOS deficiency.

A hitchhiker's guide to mitochondrial quantification.

The variety of available mitochondrial quantification tools makes it difficult to select the most reliable and accurate quantification tool. Here, we performed elaborate analyses on five open source ImageJ tools. Excessive clustering of mitochondrial structures was observed in four tools, caused by the global thresholding applied by these tools. The Mitochondrial Analyzer, which uses adaptive thresholding, outperformed the other examined tools, with accurate structural segregation and identification. Additionally, we showed that the Mitochondrial Analyzer successfully identifies mitochondrial morphology differences. Based on the observed performance, we consider the Mitochondrial Analyzer the best open source tool for mitochondrial network morphology quantification.