metPropagate: network-guided propagation of metabolomic information for prioritization of metabolic disease genes

Abstract

Many inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis is imperative. Whole-exome sequencing (WES) variant prioritization coupled with phenotype-guided clinical and bioinformatics expertise is typically used to identify disease-causing variants; however, it can be challenging to identify the causal candidate gene when a large number of rare and potentially pathogenic variants are detected. Here, we present a network-based approach, metPropagate, that uses untargeted metabolomics (UM) data from a single patient and a group of controls to prioritize candidate genes in patients with suspected IEMs. We validate metPropagate on 107 patients with IEMs diagnosed in Miller et al. (2015) and 11 patients with both CNS and metabolic abnormalities. The metPropagate method ranks candidate genes by label propagation, a graph-smoothing algorithm that considers each gene's metabolic perturbation in addition to the network of interactions between neighbors. metPropagate was able to prioritize at least one causative gene in the top 20 th percentile of candidate genes for 92% of patients with known IEMs. Applied to patients with suspected neurometabolic disease, metPropagate placed at least one causative gene in the top 20 th percentile in 9/11 patients, and ranked the causative gene more highly than Exomiser's phenotype-based ranking in 6/11 patients. Interestingly, ranking by a weighted combination of metPropagate and Exomiser scores resulted in improved prioritization. The results of this study indicate that network-based analysis of UM data can provide an additional mode of evidence to prioritize causal genes in patients with suspected IEMs.

Integrative omics approaches to advance rare disease diagnostics

“…Untargeted methods can quantify tens of thousands of features, providing an unbiased view of the human metabolome and can be used for screening for novel biomarkers. Several studies have used untargeted metabolomics to provide additional evidence to confirm the pathogenicity of variants 17,24,38,114,121,122 . The application of metabolite set enrichment analysis on untargeted metabolomics data has also been shown to prioritise relevant pathways for inherited metabolic disorders 120,123 .…”

“…Several studies have used untargeted metabolomics to provide additional evidence to confirm the pathogenicity of variants. 17,24,38,114,121,122 The application of metabolite set enrichment analysis on untargeted metabolomics data has also been shown to prioritise relevant pathways for inherited disorders. 120,123 In addition, abnormal metabolite levels and biochemical measurements could be encoded as HPO terms and, therefore, integrated with phenotype-based prioritisation algorithms.…”

Metabolomics of ethnic fermented foods and beverages: understanding new aspects through Omic techniques

“…SUMMER JS-MS 2.0, and rawR built on various programming languages such as R, C++, Java, JavaScript, and HTML are analytical tools for LC-MS-based metabolomics data. Metpropagate is another example of an analytical tool for untargeted LC-MS purely based on some of the most crucial programming languages such as R and Python (Graham Linck et al, 2020). There are some Comprehensive R Archive Network (CRAN) packages such as Lilikoi, Omu, Wilson, and MetaClean, which are helpful for metabolomics research (Misra, 2021).…”

New software tools, databases, and resources in metabolomics: updates from 2020

“…metPropagate, is a network-based approach that uses untargeted metabolomics data from a single patient and a group of controls to prioritize candidate genes in patients with suspected inborn errors of metabolism (IEMs) (Graham Linck et al 2020 ). This approach determines whether metabolomic evidence could be used to prioritize the causative gene from this list of candidate genes, where each gene in a patient’s candidate gene list is ranked using a per-gene metabolomic score termed the “metPropagate score”, which represented the likely metabolic relevance of a particular gene to each patient.…”