PubMed

F1000Res. 2020 Oct 15;9:1239. doi: 10.12688/f1000research.26429.2. eCollection 2020.

ABSTRACT

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data - a common problem in real-world data - without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

PMID:33628435 | PMC:PMC7883323 | DOI:10.12688/f1000research.26429.2

Exp Physiol. 2021 Feb 18. doi: 10.1113/EP089366. Online ahead of print.

NO ABSTRACT

PMID:33599973 | DOI:10.1113/EP089366

NPJ Genom Med. 2020 Apr 8;5(1):16. doi: 10.1038/s41525-020-0123-6.

ABSTRACT

Wilson disease is a recessive genetic disorder caused by pathogenic loss-of-function variants in the ATP7B gene. It is characterized by disrupted copper homeostasis resulting in liver disease and/or neurological abnormalities. The variant NM_000053.3 :c.1934T > G (Met645Arg) has been reported as compound heterozygous, and is highly prevalent among Wilson disease patients of Spanish descent. Accordingly, it is classified as pathogenic by leading molecular diagnostic centers. However, functional studies suggest that the amino acid change does not alter protein function, leading one ClinVar submitter to question its pathogenicity. Here, we used a minigene system and gene-edited HepG2 cells to demonstrate that c.1934T > G causes ~70% skipping of exon 6. Exon 6 skipping results in frameshift and stop-gain, leading to loss of ATP7B function. The elucidation of the mechanistic effect for this variant resolves any doubt about its pathogenicity and enables the development of genetic medicines for restoring correct splicing.

PMID:33579975 | DOI:10.1038/s41525-020-0123-6

Mol Metab. 2021 Feb 6:101185. doi: 10.1016/j.molmet.2021.101185. Online ahead of print.

ABSTRACT

OBJECTIVE: Autophagy is a physiological self-eating process that can promote cell survival or activate cell death in eukaryotic cells. In skeletal muscle, it is important for maintaining muscle mass and function that is critical to sustain mobility and regulate metabolism. The UV radiation resistance-associated gene (UVRAG) regulates the early stages of autophagy and autophagosome maturation and plays a key role in endosomal trafficking. This study investigated the essential in vivo role of UVRAG in skeletal muscle biology.

METHODS: To determine the role of UVRAG in skeletal muscle in vivo, we generated muscle-specific UVRAG knockout mice using the Cre-loxP system driven by Myf6 promoter that is exclusively expressed in skeletal muscle. Myf6-Cre+ UVRAGfl/fl (M-UVRAG-/-) mice were compared to littermate Myf6-Cre+ UVRAG+/+ (M-UVRAG+/+) controls under basal conditions on a normal chow diet. Body composition, muscle function, and mitochondria morphology were assessed in muscles of the WT and KO mice at 24 weeks of age.

RESULTS: M-UVRAG-/- mice developed accelerated sarcopenia and impaired muscle function compared to M-UVRAG+/+ littermates at 24 weeks of age. Interestingly, these mice displayed improved glucose tolerance and increased energy expenditure likely related to upregulated Fgf21, a marker of muscle dysfunction. Skeletal muscle of the M-UVRAG-/- mice showed altered mitochondrial morphology with increased mitochondrial fission and EGFR accumulation reflecting defects in endosomal trafficking. To determine whether increased EGFR signaling had a causal role in muscle dysfunction, the mice were treated with an EGFR inhibitor, gefitinib, which partially restored markers of muscle and mitochondrial deregulation. Conversely, constitutively active EGFR transgenic expression in UVRAG-deficient muscle led to further detrimental effects with non-overlapping distinct defects in muscle function, with EGFR activation affecting the muscle fiber type whereas UVRAG deficiency impaired mitochondrial homeostasis.

CONCLUSIONS: Our results show that both UVRAG and EGFR signaling are critical for maintaining muscle mass and function with distinct mechanisms in the differentiation pathway.

PMID:33561544 | DOI:10.1016/j.molmet.2021.101185