Deregulation of LRSAM1 expression impairs the levels of TSG101, UBE2N, VPS28, MDM2 and EGFR.

CMT is the most common hereditary neuromuscular disorder of the peripheral nervous system with a prevalence of 1/2500 individuals and it is caused by mutations in more than 80 genes. LRSAM1, a RING finger ubiquitin ligase also known as TSG101-associated ligase (TAL), has been associated with Charcot-Marie-Tooth disease type 2P (CMT2P) and to date eight causative mutations have been identified. Little is currently known on the pathogenetic mechanisms that lead to the disease. We investigated the effect of LRSAM1 deregulation on possible LRSAM1 interacting molecules in cell based models. Possible LRSAM1 interacting molecules were identified using protein-protein interaction databases and literature data. Expression analysis of these molecules was performed in both CMT2P patient and control lymphoblastoid cell lines as well as in LRSAM1 and TSG101 downregulated SH-SY5Y cells.TSG101, UBE2N, VPS28, EGFR and MDM2 levels were significantly decreased in the CMT2P patient lymphoblastoid cell line as well as in LRSAM1 downregulated cells. TSG101 downregulation had a significant effect only on the expression of VPS28 and MDM2 and it did not affect the levels of LRSAM1. This study confirms that LRSAM1 is a regulator of TSG101 expression. Furthermore, deregulation of LRSAM1 significantly affects the levels of UBE2N, VPS28, EGFR and MDM2

Genetic Modifiers at the Crossroads of Personalised Medicine for Haemoglobinopathies

Haemoglobinopathies are common monogenic disorders with diverse clinical manifestations, partly attributed to the influence of modifier genes. Recent years have seen enormous growth in the amount of genetic data, instigating the need for ranking methods to identify candidate genes with strong modifying effects. Here, we present the first evidence-based gene ranking metric (IthaScore) for haemoglobinopathy-specific phenotypes by utilising curated data in the IthaGenes database. IthaScore successfully reflects current knowledge for well-established disease modifiers, while it can be dynamically updated with emerging evidence. Protein–protein interaction (PPI) network analysis and functional enrichment analysis were employed to identify new potential disease modifiers and to evaluate the biological profiles of selected phenotypes. The most relevant gene ontology (GO) and pathway gene annotations for (a) haemoglobin (Hb) F levels/Hb F response to hydroxyurea included urea cycle, arginine metabolism and vascular endothelial growth factor receptor (VEGFR) signalling, (b) response to iron chelators included xenobiotic metabolism and glucuronidation, and (c) stroke included cytokine signalling and inflammatory reactions. Our findings demonstrate the capacity of IthaGenes, together with dynamic gene ranking, to expand knowledge on the genetic and molecular basis of phenotypic variation in haemoglobinopathies and to identify additional candidate genes to potentially inform and improve diagnosis, prognosis and therapeutic management

A Novel Tool for the Analysis and Detection of Copy Number Variants Associated with Haemoglobinopathies

Several types of haemoglobinopathies are caused by copy number variants (CNVs). While diagnosis is often based on haematological and biochemical parameters, a definitive diagnosis requires molecular DNA analysis. In some cases, the molecular characterisation of large deletions/duplications is challenging and inconclusive and often requires the use of specific diagnostic procedures, such as multiplex ligation-dependent probe amplification (MLPA). Herein, we collected and comprehensively analysed all known CNVs associated with haemoglobinopathies. The dataset of 291 CNVs was retrieved from the IthaGenes database and was further manually annotated to specify genomic locations, breakpoints and MLPA probes relevant for each CNV. We developed IthaCNVs, a publicly available and easy-to-use online tool that can facilitate the diagnosis of rare and diagnostically challenging haemoglobinopathy cases attributed to CNVs. Importantly, it facilitates the filtering of available entries based on the type of breakpoint information, on specific chromosomal and locus positions, on MLPA probes, and on affected gene(s). IthaCNVs brings together manually curated information about CNV genomic locations, functional effects, and information that can facilitate CNV characterisation through MLPA. It can help laboratory staff and clinicians confirm suspected diagnosis of CNVs based on molecular DNA screening and analysis

Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2, and HBB associated with haemoglobinopathies

Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. By varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity and (b) by using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Our results show that CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. This study provides evidence about the optimal use of computational tools in globin gene clusters under the ACMG/AMP framework

PDXK mutations cause polyneuropathy responsive to pyridoxal 5'-phosphate supplementation

Objective: To identify disease-causing variants in autosomal recessive axonal polyneuropathy with optic atrophy and provide targeted replacement therapy. Methods: We performed genome-wide sequencing, homozygosity mapping, and segregation analysis for novel disease-causing gene discovery. We used circular dichroism to show secondary structure changes and isothermal titration calorimetry to investigate the impact of variants on adenosine triphosphate (ATP) binding. Pathogenicity was further supported by enzymatic assays and mass spectroscopy on recombinant protein, patient-derived fibroblasts, plasma, and erythrocytes. Response to supplementation was measured with clinical validated rating scales, electrophysiology, and biochemical quantification. Results: We identified biallelic mutations in PDXK in 5 individuals from 2 unrelated families with primary axonal polyneuropathy and optic atrophy. The natural history of this disorder suggests that untreated, affected individuals become wheelchair-bound and blind. We identified conformational rearrangement in the mutant enzyme around the ATP-binding pocket. Low PDXK ATP binding resulted in decreased erythrocyte PDXK activity and low pyridoxal 5'-phosphate (PLP) concentrations. We rescued the clinical and biochemical profile with PLP supplementation in 1 family, improvement in power, pain, and fatigue contributing to patients regaining their ability to walk independently during the first year of PLP normalization. Interpretation: We show that mutations in PDXK cause autosomal recessive axonal peripheral polyneuropathy leading to disease via reduced PDXK enzymatic activity and low PLP. We show that the biochemical profile can be rescued with PLP supplementation associated with clinical improvement. As B6 is a cofactor in diverse essential biological pathways, our findings may have direct implications for neuropathies of unknown etiology characterized by reduced PLP levels.ANN NEUROL 2019;86:225-24