Complex hereditary peripheral neuropathies caused by novel variants in mitochondrial-related nuclear genes

Whole genome sequencing increases the diagnostic rate in Charcot-Marie-Tooth disease

Charcot-Marie-Tooth disease (CMT) is one of the most common and genetically heterogeneous inherited neurological diseases, with more than 130 disease-causing genes. Whole genome sequencing (WGS) has improved diagnosis across genetic diseases, but the diagnostic impact in CMT is yet to be fully reported. We present the diagnostic results from a single specialist inherited neuropathy centre, including the impact of WGS diagnostic testing. Patients were assessed at our specialist inherited neuropathy centre from 2009 to 2023. Genetic testing was performed using single gene testing, next-generation sequencing targeted panels, research whole exome sequencing and WGS and, latterly, WGS through the UK National Health Service. Variants were assessed using the American College of Medical Genetics and Genomics and Association for Clinical Genomic Science criteria. Excluding patients with hereditary ATTR amyloidosis, 1515 patients with a clinical diagnosis of CMT and related disorders were recruited. In summary, 621 patients had CMT1 (41.0%), 294 CMT2 (19.4%), 205 intermediate CMT (CMTi, 13.5%), 139 hereditary motor neuropathy (HMN, 9.2%), 93 hereditary sensory neuropathy (HSN, 6.1%), 38 sensory ataxic neuropathy (2.5%), 72 hereditary neuropathy with liability to pressure palsies (HNPP, 4.8%) and 53 'complex' neuropathy (3.5%). Overall, a genetic diagnosis was reached in 76.9% (1165/1515). A diagnosis was most likely in CMT1 (96.8%, 601/621), followed by CMTi (81.0%, 166/205) and then HSN (69.9%, 65/93). Diagnostic rates remained less than 50% in CMT2, HMN and complex neuropathies. The most common genetic diagnosis was PMP22 duplication (CMT1A; 505/1165, 43.3%), then GJB1 (CMTX1; 151/1165, 13.0%), PMP22 deletion (HNPP; 72/1165, 6.2%) and MFN2 (CMT2A; 46/1165, 3.9%). We recruited 233 cases to the UK 100 000 Genomes Project (100KGP), of which 74 (31.8%) achieved a diagnosis; 28 had been otherwise diagnosed since recruitment, leaving a true diagnostic rate of WGS through the 100KGP of 19.7% (46/233). However, almost half of the solved cases (35/74) received a negative report from the study, and the diagnosis was made through our research access to the WGS data. The overall diagnostic uplift of WGS for the entire cohort was 3.5%. Our diagnostic rate is the highest reported from a single centre and has benefitted from the use of WGS, particularly access to the raw data. However, almost one-quarter of all cases remain unsolved, and a new reference genome and novel technologies will be important to narrow the 'diagnostic gap'.

Exploring the cuproptosis-related molecular clusters in the peripheral blood of patients with amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a progressive and lethal neurodegenerative disease. Several studies have suggested the involvement of cuproptosis in its pathogenesis. In this research, we intend to explore the cuproptosis-related molecular clusters in ALS and develop a novel cuproptosis-related genes prediction model.

METHODS

The peripheral blood gene expression data was downloaded from the Gene Expression Omnibus (GEO) online database. Based on the GSE112681 dataset, we investigated the critical cuproptosis-related genes (CuRGs) and pathological clustering of ALS. The immune microenvironment features of the peripheral blood in ALS patients were also examined using the CIBERSORT algorithm. Cluster-specific hub genes were determined by the WGCNA. The most accurate prediction model was selected by comparing the performance of four machine learning techniques. ROC curves and two independent datasets were applied to validate the prediction accuracy. The available compounds targeting these hub genes were filtered to investigate their efficacy in treating ALS.

RESULTS

We successfully determined four critical cuproptosis-related genes and two pathological clusters with various immune profiles and biological characteristics in ALS. Functional analysis showed that genes in Cluster1 were primarily enriched in pathways closely associated with immunity. The Support Vector Machine (SVM) model exhibited the best discrimination properties with a large area under the curve (AUC = 0.862). Five hub prediction genes (BAP1, SMG1, BCLAF1, DHX15, EIF4G2) were selected to establish a nomogram model, suggesting significant risk prediction potential for ALS. The accuracy of this model in predicting ALS incidence was also demonstrated through calibration curves, nomograms, and decision curve analysis. Finally, three drugs targeting BAP1 were determined through drug-gene interactions.

CONCLUSION

This study elucidated the complex associations between cuproptosis and ALS and constructed a satisfactory predictive model to explore the pathological characteristics of different clusters in ALS patients.