Identification of mutations in the ATP7B gene in 14 Wilson disease children: Case series

Copper: From enigma to therapeutic target for neurological disorder

Neurological disorders (NDs) have a negative impact on the lives of individuals. There could be two explanations for this: unclear aetiology and lack of effective therapy. However, research in the past few years has revealed the role of bio-metals dyshomeostasis in NDs. The imbalance in copper (Cu) concentration may be one of the main causative factors in NDs. In this review, we have discussed the role of Cu in NDs, especially Alzheimer's disease (AD), including the molecular mechanisms involved in Cu-associated NDs like oxidative stress, neuroinflammation, and protein misfolding. We have also summarized the recent Cu-targeting approaches and highlighted the in vitro and in vivo studies recently being reported on the subject. Based on the earlier published reports, it could be speculated that the Cu targeting strategy might be an interesting and potential therapeutic approach for NDs. Various difficulties must be overcome to develop safe and efficient Cu-targeting medications for NDs.

WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease

BACKGROUND

Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understanding of the nature of these variants, American College of Medical Genetics and Genomics and the Association of Molecular Pathologists (ACMG-AMP) have issued a set of guidelines for variant classification. However, given the vast number of variants associated with any disorder, it is impossible to manually apply these guidelines to all known variants. Machine learning methodologies offer a rapid way to classify large numbers of variants, as well as variants of uncertain significance as either pathogenic or benign. Here we classify ATP7B genetic variants by employing ML and AI algorithms trained on our well-annotated WilsonGen dataset.

METHODS

We have trained and validated two algorithms: TabNet and XGBoost on a high-confidence dataset of manually annotated, ACMG & AMP classified variants of the ATP7B gene associated with Wilson's Disease.

RESULTS

Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings.

CONCLUSION

We have created a ready to deploy tool, that can classify variants linked with Wilson's disease as pathogenic or benign, which can be utilized by both clinicians and researchers to better understand the disease through the nature of genetic variants associated with it.

Cholinesterase Deficiency Syndrome-A Pitfall in the Use of Butyrylcholinesterase as a Biomarker for Wilson's Disease

A family is described as having two recessively inherited metabolic diseases and three differently affected children. During the explantation of a drain tube grommet under general anesthesia, a prolonged resuscitation and wake-up period occurred in the key case when he was 8 years old. This led to a family screening for butyrylcholinesterase deficiency, which was confirmed not only in the key case but also in his 5-year-old sister; it was not confirmed in his 10-year-old brother. However, the key case not only had reduced serum levels of BCHE, but also elevated liver enzyme levels, which are atypical for BCHE deficiency. After the exclusion of viral and autoimmune hepatitis, Wilson's disease (WD) was eventually diagnosed and also confirmed in his elder brother, but not in his sister. This family is presented to highlight an extremely rare WD-patient in whom a low serum level of BCHE did not occur because of WD but because of BCHE deficiency.