C9ORF72 hexanucleotide repeat expansion frequency in patients with Paget's disease of bone

Distribution of the C9orf72 hexanucleotide repeat expansion in healthy subjects: a multicenter study promoted by the Italian IRCCS network of neuroscience and neurorehabilitation

Introduction

High repeat expansion (HRE) alleles in C9orf72 have been linked to both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD); ranges for intermediate allelic expansions have not been defined yet, and clinical interpretation of molecular data lacks a defined genotype-phenotype association. In this study, we provide results from a large multicenter epidemiological study reporting the distribution of C9orf72 repeats in healthy elderly from the Italian population.

Methods

A total of 967 samples were collected from neurologically evaluated healthy individuals over 70 years of age in the 13 institutes participating in the RIN (IRCCS Network of Neuroscience and Neurorehabilitation) based in Italy. All samples were genotyped using the AmplideXPCR/CE C9orf72 Kit (Asuragen, Inc.), using standardized protocols that have been validated through blind proficiency testing.

Results

All samples carried hexanucleotide G4C2 expansion alleles in the normal range. All samples were characterized by alleles with less than 25 repeats. In particular, 93.7% of samples showed a number of repeats ≤10, 99.9% ≤20 repeats, and 100% ≤25 repeats.

Conclusion

This study describes the distribution of hexanucleotide G4C2 expansion alleles in an Italian healthy population, providing a definition of alleles associated with the neurological healthy phenotype. Moreover, this study provides an effective model of federation between institutes, highlighting the importance of sharing genomic data and standardizing analysis techniques, promoting translational research. Data derived from the study may improve genetic counseling and future studies on ALS/FTD.

Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the molecular basis of the disease is still elusive, the development of high-throughput sequencing technologies, combined with data mining techniques and machine learning methods, could provide remarkable results in identifying pathogenetic mechanisms. High dimensionality is a major problem when applying machine learning techniques in biomedical data analysis, since a huge number of features is available for a limited number of samples. The aim of this study was to develop a methodology for training interpretable machine learning models in the classification of ALS and ALS-subtypes samples, using gene expression datasets.

METHODS

We performed dimensionality reduction in gene expression data using a semi-automated preprocessing systematic gene selection procedure using Statistically Equivalent Signature (SES), a causality-based feature selection algorithm, followed by Boosted Regression Trees (XGBoost) and Random Forest to train the machine learning classifiers. The SHapley Additive exPlanations (SHAP values) were used for interpretation of the machine learning classifiers. The methodology was developed and tested using two distinct publicly available ALS RNA-seq datasets. We evaluated the performance of SES as a dimensionality reduction method against: (a) Least Absolute Shrinkage and Selection Operator (LASSO), and (b) Local Outlier Factor (LOF).

RESULTS

The proposed methodology achieved 85.18% accuracy for the classification of cerebellum or frontal cortex samples as C9orf72-related familial ALS, sporadic ALS or healthy samples. Importantly, the genes identified as the most determinative have also been reported as disease-associated in ALS literature. When tested in the evaluation dataset, the methodology achieved 88.89% accuracy for the classification of sporadic ALS motor neuron samples. When LASSO was used as feature selection method instead of SES, the accuracy of the machine learning classifiers ranged from 74.07 to 96.30%, depending on tissue assessed, while LOF underperformed significantly (77.78% accuracy for the classification of pooled cerebellum and frontal cortex samples).

CONCLUSIONS

Using SES, we addressed the challenge of high dimensionality in gene expression data analysis, and we trained accurate machine learning ALS classifiers, specific for the gene expression patterns of different disease subtypes and tissue samples, while identifying disease-associated genes.

Amyotrophic lateral sclerosis (ALS) and the endocrine system: Are there any further ties to be explored?

Amyotrophic Lateral Sclerosis (ALS) belongs to the family of neurodegenerative disorders and is classified as fronto-temporal dementia (FTD), progressive muscular atrophy, primary lateral sclerosis, and pseudobulbar palsy. Even though endocrine dysfunction independently impacts the ALS-related survival rate, the complex connection between ALS and the endocrine system has not been studied in depth. Here we review earlier and recent findings on how ALS interacts with hormones a) of the hypothalamus and pituitary gland, b) the thyroid gland, c) the pancreas, d) the adipose tissue, e) the parathyroid glands, f) the bones, g) the adrenal glands, and h) the gonads (ovaries and testes). Of note, endocrine issues should always be explored in patients with ALS, especially those with low skeletal muscle and bone mass, vitamin D deficiency, and decreased insulin sensitivity (diabetes mellitus). Because ALS is a progressively deteriorating disease, addressing any potential endocrine co-morbidities in patients with this malady is quite important for decreasing the overall ALS-associated disease burden. Importantly, as this burden is estimated to increase globally in the decades to follow, in part because of an increasingly aging population, it is high time for future multi-center, multi-ethnic studies to assess the link between ALS and the endocrine system in significantly larger patient populations. Last, the psychosocial stress experienced by patients with ALS and its psycho-neuro-endocrinological sequelae, including hypothalamic-pituitaryadrenal dysregulation, should become an area of intensive study in the future.