Computational analysis of functional SNPs in Alzheimer's disease-associated endocytosis genes

Endocytosis and Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and is the most common cause of dementia. The pathogenesis of AD still remains unclear, including two main hypotheses: amyloid cascade and tau hyperphosphorylation. The hallmark neuropathological changes of AD are extracellular deposits of amyloid-β (Aβ) plaques and intracellular neurofibrillary tangles (NFTs). Endocytosis plays an important role in a number of cellular processes including communication with the extracellular environment, nutrient uptake, and signaling by the cell surface receptors. Based on the results of genetic and biochemical studies, there is a link between neuronal endosomal function and AD pathology. Taking this into account, we can state that in the results of previous research, endolysosomal abnormality is an important cause of neuronal lesions in the brain. Endocytosis is a central pathway involved in the regulation of the degradation of amyloidogenic components. The results of the studies suggest that a correlation between alteration in the endocytosis process and associated protein expression progresses AD. In this article, we discuss the current knowledge about endosomal abnormalities in AD.

In silico SNP prediction of selected protein orthologues in insect models for Alzheimer's, Parkinson's, and Huntington's diseases

Alzheimer's, Parkinson's, and Huntington's are the most common neurodegenerative diseases that are incurable and affect the elderly population. Discovery of effective treatments for these diseases is often difficult, expensive, and serendipitous. Previous comparative studies on different model organisms have revealed that most animals share similar cellular and molecular characteristics. The meta-SNP tool includes four different integrated tools (SIFT, PANTHER, SNAP, and PhD-SNP) was used to identify non synonymous single nucleotide polymorphism (nsSNPs). Prediction of nsSNPs was conducted on three representative proteins for Alzheimer's, Parkinson's, and Huntington's diseases; APPl in Drosophila melanogaster, LRRK1 in Aedes aegypti, and VCPl in Tribolium castaneum. With the possibility of using insect models to investigate neurodegenerative diseases. We conclude from the protein comparative analysis between different insect models and nsSNP analyses that D. melanogaster is the best model for Alzheimer's representing five nsSNPs of the 21 suggested mutations in the APPl protein. Aedes aegypti is the best model for Parkinson's representing three nsSNPs in the LRRK1 protein. Tribolium castaneum is the best model for Huntington's disease representing 13 SNPs of 37 suggested mutations in the VCPl protein. This study aimed to improve human neural health by identifying the best insect to model Alzheimer's, Parkinson's, and Huntington's.

Linking single nucleotide polymorphisms to signaling blueprints in abdominal aortic aneurysms

Abdominal aortic aneurysms (AAA) is a multifactorial complex disease with life-threatening consequences. While Genome-wide association studies (GWAS) have revealed several single nucleotide polymorphisms (SNPs) located in the genome of individuals with AAA, the link between SNPs with the associated pathological signals, the influence of risk factors on their distribution and their combined analysis is not fully understood. We integrated 86 AAA SNPs from GWAS and clinical cohorts from the literature to determine their phenotypical vulnerabilities and association with AAA risk factors. The SNPs were annotated using snpXplorer AnnotateMe tool to identify their chromosomal position, minor allele frequency, CADD (Combined Annotation Dependent Depletion), annotation-based pathogenicity score, variant consequence, and their associated gene. Gene enrichment analysis was performed using Gene Ontology and clustered using REVIGO. The plug-in GeneMANIA in Cytoscape was applied to identify network integration with associated genes and functions. 15 SNPs affecting 20 genes with a CADD score above ten were identified. AAA SNPs were predominantly located on chromosome 3 and 9. Stop-gained rs5516 SNP obtained high frequency in AAA and associated with proinflammatory and vascular remodeling phenotypes. SNPs presence positively correlated with hypertension, dyslipidemia and smoking history. GO showed that AAA SNPs and their associated genes could regulate lipid metabolism, extracellular matrix organization, smooth muscle cell proliferation, and oxidative stress, suggesting that part of these AAA traits could stem from genetic abnormalities. We show a library of inborn SNPs and associated genes that manifest in AAA. We uncover their pathological signaling trajectories that likely fuel AAA development.

CI-SpliceAI—Improving machine learning predictions of disease causing splicing variants using curated alternative splice sites

Background

It is estimated that up to 50% of all disease causing variants disrupt splicing. Due to its complexity, our ability to predict which variants disrupt splicing is limited, meaning missed diagnoses for patients. The emergence of machine learning for targeted medicine holds great potential to improve prediction of splice disrupting variants. The recently published SpliceAI algorithm utilises deep neural networks and has been reported to have a greater accuracy than other commonly used methods.

Methods and findings

The original SpliceAI was trained on splice sites included in primary isoforms combined with novel junctions observed in GTEx data, which might introduce noise and de-correlate the machine learning input with its output. Limiting the data to only validated and manual annotated primary and alternatively spliced GENCODE sites in training may improve predictive abilities. All of these gene isoforms were collapsed (aggregated into one pseudo-isoform) and the SpliceAI architecture was retrained (CI-SpliceAI). Predictive performance on a newly curated dataset of 1,316 functionally validated variants from the literature was compared with the original SpliceAI, alongside MMSplice, MaxEntScan, and SQUIRLS. Both SpliceAI algorithms outperformed the other methods, with the original SpliceAI achieving an accuracy of ∼91%, and CI-SpliceAI showing an improvement at ∼92% overall. Predictive accuracy increased in the majority of curated variants.

Conclusions

We show that including only manually annotated alternatively spliced sites in training data improves prediction of clinically relevant variants, and highlight avenues for further performance improvements.

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.