Machine learning-based predictions of dietary restriction associations across ageing-related genes

Abstract

Background Dietary restriction (DR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel DR-related genes and DR-related genetic features. Results This work used a Machine Learning (ML) approach to classify ageing-related genes as DR-related or NotDR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein–Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype Tissue Expression (GTEx) expression features, GeneFriends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking DR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict DR-relatedness among ageing-related genes currently lacking DR-related annotations in the data, resulting in a set of promising candidate DR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted DR-relatedness remain to be validated in future wet-lab experiments. Conclusions This work demonstrated the strong potential of ML-based techniques to identify DR-associated features as our findings are consistent with literature and recent discoveries. Although the inference of new DR-related mechanistic findings based solely on GO terms and biological pathways was limited due to their knowledge-driven nature, the predictive power of these two features types remained useful as it allowed inferring new promising candidate DR-related genes.

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

“…Indeed, a number of studies by our group and others have employed computational and machine learning analysis to identify new candidates in the context of aging and AADs. These approaches have led to the detection of disease-related genes, caloric restriction genes, and longevity drugs [ 20 – 24 ]. The application of AI in the pharmaceutical industry also aims to reduce the tremendous amount of cost and time conventionally needed to discover new therapeutic targets in various diseases.…”

Calorie restriction modulates the transcription of genes related to stress response and longevity in human muscle: The CALERIE study

“…Cryptochrome circadian regulator 2 ( CRY2 ), a core clock protein upregulated by CR, which also reduces its rhythmicity, reduced circadian rhythm upregulation (Patel et al, 2016 ; Velingkaar et al, 2021 ). Insulin receptor substrate 2 ( IRS2 ) in skeletal muscle is associated with exercise and CR (Kang et al, 2023 ) and plays an important role in lipid metabolism in skeletal muscle, and longevity in mice (Kang et al, 2023 ; Masternak et al, 2005 ; Vega Magdaleno et al, 2022 ). SIK family kinase 3 ( SIK3 ) is a serine–threonine kinase in the AMPK activated protein kinase family that a well‐known energy sensor.…”

How can we modulate aging through nutrition and physical exercise? An epigenetic approach

“…Studies have shown that from an economic point of view is more advantageous for society to promote even a slight increase in healthspan, rather than investing in disease-specific adaptions that cater to an aged population [16,97]. Henceforth, it is important to identify biomarkers and develop biological clocks that can assess the value of novel strategies that may potentially overcome or delay conditions leading to poor health amongst the elderly [98][99][100].…”