Fasting Drives Nrf2-Related Antioxidant Response in Skeletal Muscle

Lettieri-Barbato, Daniele; Minopoli, Giuseppina; Caggiano, Rocco; Izzo, Rossella; Santillo, Mariarosaria; Aquilano, Katia; Faraonio, Raffaella

doi:10.3390/ijms21207780

Cited by 15 publications

(7 citation statements)

References 69 publications

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“…Its role in CR is not explicitly stated in literature. However, a recent study (Lettieri-Barbato et al, 2020) showed its increased expression during fasting in PASKdeficient mice. Since fasting and intermittent fasting are associated to CR-like benefits, a link between GCLM and CR can be investigated from this per-spective.…”

Section: Cr-related Candidate Genesmentioning

confidence: 96%

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

Vega-Magdaleno

Bespalov

Zheng

et al. 2021

Preprint

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Caloric restriction (CR) 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 CR-related genes and CR-related genetic features. This work used a Machine Learning (ML) approach to classify ageing-related genes as CR-related or NotCR-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) have 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 CR 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 CR-relatedness among ageing-related genes currently lacking CR-related annotations in the data, resulting in a set of promising candidate CR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted CR-relatedness remain to be validated in future wet-lab experiments.

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Section: Cr-related Candidate Genesmentioning

confidence: 96%

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

Vega-Magdaleno

Bespalov

Zheng

et al. 2021

Preprint

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“…Its role in DR is not explicitly stated in literature. However, a recent study [64] showed its increased expression during fasting in PASK-deficient mice. Since fasting and intermittent fasting are associated to DR-like benefits, a link between GCLM and DR can be investigated from this perspective.…”

Section: Dr-related Candidate Genesmentioning

confidence: 97%

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

et al. 2022

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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.

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“…Fasting-induced metabolic alterations in skeletal muscle result in a moderate increase in oxidative stress. The Nrf2 transcription factor is responsible for coordinating an adaptive program that helps maintain redox balance and increase glutathione synthesis [ 38 ]. As a result, nutrient restriction/fasting may be an effective technique for boosting antioxidant defenses and preventing lipid peroxidation.…”

Section: Cellular and Molecular Level Interactions During If And Calo...mentioning

confidence: 99%

Impact of Intermittent Fasting on Metabolic Syndrome and Periodontal Disease—A Suggested Preventive Strategy to Reduce the Public Health Burden

Parveen

Alhazmi

2022

IJERPH

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Metabolic syndrome (MetS) prevalence continues to climb significantly worldwide in today’s ad libitum society. MetS has tremendous societal and economic ramifications, making it imperative to develop effective strategies for preventing and controlling it to alleviate this growing burden. Periodontal disease and MetS are associated with several risk factors. Studies in the past have demonstrated that obesity, cardiovascular illness, and type 2 diabetes mellitus have a negative effect on the severity of the periodontal disease. Patients with metabolic syndrome have elevated serum levels of proinflammatory mediators such as tumor necrosis factor-alpha interleukin-6 and C-reactive protein. Similar inflammatory mediators, such as interleukin-6, tumor necrosis factor-alpha, and C-reactive protein, are increased in patients with severe periodontal disease. Remarkably, intermittent fasting is underpinned by scientific evidence, claiming to be the most effective non-pharmacological, potential therapeutic alternative for combating a wide range of metabolic, inflammatory, and lifestyle-related diseases. Nonetheless, an insufficient investigation has been performed to determine whether intermittent fasting has therapeutic benefits on periodontal inflammation and diseases. Here, we show the interrelationship between metabolic syndrome and periodontal disease and contextualize the beneficial impact of intermittent fasting in modulating the chronic metabolic and periodontal inflammatory response. We also anticipate that this review paves the way for further exploration of intermittent fasting as a unique research paradigm representing a cost-effective alternative strategy to conventional disease management in patients with periodontal diseases and metabolic syndrome which may serve as the foundation for an integrative vision relevant to primary, diagnostic, and therapeutic purposes.

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Fasting Drives Nrf2-Related Antioxidant Response in Skeletal Muscle

Cited by 15 publications

References 69 publications

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

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

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

Impact of Intermittent Fasting on Metabolic Syndrome and Periodontal Disease—A Suggested Preventive Strategy to Reduce the Public Health Burden

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