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A groundbreaking computational platform has been developed to systematically identify interventions capable of transcriptionally “rejuvenating” human brain cells. This platform, centered on a brain-specific transcriptional aging clock, accurately predicts brain age based on gene expression and can detect age- and neurodegeneration-related changes. The research, which screened over 43,000 transcriptional profiles, has identified 453 unique interventions, many of which are already known to extend lifespan in animal models or treat neurological disorders like Alzheimer’s.
The Discovery:
The computational platform uses a brain-specific transcriptional aging clock trained on healthy individuals aged 20 to 97. This clock employs 365 genes to make predictions, achieving high accuracy in determining chronological age.
When applied to samples from individuals with neurodegenerative diseases, the platform revealed that the presence and severity of these diseases significantly increase predicted brain age. This suggests that neurodegeneration is reflected as an acceleration of the aging process at a transcriptional level.
By screening 43,840 transcriptional profiles from chemical and genetic perturbations, the platform identified 453 unique interventions that significantly shifted the transcriptional age of brain cells towards a younger state.
The Science:
The brain-specific transcriptional aging clock achieved a coefficient of determination (R²) of 0.945 in the training set and 0.808 in the testing set, with a mean absolute error (MAE) of 4.81 years in the test set.
The 365 genes comprising the clock’s signature are significantly involved in biological processes related to DNA metabolism and repair, genomic instability, and chromatin dynamics, all hallmarks of aging.
Applying the bulk transcriptome-trained model to single-cell RNA-seq data showed that it effectively captured aging trends in individual cell types, particularly in excitatory neurons, inhibitory neurons, and oligodendrocytes.
Neurodegenerative samples exhibited a transcriptional age up to 15.23 years higher than healthy controls, especially in the 60-70 age range, with a significant association between Braak index (a measure of neurodegeneration severity) and transcriptional age.
Among the identified rejuvenating compounds, several are known to extend lifespan in animal models (e.g., curcumin, erythromycin-ethylsuccinate, azacitidine, everolimus), and others are used to treat neurological disorders like Alzheimer’s disease and Parkinson’s disease.
A combination of three predicted compounds (5-azacytidine, tranylcypromine, and JNK-IN-8) administered to aged mice reduced anxiety, improved memory, and rejuvenated the brain cortex transcriptome, shifting gene expression towards a younger phenotype.
Your Action:
This research introduces a powerful tool to identify compounds that reverse transcriptional aging in the brain. Some of the 453 interventions, like curcumin, azacitidine, and everolimus, already show lifespan-extending or neuroprotective effects in animal studies. To support your brain health today, consider natural anti-inflammatory compounds like curcumin, optimize sleep and metabolic health, and stay informed as more of these interventions move toward human testing.
Bottom Line:
A novel machine-learning platform has identified hundreds of chemical and genetic interventions that can transcriptionally rejuvenate human brain cells, offering promising avenues for treating age-related brain disorders and improving cognitive function.
Source:
A Machine-Learning Approach Identifies Rejuvenating Interventions in the Human Brain, Advanced Science, Guillem Santamaria, Cristina Iglesias, Sascha Jung, Javier Arcos Hodar, Ruben Nogueiras, and Antonio del Sol
https://doi.org/10.1002/advs.202503344
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