Decay in transcriptional information flow is a hallmark of cellular aging
Emison, B.; Lynn, C. W.; Mugler, A.; Ambrosio, F.; Dixit, P. D.
Show abstract
Aging is marked by the progressive loss of cellular function, yet the organizing principles underlying this decline remain unclear. Although molecular fingerprints of aging are diverse, many converge on disruption of the interrelated and overlapping communication networks that coordinate molecular activity. Here, we apply information theory to quantify age-related corruption in gene regulation by modeling regulatory interactions between transcription factors (TFs) and their target genes (TGs) as a multi-input multi-output communication channel. Using an analytically tractable probabilistic model and single-cell RNA-sequencing data from multiple tissues, we find that the mutual information (a measure of information transfer) between TFs and TGs declines with age across all ten tissues analyzed, establishing loss of regulatory information transmission as a hallmark of aging. Structural analysis of the regulatory network reveals that aging degrades communication primarily through input distribution mismatch, reflecting a loss of coordinated TF activity, rather than channel corruption, or the inability of TFs to reliably activate or inhibit their targets. This mismatch is caused by increased network centralization and loss of stabilizing feedback motifs, leading to reduced robustness to random perturbations. Notably, in silico upregulation of a small set of TFs restores youthful information transfer and gene expression levels, suggesting that targeted reinforcement of key regulatory nodes may rejuvenate aged networks.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts ultimate limit of human lifespan 95%
- Compression of morbidity by interventions that steepen the survival curve 94%
- Identification of a blood test-based biomarker of aging through deep learning of aging trajectories in large phenotypic datasets of mice 94%
Similar papers in this journal
- Age-related behavioral resilience in smartphone touchscreen interaction dynamics 94%
- Systematic Approach Identifies Multiple Transcription Factor Perturbations That Rejuvenate Replicatively Aged Human Skin Fibroblasts 93%
- Pervasive convergent evolution and extreme phenotypes define chaperone requirements of protein homeostasis 93%
Similar papers in this journal
- A mathematical model that predicts human biological age from physiological traits identifies environmental and genetic factors that influence aging 94%
- Automated, high-dimensional evaluation of physiological aging and resilience in outbred mice 94%
- Early life imprints the hierarchy of T cell clone sizes 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.