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Aging manifests itself through a decline in organismal homeostasis and a multitude of cellular and physiological functions. Efforts to identify a common basis for vertebrate aging face many challenges; for example, while there have been documented changes in the expression of many hundreds of mRNAs, the results across tissues and species have been inconsistent. We therefore analyzed age-resolved transcriptomic data from 17 mouse organs and 51 human organs using unsupervised machine learning to identify the architectural and regulatory characteristics most informative on the differential expression of genes with age. We report a hitherto unknown phenomenon, a systemic age-dependent length-driven transcriptome imbalance that for older organisms disrupts the homeostatic balance between short and long transcript molecules for mice, rats, killifishes, and humans. We also demonstrate that in a mouse model of healthy aging, length-driven transcriptome imbalance correlates with changes in expression of splicing factor proline and glutamine rich (Sfpq), which regulates transcriptional elongation according to gene length. Furthermore, we demonstrate that length-driven transcriptome imbalance can be triggered by environmental hazards and pathogens. Our findings reinforce the picture of aging as a systemic homeostasis breakdown and suggest a promising explanation for why diverse insults affect multiple age-dependent phenotypes in a similar manner.

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