How and why gene expression changes
As organisms age, their gene activity patterns change. To understand why, some researchers look into cross-species analysis to find common patterns. Others look into long-lived animals to identify what genetic features make them live longer.
These researchers combined those approaches. Analyzing the gene expression of several tissues in mice, they found that any gene expression that is associated with maximum lifespan in mammals is statistically more likely than usual to be associated with aging. Similarly, a gene expression that is negatively associated with maximum lifespan is less likely to be associated with aging.
These findings were further corroborated by an analysis of kidney gene expression. This analysis found that it is statistically less likely than usual that a gene expression that is positively associated with maximum lifespan is negatively associated with aging. It is similarly unlikely that a gene expression that is negatively associated with maximum lifespan is positively associated with aging.
In total, these observations suggest that mice have similarities between gene expressions associated with aging and gene expressions associated with maximum lifespan. These similarities were also identified in human tissues.
Complex roles of inflammation
When analyzing gene expression in kidneys of different mammalian species, researchers found that many genes positively associated with maximum lifespan were involved in inflammatory processes. Many genes negatively associated with maximum lifespan involved the mitochondria.
The expression of inflammatory genes is considered a common feature of aging [2, 3]. The chronic inflammatory state observed in older organisms is known as inflammaging.
In this study, the authors analyzed more than one previously published data set that had included different mammalian species. Two separate datasets show differences regarding immune regulation, which led to a curious observation regarding inflammatory genes.
In the first dataset, the authors observed an overrepresentation of inflammatory genes among genes positively correlated with mammalian maximum lifespan. In the second dataset, which included data from rodents, hedgehogs, and gymnures, inflammatory gene representation was found among genes negatively correlated with maximum lifespan.
Based on a detailed analysis of specific genes, researchers concluded that immune system regulation for longer lifespans evolved differently in rodents, hedgehogs, and gymnures compared to other mammals. This and previous research point to the complex role of inflammatory responses in aging.
On the one hand, dysregulated inflammatory responses are linked to the pathogenesis of many diseases. Conversely, their functions are important for tumor suppression [4]. The authors also point out that high expression of inflammatory genes can be linked to better immunity in long-lived animals.
Regulation of aging-associated genes
In this and previous research, these researchers expressed the idea that many changes in the expression of aging-related genes could be the result of the adaptation to aging.
For example, they present the transcription repressor called REST as one of the proteins that can support such a hypothesis. Although REST expression increases with aging, increased expression of REST in the human brain benefits longevity and cognitive performance [5].
The authors showed that in the brain, REST target genes were overrepresented among genes whose expression negatively correlated with maximum lifespan and REST target genes were underrepresented among genes whose expression positively correlates with maximum lifespan.
They further note that observed gene expression doesn’t prove that REST activity correlates with maximum lifespan-associated genes. Alternatively, DNA sites responsible for binding REST can be increased in the parts of the DNA that regulate the activity of maximum lifespan-associated genes.
Transcription factors are proteins that regulate the expression of genes. To fulfill their role, they bind to very specific DNA sequences. The authors explored the possibility that such transcription factors are regulating this interaction, as they found DNA sequences that bind to transcription factors that can potentially drive the expression of maximum lifespan-associated genes. Those transcription factor binding sequences were found to be correlated with maximum lifespan.
This suggests that maximum lifespan evolved together within maximum lifespan-associated genes and the sequences that aid in regulating the expression of those genes.
Alternative interpretations
These researchers suggest that similarities in gene expression associated with aging and maximum lifespan could indicate that some age-related gene expression changes are beneficial rather than harmful. However, they are aware of the limitations of their observations.
They point out that looking into gene expression levels may not reflect the levels of proteins in the cells, which can change due to a loss of proper functioning of protein-building machinery and the accumulation of misfolded proteins. This is known as the loss of proteostasis, and it is one of the Hallmarks of Aging. The authors did not directly observe functional proteins, and they note that such an analysis might not result in the same conclusions.
Another limitation of the study was that changes in gene expression levels “during aging and in the evolution of longevity may be the result rather than the cause of aging/longevity.” For example, the authors explain that if a gene’s transcription decreases with age, it can be caused by damaged DNA that prevents transcript production. Species with long lifespans are known to have better protected DNA.
Finally, this paper points out that ”most aging-related gene expression changes represent reversal or extension of developmental gene expression changes” [6, 7]. Therefore, those age-related changes in gene expression may be the result of a program and not the result of damage accumulation.
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Literature
[1] Takasugi, M., Yoshida, Y., Nonaka, Y., & Ohtani, N. (2023). Gene expressions associated with longer lifespan and aging exhibit similarity in mammals. Nucleic acids research, gkad544. Advance online publication. https://doi.org/10.1093/nar/gkad544
[2] Frenk, S., & Houseley, J. (2018). Gene expression hallmarks of cellular ageing. Biogerontology, 19(6), 547–566. https://doi.org/10.1007/s10522-018-9750-z
[3] de Magalhães, J. P., Curado, J., & Church, G. M. (2009). Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics (Oxford, England), 25(7), 875–881. https://doi.org/10.1093/bioinformatics/btp073
[4] Egen, J. G., Ouyang, W., & Wu, L. C. (2020). Human Anti-tumor Immunity: Insights from Immunotherapy Clinical Trials. Immunity, 52(1), 36–54. https://doi.org/10.1016/j.immuni.2019.12.010
[5] Lu, T., Aron, L., Zullo, J., Pan, Y., Kim, H., Chen, Y., Yang, T. H., Kim, H. M., Drake, D., Liu, X. S., Bennett, D. A., Colaiácovo, M. P., & Yankner, B. A. (2014). REST and stress resistance in ageing and Alzheimer’s disease. Nature, 507(7493), 448–454. https://doi.org/10.1038/nature13163
[6] de Magalhães J. P. (2012). Programmatic features of aging originating in development: aging mechanisms beyond molecular damage?. FASEB journal : official publication of the Federation of American Societies for Experimental Biology, 26(12), 4821–4826. https://doi.org/10.1096/fj.12-210872
