Polinski JM et al. (2020), Unique age-related transcriptional signature in...

ECB-ART-49795

Sci Rep 2020 Jun 08;101:9182. doi: 10.1038/s41598-020-66052-3.

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Unique age-related transcriptional signature in the nervous system of the long-lived red sea urchin Mesocentrotus franciscanus.

Polinski JM , Kron N , Smith DR , Bodnar AG .

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The red sea urchin, Mesocentrotus franciscanus, is one the earth's longest-lived animals, reported to live more than 100 years with indeterminate growth, life-long reproduction and no increase in mortality rate with age. To gain insight into mechanisms associated with longevity and negligible senescence, age-related transcriptional profiles were examined in tissues of the red sea urchin. Genome-wide transcriptional profiling using RNA-Seq revealed few age-related changes in gene expression in muscle and esophagus tissue. In contrast, radial nerve showed an unexpected level of complexity with the expression of 3,370 genes significantly altered more than two-fold with age, including genes involved in nerve function, signaling, metabolism, transcriptional regulation and chromatin modification. There was an age-related upregulation in expression of genes involved in synaptogenesis, axonogenesis and neuroprotection suggesting preservation of neuronal processes with age. There was also an upregulation in expression of positive regulators and key components of the AMPK pathway, autophagy, proteasome function, and the unfolded protein response. This unique age-related gene expression profile in the red sea urchin nervous system may play a role in mitigating the detrimental effects of aging in this long-lived animal.

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	Figure 1. Representative M. franciscanus from Small (young) and Large (old) size categories (scale bar = 1âcm).
	Figure 2. qRT-PCR validation of expression of selected genes in radial nerve cord (RN) and Aristotleâs lantern muscle (ALM) using M. franciscanus tissue from the 2010 collection. Comparison of log2 fold-change in gene expression with age for the selected genes determined by qRT-PCR (open bars) and RNA-Seq (solid bars). The black bars represent RN and the red bars represent ALM. Gene names: Tnks1 â Tankyrase-1, Capn â calpain, Wnt1 â Wingless-type MMTV integration site-1, Mab21 â Mab-21-like 2, Lkb1 â Liver Kinase B1/Serine Threonine Kinase 11, Hyou1 â Hypoxia up-regulated 1, Neurx â Neurexin, Glur6 â Glutamate receptor ionotropic kainite-like 2, MAP3K9 â mitogen activated protein kinase kinase kinase 9, EBP â echinenone-binding protein, SM30E â spicule matrix protein 30E, MstnB â myostatin B.
	Figure 3. Distribution of genes upregulated and downregulated with age in M. franciscanus Aristotleâs Lantern Muscle (a) and Esophagus (b) curated into modified sea urchin custom gene ontology categories. Blue and red bars represent the number of genes in each category upregulated and downregulated, respectively.
	Figure 4. Distribution of genes upregulated and downregulated with age in M. franciscanus radial nerve cord (RN) curated into sea urchin custom gene ontology categories. Blue and red bars represent the number of genes in each category upregulated and downregulated, respectively.
	Figure 5. IPA canonical pathways significantly affected by age in sea urchin radial nerve cord (BH FDR adj P-value <0.05). Blue and red bars represent the number of genes in each pathway upregulated and downregulated, respectively.
	Figure 6. Schematic representation of the predicted activation of Lkb1, AMPK, Ulk1, and mTORC2 and inhibition of mTORC1 in sea urchin radial nerve tissue inferred from the age-related differential gene expression. Predicted increased activity is depicted in blue while predicted inhibition of activity is depicted in red with the predicted outcomes being increased autophagy, cytoskeletal rearrangement, and cell survival and decreased protein synthesis and mitochondrial energy metabolism.

References [+] :

Allison, Microarray data analysis: from disarray to consolidation and consensus. 2006, Pubmed