Back

Loss of age-accumulated crh-1 circRNAs ameliorate amyloid β-induced toxicity in a C. elegans model for Alzheimers disease

Alshareef, H.; Ballinger, T.; Rojas, E.; van der Linden, A. M.

2024-04-13 neuroscience
10.1101/2024.04.09.588761 bioRxiv
Show abstract

Circular RNAs (circRNAs) are non-coding RNAs mostly derived from exons of protein-coding genes via a back-splicing process. The expression of hundreds of circRNAs accumulates during healthy aging and is associated with Alzheimers disease (AD), characterized by the accumulation of amyloid-beta (A{beta}) proteins. In C. elegans, many circRNAs were previously found to accumulate during aging, with loss of age-accumulated circRNAs derived from the CREB gene (circ-crh-1) to increase mean lifespan. Here, we used C. elegans to study the effects of age-accumulated circRNAs on the age-related onset of A{beta}-toxicity. We found that circ-crh-1 mutations delayed A{beta}-induced muscle paralysis and lifespan phenotypes in a transgenic C. elegans strain expressing a full-length human A{beta}-peptide (A{beta}1-42) selectively in muscle cells (GMC101). The delayed A{beta} phenotypic defects were associated with inhibiting the deposition of A{beta} aggregates, and thus, genetic removal of circ-crh-1 provides protection against A{beta}-induced toxicity. Consistent with a detrimental role for age-accumulated circRNAs in AD, circ-crh-1 expression level is elevated after induction of A{beta} during aging, whereas linear crh-1 mRNA expression remains unchanged. Finally, we show that a circ-crh-1 upregulated collagen gene, col-49, promotes A{beta}-induced paralysis. Taken together, our results show that the loss of an age-accumulated circRNA exerts a protective role on A{beta}-induced toxicity, demonstrating the utility of C. elegans for studying circRNAs in AD and its relationship to aging.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.