Hoppe, F. | Search bioRxiv

bioRxiv2026

Natural sleep, but not propofol-induced anesthesia preserves spatial and procedural memory consolidation

Propofol-induced general anesthesia and natural sleep are both associated with GABA-A-ergic inhibition and slow-wave activity. Whereas sleep actively supports memory consolidation, consolidation may be disrupted during anesthesia. Here, we compared EEG and memory data from 19 participants undergoing surgery under propofol-induced anesthesia with data from 17 participants taking a nap. In both groups, participants completed a hippocampus-dependent spatial memory task (Virtual Water Maze) and a procedural memory task (Mirror Tracing) before (pre) and after (post) an interval filled with either anesthesia or sleep. Performance declined from pre to post in the propofol group for both tasks, whereas it remained stable in the sleep group. Slow oscillations (SO) occurred during both non-REM sleep and propofol-induced anesthesia. However, only during non-REM sleep did SOs show increased spindle activity during the SO upstate. Together, these findings suggest that propofol anesthesia disrupts memory consolidation, most likely because SOs under anesthesia lack the spindle coupling that mediates the hippocampal-neocortical information transfer underlying systems memory consolidation.

neuroscience↗

bioRxiv2025

RNAprecis: Prediction of full-detail RNA conformation from the experimentally best-observed sparse parameters

We address the problem of predicting high-detail RNA structure geometry from the information available in low-detail experimental maps. Here, low-detail refers to resolutions {approx} 2.5-3.5[A], where the location of the phosphate groups and the glycosidic bonds can be determined from experimental maps but all other backbone atom positions cannot. In contrast, higher-resolution maps allow high-detail determinations of all backbone atomic positions. To this end, we first create a gold standard dataset of highly curated, experimentally supported RNA suites. Second, we develop and employ a modified version of the previously devised algorithm MINT-AGE to learn clusters that are in high correspondence with the gold standards conformational classes of suites based on 3D RNA structure. Since some of the gold standard classes are of very small size, a new modified version of MINT-AGE is able to also identify very small clusters. Third, we create a new conformer prediction algorithm, RNAprecis, which assigns low-detail structures to newly designed 3D shape coordinates. Our improvements include: (i) learned classes augmented to cover also very low sample sizes and (ii) replacing distances from clusters by Bayesian posterior probabilities. On test data containing suites modeled as conformational outliers, RNAprecis shows good results suggesting that our learning method generalizes well. In particular, we show that the modified MINT-AGE clustering can more finely delineate between previously unseen suite conformer separations. For example, the 0a conformer has been separated into two clusters seen in different structural contexts. Such new distinctions can have implications for biochemical interpretation of RNA structure.

molecular biology↗