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Myomaker and ether lipids cooperate to promote fusion-competent membrane states

This study identifies ether-linked phospholipids as modulators of Myomaker-mediated membrane fusion, revealing a lipid-centric perspective on the mechanisms driving myocyte fusion. Although we found no evidence of ceramidase activity for Myomaker, inhibiting sphingolipid biosynthesis enhanced fusion in both myocytes and BHK cells expressing Myomaker and Myomerger. These findings indicate that sphingolipids are not required for Myomaker function and may even act as antagonists of fusion. Lipidomic analyses under sphingolipid inhibition revealed an enrichment in ether lipids. Known for their fusogenic properties, these lipids were also enriched in Myomaker-containing lentiviral particles, indicating that membranes rendered fusion competent by Myomaker have higher concentrations of ether lipids. One possibility is that Myomaker may reside in, or help establish, lipid microdomains enriched in ether lipids. Functionally, increasing ether lipid levels, via Far1 overexpression or supplementation with the ether lipid precursor HG, was sufficient to induce Myomaker-dependent fusion even in the absence of Myomerger. Additionally, elevated ether lipid levels enhanced Myomaker’s localization to the plasma membrane and promoted externalization of PE and PS, hallmarks of membrane remodeling. Together, these findings suggest that ether lipids act as regulators of Myomaker activity and reveal a relationship between membrane lipid remodeling and Myomaker-mediated fusion.

Our work indicates that specific lipid classes, beyond their general fusogenic characteristics, can regulate protein-driven cell-cell fusion. One possible explanation for the ability of ether lipids to induce fusion in the presence of Myomaker is that they simply increase the amount of protein on the plasma membrane. While we detected an increase in plasma membrane-associated Myomaker after elevation of ether lipids, alternative ways to increase levels of Myomaker on the membrane, such as inhibition of autophagy, did not induce fusion, indicating that increases in plasma membrane Myomaker are not sufficient to induce fusion. This suggests that ether lipids influence the activity of Myomaker through additional mechanisms. One can hypothesize that an elevation in ether lipids promotes hemifusion-to-fusion transition by compensating for Myomerger’s activity.

A group of researchers compared the DNA of Italian centenarians with prehistoric genomes… and what they discovered left them stunned: the secret to living to 100 could lie in genes from 14,000 years ago

Italian centenarians share DNA links to Ice Age hunter gatherers, hinting that ancient genes may support extreme longevity.

AI Discovers Geophysical Turbulence Model

One of the biggest challenges in climate science and weather forecasting is predicting the effects of turbulence at spatial scales smaller than the resolution of atmospheric and oceanic models. Simplified sets of equations known as closure models can predict the statistics of this “subgrid” turbulence, but existing closure models are prone to dynamic instabilities or fail to account for rare, high-energy events. Now Karan Jakhar at the University of Chicago and his colleagues have applied an artificial-intelligence (AI) tool to data generated by numerical simulations to uncover an improved closure model [1]. The finding, which the researchers subsequently verified with a mathematical derivation, offers insights into the multiscale dynamics of atmospheric and oceanic turbulence. It also illustrates that AI-generated prediction models need not be “black boxes,” but can be transparent and understandable.

The team trained their AI—a so-called equation-discovery tool—on “ground-truth” data that they generated by performing computationally costly, high-resolution numerical simulations of several 2D turbulent flows. The AI selected the smallest number of mathematical functions (from a library of 930 possibilities) that, in combination, could reproduce the statistical properties of the dataset. Previously, researchers have used this approach to reproduce only the spatial structure of small-scale turbulent flows. The tool used by Jakhar and collaborators filtered for functions that correctly represented not only the structure but also energy transfer between spatial scales.

They tested the performance of the resulting closure model by applying it to a computationally practical, low-resolution version of the dataset. The model accurately captured the detailed flow structures and energy transfers that appeared in the high-resolution ground-truth data. It also predicted statistically rare conditions corresponding to extreme-weather events, which have challenged previous models.

Current flows without heat loss in newly engineered fractional quantum material

A team of US researchers has unveiled a device that can conduct electricity along its fractionally charged edges without losing energy to heat. Described in Nature Physics, the work, led by Xiaodong Xu at the University of Washington, marks the first demonstration of a “dissipationless fractional Chern insulator,” a long-sought state of matter with promising implications for future quantum technologies.

The quantum Hall effect emerges when electrons are confined to a two-dimensional material, cooled to extremely low temperatures, and exposed to strong magnetic fields. Much like the classical Hall effect, it describes how a voltage develops across a material perpendicular to the direction of current flow. In this case, however, that voltage increases in discrete, or quantized steps.

Under even more extreme conditions, an exotic variant appears named the “fractional quantum Hall” (FQH) effect. Here, electrons no longer behave as independent particles but move collectively, giving rise to voltage steps that correspond to fractions of an electron’s charge. This unusual collective behavior unlocks a whole host of exotic properties, and has made such states particularly appealing for emerging quantum technologies.

Old galaxies in a young universe?

The standard cosmological model (present-day version of “Big Bang,” called Lambda-CDM) gives an age of the universe close to 13.8 billion years and much younger when we explore the universe at high-redshift. The redshift of galaxies is produced by the expansion of the universe, which causes emitted wavelengths to lengthen and move toward the red end of the electromagnetic spectrum.

The further away a galaxy is, the more rapidly it is moving with respect to us, and so the greater is its redshift; and, given that the speed of light is finite, the more we travel to the past. Hence, measuring the age of very high redshift galaxies would be a way to test the cosmological model. Galaxies cannot be older than the age of the universe in which they are; it would be absurd, like a son older than his mother.

In work carried out with my colleague, Carlos M. Gutiérrez, at the Canary Islands Astrophysics Institute (IAC; Spain), we analyzed 31 galaxies with average redshift 7.3 (when the universe was 700 Myr old, according to the standard model) observed with the most powerful available telescope available: the James Webb Space Telescope (JWST).

Software tool can detect hidden errors in complex tissue analyses

A new software tool, ovrlpy, improves quality control in spatial transcriptomics, a key technology in biomedical research. Developed by the Berlin Institute of Health at Charité (BIH) in international collaboration, ovrlpy is the first tool to identify cell overlaps and folds in tissue sections, thereby reducing previously unrecognized sources of misinterpretations. The researchers have published their results in the journal Nature Biotechnology.

Spatial transcriptomics is a pioneering field of research in biomedicine that visualizes cellular activity within a tissue by mapping RNA transcripts and assigning this molecular activity to individual cells. So far, such analyses of tissue samples have mostly been interpreted in two dimensions. However, even very thin tissue sections of 5 to 10 micrometers thick, about one-tenth the width of a human hair, have a complex three-dimensional structure.

If this 3D arrangement is interpreted only as a flat surface, analytical errors can occur, for example, due to cell overlaps or tissue folds. This impedes the precise assignment of transcripts to individual cells and can distort downstream analysis and interpretation.

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