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Category: biological

Algal Swimming Patterns Change with Light Intensity

In response to changes in illumination, a swimming microorganism reverses the direction of its circular trajectory by tilting its flagella’s planes of motion.

Many microorganisms adjust their swimming trajectories in response to environmental signals such as nutrients or light. Researchers have now discovered a new mode of such behavior in a species of green algae [1]. The microbes swim in wide circles when illuminated and switch from counterclockwise (CCW) to clockwise (CW) swimming when the light intensity is above a threshold value. The researchers determined how this change is generated by the algae’s two whip-like flagella. They say that the results reveal a new navigation strategy that microorganisms can use to find optimal environments.

The single-celled green alga Chlamydomonas reinhardtii is photosynthetic and moves toward light by beating its two flagella, situated close together on its front surface, in a breaststroke pattern. In 2021, Kirsty Wan and Dario Cortese of the University of Exeter in the UK figured out the beating pattern that produces the microbe’s typical corkscrew-shaped trajectory, which follows a tight helix [2]. They showed how changing the frequency, amplitude, and synchronization of the flagellar beating allows the cell to change the overall direction of motion, perhaps to steer it toward or away from a light source and optimize the intensity of light it receives.

Sleep loss induces cholesterol-associated myelin dysfunction

The increasing prevalence of sleep deprivation poses a public health challenge in modern society. Manifestations of reduced alertness, such as slowed reaction times and increased errors, are well-documented behavioral indicators of sleep loss (SL). Yet, the biological consequences of sleep deprivation and their role in behavioral impairment remain elusive. Our study reveals significant effects of sleep deprivation on myelin integrity. As a result, we identify increased conduction delays in nerve signal propagation, hindered interhemispheric synchronization, and impaired cognitive and motor performance associated with SL. By profiling oligodendrocyte transcriptome and lipidome, we observe SL-induced endoplasmic reticulum stress and lipid metabolism dysregulation, particularly affecting cholesterol homeostasis.

Rapid Evolution of Complex Multi-mutant Proteins

The researchers developed MULTI-evolve, a framework for efficient protein evolution that applies machine learning models trained on datasets of ~200 variants focused specifically on pairs of function-enhancing mutations.

Published in Science, this work represents the first lab-in-the-loop framework for biological design, where computational prediction and experimental design are tightly integrated from the outset, reflecting our broader investment in AI-guided research.

Our insight was to focus on quality over quantity. First identify ~15–20 function-enhancing mutations (using protein language models or experimental screens), then systematically test all pairwise combinations of those beneficial mutations. This generates ~100–200 measurements, and every one is informative for learning beneficial epistatic interactions.

We validated this computationally using 12 existing protein datasets from published studies. Training neural networks on only the single and double mutants, we found models could accurately predict complex multi-mutants (variants with 3–12 mutations) across all 12 diverse protein families. This result held even when we reduced training data to just 10% of what was available.

Training on double mutants works because they reveal epistasis. A double mutant might perform better than the sum of its parts (synergy), worse than expected (antagonism), or exactly as predicted (additivity). These pairwise interaction patterns teach models the rules for how mutations combine, enabling extrapolation to predict which 5-, 6-, or 7-mutation combinations will work synergistically.

We then applied MULTI-evolve to three new proteins: APEX (up to 256-fold improvement over wild-type, 4.8-fold beyond already-optimized APEX2), dCasRx for trans-splicing (up to 9.8-fold improvement), and an anti-CD122 antibody (2.7-fold binding improvement to 1.0 nM, 6.5-fold expression increase). For dCasRx, we started with a deep mutational scan of 11,000 variants, extracted only the function-enhancing mutations, and tested their pairwise combinations—demonstrating the value of strategic data curation for efficient engineering.

Each required experimentally testing only ~100–200 variants in a single round to train models that accurately predicted complex multi-mutants, compressing what traditionally takes 5–10 iterative cycles over many months into weeks. Science Mission sciencenewshighlights.

Continue reading “Rapid Evolution of Complex Multi-mutant Proteins” | >

Tuning in to fluorescence to farm smarter: Monitoring plant light use saves indoor farm energy costs

Plant owners with a so-called green thumb often seem to have a more finely tuned sense of what their plants need than the rest of us. A new “smart lighting” system for indoor vertical farms grants this ability on a facility-wide scale, responsively meeting plants’ needs while reducing energy inefficiencies, clearing a path for indoor farms as an energy-efficient food security strategy.

The system was designed and tested in a study led by Professor of Plant Biology Tracy Lawson, who conducted the research at the University of Essex and is now a member of the Carl R. Woese Institute for Genomic Biology at the University of Illinois Urbana-Champaign. The work, published in Smart Agricultural Technology, emerged from her goal to help establish the viability of vertical farming for large-scale food production.

“One of the key aspects of [vertical farming], of course, is the energy cost associated with using LED lighting,” Lawson said. “So that’s where it all started, trying to save energy.”

Frontiers: Biological membranes are complex, heterogeneous, and dynamic systems that play roles in the compartmentalization and protection of cells from the environment

It is still a challenge to elucidate kinetics and real-time transport routes for molecules through biological membranes in live cells. Currently, by developing and employing super-resolution microscopy; increasing evidence indicates channels and transporter nano-organization and dynamics within membranes play an important role in these regulatory mechanisms. Here we review recent advances and discuss the major advantages and disadvantages of using super-resolution microscopy to investigate protein organization and transport within plasma membranes.

The mammalian plasma membrane (PM) is a complex assembly of lipids and proteins that separates the cell’s interior from the outside environment (Ingolfsson et al., 2014). The multiple collective processes that take place within membranes have a strong impact not only on the cellular behavior but also on its biochemistry. Understanding these processes poses a challenge due to the often complex and multiple interactions among membrane components (Stone et al., 2017). Moreover, the PM surface accommodates different types of lipid and protein clusters (Saka et al., 2014; Owen et al., 2012; Sezgin, 2017), even though the functional role of the clustering on the membrane surface has not yet been fully understood.

Scientists Believe Quantum Computers AreAbout to Cross a Major Line

We began this inquiry by looking at the mismatch between our computers and our brains. We realized that we were trying to run biological software on the wrong hardware. That era is ending. As we refine these quantum processors, we are finally building a mirror that is accurate enough to reflect the true nature of the mind. We are not just building faster computers. We are building a vessel that can hold the physics of thought.

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Timestamps:
0:00 Quantum Computers.
1:18 The Scale Problem.
4:40 The Thermodynamic Wall.
8:11 Quantum Mechanics in Wetware.
13:58 The \

Chemistry-powered ‘breathing’ membrane opens and closes tiny pores on its own

Ion channels are narrow passageways that play a pivotal role in many biological processes. To model how ions move through these tight spaces, pores need to be fabricated at very small length scales. The narrowest regions of ion channels can be just a few angstroms wide, about the size of individual atoms, making reproducible and precise fabrication a major challenge in modern nanotechnology.

In a study published in Nature Communications, researchers at The University of Osaka have addressed this challenge by using a miniature electrochemical reactor to create ultra-small pores approaching subnanometer dimensions.

In biological cells, ions flow in and out through channels in cell membranes. This ion flow is the basis for generating electrical signals, such as nerve impulses that trigger muscle contraction. The channels themselves are made of proteins and can have angstrom-wide narrow regions. Conformational changes of these proteins in response to external stimuli open and close the channels.

Lab-in-the-loop framework enables rapid evolution of complex multi-mutant proteins

The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20100 possible variants—more combinations than atoms in the observable universe. Traditional engineering methods might test hundreds of variants but limit exploration to narrow regions of the sequence space. Recent machine learning approaches enable broader searches through computational screening. However, these approaches still require tens of thousands of measurements, or 5–10 iterative rounds.

With the advent of these foundational protein models, the bottleneck for protein engineering swings back to the lab. For a single protein engineering campaign, researchers can only efficiently build and test hundreds of variants. What is the best way to choose those hundreds to most effectively uncover an evolved protein with substantially increased function? To address this problem, researchers have developed MULTI-evolve, a framework for efficient protein evolution that applies machine learning models trained on datasets of ~200 variants focused specifically on pairs of function-enhancing mutations.

Published in Science, this work represents Arc Institute’s first lab-in-the-loop framework for biological design, where computational prediction and experimental design are tightly integrated from the outset, reflecting a broader investment in AI-guided research.

Particles don’t always go with the flow (and why that matters)

It is commonly assumed that tiny particles just go with the flow as they make their way through soil, biological tissue, and other complex materials. But a team of Yale researchers led by Professor Amir Pahlavan shows that even gentle chemical gradients, such as a small change in salt concentration, can dramatically reshape how particles move through porous materials. Their results are published in Science Advances.

How small particles known as colloids, like fine clays, microbes, or engineered particles, move through porous materials such as soil, filters, and biological tissue can have significant and wide-ranging effects on everything from environmental cleanups to agriculture.

It’s long been known that chemical gradients—that is, gradual changes in the concentration of salt or other chemicals—can drive colloids to migrate directionally, a phenomenon known as diffusiophoresis. But it was often assumed that this effect would matter only when there was little or no flow, because phoretic speeds are typically orders of magnitude smaller than average flow speeds in porous media. Experiments set up in Pahlavan’s lab demonstrated a very different outcome.

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