Shuttling of macromolecules between different cellular compartments helps regulate the timing and extent of different cellular activities. Here, we report that LC3, a key initiator of autophagy that cycles between the nucleus and cytoplasm, becomes selectively activated in the nucleus during starvation through deacetylation by the nuclear deacetylase Sirt1. Deacetylation of LC3 at K49 and K51 by Sirt1 allows LC3 to interact with the nuclear protein DOR and return to the cytoplasm with DOR, where it is able to bind Atg7 and other autophagy factors and undergo phosphatidylethanolamine conjugation to preautophagic membranes. The association of deacetylated LC3 with autophagic factors shifts LC3's distribution from the nucleus toward the cytoplasm. Thus, an acetylation-deacetylation cycle ensures that LC3 effectively redistributes in an activated form from nucleus to cytoplasm, where it plays a central role in autophagy to enable the cell to cope with the lack of external nutrients.

The evolution of the ancestral eukaryotic flagellum is an example of a cellular organelle that became dispensable in some modern eukaryotes while remaining an essential motile and sensory apparatus in others. To help define the repertoire of specialized proteins needed for the formation and function of cilia, we used comparative genomics to analyze the genomes of organisms with prototypical cilia, modified cilia, or no cilia and identified approximately 200 genes that are absent in the genomes of nonciliated eukaryotes but are conserved in ciliated organisms. Importantly, over 80% of the known ancestral proteins involved in cilia function are included in this small collection. Using Drosophila as a model system, we then characterized a novel family of proteins (OSEGs: outer segment) essential for ciliogenesis. We show that osegs encode components of a specialized transport pathway unique to the cilia compartment and are related to prototypical intracellular transport proteins.

This special feature of , titled 'Advances in Quantitative Bioimaging', proposes an overview of the latest advancements in quantitative bioimaging techniques and their wide-ranging applications. The articles cover various topics, including modern imaging methods that enable visualization on a nanoscale, such as super-resolution microscopy and single-particle analysis. These techniques offer unparalleled insights into complex molecular structures and dynamic cellular processes , such as mapping nuclear pore proteins or tracking single histone deposition events throughout the cell cycle. The articles presented in this edition showcase cutting-edge quantitative imaging techniques coupled with advanced computational analysis capable of precisely measuring biological structures and processes. Examples range from correlating calcium release events to underlying protein organization in heart cells to pioneering tools for categorizing changes in microglia morphology under various conditions. This editorial highlights how these advancements are revolutionizing our understanding of living systems, while acknowledging challenges that must be addressed to fully exploit the potential of these emerging technologies, such as improving molecular probes, algorithms and correlation protocols.

Natural physical, chemical, and biological dynamical systems are often complex, with heterogeneous components interacting in diverse ways. We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneity from data alone. The learned latent structure and dynamics can be used to virtually decompose the complex system which is necessary to parameterize and infer the underlying governing equations. We tested the approach with simulation experiments of moving particles and vector fields that interact with each other. While our current aim is to better understand and validate the approach with simulated data, we anticipate it to become a generally applicable tool to uncover the governing rules underlying complex dynamics observed in nature.

Behavior has molecular, cellular, and circuit determinants. However, because many proteins are broadly expressed, their acute manipulation within defined cells has been difficult. Here, we combined the speed and molecular specificity of pharmacology with the cell type specificity of genetic tools. DART (drugs acutely restricted by tethering) is a technique that rapidly localizes drugs to the surface of defined cells, without prior modification of the native target. We first developed an AMPAR antagonist DART, with validation in cultured neuronal assays, in slices of mouse dorsal striatum, and in behaving mice. In parkinsonian animals, motor deficits were causally attributed to AMPARs in indirect spiny projection neurons (iSPNs) and to excess phasic firing of tonically active interneurons (TANs). Together, iSPNs and TANs (i.e., D2 cells) drove akinesia, whereas movement execution deficits reflected the ratio of AMPARs in D2 versus D1 cells. Finally, we designed a muscarinic antagonist DART in one iteration, demonstrating applicability of the method to diverse targets.

Hunger is a complex behavioural state that elicits intense food seeking and consumption. These behaviours are rapidly recapitulated by activation of starvation-sensitive AGRP neurons, which present an entry point for reverse-engineering neural circuits for hunger. Here we mapped synaptic interactions of AGRP neurons with multiple cell populations in mice and probed the contribution of these distinct circuits to feeding behaviour using optogenetic and pharmacogenetic techniques. An inhibitory circuit with paraventricular hypothalamus (PVH) neurons substantially accounted for acute AGRP neuron-evoked eating, whereas two other prominent circuits were insufficient. Within the PVH, we found that AGRP neurons target and inhibit oxytocin neurons, a small population that is selectively lost in Prader-Willi syndrome, a condition involving insatiable hunger. By developing strategies for evaluating molecularly defined circuits, we show that AGRP neuron suppression of oxytocin neurons is critical for evoked feeding. These experiments reveal a new neural circuit that regulates hunger state and pathways associated with overeating disorders.

Serine hydrolases have diverse intracellular substrates, biological functions, and structural plasticity, and are thus important for biocatalyst design. Amongst serine hydrolases, the recently described ybfF enzyme family are promising novel biocatalysts with an unusual bifurcated substrate-binding cleft and the ability to recognize commercially relevant substrates. We characterized in detail the substrate selectivity of a novel ybfF enzyme from Vibrio cholerae (Vc-ybfF) by using a 21-member library of fluorogenic ester substrates. We assigned the roles of the two substrate-binding clefts in controlling the substrate selectivity and folded stability of Vc-ybfF by comprehensive substitution analysis. The overall substrate preference of Vc-ybfF was for short polar chains, but it retained significant activity with a range of cyclic and extended esters. This broad substrate specificity combined with the substitutional analysis demonstrates that the larger binding cleft controls the substrate specificity of Vc-ybfF. Key selectivity residues (Tyr116, Arg120, Tyr209) are also located at the larger binding pocket and control the substrate specificity profile. In the structure of ybfF the narrower binding cleft contains water molecules prepositioned for hydrolysis, but based on substitution this cleft showed only minimal contribution to catalysis. Instead, the residues surrounding the narrow binding cleft and at the entrance to the binding pocket contributed significantly to the folded stability of Vc-ybfF. The relative contributions of each cleft of the binding pocket to the catalytic activity and folded stability of Vc-ybfF provide a valuable map for designing future biocatalysts based on the ybfF scaffold.

Young birds learn to sing by using auditory feedback to compare their own vocalizations to a memorized or innate song pattern; if they are deafened as juveniles, they will not develop normal songs. The completion of song development is called crystallization. After this stage, song shows little variation in its temporal or spectral properties. However, the mechanisms underlying this stability are largely unknown. Here we present evidence that auditory feedback is actively used in adulthood to maintain the stability of song structure. We found that perturbing auditory feedback during singing in adult zebra finches caused their song to deteriorate slowly. This ’decrystallization’ consisted of a marked loss of the spectral and temporal stereotypy seen in crystallized song, including stuttering, creation, deletion and distortion of song syllables. After normal feedback was restored, these deviations gradually disappeared and the original song was recovered. Thus, adult birds that do not learn new songs nevertheless retain a significant amount of plasticity in the brain.

To integrate changing environmental cues with high spatial and temporal resolution is critical for animals to orient themselves. Drosophila larvae show an effective motor program to navigate away from light sources. How the larval visual circuit processes light stimuli to control navigational decision remains unknown. The larval visual system is composed of two sensory input channels, Rhodopsin5 (Rh5) and Rhodopsin6 (Rh6) expressing photoreceptors (PRs). We here characterize how spatial and temporal information are used to control navigation. Rh6-PRs are required to perceive temporal changes of light intensity during head casts, while Rh5-PRs are required to control behaviors that allow navigation in response to spatial cues. We characterize how distinct behaviors are modulated and identify parallel acting and converging features of the visual circuit. Functional features of the larval visual circuit highlight the principle of how early in a sensory circuit distinct behaviors may be computed by partly overlapping sensory pathways.

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.Competing Interest StatementThe authors have declared no competing interest.