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Abstract The universally conserved α-oxoaldehydes glyoxal (GO) and methylglyoxal (MGO) are toxic metabolic byproducts whose accumulation can lead to cell death. In the absence of a known, natural inducer of the GO-specific response in prokaryotes, we exploited RNA-seq to define a GO response in the bacterial pathogenPseudomonas aeruginosa. The highest upregulated operon consisted of the known glyoxalase (gloA2) and an antibiotic monooxygenase (ABM) domain of unknown function - renamed hereAldehyderesponsivequorum-sensingInhibitor (ArqI). ThearqI-gloA2operon is highly specific to GO induction and ArqI protein responds by migrating to the flagellar pole. An ArqI atomic structure revealed several unique features to the ABM family, including a ‘pinwheel’ hexamer harboring a GO-derived post-translational modification on a conserved arginine residue (Arg49). Induction of ArqI abrogates production of the Pseudomonas Quinolone Signal (PQS) quorum sensing molecule and was found to directly interact with PqsA; the first enzyme in the PQS biosynthesis pathway. Finally, we use a sepsis model of infection to reveal a survival requirement forarqI-gloA2in blood-rich organs (heart, spleen, liver and lung). Here we define a global GO response in a pathogen, identify and characterize the first GO-specific operon and implicate its role in PQS production and host survival. 

Abstract Animals rely on their sense of smell to survive, but important olfactory cues are mixed with confounding background odors that fluctuate due to atmospheric turbulence. It is unclear how the olfactory system habituates to such stochastic backgrounds to detect behaviorally important odors. Here, we explicitly consider the high-dimensional nature of odor coding, the natural statistics of odor fluctuations and the architecture of the early olfactory pathway. We show that their combination favors a manifold learning mechanism for olfactory habituation over alternatives based on predictive filtering. Manifold learning is implemented in our model by a biologically plausible network of inhibitory interneurons in the early olfactory pathway. We demonstrate that plasticity rules based on IBCM or online PCA are effective at implementing this mechanism in turbulent conditions and outperform previous models relying on mean background subtraction. Interneurons with an IBCM plasticity rule acquire selectivity to independently varying odors. This manifold learning mechanism offers a path towards distinguishing plasticity rules in experiments and could be leveraged by other biological circuits facing fluctuating environments. 

Abstract Diet influences the levels of small molecules that circulate in plasma and interstitial fluid, altering the biochemical composition of the tumor microenvironment (TME). These circulating nutrients have been associated with how tumors grow and respond to treatment, but it remains difficult to parse their direct effects on cancer cells. Here, we combine a three-dimensional (3D) microfluidic tumor model with physiologically relevant culture media to investigate how concentrations of circulating nutrients influence tumor growth, cancer cell invasion, and overall tumor metabolism. Human triple-negative breast cancer cells cultured in 2D under media conditions mimicking five different dietary states show no observable differences in proliferation or morphology. Nonetheless, those exposed to high-fat conditions exhibit increased metabolic activity and upregulate genes associated with motility and extracellular matrix remodeling. In the 3D microfluidic model, high-fat conditions accelerate tumor growth and invasion and induce the formation of hollow cavities. Surprisingly, the presence of these cavities does not correlate with an increase in apoptosis or ferroptosis. Instead, RNA-sequencing analysis revealed that high-fat conditions induce the expression ofMMP1, consistent with cavitation via cell invasion. Mimicking the flow of circulating nutrients within the TME can thus be used to identify novel connections between metabolic states and tumor phenotype. 

Abstract Recent advances in brain-wide recordings of small animals such as worms, fish, and flies have revealed complex activity involving large populations of neurons. In theDrosophilabrain, with about 140,000 neurons, brain-wide recordings have been critical to uncovering widespread sensory and motor activity. However, current limitations in volumetric imaging rates hinder the accurate capture of fast neural dynamics. To improve the speed of volumetric imaging inDrosophila, we leverage the recently introduced light beads microscopy (LBM) method. We built a microscope and a LBM module tailored to fly brain experiments and used it to record brain-wide calcium signals in adult behaving flies at either 28 volumes per second or at 60 volumes per second (when selecting the central brain alone). We uncover fast-timescale auditory responses that are missed with standard volumetric imaging. We also demonstrate how temporal super-resolution can be combined with LBM data to uncover responses to singleDrosophilacourtship song pulses. This establishes LBM as a viable tool for capturing whole-brain activity at high spatial and temporal resolution in the fly. 

Abstract We describe a DNA-array-based method to infer intramolecular connections in a population of RNA molecules in vitro. First we add DNA oligonucleotide “patches” that perturb the RNA connections, and then we use a microarray containing a complete set of DNA oligonucleotide “probes” to record where perturbations occur. The pattern of perturbations reveals couplings between regions of the RNA sequence, from which we infer connections as well as their prevalences in the population, without reference to folding models. We validate this patch–probe method using the 1058-nucleotide RNA genome of satellite tobacco mosaic virus (STMV), which has been shown to have multiple long-range connections. Our results not only indicate long-range connections that agree with previous structures but also reveal the prevalence of competing connections. Together, these results suggest that multiple structures with different connectivity coexist in solution. Furthermore, we show that the prevalence of certain connections changes when pseudouridine, an important component of natural and synthetic RNAs, is substituted for uridine in STMV RNA, and that the connectivity of STMV minus strands is qualitatively distinct from that of plus strands. Finally, we use a simplified version of the method to validate a predicted 317-nucleotide connection within the 3569-nucleotide RNA genome of bacteriophage MS2. 

Summary Dominant individuals often structure group organization, but less is known about how social networks differ in their absence or how variation among subordinates contributes to collective outcomes. Bumble bees (Bombus impatiens) provide an ideal system to study how individual behavior shapes colony organization: queens typically monopolize reproduction, but in some contexts individual workers can adopt queen-like social roles. We asked how this process shapes the collective phenotype. Using multi-animal pose tracking to quantify social behaviors, we compared matched queenright and queenless partitions from the same source colonies. Queenless colonies were more interactive and contained a subset of behaviorally extreme queen-like workers with higher movement, spatial centrality, and reproductive potential. Such variation, absent in queenright colonies, coincided with a shift to decentralized, efficient network structures. These results demonstrate how social context shapes the expression of individual phenotypes, revealing a mechanism by which seemingly hierarchical societies can retain latent social flexibility and underscoring the link between individual variation and collective organization. 

Abstract Protein turnover is critical for proteostasis, but turnover quantification is challenging, and even in well-studiedE. coli, proteome-wide measurements remain scarce. Here, we quantify the turnover rates of ~3200E. coliproteins under 13 conditions by combining heavy isotope labeling with complement reporter ion quantification and find that cytoplasmic proteins are recycled when nitrogen is limited. We use knockout experiments to assign substrates to the known cytoplasmic ATP-dependent proteases. Surprisingly, none of these proteases are responsible for the observed cytoplasmic protein degradation in nitrogen limitation, suggesting that a major proteolysis pathway inE. coliremains to be discovered. Lastly, we show that protein degradation rates are generally independent of cell division rates. Thus, we present broadly applicable technology for protein turnover measurements and provide a rich resource for protein half-lives and protease substrates inE. coli, complementary to genomics data, that will allow researchers to study the control of proteostasis. 

Abstract Many bacteria inhabit thin layers of water on solid surfaces both naturally in soils or on hosts or textiles and in the lab on agar hydrogels. In these environments, cells experience capillary forces, yet an understanding of how these forces shape bacterial collective behaviors remains elusive. Here, we show that the water menisci formed around bacteria lead to capillary attraction between cells while still allowing them to slide past one another. We develop an experimental apparatus that allows us to control bacterial collective behaviors by varying the strength and range of capillary forces. Combining 3D imaging and cell tracking with agent-based modeling, we demonstrate that capillary attraction organizes rod-shaped bacteria into densely packed, nematic groups, and profoundly influences their collective dynamics and morphologies. Our results suggest that capillary forces may be a ubiquitous physical ingredient in shaping microbial communities in partially hydrated environments. 

Abstract Season length and its associated variables can influence the expression of social behaviors, including the occurrence of eusociality in insects. Eusociality can vary widely across environmental gradients, both within and between different species. Numerous theoretical models have been developed to examine the life history traits that underlie the emergence and maintenance of eusociality, yet the impact of seasonality on this process is largely uncharacterized. Here, we present a theoretical model that incorporates season length and offspring development time into a single, individual-focused model to examine how these factors can shape the costs and benefits of social living. We find that longer season lengths and faster brood development times are sufficient to favor the emergence and maintenance of a social strategy, while shorter seasons favor a solitary one. We also identify a range of season lengths where social and solitary strategies can coexist. Moreover, our theoretical predictions are well-matched to the natural history and behavior of two flexibly-eusocial bee species, suggesting our model can make realistic predictions about the evolution of different social strategies. Broadly, this work reveals the crucial role that environmental conditions can have in shaping social behavior and its evolution and underscores the need for further models that explicitly incorporate such variation to study evolutionary trajectories of eusociality. 

Abstract Non-equilibrium systems, in particular, living organisms, are maintained by irreversible transformations of energy that drive diverse functions. Quantifying their irreversibility, as measured by energy dissipation, is essential for understanding the underlying mechanisms. However, existing techniques usually overlook experimental limitations, either by assuming full information or by employing a coarse-graining method that requires knowledge of the structure behind hidden degrees of freedom. Here, we study the inference of dissipation from finite-resolution measurements by employing a recently developed model-free estimator that considers both the sequence of coarse-grained transitions and the waiting time distributions: ${\sigma }_2={\sigma }_2^{\ell }+{\sigma }_2^t$. The dominant term ${\sigma }_2^{\ell }$originates from the sequence of observed transitions. We find that it scales with resolution following a power law. Comparing the scaling exponent with a previous estimator highlights the importance of accounting for flux correlations at lower resolutions. ${\sigma }_2^t$comes from asymmetries in waiting time distributions. It is non-monotonic in resolution, with its peak position revealing characteristic scales of the underlying dissipative process, consistent with observations in the actomyosin cortex of starfish oocytes. Alternatively, the characteristic scale can be detected in a crossover of the scaling of ${\sigma }_2^{\ell }$. This provides a novel perspective for extracting otherwise hidden characteristic dissipative scales directly from dissipation measurements. We illustrate these results in biochemical models as well as complex networks. Overall, this study highlights the significance of resolution considerations in non-equilibrium systems, providing insight into the interplay between experimental resolution, entropy production and underlying complexity.