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Chemotactic bacteria not only navigate chemical gradients, but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior, and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities. [ABSTRACT FROM AUTHOR]

Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible--a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise. [ABSTRACT FROM AUTHOR]

Collective behaviors require coordination among a group of individuals. As a result, individuals that are too phenotypically different from the rest of the group can be left out, reducing heterogeneity, but increasing coordination. If individuals also reproduce, the offspring can have different phenotypes from their parent(s). This raises the question of how these two opposing processes--loss of diversity by collective behaviors and generation of it through growth and inheritance--dynamically shape the phenotypic composition of an isogenic population. We examine this question theoretically using collective migration of chemotactic bacteria as a model system, where cells of different swimming phenotypes are better suited to navigate in different environments. We find that the differential loss of phenotypes caused by collective migration is environmentdependent. With cell growth, this differential loss enables migrating populations to dynamically adapt their phenotype compositions to the environment, enhancing migration through multiple environments. Which phenotypes are produced upon cell division depends on the level of nongenetic inheritance, and higher inheritance leads to larger composition adaptation and faster migration at steady state. However, this comes at the cost of slower responses to new environments. Due to this trade-off, there is an optimal level of inheritance that maximizes migration speed through changing environments, which enables a diverse population to outperform a nondiverse one. Growing populations might generally leverage the selection-like effects provided by collective behaviors to dynamically shape their own phenotype compositions, without mutations. [ABSTRACT FROM AUTHOR]

Most natural odors are mixtures and often elicit percepts distinct from those elicited by their constituents. This emergence of a unique odor quality has long been attributed to central processing. Here we show that sophisticated integration of olfactory information begins in olfactory receptor neurons (ORNs) in Drosophila. Odor mixtures are encoded in the temporal dynamics as well as in the magnitudes of ORN responses. ORNs can respond to an inhibitory odorant with different durations depending on the level of background excitation. ORNs respond to mixtures with distinctive temporal dynamics that reflect the physicochemical properties of the constituent odorants. The insect repellent DEET (N,N-diethyl-m-toluamide), which attenuates odor responses of multiple ORNs, differs from an ORN-specific inhibitor in its effects on temporal dynamics. Our analysis reveals a means by which integration of information from odor mixtures begins in ORNs and provides insight into the contribution of inhibitory stimuli to sensory coding. [ABSTRACT FROM AUTHOR]

The propagation of cell shape across generations is remarkably robust in most bacteria. Even when deformations are acquired. growing cells progressively recover their original shape once the deforming factors are eliminated. For instance, straight-rod-shaped bacteria grow curved when confined to circular microchambers, but straighten in a growth-dependent fashion when released. Bacterial cell shape is maintained by the peptidoglycan (PG) cell wall, a giant macromolecule of glycan strands that are synthesized by processive enzymes and cross-linked by peptide chains. Changes in cell geometry require modifying the PG and therefore depend directly on the molecular-scale properties of PG structure and synthesis. Using a mathematical model we quantify the straightening of curved Cau!obacter crescentus cells after disruption of the cell-curving crescentin structure. We observe that cells straighten at a rate that is about half (57%) the cell growth rate. Next we show that in the absence of other effects there exists a mathematical relationship between the rate of cell straightening and the processivity of PG synthesis-the number of subunits incorporated before termination of synthesis. From the measured rate of cell straightening this relationship predicts processivity values that are in good agreement with our estimates from published data. Finally, we consider the possible role of three other mechanisms in cell straightening. We conclude that regardless of the involvement of other factors, intrinsic properties of PG processivity provide a robust mechanism for cell straightening that is hardwired to the cell wall synthesis machinery. [ABSTRACT FROM AUTHOR]

The molecular basis of bacterial cell morphogenesis remains largely an open question. Here we discover a morphogenic protein, RodZ, which is widely conserved across the bacterial kingdom. In Caulobacter crescentus, RodZ is essential for viability and is involved in all aspects of this organism's complex morphology. Depletion or over-production of RodZ results in grossly misshapen cells with stalk defects. RodZ exhibits a localization pattern during the cell cycle corresponding to sites of active peptidoglycan synthesis. The temporal transition of RodZ between patchy/helical and mid-cell localization mimics and depends on the actin-like MreB cytoskeleton. In Escherichia coli, an organism with a distinct mode of growth and MreB localization dynamics, RodZ follows MreB and retains its crucial role in cell morphogenesis, demonstrating conservation of function. Genomic analysis shows that RodZ represents an ancient function unique to bacteria. Multiple sequence alignment of 143 RodZ sequences from species across bacterial phyla identifies an N-terminal cytoplasmic domain with a helix-turn-helix motif, a transmembrane sequence, and a previously unidentified, conserved periplasmic or extracellular C-terminal domain. Both the N- and C-terminal domains are important for function, with the N-terminal domain containing localization determinants. This study uncovers a key missing player in the cytoskeleton-based growth machinery enabling heritable and defined cellular forms in bacteria. [ABSTRACT FROM AUTHOR]

Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible-a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.