David J. Schwab

α-ketoglutarate coordinates carbon and nitrogen utilization via enzyme I inhibition

Microbes survive in a variety of nutrient environments by modulating their intracellular metabolism. Balanced growth requires coordinated uptake of carbon and nitrogen, the primary substrates for biomass production. The mechanisms that balance carbon and nitrogen uptake are, however, poorly understood. We find in Escherichia coli that a sudden increase in nitrogen availability results in an almost immediate increase in glucose uptake. The concentrations of known glycolytic intermediates and regulators, however, remain homeostatic. Instead, we find that α-ketoglutarate, which accumulates in nitrogen limitation, directly blocks glucose uptake by inhibiting Enzyme I, the first step of the phosphotransferase system (PTS). This enables rapid modulation of glycolytic flux without marked concentration changes in glycolytic intermediates by simultaneously accelerating glucose import and consumption of the terminal glycolytic intermediate phosphoenolpyruvate. Quantitative modeling shows that this previously unidentified regulatory connection is in principle sufficient to coordinate carbon and nitrogen utilization.

Energetic costs of cellular computation

Cells often perform computations in order to respond to environmental cues. A simple example is the classic problem, first considered by Berg and Purcell, of determining the concentration of a chemical ligand in the surrounding media. On general theoretical grounds, it is expected that such computations require cells to consume energy. In particular, Landauer’s principle states that energy must be consumed in order to erase the memory of past observations. Here, we explicitly calculate the energetic cost of steady-state computation of ligand concentration for a simple two-component cellular network that implements a noisy version of the Berg–Purcell strategy. We show that learning about external concentrations necessitates the breaking of detailed balance and consumption of energy, with greater learning requiring more energy. Our calculations suggest that the energetic costs of cellular computation may be an important constraint on networks designed to function in resource poor environments, such as the spore germination networks of bacteria.

Constant Growth Rate Can Be Supported by Decreasing Energy Flux and Increasing Aerobic Glycolysis

Fermenting glucose in the presence of enough oxygen to support respiration, known as aerobic glycolysis, is believed to maximize growth rate. Wexa0observed increasing aerobic glycolysis during exponential growth, suggesting additional physiological roles for aerobic glycolysis. We investigatedxa0such roles in yeast batch cultures by quantifying O2 consumption, CO2 production, amino acids, mRNAs, proteins, posttranslational modifications, and stress sensitivity in the course of nine doublings at constant rate. During this course, the cells support a constant biomass-production rate with decreasing rates of respiration and ATP production but also decrease their stress resistance. As the respiration rate decreases, so do the levels of enzymes catalyzing rate-determining reactions of the tricarboxylic-acid cycle (providing NADH for respiration) and of mitochondrial folate-mediated NADPH production (required for oxidative defense). The findings demonstrate that exponential growth can represent not a single metabolic/physiological state but a continuum of changing states and that aerobic glycolysis can reduce the energy demands associated with respiratory metabolism and stress survival.

Zipf's Law and Criticality in Multivariate Data without Fine-Tuning

The joint probability distribution of states of many degrees of freedom in biological systems, such as firing patterns in neural networks or antibody sequence compositions, often follows Zipfs law, where a power law is observed on a rank-frequency plot. This behavior has been shown to imply that these systems reside near a unique critical point where the extensive parts of the entropy and energy are exactly equal. Here, we show analytically, and via numerical simulations, that Zipf-like probability distributions arise naturally if there is a fluctuating unobserved variable (or variables) that affects the system, such as a common input stimulus that causes individual neurons to fire at time-varying rates. In statistics and machine learning, these are called latent-variable or mixture models. We show that Zipfs law arises generically for large systems, without fine-tuning parameters to a point. Our work gives insight into the ubiquity of Zipfs law in a wide range of systems.

Specific wiring of distinct amacrine cells in the directionally selective retinal circuit permits independent coding of direction and size

Local and global forms of inhibition controlling directionally selective ganglion cells (DSGCs) in the mammalian retina are well documented. It is established that local inhibition arising from GABAergic starburst amacrine cells (SACs) strongly contributes to direction selectivity. Here, we demonstrate that increasing ambient illumination leads to the recruitment of GABAergic wide-field amacrine cells (WACs) endowing the DS circuit with an additional feature: size selectivity. Using a combination of electrophysiology, pharmacology, and light/electron microscopy, we show that WACs predominantly contact presynaptic bipolar cells, which drive direct excitation and feedforward inhibition (through SACs) to DSGCs, thus maintaining the appropriate balance of inhibition/excitation required for generating DS. This circuit arrangement permits high-fidelity direction coding over a range of ambient light levels, over which size selectivity is adjusted. Together, these results provide novel insights into the anatomical and functional arrangement of multiple inhibitory interneurons within a single computational module in the retina.

Lag normalization in an electrically coupled neural network

Moving objects can cover large distances while they are processed by the eye, usually resulting in a spatially lagged retinal response. We identified a network of electrically coupled motion–coding neurons in mouse retina that act collectively to register the leading edges of moving objects at a nearly constant spatial location, regardless of their velocity. These results reveal a previously unknown neurophysiological substrate for lag normalization in the visual system.

From intracellular signaling to population oscillations: Bridging size- and time-scales in collective behavior

Collective behavior in cellular populations is coordinated by biochemical signaling networks within individual cells. Connecting the dynamics of these intracellular networks to the population phenomena they control poses a considerable challenge because of network complexity and our limited knowledge of kinetic parameters. However, from physical systems, we know that behavioral changes in the individual constituents of a collectively behaving system occur in a limited number of well‐defined classes, and these can be described using simple models. Here, we apply such an approach to the emergence of collective oscillations in cellular populations of the social amoeba Dictyostelium discoideum. Through direct tests of our model with quantitative in vivo measurements of single‐cell and population signaling dynamics, we show how a simple model can effectively describe a complex molecular signaling network at multiple size and temporal scales. The model predicts novel noise‐driven single‐cell and population‐level signaling phenomena that we then experimentally observe. Our results suggest that like physical systems, collective behavior in biology may be universal and described using simple mathematical models.

Nonlinear dendritic integration of electrical and chemical synaptic inputs drives fine-scale correlations

Throughout the CNS, gap junction–mediated electrical signals synchronize neural activity on millisecond timescales via cooperative interactions with chemical synapses. However, gap junction–mediated synchrony has rarely been studied in the context of varying spatiotemporal patterns of electrical and chemical synaptic activity. Thus, the mechanism underlying fine-scale synchrony and its relationship to neural coding remain unclear. We examined spike synchrony in pairs of genetically identified, electrically coupled ganglion cells in mouse retina. We found that coincident electrical and chemical synaptic inputs, but not electrical inputs alone, elicited synchronized dendritic spikes in subregions of coupled dendritic trees. The resulting nonlinear integration produced fine-scale synchrony in the cells spike output, specifically for light stimuli driving input to the regions of dendritic overlap. In addition, the strength of synchrony varied inversely with spike rate. Together, these features may allow synchronized activity to encode information about the spatial distribution of light that is ambiguous on the basis of spike rate alone.

Dynamic tuning of electrical and chemical synaptic transmission in a network of motion coding retinal neurons.

Recently, we demonstrated that gap junction coupling in the population of superior coding ON-OFF directionally selective ganglion cells (DSGCs) genetically labeled in the Hb9::eGFP mouse retina allows the passage of lateral anticipatory signals that help track moving stimuli. Here, we examine the properties of gap junctions in the DSGC network, and address how interactions between electrical and chemical synapses and intrinsic membrane properties contribute to the dynamic tuning of lateral anticipatory signals. When DSGC subtypes coding all four cardinal directions were individually loaded with the gap junction-permeable tracer Neurobiotin, only superior coding DSGCs exhibited homologous coupling. Consistent with these anatomical findings, gap junction-dependent feedback spikelets were only observed in Hb9+ DSGCs. Recordings from pairs of neighboring Hb9+ DSGCs revealed that coupling was reciprocal, non-inactivating, and relatively weak, and provided a substrate for an extensive subthreshold excitatory receptive field around each cell. This subthreshold activity appeared to boost coincident light-driven chemical synaptic responses. However, during responses to moving stimuli, gap junction-mediated boosting appeared to be dynamically modulated such that upstream DSGCs primed downstream cells, but not vice versa, giving rise to highly skewed responses in individual cells. We show that the asymmetry in priming arises from a combination of spatially offset GABAergic inhibition and activity-dependent changes in intrinsic membrane properties of DSGCs. Thus, dynamic interactions between electrical and chemical synapses and intrinsic membrane properties allow the network of DSGCs to propagate anticipatory responses most effectively along their preferred direction without leading to runaway excitation.

A Central Role for Mixed Acetylcholine/GABA Transmission in Direction Coding in the Retina

A surprisingly large number of neurons throughout the brain are endowed with the ability to co-release both a fast excitatory and inhibitory transmitter. The computational benefits of dual transmitter release, however, remain poorly understood. Here, we address the role of co-transmission of acetylcholine (ACh) and GABA from starburst amacrine cells (SACs) to direction-selective ganglion cells (DSGCs). Using a combination of pharmacology, optogenetics, and linear regression methods, we estimated the spatiotemporal profiles of GABA, ACh, and glutamate receptor-mediated synaptic activity in DSGCs evoked by motion. We found that ACh initiates responses to motion in natural scenes or under low-contrast conditions. In contrast, classical glutamatergic pathways play a secondary role, amplifying cholinergic responses via NMDA receptor activation. Furthermore, under these conditions, the network ofxa0SACs differentially transmits ACh and GABA to DSGCs in a directional manner. Thus, mixed transmission plays a central role in shaping directional responses of DSGCs.