You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Predicting Chemical Environments of Bacteria from Receptor Signaling}
Article:
Abstract:
Sensory systems have evolved to respond to input stimuli of certain statistical properties, and to reliably transmit this information through biochemical pathways. Hence, for an experimentally well-characterized sensory system, one ought to be able to extract valuable information about the statistics of the stimuli. Based on dose-response curves from in vivo fluorescence resonance energy transfer( FRET) experiments of the bacterial chemotaxis sensory system, we predict the chemical gradients chemotactic Escherichia coli cells typically encounter in their natural environment. To predict average gradients cells experience, we revaluate the phenomenological Weber's law and its generalizations to the Weber-Fechner law and fold-change detection. To obtain full distributions of gradients we use information theory and simulations, considering limitations of information transmission from both cell-external and internal noise. We identify broad distributions of exponential gradients, which lead to log-normal stimuli and maximal drift velocity. Our results thus provide a first step towards deciphering the chemical nature of complex, experimentally inaccessible cellular microenvironments, such as the human intestine.
Introduction:
Bacteria live in complex chemical microenvironments with the spatio-temporal chemical concentration profiles shaped by nutrient supply, as well as nutrient depletion by surrounding bacteria of the same or other species( Fig. 1). From studies of the composition of commensal bacteria in our intestines, it is emerging that these bacterial communities have important roles for breaking down undigested food, and for increasing resistance against colonization by pathogens[1]. Although of physiological importance, we currently do not have a good understanding of what chemical gradients microorganisms in the gut typically encounter. One way forward is to exploit our accumulated knowledge about their sensory systems, evolved to detect typical stimuli relevant to the organism, e. g. the concentration of a particular nutrient. Therefore, knowing the design and functioning of sensory systems we ought to be able to predict typical stimuli such as concentration gradients. The best-characterized bacterium is Escherichia coli, inhabiting the gastrointestinal tract of humans and animals. This bacterium grows in the mucus layer secreted by the intestinal epithelium[2],[3]. The chemotaxis sensory system enables these bacteria to detect and migrate in chemical gradients of nutrients and toxins. Unlike many sensory systems, it is relatively simple with only a few components and well understood at the molecular level[4]–[8]. This and other sensory systems share common design principles including Weber's law, the Weber-Fechner law and fold-change detection( FCD)[9]–[15]( see Text S1 and Fig. S1A for an introduction). Weber's law predicts that the minimum change in stimulus perceived against a background stimulus increases proportionally to the background stimulus, thus implementing efficient contrast coding[11],[16],[17]. The integrated version of Weber's law is the Weber-Fechner law, stressing the internal representation of stimuli[12]. FCD is a generalization of Weber's law to large stimuli and dynamics[18] that postulates the invariance of responses to time-dependent stimuli when scaled up or down by a constant factor[15],[19]. While these laws relate to average gradients[19],[20], they do not quantify the complexity of the chemical environment in terms of distributions of concentrations and gradients, or provide information on their functional importance in terms of cell-swimming behavior. To fully characterize the chemical environment of chemotactic bacteria in terms of distributions, we expect a matching between the statistical properties of typical stimuli and the corresponding cellular responses assuming certain optimization criteria. For instance, taking into account cellular limitations of signal processing due to noise and limited energy resources, a reasonable assumption is that cells translate a stimulus( input) into a cellular response( output) in a way that maximizes the transmission of information[21]–[24]. An appropriate measure for the information contained in the output about the input is Shannon's mutual information( see Text S1 and Fig. S1B for an introduction to information theory)[25], which effectively describes the correlations between the inputs and outputs in bits, with a fundamental link to thermodynamics[26]. It is furthermore unclear how the above mentioned phenomenological laws follow from first principles, such as information theory. Here, we demonstrate the ability to predict distributions of typical gradients of chemoattractant aspartate E. coli likely encounters in its physiological microenvironment. For this purpose we use quantitative dose-response curves from in vivo fluorescence resonance energy transfer( FRET) measurements of the chemotactic sensory pathway, information theory, as well as measurements and simulations of swimming bacteria. Using our multidisciplinary approach, we show that maximizing mutual information implies maximizing the alignment of cell trajectories with the gradient( chemotactic index), and hence the drift velocity of cells swimming up the gradient. We thus go significantly beyond Weber's law, Weber-Fechner law and fold-change detection, which only predict average chemical gradients. Our approach is a first step towards the prediction of typical chemical environments of bacteria, shaped by cellular communities and dynamic turnover of nutrients. These environments are difficult to determine experimentally, but are biomedically highly important for gut metabolism, tissue homeostasis, and disease development in the gastrointestinal tract[27],[28].
Discussion:
In the large intestine, hundreds of different bacterial species are present at high cell densities[2], forming a dynamic microenvironment with complex spatio-temporal chemical gradients from partially digested food and host secretions[54]. Previous models accounted for Weber's law and fold-change detection( FCD)[17],[19],[20], but were unable to make predictions beyond average gradients. Here, we first critically reevaluated FCD and Weber's law, also addressing the Weber-Fechner law. To predict distributions of concentration we then applied information theory to in vivo FRET data in E. coli. By comparing with simulations of swimming bacteria in different linear gradients, we found that the predicted distributions of input concentrations from information theory are consistent with bacteria experiencing a broad range of exponential gradients. In particular, fluctuating gradients( cell-external noise) and methylation levels( cell-internal noise) were needed to explain the long tails of the distributions in line with expectation[55],[56]. Importantly, our results show that maximizing information transmission leads to maximizing the chemotactic index( CI), and hence the drift velocity up the gradient( Fig. 5)[49]. Although expected, the existence of such a link between information transmission and drift has recently been questioned in bacterial chemotaxis[51]. Our model is thus able to connect a large number of phenomenological laws with information theory at the molecular level. In contrast to the report by Lazova et al.[19], we found that at large concentration changes the resulting large-amplitude responses and their subsequent adaptation curves do not show FCD( cf. Fig. 2C, D). A possible reason for this discrepancy is that Lazova et al. used small temporal variations of stimuli which varied on a time scale close to adaptation[19]. Hence, their experiments might have probed FCD with respect to small response amplitudes with temporal responses mainly determined by the external oscillatory stimulus rather than by internal signaling[30]. The Weber-Fechner law requires a quantity called “perception”, which has not been identified previously in bacterial chemotaxis. The free-energy difference between the on and off states of the receptor complex fulfills this purpose; it is a logarithmic function of the concentration in the validity range of Weber's law, thus leading to a compressed internal representation of cell-external stimuli. This functional form arises from the molecular interaction between ligand and receptor, in particular from the competition between the gain of binding-free energy and loss of ligand-volume entropy upon ligand-receptor binding[35]. As logarithmic response functions are implicated for ligand-receptor interactions[20],[57], as well as simple membrane potentials( Nernst equation), our findings may apply to a wide range of signal transduction processes in cells[10],[58],[59]. Information theory can predict the distributions of input concentrations, and using simulations the distributions of gradients. Interestingly, our predicted distribution of inputs are log-normal, similar to the identified scale invariance of light intensities in the visual system[11]. This observation may contain deeper insights into the workings of sensory systems. Specifically, log-normal distributions arise from multiplicative interactions in molecular components[60]. Indeed, our dose-response curves closely resemble Hill equations due to receptor cooperativity[35]( note Hill equations are the cumulative distributions of log-normal distributions). Our deduced exponential gradients of aspartate may arise from local nutrient sources and degradation by bacteria in the surroundings. Although our information-theoretic/simulation results are consistent with exponential gradients predicted by phenomenological laws, our model is based on a number of assumptions. These include small Gaussian noise and a certain functional form of the noise( Eq. 11, both assumptions were successfully tested in Fig. S4). Most importantly, we assume maximal information transmission. However, Tar receptors are also used for pH and temperature sensing[61], and global optimization may result in suboptimal sensing of individual stimuli types[62]. Additionally, expression of chemotactic genes is linked to nutrient supply, growth conditions, and life cycle[63], further restricting the predictive power of our information-theoretic approach. Furthermore, this approach only applies to( nearly) instantaneous receptor signaling, and hence excludes slower downstream dynamics, in particular of the motor[64]. However, we expect that if information transmission is optimized by the whole chemotaxis pathway, this should also be true for every intermediate stage, as information can only be lost, not gained. Note that our data of non-adapting cells was restricted to Tar-only cells. While this restricted the dynamic range, Tar is the cognate receptor for our stimuli. To fully characterize the microenvironment of E. coli future work may need to factor in additional types of stimuli, such as other chemicals, temperature[65] and pH[66]. Models can first be tested in well-defined gradients of stimuli produced in microfabricated devices[42],[50],[66]–[68], thus establishing the stimuli cells sense best. By measuring FRET in single cells[69], the fidelity of information transmission can be tested more directly without any masking effects at the population level. This may also help answering whether information is conserved between graded receptor signaling and the binary-like motor response. Ultimately, imaging and tracking bacteria in complex microenvironments will enhance our understanding of chemotaxis under natural conditions.
Outside the laboratory , bacteria live in complex microenvironments characterized by competition for space and available nutrients . Although often inaccessible by experiments , understanding the spatio-temporal dynamics of bacterial microenvironments is biomedically important . For instance , the chemical environment that symbiotic Escherichia coli encounter in the human gut relates to health of the gastrointestinal tract , gut metabolism , immune response , and tissue homeostasis . Other complex microenvironments include soil and biofilms . Assuming that bacterial sensory systems have evolved to optimally sense typical gradients , we treat signaling data , the signaling pathway with its architecture and reaction rates , and computer simulations of swimming bacteria in different gradients as “prior knowledge” to “reverse engineer” E . coli's habitat . Our identified gradients are exponentially shaped with wide-ranging rate values . These microenvironments most likely stem from local fluctuating nutrient sources and degradation by competing species , in which bacteria have evolved to swim with optimal performance .