The protein organization of a red blood cell.

Red blood cells (RBCs) (erythrocytes) are the simplest primary human cells, lacking nuclei and major organelles and instead employing about a thousand proteins to dynamically control cellular function and morphology in response to physiological cues. In this study, we define a canonical RBC proteome and interactome using quantitative mass spectrometry and machine learning. Our data reveal an RBC interactome dominated by protein homeostasis, redox biology, cytoskeletal dynamics, and carbon metabolism. We validate protein complexes through electron microscopy and chemical crosslinking and, with these data, build 3D structural models of the ankyrin/Band 3/Band 4.2 complex that bridges the spectrin cytoskeleton to the RBC membrane. The model suggests spring-like compression of ankyrin may contribute to the characteristic RBC cell shape and flexibility. Taken together, our study provides an in-depth view of the global protein organization of human RBCs and serves as a comprehensive resource for future research.

A predictive model of gene expression reveals the role of network motifs in the mating response of yeast.

Cells use signaling pathways to receive and process information about their environment. These nonlinear systems rely on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models describing signaling pathways often lack predictive power because they are not trained on data that encompass the diverse time scales on which these regulatory mechanisms operate. We addressed this limitation by measuring transcriptional changes induced by the mating response in Saccharomyces cerevisiae exposed to different dynamic patterns of pheromone. We found that pheromone-induced transcription persisted after pheromone removal and showed long-term adaptation upon sustained pheromone exposure. We developed a model of the regulatory network that captured both characteristics of the mating response. We fit this model to experimental data with an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four architectural elements of the network in regulating gene expression. These network motifs are incoherent feedforward, positive feedback, negative feedback, and repressor binding. Experimental and computational perturbations to these network motifs established a specific role for each in coordinating the mating response to persistent and dynamic stimulation.

Metabolic crosstalk regulates Porphyromonas gingivalis colonization and virulence during oral polymicrobial infection.

Many human infections are polymicrobial in origin, and interactions among community inhabitants shape colonization patterns and pathogenic potential 1 . Periodontitis, which is the sixth most prevalent infectious disease worldwide 2 , ensues from the action of dysbiotic polymicrobial communities 3 . The keystone pathogen Porphyromonas gingivalis and the accessory pathogen Streptococcus gordonii interact to form communities in vitro and exhibit increased fitness in vivo 3,4 . The mechanistic basis of this polymicrobial synergy, however, has not been fully elucidated. Here we show that streptococcal 4-aminobenzoate/para-amino benzoic acid (pABA) is required for maximal accumulation of P. gingivalis in dual-species communities. Metabolomic and proteomic data showed that exogenous pABA is used for folate biosynthesis, and leads to decreased stress and elevated expression of fimbrial adhesins. Moreover, pABA increased the colonization and survival of P. gingivalis in a murine oral infection model. However, pABA also caused a reduction in virulence in vivo and suppressed extracellular polysaccharide production by P. gingivalis. Collectively, these data reveal a multidimensional aspect to P. gingivalis-S. gordonii interactions and establish pABA as a critical cue produced by a partner species that enhances the fitness of P. gingivalis while diminishing its virulence.

The E. coli molecular phenotype under different growth conditions.

Modern systems biology requires extensive, carefully curated measurements of cellular components in response to different environmental conditions. While high-throughput methods have made transcriptomics and proteomics datasets widely accessible and relatively economical to generate, systematic measurements of both mRNA and protein abundances under a wide range of different conditions are still relatively rare. Here we present a detailed, genome-wide transcriptomics and proteomics dataset of E. coli grown under 34 different conditions. Additionally, we provide measurements of doubling times and in-vivo metabolic fluxes through the central carbon metabolism. We manipulate concentrations of sodium and magnesium in the growth media, and we consider four different carbon sources glucose, gluconate, lactate, and glycerol. Moreover, samples are taken both in exponential and stationary phase, and we include two extensive time-courses, with multiple samples taken between 3 hours and 2 weeks. We find that exponential-phase samples systematically differ from stationary-phase samples, in particular at the level of mRNA. Regulatory responses to different carbon sources or salt stresses are more moderate, but we find numerous differentially expressed genes for growth on gluconate and under salt and magnesium stress. Our data set provides a rich resource for future computational modeling of E. coli gene regulation, transcription, and translation.

Controlled Measurement and Comparative Analysis of Cellular Components in E. coli Reveals Broad Regulatory Changes in Response to Glucose Starvation.

How do bacteria regulate their cellular physiology in response to starvation? Here, we present a detailed characterization of Escherichia coli growth and starvation over a time-course lasting two weeks. We have measured multiple cellular components, including RNA and proteins at deep genomic coverage, as well as lipid modifications and flux through central metabolism. Our study focuses on the physiological response of E. coli in stationary phase as a result of being starved for glucose, not on the genetic adaptation of E. coli to utilize alternative nutrients. In our analysis, we have taken advantage of the temporal correlations within and among RNA and protein abundances to identify systematic trends in gene regulation. Specifically, we have developed a general computational strategy for classifying expression-profile time courses into distinct categories in an unbiased manner. We have also developed, from dynamic models of gene expression, a framework to characterize protein degradation patterns based on the observed temporal relationships between mRNA and protein abundances. By comparing and contrasting our transcriptomic and proteomic data, we have identified several broad physiological trends in the E. coli starvation response. Strikingly, mRNAs are widely down-regulated in response to glucose starvation, presumably as a strategy for reducing new protein synthesis. By contrast, protein abundances display more varied responses. The abundances of many proteins involved in energy-intensive processes mirror the corresponding mRNA profiles while proteins involved in nutrient metabolism remain abundant even though their corresponding mRNAs are down-regulated.

Cellular noise suppression by the regulator of G protein signaling Sst2.

G proteins and their associated receptors process information from a variety of environmental stimuli to induce appropriate cellular responses. Generally speaking, each cell in a population responds within defined limits, despite large variation in the expression of protein signaling components. Therefore, we postulated that noise suppression is encoded within the signaling system. Using the yeast mating pathway as a model, we evaluated the ability of a regulator of G protein signaling (RGS) protein to suppress noise. We found that the RGS protein Sst2 limits variability in transcription and morphogenesis in response to pheromone stimulation. While signal suppression is a result of both the GAP (GTPase accelerating) and receptor binding functions of Sst2, noise suppression requires only the GAP activity. Taken together, our findings reveal a hitherto overlooked role of RGS proteins as noise suppressors and demonstrate an ability to uncouple signal and noise in a prototypical stimulus-response pathway.

An improved short-lived fluorescent protein transcriptional reporter for Saccharomyces cerevisiae.

Ideal reporter genes for temporal transcription programmes have short half-lives that restrict their detection to the window in which their transcripts are present and translated. In an effort to meet this criterion for reporters of transcription in individual living cells, we adapted the ubiquitin fusion strategy for programmable N-end rule degradation to generate an N-degron version of green fluorescent protein (GFP) with a half-life of ~7 min. The GFP variant we used here (designated GFP*) has excellent fluorescence brightness and maturation properties, which make the destabilized reporter well suited for tracking the induction and attenuation kinetics of gene expression in living cells. These attributes are illustrated by its ability to track galactose- and pheromone-induced transcription in S. cerevisiae. We further show that the fluorescence measurements using the short-lived N-degron GFP* reporter gene accurately predict the transient mRNA profile of the prototypical pheromone-induced FUS1 gene.

Positive roles for negative regulators in the mating response of yeast.

All cells must detect and respond to changes in their environment, often through changes in gene expression. The yeast pheromone pathway has been extensively characterized, and is an ideal system for studying transcriptional regulation. Here we combine computational and experimental approaches to study transcriptional regulation mediated by Ste12, the key transcription factor in the pheromone response. Our mathematical model is able to explain multiple counterintuitive experimental results and led to several novel findings. First, we found that the transcriptional repressors Dig1 and Dig2 positively affect transcription by stabilizing Ste12. This stabilization through protein-protein interactions creates a large pool of Ste12 that is rapidly activated following pheromone stimulation. Second, we found that protein degradation follows saturating kinetics, explaining the long half-life of Ste12 in mutants expressing elevated amounts of Ste12. Finally, our model reveals a novel mechanism for robust perfect adaptation through protein-protein interactions that enhance complex stability. This mechanism allows the transcriptional response to act on a shorter time scale than upstream pathway activity.

Evidence that αC region is origin of low modulus, high extensibility, and strain stiffening in fibrin fibers.

Fibrin fibers form the structural scaffold of blood clots and perform the mechanical task of stemming blood flow. Several decades of investigation of fibrin fiber networks using macroscopic techniques have revealed remarkable mechanical properties. More recently, the microscopic origins of fibrin's mechanics have been probed through direct measurements on single fibrin fibers and individual fibrinogen molecules. Using a nanomanipulation system, we investigated the mechanical properties of individual fibrin fibers. The fibers were stretched with the atomic force microscope, and stress-versus-strain data was collected for fibers formed with and without ligation by the activated transglutaminase factor XIII (FXIIIa). We observed that ligation with FXIIIa nearly doubled the stiffness of the fibers. The stress-versus-strain behavior indicates that fibrin fibers exhibit properties similar to other elastomeric biopolymers. We propose a mechanical model that fits our observed force extension data, is consistent with the results of the ligation data, and suggests that the large observed extensibility in fibrin fibers is mediated by the natively unfolded regions of the molecule. Although some models attribute fibrin's force-versus-extension behavior to unfolding of structured regions within the monomer, our analysis argues that these models are inconsistent with the measured extensibility and elastic modulus.

Stiffening of individual fibrin fibers equitably distributes strain and strengthens networks.

As the structural backbone of blood clots, fibrin networks carry out the mechanical task of stemming blood flow at sites of vascular injury. These networks exhibit a rich set of remarkable mechanical properties, but a detailed picture relating the microscopic mechanics of the individual fibers to the overall network properties has not been fully developed. In particular, how the high strain and failure characteristics of single fibers affect the overall strength of the network is not known. Using a combined fluorescence/atomic force microscope nanomanipulation system, we stretched 2-D fibrin networks to the point of failure, while recording the strain of individual fibers. Our results were compared to a pair of model networks: one composed of linearly responding elements and a second of nonlinear, strain-stiffening elements. We find that strain-stiffening of the individual fibers is necessary to explain the pattern of strain propagation throughout the network that we observe in our experiments. Fiber strain-stiffening acts to distribute strain more equitably within the network, reduce strain maxima, and increase network strength. Along with its physiological implications, a detailed understanding of this strengthening mechanism may lead to new design strategies for engineered polymeric materials.