Fund Black scientists.

Our nationwide network of BME women faculty collectively argue that racial funding disparity by the National Institutes of Health (NIH) remains the most insidious barrier to success of Black faculty in our profession. We thus refocus attention on this critical barrier and suggest solutions on how it can be dismantled.

Real-Time Genetic Compensation Defines the Dynamic Demands of Feedback Control.

Biological signaling networks use feedback control to dynamically adjust their operation in real time. Traditional static genetic methods such as gene knockouts or rescue experiments can often identify the existence of feedback interactions but are unable to determine what feedback dynamics are required. Here, we implement a new strategy, closed-loop optogenetic compensation (CLOC), to address this problem. Using a custom-built hardware and software infrastructure, CLOC monitors, in real time, the output of a pathway deleted for a feedback regulator. A minimal model uses these measurements to calculate and deliver-on the fly-an optogenetically enabled transcriptional input designed to compensate for the effects of the feedback deletion. Application of CLOC to the yeast pheromone response pathway revealed surprisingly distinct dynamic requirements for three well-studied feedback regulators. CLOC, a marriage of control theory and traditional genetics, presents a broadly applicable methodology for defining the dynamic function of biological feedback regulators.

Publisher Correction: Modular and tunable biological feedback control using a de novo protein switch.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Modular and tunable biological feedback control using a de novo protein switch.

De novo-designed proteins1-3 hold great promise as building blocks for synthetic circuits, and can complement the use of engineered variants of natural proteins4-7. One such designer protein-degronLOCKR, which is based on 'latching orthogonal cage-key proteins' (LOCKR) technology8-is a switch that degrades a protein of interest in vivo upon induction by a genetically encoded small peptide. Here we leverage the plug-and-play nature of degronLOCKR to implement feedback control of endogenous signalling pathways and synthetic gene circuits. We first generate synthetic negative and positive feedback in the yeast mating pathway by fusing degronLOCKR to endogenous signalling molecules, illustrating the ease with which this strategy can be used to rewire complex endogenous pathways. We next evaluate feedback control mediated by degronLOCKR on a synthetic gene circuit9, to quantify the feedback capabilities and operational range of the feedback control circuit. The designed nature of degronLOCKR proteins enables simple and rational modifications to tune feedback behaviour in both the synthetic circuit and the mating pathway. The ability to engineer feedback control into living cells represents an important milestone in achieving the full potential of synthetic biology10,11,12. More broadly, this work demonstrates the large and untapped potential of de novo design of proteins for generating tools that implement complex synthetic functionalities in cells for biotechnological and therapeutic applications.

De novo design of bioactive protein switches.

Allosteric regulation of protein function is widespread in biology, but is challenging for de novo protein design as it requires the explicit design of multiple states with comparable free energies. Here we explore the possibility of designing switchable protein systems de novo, through the modulation of competing inter- and intramolecular interactions. We design a static, five-helix 'cage' with a single interface that can interact either intramolecularly with a terminal 'latch' helix or intermolecularly with a peptide 'key'. Encoded on the latch are functional motifs for binding, degradation or nuclear export that function only when the key displaces the latch from the cage. We describe orthogonal cage-key systems that function in vitro, in yeast and in mammalian cells with up to 40-fold activation of function by key. The ability to design switchable protein functions that are controlled by induced conformational change is a milestone for de novo protein design, and opens up new avenues for synthetic biology and cell engineering.

CoRa-A general approach for quantifying biological feedback control.

Feedback control is a fundamental underpinning of life, underlying homeostasis of biological processes at every scale of organization, from cells to ecosystems. The ability to evaluate the contribution and limitations of feedback control mechanisms operating in cells is a critical step for understanding and ultimately designing feedback control systems with biological molecules. Here, we introduce CoRa-or Control Ratio-a general framework that quantifies the contribution of a biological feedback control mechanism to adaptation using a mathematically controlled comparison to an identical system that does not contain the feedback. CoRa provides a simple and intuitive metric with broad applicability to biological feedback systems.

Defining network topologies that can achieve biochemical adaptation.

Many signaling systems show adaptation-the ability to reset themselves after responding to a stimulus. We computationally searched all possible three-node enzyme network topologies to identify those that could perform adaptation. Only two major core topologies emerge as robust solutions: a negative feedback loop with a buffering node and an incoherent feedforward loop with a proportioner node. Minimal circuits containing these topologies are, within proper regions of parameter space, sufficient to achieve adaptation. More complex circuits that robustly perform adaptation all contain at least one of these topologies at their core. This analysis yields a design table highlighting a finite set of adaptive circuits. Despite the diversity of possible biochemical networks, it may be common to find that only a finite set of core topologies can execute a particular function. These design rules provide a framework for functionally classifying complex natural networks and a manual for engineering networks. For a video summary of this article, see the PaperFlick file with the Supplemental Data available online.

A population shift between two heritable cell types of the pathogen Candida albicans is based both on switching and selective proliferation.

Differentiated cell types often retain their characteristics through many rounds of cell division. A simple example is found in Candida albicans, a member of the human microbiota and also the most prevalent fungal pathogen of humans; here, two distinct cell types (white and opaque) exist, and each one retains its specialized properties across many cell divisions. Switching between the two cell types is rare in standard laboratory medium (2% glucose) but can be increased by signals in the environment, for example, certain sugars. When these signals are removed, switching ceases and cells remain in their present state, which is faithfully passed on through many generations of daughter cells. Here, using an automated flow cytometry assay to monitor white-opaque switching over 96 different sugar concentrations, we observed a wide range of opaque-to-white switching that varied continuously across different sugar compositions of the medium. By also measuring white cell proliferation rates under each condition, we found that both opaque-to-white switching and selective white cell proliferation are required for entire populations to shift from opaque to white. Moreover, the switching frequency correlates with the preference of the resulting cell type for the growth medium; that is, the switching is adjusted to increase in environments that favor white cell proliferation. The widely adjustable, all-or-none nature of the switch, combined with the long-term heritability of each state, is distinct from conventional forms of gene regulation, and we propose that it represents a strategy used by C. albicans to efficiently colonize different niches of its human host.

Cellular noise regulons underlie fluctuations in Saccharomyces cerevisiae.

Stochasticity is a hallmark of cellular processes, and different classes of genes show large differences in their cell-to-cell variability (noise). To decipher the sources and consequences of this noise, we systematically measured pairwise correlations between large numbers of genes, including those with high variability. We find that there is substantial pathway variability shared across similarly regulated genes. This induces quantitative correlations in the expression of functionally related genes such as those involved in the Msn2/4 stress response pathway, amino-acid biosynthesis, and mitochondrial maintenance. Bioinformatic analyses and genetic perturbations suggest that fluctuations in PKA and Tor signaling contribute to pathway-specific variability. Our results argue that a limited number of well-delineated "noise regulons" operate across a yeast cell and that such coordinated fluctuations enable a stochastic but coherent induction of functionally related genes. Finally, we show that pathway noise is a quantitative tool for exploring pathway features and regulatory relationships in un-stimulated systems.

Conformational biosensors reveal GPCR signalling from endosomes.

A long-held tenet of molecular pharmacology is that canonical signal transduction mediated by G-protein-coupled receptor (GPCR) coupling to heterotrimeric G proteins is confined to the plasma membrane. Evidence supporting this traditional view is based on analytical methods that provide limited or no subcellular resolution. It has been subsequently proposed that signalling by internalized GPCRs is restricted to G-protein-independent mechanisms such as scaffolding by arrestins, or GPCR activation elicits a discrete form of persistent G protein signalling, or that internalized GPCRs can indeed contribute to the acute G-protein-mediated response. Evidence supporting these various latter hypotheses is indirect or subject to alternative interpretation, and it remains unknown if endosome-localized GPCRs are even present in an active form. Here we describe the application of conformation-specific single-domain antibodies (nanobodies) to directly probe activation of the ß2-adrenoceptor, a prototypical GPCR, and its cognate G protein, Gs (ref. 12), in living mammalian cells. We show that the adrenergic agonist isoprenaline promotes receptor and G protein activation in the plasma membrane as expected, but also in the early endosome membrane, and that internalized receptors contribute to the overall cellular cyclic AMP response within several minutes after agonist application. These findings provide direct support for the hypothesis that canonical GPCR signalling occurs from endosomes as well as the plasma membrane, and suggest a versatile strategy for probing dynamic conformational change in vivo.

Population diversification in a yeast metabolic program promotes anticipation of environmental shifts.

Delineating the strategies by which cells contend with combinatorial changing environments is crucial for understanding cellular regulatory organization. When presented with two carbon sources, microorganisms first consume the carbon substrate that supports the highest growth rate (e.g., glucose) and then switch to the secondary carbon source (e.g., galactose), a paradigm known as the Monod model. Sequential sugar utilization has been attributed to transcriptional repression of the secondary metabolic pathway, followed by activation of this pathway upon depletion of the preferred carbon source. In this work, we demonstrate that although Saccharomyces cerevisiae cells consume glucose before galactose, the galactose regulatory pathway is activated in a fraction of the cell population hours before glucose is fully consumed. This early activation reduces the time required for the population to transition between the two metabolic programs and provides a fitness advantage that might be crucial in competitive environments.

Systematic analysis of asymmetric partitioning of yeast proteome between mother and daughter cells reveals "aging factors" and mechanism of lifespan asymmetry.

Budding yeast divides asymmetrically, giving rise to a mother cell that progressively ages and a daughter cell with full lifespan. It is generally assumed that mother cells retain damaged, lifespan limiting materials ("aging factors") through asymmetric division. However, the identity of these aging factors and the mechanisms through which they limit lifespan remain poorly understood. Using a flow cytometry-based, high-throughput approach, we quantified the asymmetric partitioning of the yeast proteome between mother and daughter cells during cell division, discovering 74 mother-enriched and 60 daughter-enriched proteins. While daughter-enriched proteins are biased toward those needed for bud construction and genome maintenance, mother-enriched proteins are biased towards those localized in the plasma membrane and vacuole. Deletion of 23 of the 74 mother-enriched proteins leads to lifespan extension, a fraction that is about six times that of the genes picked randomly from the genome. Among these lifespan-extending genes, three are involved in endosomal sorting/endosome to vacuole transport, and three are nitrogen source transporters. Tracking the dynamic expression of specific mother-enriched proteins revealed that their concentration steadily increases in the mother cells as they age, but is kept relatively low in the daughter cells via asymmetric distribution. Our results suggest that some mother-enriched proteins may increase to a concentration that becomes deleterious and lifespan-limiting in aged cells, possibly by upsetting homeostasis or leading to aberrant signaling. Our study provides a comprehensive resource for analyzing asymmetric cell division and aging in yeast, which should also be valuable for understanding similar phenomena in other organisms.

Combinatorial, site-specific requirement for heterochromatic silencing factors in the elimination of nucleosome-free regions.

High-resolution nucleosome occupancy maps of heterochromatic regions of wild-type and silencing-defective mutants of the fission yeast Schizosaccharomyces pombe revealed that heterochromatin induces the elimination of nucleosome-free regions (NFRs). NFRs associated with transcription initiation sites as well as those not associated with promoters are affected. We dissected the roles of the histone H3K9 methyltransferase Clr4 and the HP1 proteins Swi6 and Chp2, as well as the two catalytic activities of the SHREC histone deacetylase (HDAC)/ATPase effector complex. Strikingly, different DNA sites have distinct combinatorial requirements for these factors: Five classes of NFRs were identified that are eliminated by silencing factors through a mechanistic hierarchy governed by Clr4. The SHREC HDAC activity plays a major role in the elimination of class I-IV NFRs by antagonizing the action of RSC, a remodeling complex implicated in NFR formation. We propose that heterochromatin formation involves the deployment in several sequence-specific mechanisms to eliminate gaps between nucleosomes, thereby blocking access to the DNA.

Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry.

Cells adjust to changes in environmental conditions using complex regulatory programs. These cellular programs are the result of an intricate interplay between gene expression, cellular growth and protein degradation. Technologies that enable simultaneous and time-resolved measurements of these variables are necessary to dissect cellular homeostatic strategies. Here we report the development of an automated flow cytometry robotic setup that enables real-time measurement of precise and simultaneous relative growth and protein synthesis rates of multiplexed microbial populations across many conditions. These measurements generate quantitative profiles of dynamically evolving protein synthesis and degradation rates. We demonstrate this setup in the context of gene regulation of the unfolded protein response (UPR) of Saccharomyces cerevisiae and uncover a dynamic and complex landscape of gene expression, growth dynamics and proteolysis following perturbations.

Delayed Ras/PKA signaling augments the unfolded protein response.

During environmental, developmental, or genetic stress, the cell's folding capacity can become overwhelmed, and misfolded proteins can accumulate in all cell compartments. Eukaryotes evolved the unfolded protein response (UPR) to counteract proteotoxic stress in the endoplasmic reticulum (ER). Although the UPR is vital to restoring homeostasis to protein folding in the ER, it has become evident that the response to ER stress is not limited to the UPR. Here, we used engineered orthogonal UPR induction, deep mRNA sequencing, and dynamic flow cytometry to dissect the cell's response to ER stress comprehensively. We show that budding yeast augments the UPR with time-delayed Ras/PKA signaling. This second wave of transcriptional dynamics is independent of the UPR and is necessary for fitness in the presence of ER stress, partially due to a reduction in general protein synthesis. This Ras/PKA-mediated effect functionally mimics other mechanisms, such as translational control by PKR-like ER kinase (PERK) and regulated inositol-requiring enzyme 1 (IRE1)-dependent mRNA decay (RIDD), which reduce the load of proteins entering the ER in response to ER stress in metazoan cells.

The Impact of Different Sources of Fluctuations on Mutual Information in Biochemical Networks.

Stochastic fluctuations in signaling and gene expression limit the ability of cells to sense the state of their environment, transfer this information along cellular pathways, and respond to it with high precision. Mutual information is now often used to quantify the fidelity with which information is transmitted along a cellular pathway. Mutual information calculations from experimental data have mostly generated low values, suggesting that cells might have relatively low signal transmission fidelity. In this work, we demonstrate that mutual information calculations might be artificially lowered by cell-to-cell variability in both initial conditions and slowly fluctuating global factors across the population. We carry out our analysis computationally using a simple signaling pathway and demonstrate that in the presence of slow global fluctuations, every cell might have its own high information transmission capacity but that population averaging underestimates this value. We also construct a simple synthetic transcriptional network and demonstrate using experimental measurements coupled to computational modeling that its operation is dominated by slow global variability, and hence that its mutual information is underestimated by a population averaged calculation.

TRADEOFFS IN ADAPTING BIOLOGICAL SYSTEMS.

Biological systems must sense and adapt to changes in their environment. Molecular networks capable of such adaptation belong to two well-known classes, feed-forward and feedback structures, but the fundamental limitations and tradeoffs of these two classes remain unknown. Here we study the advantages and limitations of the feedforward class using three-node circuits representative of these architectures. The feedforward model we investigate displays a tradeoff between the sensitivity of the response (its peak response) and its precision (its error in its return to steady-state). We suggest two ways in which this tradeoff can be alleviated: (1) by introducing a nonlinearity in the production of a specific node in the network, or (2) by adding a feedback loop to the input. We present analytical and numerical examples to support our findings.

Synergistic dual positive feedback loops established by molecular sequestration generate robust bimodal response.

Feedback loops are ubiquitous features of biological networks and can produce significant phenotypic heterogeneity, including a bimodal distribution of gene expression across an isogenic cell population. In this work, a combination of experiments and computational modeling was used to explore the roles of multiple feedback loops in the bimodal, switch-like response of the Saccharomyces cerevisiae galactose regulatory network. Here, we show that bistability underlies the observed bimodality, as opposed to stochastic effects, and that two unique positive feedback loops established by Gal1p and Gal3p, which both regulate network activity by molecular sequestration of Gal80p, induce this bimodality. Indeed, systematically scanning through different single and multiple feedback loop knockouts, we demonstrate that there is always a concentration regime that preserves the system's bimodality, except for the double deletion of GAL1 and the GAL3 feedback loop, which exhibits a graded response for all conditions tested. The constitutive production rates of Gal1p and Gal3p operate as bifurcation parameters because variations in these rates can also abolish the system's bimodal response. Our model indicates that this second loss of bistability ensues from the inactivation of the remaining feedback loop by the overexpressed regulatory component. More broadly, we show that the sequestration binding affinity is a critical parameter that can tune the range of conditions for bistability in a circuit with positive feedback established by molecular sequestration. In this system, two positive feedback loops can significantly enhance the region of bistability and the dynamic response time.

Basic leucine zipper transcription factor Hac1 binds DNA in two distinct modes as revealed by microfluidic analyses.

A quantitative understanding of how transcription factors interact with genomic target sites is crucial for reconstructing transcriptional networks in vivo. Here, we use Hac1, a well-characterized basic leucine zipper (bZIP) transcription factor involved in the unfolded protein response (UPR) as a model to investigate interactions between bZIP transcription factors and their target sites. During the UPR, the accumulation of unfolded proteins leads to unconventional splicing and subsequent translation of HAC1 mRNA, followed by transcription of UPR target genes. Initial candidate-based approaches identified a canonical cis-acting unfolded protein response element (UPRE-1) within target gene promoters; however, subsequent studies identified a large set of Hac1 target genes lacking this UPRE-1 and containing a different motif (UPRE-2). Using a combination of unbiased and directed microfluidic DNA binding assays, we established that Hac1 binds in two distinct modes: (i) to short (6-7 bp) UPRE-2-like motifs and (ii) to significantly longer (11-13 bp) extended UPRE-1-like motifs. Using a genetic screen, we demonstrate that a region of extended homology N-terminal to the basic DNA binding domain is required for this dual site recognition. These results establish Hac1 as the first bZIP transcription factor known to adopt more than one binding mode and unify previously conflicting and discrepant observations of Hac1 function into a cohesive model of UPR target gene activation. Our results also suggest that even structurally simple transcription factors can recognize multiple divergent target sites of very different lengths, potentially enriching their downstream target repertoire.

A master equation and moment approach for biochemical systems with creation-time-dependent bimolecular rate functions.

Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-to-cell variability even in clonal populations. Stochastic biochemical networks have been traditionally modeled as continuous-time discrete-state Markov processes whose probability density functions evolve according to a chemical master equation (CME). In diffusion reaction systems on membranes, the Markov formalism, which assumes constant reaction propensities is not directly appropriate. This is because the instantaneous propensity for a diffusion reaction to occur depends on the creation times of the molecules involved. In this work, we develop a chemical master equation for systems of this type. While this new CME is computationally intractable, we make rational dimensional reductions to form an approximate equation, whose moments are also derived and are shown to yield efficient, accurate results. This new framework forms a more general approach than the Markov CME and expands upon the realm of possible stochastic biochemical systems that can be efficiently modeled.