Alvaro Sanchez

Genetic Determinants and Cellular Constraints in Noisy Gene Expression

In individual cells, transcription is a random process obeying single-molecule kinetics. Often, it occurs in a bursty, intermittent manner. The frequency and size of these bursts affect the magnitude of temporal fluctuations in messenger RNA and protein content within a cell, creating variation or “noise” in gene expression. It is still unclear to what degree transcriptional kinetics are specific to each gene and determined by its promoter sequence. Alternative scenarios have been proposed, in which the kinetics of transcription are governed by cellular constraints and follow universal rules across the genome. Evidence from genome-wide noise studies and from systematic perturbations of promoter sequences suggest that both scenarios—namely gene-specific versus genome-wide regulation of transcription kinetics—may be present to different degrees in bacteria, yeast, and animal cells.

Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.

Abstract Deciphering how the regulatory DNA sequence of a gene dictates its expression in response to intra and extracellular cues is one of the leading challenges in modern genomics. The development of novel single-cell sequencing and imaging techniques, as well as a better exploitation of currently available single-molecule imaging techniques, provides an avenue to interrogate the process of transcription and its dynamics in cells by quantifying the number of RNA polymerases engaged in the transcription of a gene (or equivalently the number of nascent RNAs) at a given moment in time. In this paper, we propose that measurements of the cell-to-cell variability in the number of nascent RNAs provide a mostly unexplored method for deciphering mechanisms of transcription initiation in cells. We propose a simple kinetic model of transcription initiation and elongation from which we calculate nascent RNA copy-number fluctuations. To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes. Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps. Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism. Our analytical framework can be used to extract quantitative information about dynamics of transcription from single-cell sequencing data, as well as from single-molecule imaging and electron micrographs of fixed cells, and provides the mathematical means to exploit the quantitative power of these technologies.

Emergent simplicity in microbial community assembly

Interchanging species of similar function Under natural conditions, bacteria form mixed, interacting communities. Understanding how such communities assemble and stabilize is important in a range of contexts, from biotechnological applications to what happens in our guts. Goldford et al. sampled the microbial communities from soil and plants containing hundreds to thousands of sequence variants. The organisms were passaged after culture in low concentrations of single carbon sources and were cross-fed with each others metabolites; then, the resulting communities were sequenced using 16S ribosomal RNA, and the outcomes were modeled mathematically. The mix of species that survived under steady conditions converged reproducibly to reflect the experimentally imposed conditions rather than the mix of species initially inoculated—although at coarse phylogenetic levels, taxonomic patterns persisted. Science, this issue p. 469 Microbial communities assemble into similar family-level compositions containing divergent genera and species. A major unresolved question in microbiome research is whether the complex taxonomic architectures observed in surveys of natural communities can be explained and predicted by fundamental, quantitative principles. Bridging theory and experiment is hampered by the multiplicity of ecological processes that simultaneously affect community assembly in natural ecosystems. We addressed this challenge by monitoring the assembly of hundreds of soil- and plant-derived microbiomes in well-controlled minimal synthetic media. Both the community-level function and the coarse-grained taxonomy of the resulting communities are highly predictable and governed by nutrient availability, despite substantial species variability. By generalizing classical ecological models to include widespread nonspecific cross-feeding, we show that these features are all emergent properties of the assembly of large microbial communities, explaining their ubiquity in natural microbiomes.

Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network

Microorganisms often exhibit a history-dependent phenotypic response after exposure to a stimulus which can be imperative for proper function. However, cells frequently experience unexpected environmental perturbations that might induce phenotypic switching. How cells maintain phenotypic states in the face of environmental fluctuations remains an open question. Here, we use environmental perturbations to characterize the resilience of phenotypic states in a synthetic gene network near a critical transition. We find that far from the critical transition an environmental perturbation may induce little to no phenotypic switching, whereas close to the critical transition the same perturbation can cause many cells to switch phenotypic states. This loss of resilience was observed for perturbations that interact directly with the gene circuit as well as for a variety of generic perturbations-such as salt, ethanol, or temperature shocks-that alter the state of the cell more broadly. We obtain qualitatively similar findings in natural gene circuits, such as the yeast GAL network. Our findings illustrate how phenotypic memory can become destabilized by environmental variability near a critical transition. DOI: http://dx.doi.org/10.7554/eLife.07935.001

Combinatorial Gene Regulation through Kinetic Control of the Transcription Cycle

Cells decide when, where, and to what level to express their genes by computing information from transcription factors (TFs) binding to regulatory DNA. How is the information contained in multiple TF-binding sites integrated to dictate the rate of transcription? The dominant conceptual and quantitative model is that TFs combinatorially recruit one another and RNA polymerase to the promoter by direct physical interactions. Here, we develop a quantitative framework to explore kinetic control, an alternative model in which combinatorial gene regulation can result from TFs working on different kinetic steps of the transcription cycle. Kinetic control can generate a wide range of analog and Boolean computations without requiring the input TFs to be simultaneously bound to regulatory DNA. We propose experiments that will illuminate the role of kinetic control in transcription and discuss implications for deciphering the cis-regulatory code.

Integrating regulatory information via combinatorial control of the transcription cycle

Combinatorial regulation of gene expression by multiple transcription factors (TFs) enables cells to carry out sophisticated computations that are key to cellular decision-making. How is the information contained in multiple TF binding sites integrated to dictate the rate of transcription? The dominant model is that direct or indirect physical interactions between TFs enable them to combinatorially recruit each other and RNA polymerase to the promoter. Here we develop a quantitative framework to explore an alternative model, where combinatorial gene regulation can result from TFs working on different kinetic steps of the transcription cycle. Our results clarify the null hypotheses for independent action of TFs and show that combinatorial control of the transcription cycle can generate a wide range of analog and Boolean computations without requiring the input regulators to be simultaneously co-localized in the nucleus. This work emphasizes the importance of deciphering TF function beyond activation and repression, highlights the role of the basal promoter in processing regulatory information and suggests qualitative explanations for the flexibility of regulatory evolution.

High-order interactions dominate the functional landscape of microbial consortia

Understanding the link between community composition and function is a major challenge in microbial ecology, with implications for the management of natural microbiomes and the design of synthetic consortia. For this purpose, it is critical to understand the extent to which community functions and properties can be predicted from species traits and what role is played by complex interactions. Inspired by the study of complex genetic interactions and fitness landscapes, here we have examined how the amylolytic function of combinatorial assemblages of seven starch-degrading soil bacteria depends on the functional contributions from each species and their interactions. Filtering our experimental results through the theory of enzyme kinetics, we show that high-order functional interactions dominate the amylolytic rate of our consortia, even though this function is biochemically simple, redundantly distributed in the community, and additive in the absence of inter-species interactions. As the community grows in size, the contribution of high-order functional interactions grows too, making the community function increasingly unpredictable. We can explain the prevalence of high order effects and their sign from the redundancy of ecological interactions in the network, in particular from redundant facilitation towards a high-performing community member. Our results suggest that even simple functions can be dominated by complex interactions, posing challenges for the predictability and bottom-up engineering of ecosystem function in complex multi-species communities.

Cohesiveness in microbial community coalescence

Microbial invasions exhibit many unique properties; notably, entire microbial communities often invade one another, a phenomenon known as community coalescence. In spite of the potential importance of this process for the dynamics and stability of microbiome assembly, our understanding of it is still very limited. Recent theoretical and empirical work has proposed that large microbial communities may exhibit an emergent cohesiveness, as a result of collective consumer-resource interactions and metabolic feedbacks between microbial growth and the environment. A fundamental prediction of this proposal is the presence of ecological co-selection during community coalescence, where the invasion success of a given taxon is determined by its community members. To establish the generality of this prediction in experimental microbiomes, we have performed over one hundred invasion and coalescence experiments with environmental communities of different origins that had spontaneously and stably assembled in two different synthetic aerobic environments. We show that the dominant species of the coalesced communities can both recruit their community members (top-down co-selection) and be recruited by them (bottom-up co-selection) into the coalesced communities. Our results provide direct evidence that collective invasions generically produce ecological co-selection of interacting species, emphasizing the importance of community-level interactions during microbial community assembly.

Available energy fluxes drive a phase transition in the diversity, stability, and functional structure of microbial communities

A fundamental goal of microbial ecology is to understand what determines the diversity, stability, and structure of microbial ecosystems. The microbial context poses special conceptual challenges because of the strong mutual influences between the microbes and their chemical environment through the consumption and production of metabolites. By analyzing a generalized consumer resource model that explicitly includes cross-feeding, stochastic colonization, and thermodynamics, we show that complex microbial communities generically exhibit a sharp transition as a function of available energy fluxes from a “resource-limited” regime where community structure and stability is shaped by energetic and metabolic considerations to a diverse regime where the dominant force shaping microbial communities is competition. These two regimes have distinct species abundance patterns, different functional profiles, and respond differently to environmental perturbations. Our model reproduces large-scale ecological patterns observed across multiple experimental settings such as nestedness and differential beta diversity patterns along energy gradients. We discuss the experi-mental implications of our results and possible connections with disorder-induced phase transitions in statistical physics.

On the deformability of an empirical fitness landscape by microbial evolution

Significance Fitness landscapes map the relationship between genotype and phenotype, and are a core tool for predicting evolutionary processes from the emergence of resistant pathogens to cancer. The topography of fitness landscapes is determined by the environment. However, populations can also dynamically modify their environment, for instance by releasing metabolites to it, and thus they may potentially deform their own adaptive landscape. Using a combination of genome-scale metabolic simulations and experiments with Escherichia coli strains from the Lenski laboratory Long-Term Evolution Experiment, we systematically and quantitatively characterize the deformability of an empirical fitness landscape. We show that fitness landscapes retain their power to forecast evolution over short mutational distances but environment building may attenuate this capacity over longer adaptive trajectories. A fitness landscape is a map between the genotype and its reproductive success in a given environment. The topography of fitness landscapes largely governs adaptive dynamics, constraining evolutionary trajectories and the predictability of evolution. Theory suggests that this topography can be deformed by mutations that produce substantial changes to the environment. Despite its importance, the deformability of fitness landscapes has not been systematically studied beyond abstract models, and little is known about its reach and consequences in empirical systems. Here we have systematically characterized the deformability of the genome-wide metabolic fitness landscape of the bacterium Escherichia coli. Deformability is quantified by the noncommutativity of epistatic interactions, which we experimentally demonstrate in mutant strains on the path to an evolutionary innovation. Our analysis shows that the deformation of fitness landscapes by metabolic mutations rarely affects evolutionary trajectories in the short range. However, mutations with large environmental effects produce long-range landscape deformations in distant regions of the genotype space that affect the fitness of later descendants. Our results therefore suggest that, even in situations in which mutations have strong environmental effects, fitness landscapes may retain their power to forecast evolution over small mutational distances despite the potential attenuation of that power over longer evolutionary trajectories. Our methods and results provide an avenue for integrating adaptive and eco-evolutionary dynamics with complex genetics and genomics.