Gábor Balázsi

Experimental Determination and System Level Analysis of Essential Genes in Escherichia coli MG1655

Defining the gene products that play an essential role in an organisms functional repertoire is vital to understanding the system level organization of living cells. We used a genetic footprinting technique for a genome-wide assessment of genes required for robust aerobic growth of Escherichia coli in rich media. We identified 620 genes as essential and 3,126 genes as dispensable for growth under these conditions. Functional context analysis of these data allows individual functional assignments to be refined. Evolutionary context analysis demonstrates a significant tendency of essential E. coli genes to be preserved throughout the bacterial kingdom. Projection of these data over metabolic subsystems reveals topologic modules with essential and evolutionarily preserved enzymes with reduced capacity for error tolerance.

Cellular Decision Making and Biological Noise: From Microbes to Mammals

Cellular decision making is the process whereby cells assume different, functionally important and heritable fates without an associated genetic or environmental difference. Such stochastic cell fate decisions generate nongenetic cellular diversity, which may be critical for metazoan development as well as optimized microbial resource utilization and survival in a fluctuating, frequently stressful environment. Here, we review several examples of cellular decision making from viruses, bacteria, yeast, lower metazoans, and mammals, highlighting the role of regulatory network structure and molecular noise. We propose that cellular decision making is one of at least three key processes underlying development at various scales of biological organization.

Negative autoregulation linearizes the dose–response and suppresses the heterogeneity of gene expression

Although several recent studies have focused on gene autoregulation, the effects of negative feedback (NF) on gene expression are not fully understood. Our purpose here was to determine how the strength of NF regulation affects the characteristics of gene expression in yeast cells harboring chromosomally integrated transcriptional cascades that consist of the yEGFP reporter controlled by (i) the constitutively expressed tetracycline repressor TetR or (ii) TetR repressing its own expression. Reporter gene expression in the cascade without feedback showed a steep (sigmoidal) dose–response and a wide, nearly bimodal yEGFP distribution, giving rise to a noise peak at intermediate levels of induction. We developed computational models that reproduced the steep dose–response and the noise peak and predicted that negative autoregulation changes reporter expression from bimodal to unimodal and transforms the dose–response from sigmoidal to linear. Prompted by these predictions, we constructed a “linearizer” circuit by adding TetR autoregulation to our original cascade and observed a massive (7-fold) reduction of noise at intermediate induction and linearization of dose–response before saturation. A simple mathematical argument explained these findings and indicated that linearization is highly robust to parameter variations. These findings have important implications for gene expression control in eukaryotic cells, including the design of synthetic expression systems.

Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli

Recent evidence indicates that potential interactions within metabolic, protein–protein interaction, and transcriptional regulatory networks are used differentially according to the environmental conditions in which a cell exists. However, the topological units underlying such differential utilization are not understood. Here we use the transcriptional regulatory network of Escherichia coli to identify such units, called origons, representing regulatory subnetworks that originate at a distinct class of sensor transcription factors. Using microarray data, we find that specific environmental signals affect mRNA expression levels significantly only within the origons responsible for their detection and processing. We also show that small regulatory interaction patterns, called subgraphs and motifs, occupy distinct positions in and between origons, offering insights into their dynamical role in information processing. The identified features are likely to represent a general framework for environmental signal processing in prokaryotes.

Combinatorial promoter design for engineering noisy gene expression

Understanding the behavior of basic biomolecular components as parts of larger systems is one of the goals of the developing field of synthetic biology. A multidisciplinary approach, involving mathematical and computational modeling in parallel with experimentation, is often crucial for gaining such insights and improving the efficiency of artificial gene network design. Here we used such an approach and developed a combinatorial promoter design strategy to characterize how the position and multiplicity of tetO2 operator sites within the GAL1 promoter affect gene expression levels and gene expression noise in Saccharomyces cerevisiae. We observed stronger transcriptional repression and higher gene expression noise as a single operator site was moved closer to the TATA box, whereas for multiple operator-containing promoters, we found that the position and number of operator sites together determined the dose–response curve and gene expression noise. We developed a generic computational model that captured the experimentally observed differences for each of the promoters, and more detailed models to successively predict the behavior of multiple operator-containing promoters from single operator-containing promoters. Our results suggest that the independent binding of single repressors is not sufficient to explain the more complex behavior of the multiple operator-containing promoters. Taken together, our findings highlight the importance of joint experimental–computational efforts and some of the challenges of using a bottom-up approach based on well characterized, isolated biomolecular components for predicting the behavior of complex, synthetic gene networks, e.g., the whole can be different from the sum of its parts.

Linking the Transcriptional Profiles and the Physiological States of Mycobacterium tuberculosis during an Extended Intracellular Infection

Intracellular pathogens such as Mycobacterium tuberculosis have evolved strategies for coping with the pressures encountered inside host cells. The ability to coordinate global gene expression in response to environmental and internal cues is one key to their success. Prolonged survival and replication within macrophages, a key virulence trait of M. tuberculosis, requires dynamic adaptation to diverse and changing conditions within its phagosomal niche. However, the physiological adaptations during the different phases of this infection process remain poorly understood. To address this knowledge gap, we have developed a multi-tiered approach to define the temporal patterns of gene expression in M. tuberculosis in a macrophage infection model that extends from infection, through intracellular adaptation, to the establishment of a productive infection. Using a clock plasmid to measure intracellular replication and death rates over a 14-day infection and electron microscopy to define bacterial integrity, we observed an initial period of rapid replication coupled with a high death rate. This was followed by period of slowed growth and enhanced intracellular survival, leading finally to an extended period of net growth. The transcriptional profiles of M. tuberculosis reflect these physiological transitions as the bacterium adapts to conditions within its host cell. Finally, analysis with a Transcriptional Regulatory Network model revealed linked genetic networks whereby M. tuberculosis coordinates global gene expression during intracellular survival. The integration of molecular and cellular biology together with transcriptional profiling and systems analysis offers unique insights into the host-driven responses of intracellular pathogens such as M. tuberculosis.

The temporal response of the Mycobacterium tuberculosis gene regulatory network during growth arrest

The virulence of Mycobacterium tuberculosis depends on the ability of the bacilli to switch between replicative (growth) and non‐replicative (dormancy) states in response to host immunity. However, the gene regulatory events associated with transition to dormancy are largely unknown. To address this question, we have assembled the largest M. tuberculosis transcriptional‐regulatory network to date, and characterized the temporal response of this network during adaptation to stationary phase and hypoxia, using published microarray data. Distinct sets of transcriptional subnetworks (origons) were responsive at various stages of adaptation, showing a gradual progression of network response under both conditions. Most of the responsive origons were in common between the two conditions and may help define a general transcriptional signature of M. tuberculosis growth arrest. These results open the door for a systems‐level understanding of transition to non‐replicative persistence, a phenotypic state that prevents sterilization of infection by the host immune response and promotes the establishment of latent M. tuberculosis infection, a condition found in two billion people worldwide.

Tuning and controlling gene expression noise in synthetic gene networks

Synthetic gene networks can be used to control gene expression and cellular phenotypes in a variety of applications. In many instances, however, such networks can behave unreliably due to gene expression noise. Accordingly, there is a need to develop systematic means to tune gene expression noise, so that it can be suppressed in some cases and harnessed in others, e.g. in cellular differentiation to create population-wide heterogeneity. Here, we present a method for controlling noise in synthetic eukaryotic gene expression systems, utilizing reduction of noise levels by TATA box mutations and noise propagation in transcriptional cascades. Specifically, we introduce TATA box mutations into promoters driving TetR expression and show that these mutations can be used to effectively tune the noise of a target gene while decoupling it from the mean, with negligible effects on the dynamic range and basal expression. We apply mathematical and computational modeling to explain the experimentally observed effects of TATA box mutations. This work, which highlights some important aspects of noise propagation in gene regulatory cascades, has practical implications for implementing gene expression control in synthetic gene networks.

A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli.

While Escherichia coli has one of the most comprehensive datasets of experimentally verified transcriptional regulatory interactions of any organism, it is still far from complete. This presents a problem when trying to combine gene expression and regulatory interactions to model transcriptional regulatory networks. Using the available regulatory interactions to predict new interactions may lead to better coverage and more accurate models. Here, we develop SEREND (SEmi-supervised REgulatory Network Discoverer), a semi-supervised learning method that uses a curated database of verified transcriptional factor–gene interactions, DNA sequence binding motifs, and a compendium of gene expression data in order to make thousands of new predictions about transcription factor–gene interactions, including whether the transcription factor activates or represses the gene. Using genome-wide binding datasets for several transcription factors, we demonstrate that our semi-supervised classification strategy improves the prediction of targets for a given transcription factor. To further demonstrate the utility of our inferred interactions, we generated a new microarray gene expression dataset for the aerobic to anaerobic shift response in E. coli. We used our inferred interactions with the verified interactions to reconstruct a dynamic regulatory network for this response. The network reconstructed when using our inferred interactions was better able to correctly identify known regulators and suggested additional activators and repressors as having important roles during the aerobic–anaerobic shift interface.

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell populations growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings.