Integration of metabolism and regulation reveals rapid adaptability to growth on non-native substrates.

Engineering synthetic heterotrophy is a key to the efficient bio-based valorization of renewable and waste substrates. Among these, engineering hemicellulosic pentose utilization has been well-explored in Saccharomyces cerevisiae (yeast) over several decades-yet the answer to what makes their utilization inherently recalcitrant remains elusive. Through implementation of a semi-synthetic regulon, we find that harmonizing cellular and engineering objectives are a key to obtaining highest growth rates and yields with minimal metabolic engineering effort. Concurrently, results indicate that "extrinsic" factors-specifically, upstream genes that direct flux of pentoses into central carbon metabolism-are rate-limiting. We also reveal that yeast metabolism is innately highly adaptable to rapid growth on non-native substrates and that systems metabolic engineering (i.e., functional genomics, network modeling, etc.) is largely unnecessary. Overall, this work provides an alternate, novel, holistic (and yet minimalistic) approach based on integrating non-native metabolic genes with a native regulon system.

Evolution of Escherichia coli in different carbon environments for 2,000 generations.

Cellular energetics is thought to have played a key role in dictating all major evolutionary transitions in the history of life on Earth. However, how exactly cellular energetics and metabolism come together to shape evolutionary paths is not well understood. In particular, when an organism is evolved in different energy environments, what are the phenomenological differences in the chosen evolutionary trajectories, is a question that is not well understood. In this context, starting from an Escherichia coli K-12 strain, we evolve the bacterium in five different carbon environments-glucose, arabinose, xylose, rhamnose and a mixture of these four sugars (in a predefined ratio) for approximately 2,000 generations. At the end of the adaptation period, we quantify and compare the growth dynamics of the strains in a variety of environments. The evolved strains show no specialized adaptation towards growth in the carbon medium in which they were evolved. Rather, in all environments, the evolved strains exhibited a reduced lag phase and an increased growth rate. Sequencing results reveal that these dynamical properties are not introduced via mutations in the precise loci associated with utilization of the sugar in which the bacterium evolved. These phenotypic changes are rather likely introduced via mutations elsewhere on the genome. Data from our experiments indicate that evolution in a defined environment does not alter hierarchy in mixed-sugar utilization in bacteria.

Robustness versus evolvability analysis for regulatory feed-forward loops.

From the definition, it appears that phenotypic robustness and evolvability of an organism are inversely related to each other. However, a number of studies exploring this question have found conflicting evidences in this regard. This question motivated the current work where we explore the relationship between robustness and evolvability. As a model system, we pick the Feed Forward Loops (FFLs), and develop a framework to characterize their performance in terms of their ability to resist changes to steady state expression (robustness), and their ability to evolve towards novel phenotypes (evolvability). We demonstrate that robustness and evolvability are positively correlated in some FFL topologies. We compare this against other small regulatory topologies, and show that the same trend does not hold among them. We postulate that the ability to positively link robustness and evolvability could be an additional reason for over-representation of FFLs in living organisms, as compared to other regulatory topologies.

Revisiting demand rules for gene regulation.

Starting with Savageau's pioneering work regarding demand rules for gene regulation from the 1970s, here, we choose the simplest transcription network and ask: how does the cell choose a particular regulatory topology from all available possibilities? According to the demand rules, a cell chooses an activator based regulation of a target if the target protein is required for most of the time. On the other hand, if the target protein is only required sporadically, its control tends to be via a repressor-based regulatory topology. We study the natural distribution of topologies at genome, systems, and micro-levels in E. coli and observe deviations from demand rules. Analyzing the regulation of amino acid biosynthesis, transport, and carbon utilization in E. coli and B. subtilis, and comparing choice of topology with demand, we observe an alternate pattern emerging. Simulations of networks are used to help explain the natural distribution of topologies in nature. Overall, our results indicate that the choice of topology is drawn randomly from a pool of all networks which satisfy the dynamic requirements of the cell, as dictated by physiology. In short, our results suggest that the cell picks "whatever works".