Adaptive Genetic Robustness of Escherichia coli Metabolic Fluxes

Plant biochemical genetics in the multiomics era

Our understanding of plant biology has been revolutionized by modern genetics and biochemistry. However, biochemical genetics can be traced back to the foundation of Mendelian genetics; indeed, one of Mendel's milestone discoveries of seven characteristics of pea plants later came to be ascribed to a mutation in a starch branching enzyme. Here, we review both current and historical strategies for the elucidation of plant metabolic pathways and the genes that encode their component enzymes and regulators. We use this historical review to discuss a range of classical genetic phenomena including epistasis, canalization, and heterosis as viewed through the lens of contemporary high-throughput data obtained via the array of approaches currently adopted in multiomics studies.

Opportunities at the Interface of Network Science and Metabolic Modeling

Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.

Environmental flexibility does not explain metabolic robustness

Cells show remarkable resilience against genetic and environmental perturbations. However, its evolutionary origin remains obscure. In order to leverage methods of systems biology for examining cellular robustness, a computationally accessible way of quantification is needed. Here, we present an unbiased metric of structural robustness in genome-scale metabolic models based on concepts prevalent in reliability engineering and fault analysis. The probability of failure (PoF) is defined as the (weighted) portion of all possible combinations of loss-of-function mutations that disable network functionality. It can be exactly determined if all essential reactions, synthetic lethal pairs of reactions, synthetic lethal triplets of reactions etc. are known. In theory, these minimal cut sets (MCSs) can be calculated for any network, but for large models the problem remains computationally intractable. Herein, we demonstrate that even at the genome scale only the lowest-cardinality MCSs are required to efficiently approximate the PoF with reasonable accuracy. Building on an improved theoretical understanding, we analysed the robustness of 489 E. coli, Shigella, Salmonella, and fungal genome-scale metabolic models (GSMMs). In contrast to the popular "congruence theory", which explains the origin of genetic robustness as a byproduct of selection for environmental flexibility, we found no correlation between network robustness and the diversity of growth-supporting environments. On the contrary, our analysis indicates that amino acid synthesis rather than carbon metabolism dominates metabolic robustness.

The impact of cell structure, metabolism and group behavior for the survival of bacteria under stress conditions

Microbes from diverse types of habitats are continuously exposed to external challenges, which may include acidic, alkaline, and toxic metabolites stress as well as nutrient deficiencies. To promote their own survival, bacteria have to rapidly adapt to external perturbations by inducing particular stress responses that typically involve genetic and/or cellular changes. In addition, pathogenic bacteria need to sense and withstand these environmental stresses within a host to establish and maintain infection. These responses can be, in principle, induced by changes in bacterial cell structure, metabolism and group behavior. Bacterial nucleic acids may serve as the core part of the stress response, and the cell envelope and ribosomes protect genetic structures from damage. Cellular metabolism and group behavior, such as quorum sensing system, can play a more important role in resisting stress than we have now found. Since bacteria survival can be only appreciated if we better understand the mechanisms behind bacterial stress response, here we review how morphological and physiological features may lead to bacterial resistance upon exposure to particular stress-inducing factors.

Genetic buffering and potentiation in metabolism

Cells adjust their metabolism in response to mutations, but how this reprogramming depends on the genetic context is not well known. Specifically, the absence of individual enzymes can affect reprogramming, and thus the impact of mutations in cell growth. Here, we examine this issue with an in silico model of Saccharomyces cerevisiae's metabolism. By quantifying the variability in the growth rate of 10000 different mutant metabolisms that accumulated changes in their reaction fluxes, in the presence, or absence, of a specific enzyme, we distinguish a subset of modifier genes serving as buffers or potentiators of variability. We notice that the most potent modifiers refer to the glycolysis pathway and that, more broadly, they show strong pleiotropy and epistasis. Moreover, the evidence that this subset depends on the specific growing condition strengthens its systemic underpinning, a feature only observed before in a toy model of a gene-regulatory network. Some of these enzymes also modulate the effect that biochemical noise and environmental fluctuations produce in growth. Thus, the reorganization of metabolism induced by mutations has not only direct physiological implications but also transforms the influence that other mutations have on growth. This is a general result with implications in the development of cancer therapies based on metabolic inhibitors.

Network Architecture and Mutational Sensitivity of the C. elegans Metabolome

A fundamental issue in evolutionary systems biology is understanding the relationship between the topological architecture of a biological network, such as a metabolic network, and the evolution of the network. The rate at which an element in a metabolic network accumulates genetic variation via new mutations depends on both the size of the mutational target it presents and its robustness to mutational perturbation. Quantifying the relationship between topological properties of network elements and the mutability of those elements will facilitate understanding the variation in and evolution of networks at the level of populations and higher taxa. We report an investigation into the relationship between two topological properties of 29 metabolites in the C. elegans metabolic network and the sensitivity of those metabolites to the cumulative effects of spontaneous mutation. The correlations between measures of network centrality and mutability are not statistically significant, but several trends point toward a weak positive association between network centrality and mutational sensitivity. There is a small but significant negative association between the mutational correlation of a pair of metabolites (r_M) and the shortest path length between those metabolites. Positive association between the centrality of a metabolite and its mutational heritability is consistent with centrally-positioned metabolites presenting a larger mutational target than peripheral ones, and is inconsistent with centrality conferring mutational robustness, at least in toto. The weakness of the correlation between r_M and the shortest path length between pairs of metabolites suggests that network locality is an important but not overwhelming factor governing mutational pleiotropy. These findings provide necessary background against which the effects of other evolutionary forces, most importantly natural selection, can be interpreted.

Heterosis Is a Systemic Property Emerging From Non-linear Genotype-Phenotype Relationships: Evidence From in Vitro Genetics and Computer Simulations

Heterosis, the superiority of hybrids over their parents for quantitative traits, represents a crucial issue in plant and animal breeding as well as evolutionary biology. Heterosis has given rise to countless genetic, genomic and molecular studies, but has rarely been investigated from the point of view of systems biology. We hypothesized that heterosis is an emergent property of living systems resulting from frequent concave relationships between genotypic variables and phenotypes, or between different phenotypic levels. We chose the enzyme-flux relationship as a model of the concave genotype-phenotype (GP) relationship, and showed that heterosis can be easily created in the laboratory. First, we reconstituted in vitro the upper part of glycolysis. We simulated genetic variability of enzyme activity by varying enzyme concentrations in test tubes. Mixing the content of "parental" tubes resulted in "hybrids," whose fluxes were compared to the parental fluxes. Frequent heterotic fluxes were observed, under conditions that were determined analytically and confirmed by computer simulation. Second, to test this model in a more realistic situation, we modeled the glycolysis/fermentation network in yeast by considering one input flux, glucose, and two output fluxes, glycerol and acetaldehyde. We simulated genetic variability by randomly drawing parental enzyme concentrations under various conditions, and computed the parental and hybrid fluxes using a system of differential equations. Again we found that a majority of hybrids exhibited positive heterosis for metabolic fluxes. Cases of negative heterosis were due to local convexity between certain enzyme concentrations and fluxes. In both approaches, heterosis was maximized when the parents were phenotypically close and when the distributions of parental enzyme concentrations were contrasted and constrained. These conclusions are not restricted to metabolic systems: they only depend on the concavity of the GP relationship, which is commonly observed at various levels of the phenotypic hierarchy, and could account for the pervasiveness of heterosis.

Control of primary metabolism by a virulence regulatory network promotes robustness in a plant pathogen

Robustness is a key system-level property of living organisms to maintain their functions while tolerating perturbations. We investigate here how a regulatory network controlling multiple virulence factors impacts phenotypic robustness of a bacterial plant pathogen. We reconstruct a cell-scale model of Ralstonia solanacearum connecting a genome-scale metabolic network, a virulence macromolecule network, and a virulence regulatory network, which includes 63 regulatory components. We develop in silico methods to quantify phenotypic robustness under a broad set of conditions in high-throughput simulation analyses. This approach reveals that the virulence regulatory network exerts a control of the primary metabolism to promote robustness upon infection. The virulence regulatory network plugs into the primary metabolism mainly through the control of genes likely acquired via horizontal gene transfer, which results in a functional overlay with ancestral genes. These results support the view that robustness may be a selected trait that promotes pathogenic fitness upon infection.

Evolutionary adaptations to new environments generally reverse plastic phenotypic changes

Organismal adaptation to a new environment may start with plastic phenotypic changes followed by genetic changes, but whether the plastic changes are stepping stones to genetic adaptation is debated. Here we address this question by investigating gene expression and metabolic flux changes in the two-phase adaptation process using transcriptomic data from multiple experimental evolution studies and computational metabolic network analysis, respectively. We discover that genetic changes more frequently reverse than reinforce plastic phenotypic changes in virtually every adaptation. Metabolic network analysis reveals that, even in the presence of plasticity, organismal fitness drops after environmental shifts, but largely recovers through subsequent evolution. Such fitness trajectories explain why plastic phenotypic changes are genetically compensated rather than strengthened. In conclusion, although phenotypic plasticity may serve as an emergency response to a new environment that is necessary for survival, it does not generally facilitate genetic adaptation by bringing the organismal phenotype closer to the new optimum.

Phenotypic plasticity has been suggested to facilitate survival in new environments and subsequent adaptation. Here, the authors reanalyze transcriptomic data from experimental evolution studies in combination with computational metabolic network analysis and show that genetic adaptation tends to reverse plastic changes in order to recover fitness.

Why Phenotype Robustness Promotes Phenotype Evolvability

Robustness and evolvability are fundamental characteristics of life whose relationship has intrigued generations of biologists. Studies of several genotype–phenotype maps (GPMs) such as the map between short DNA sequences and their bindings to transcription factors showed that phenotype robustness (PR) promotes phenotype evolvability (PE), but the underlying reason is unclear. Here, we show mathematically that the expected PE is a monotonically increasing function of the expected PR in random GPMs. Population genetic simulations confirm that increasing PR raises the probability that a target phenotype appears in a population within a given time, under empirical as well as randomly rewired GPMs. These and other results demonstrate that the positive correlation between PR and PE is mathematical rather than biological. Hence, it is unsurprising to observe this correlation in every empirical GPM investigated, although the magnitude of the correlation may vary due to influences of various biological factors.

Canalization of Tomato Fruit Metabolism

To explore the genetic robustness (canalization) of metabolism, we examined the levels of fruit metabolites in multiple harvests of a tomato introgression line (IL) population. The IL partitions the whole genome of the wild species Solanum pennellii in the background of the cultivated tomato (Solanum lycopersicum). We identified several metabolite quantitative trait loci that reduce variability for both primary and secondary metabolites, which we named canalization metabolite quantitative trait loci (cmQTL). We validated nine cmQTL using an independent population of backcross inbred lines, derived from the same parents, which allows increased resolution in mapping the QTL previously identified in the ILs. These cmQTL showed little overlap with QTL for the metabolite levels themselves. Moreover, the intervals they mapped to harbored few metabolism-associated genes, suggesting that the canalization of metabolism is largely controlled by regulatory genes.

Nonmutational compensation of the fitness cost of antibiotic resistance in mycobacteria by overexpression of tlyA rRNA methylase

Several studies over the last few decades have shown that antibiotic resistance mechanisms frequently confer a fitness cost and that these costs can be genetically ameliorated by intra- or extragenic second-site mutations, often without loss of resistance. Another, much less studied potential mechanism by which the fitness cost of antibiotic resistance could be reduced is via a regulatory response where the deleterious effect of the resistance mechanism is lowered by a physiological alteration that buffers the mutational effect. In mycobacteria, resistance to the clinically used tuberactinomycin antibiotic capreomycin involves loss-of-function mutations in rRNA methylase TlyA or point mutations in 16S rRNA (in particular the A1408G mutation). Both of these alterations result in resistance by reducing drug binding to the ribosome. Here we show that alterations of tlyA gene expression affect both antibiotic drug susceptibility and fitness cost of drug resistance. In particular, we demonstrate that the common resistance mutation A1408G is accompanied by a physiological change that involves increased expression of the tlyA gene. This gene encodes an enzyme that methylates neighboring 16S rRNA position C1409, and as a result of increased TlyA expression the fitness cost of the A1408G mutation is significantly reduced. Our findings suggest that in mycobacteria, a nonmutational mechanism (i.e., gene regulatory) can restore fitness to genetically resistant bacteria. Our results also point to a new and clinically relevant treatment strategy to combat evolution of resistance in multidrug-resistant tuberculosis. Thus, by utilizing antagonistic antibiotic interactions, resistance evolution could be reduced.

Nitric Oxide Overproduction in Tomato shr Mutant Shifts Metabolic Profiles and Suppresses Fruit Growth and Ripening

Nitric oxide (NO) plays a pivotal role in growth and disease resistance in plants. It also acts as a secondary messenger in signaling pathways for several plant hormones. Despite its clear role in regulating plant development, its role in fruit development is not known. In an earlier study, we described a short root (shr) mutant of tomato, whose phenotype results from hyperaccumulation of NO. The molecular mapping localized shr locus in 2.5 Mb region of chromosome 9. The shr mutant showed sluggish growth, with smaller leaves, flowers and was less fertile than wild type. The shr mutant also showed reduced fruit size and slower ripening of the fruits post-mature green stage to the red ripe stage. Comparison of the metabolite profiles of shr fruits with wild-type fruits during ripening revealed a significant shift in the patterns. In shr fruits intermediates of the tricarboxylic acid (TCA) cycle were differentially regulated than WT indicating NO affected the regulation of TCA cycle. The accumulation of several amino acids, particularly tyrosine, was higher, whereas most fatty acids were downregulated in shr fruits. Among the plant hormones at one or more stages of ripening, ethylene, Indole-3-acetic acid and Indole-3-butyric acid increased in shr, whereas abscisic acid declined. Our analyses indicate that the retardation of fruit growth and ripening in shr mutant likely results from the influence of NO on central carbon metabolism and endogenous phytohormones levels.