Daniel Segrè

Analysis of optimality in natural and perturbed metabolic networks

An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism.

The lipid world.

The continuity of abiotically formed bilayer membraneswith similar structures in contemporary cellular life,and the requirement for microenvironments in whichlarge and small molecules could be compartmentalized, support the idea that amphiphilic boundary structurescontributed to the emergence of life. As an extensionof this notion, we propose here a `Lipid Worldscenario as an early evolutionary step in theemergence of cellular life on Earth. This conceptcombines the potential chemical activities of lipidsand other amphiphiles, with their capacity to undergospontaneous self-organization into supramolecularstructures such as micelles and bilayers. Inparticular, the documented chemical rate enhancementswithin lipid assemblies suggest that energy-dependentsynthetic reactions could lead to the growth andincreased abundance of certain amphiphilic assemblies.We further propose that selective processes might acton such assemblies, as suggested by our computersimulations of mutual catalysis among amphiphiles. Asdemonstrated also by other researchers, such mutualcatalysis within random molecular assemblies couldhave led to a primordial homeostatic system displayingrudimentary life-like properties. Taken together,these concepts provide a theoretical framework, andsuggest experimental tests for a Lipid World model forthe origin of life.

The Effect of Oxygen on Biochemical Networks and the Evolution of Complex Life

The evolution of oxygenic photosynthesis and ensuing oxygenation of Earths atmosphere represent a major transition in the history of life. Although many organisms retreated to anoxic environments, others evolved to use oxygen as a high–potential redox couple while concomitantly mitigating its toxicity. To understand the changes in biochemistry and enzymology that accompanied adaptation to O2, we integrated network analysis with information on enzyme evolution to infer how oxygen availability changed the architecture of metabolic networks. Our analysis revealed the existence of four discrete groups of networks of increasing complexity, with transitions between groups being contingent on the presence of key metabolites, including molecular oxygen, which was required for transition into the largest networks.

Diminishing Returns Epistasis Among Beneficial Mutations Decelerates Adaptation

Interactions between genes reduce the benefits of a mutation and decrease the rate of fitness gain during adaptation. Epistasis has substantial impacts on evolution, in particular, the rate of adaptation. We generated combinations of beneficial mutations that arose in a lineage during rapid adaptation of a bacterium whose growth depended on a newly introduced metabolic pathway. The proportional selective benefit for three of the four loci consistently decreased when they were introduced onto more fit backgrounds. These three alleles all reduced morphological defects caused by expression of the foreign pathway. A simple theoretical model segregating the apparent contribution of individual alleles to benefits and costs effectively predicted the interactions between them. These results provide the first evidence that patterns of epistasis may differ for within- and between-gene interactions during adaptation and that diminishing returns epistasis contributes to the consistent observation of decelerating fitness gains during adaptation.

Deep sequencing of the oral microbiome reveals signatures of periodontal disease.

The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (∼2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ∼90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.

Environments that Induce Synthetic Microbial Ecosystems

Interactions between microbial species are sometimes mediated by the exchange of small molecules, secreted by one species and metabolized by another. Both one-way (commensal) and two-way (mutualistic) interactions may contribute to complex networks of interdependencies. Understanding these interactions constitutes an open challenge in microbial ecology, with applications ranging from the human microbiome to environmental sustainability. In parallel to natural communities, it is possible to explore interactions in artificial microbial ecosystems, e.g. pairs of genetically engineered mutualistic strains. Here we computationally generate artificial microbial ecosystems without re-engineering the microbes themselves, but rather by predicting their growth on appropriately designed media. We use genome-scale stoichiometric models of metabolism to identify media that can sustain growth for a pair of species, but fail to do so for one or both individual species, thereby inducing putative symbiotic interactions. We first tested our approach on two previously studied mutualistic pairs, and on a pair of highly curated model organisms, showing that our algorithms successfully recapitulate known interactions, robustly predict new ones, and provide novel insight on exchanged molecules. We then applied our method to all possible pairs of seven microbial species, and found that it is always possible to identify putative media that induce commensalism or mutualism. Our analysis also suggests that symbiotic interactions may arise more readily through environmental fluctuations than genetic modifications. We envision that our approach will help generate microbe-microbe interaction maps useful for understanding microbial consortia dynamics and evolution, and for exploring the full potential of natural metabolic pathways for metabolic engineering applications.

The COMBREX Project: Design, Methodology, and Initial Results

Experimental data exists for only a vanishingly small fraction of sequenced microbial genes. This community page discusses the progress made by the COMBREX project to address this important issue using both computational and experimental resources.

Dichotomy of single-nucleotide polymorphism haplotypes in olfactory receptor genes and pseudogenes

Substantial efforts are focused on identifying single-nucleotide polymorphisms (SNPs) throughout the human genome, particularly in coding regions (cSNPs), for both linkage disequilibrium and association studies. Less attention, however, has been directed to the clarification of evolutionary processes that are responsible for the variability in nucleotide diversity among different regions of the genome. We report here the population sequence diversity of genomic segments within a 450-kb cluster of olfactory receptor (OR) genes on human chromosome 17. We found a dichotomy in the pattern of nucleotide diversity between OR pseudogenes and introns on the one hand and the closely interspersed intact genes on the other. We suggest that weak positive selection is responsible for the observed patterns of genetic variation. This is inferred from a lower ratio of polymorphism to divergence in genes compared with pseudogenes or introns, high non-synonymous substitution rates in OR genes, and a small but significant overall reduction in variability in the entire OR gene cluster compared with other genomic regions. The dichotomy among functionally different segments within a short genomic distance requires high recombination rates within this OR cluster. Our work demonstrates the impact of weak positive selection on human nucleotide diversity, and has implications for the evolution of the olfactory repertoire.

Graded Autocatalysis Replication Domain (GARD): kinetic analysis of self-replication in mutually catalytic sets.

A Graded Autocatalysis Replication Domain (GARD) model is proposed, which provides a rigorous kinetic analysis of simple chemical sets that manifest mutual catalysis. It is shown that catalytic closure can sustain self-replication up to a critical dilution rate, λc, related to the graded extent of mutual catalysis. We explore the behavior of vesicles containing GARD species whose mutual catalysis is governed by a previously published statistical distribution. In the population thus generated, some GARD vesicles display a significantly higher replication efficiency than most others. GARD thus represents a simple model for primordial chemical selection of mutually catalytic sets.

Ecosystems biology of microbial metabolism

The metabolic capabilities of many environmentally and medically important microbes can be quantitatively explored using systems biology approaches to metabolic networks. Yet, as we learn more about the complex microbe-microbe and microbe-environment interactions in microbial communities, it is important to understand whether and how system-level approaches can be extended to the ecosystem level. Here we summarize recent work that addresses these challenges at multiple scales, starting from two-species natural and synthetic ecology models, up to biosphere-level approaches. Among the many fascinating open challenges in this field is whether the integration of high throughput sequencing methods and mathematical models will help us capture emerging principles of ecosystem-level metabolic organization and evolution.