Terpene synthases and pathways in animals: enzymology and structural evolution in the biosynthesis of volatile infochemicals.

Covering: up to the beginning of 2023Many animals release volatile or semi-volatile terpenes as semiochemicals in intra- and inter-specific interactions. Terpenes are important constituents of pheromones and serve as chemical defenses to ward off predators. Despite the occurrence of terpene specialized metabolites from soft corals to mammals, the biosynthetic origin of these compounds has largely remained obscure. An increasing number of animal genome and transcriptome resources is facilitating the identification of enzymes and pathways that allow animals to produce terpenes independent of their food sources or microbial endosymbionts. Substantial evidence has emerged for the presence of terpene biosynthetic pathways such as in the formation of the iridoid sex pheromone nepetalactone in aphids. In addition, terpene synthase (TPS) enzymes have been discovered that are evolutionary unrelated to canonical plant and microbial TPSs and instead resemble precursor enzymes called isoprenyl diphosphate synthases (IDSs) in central terpene metabolism. Structural modifications of substrate binding motifs in canonical IDS proteins presumably facilitated the transition to TPS function at an early state in insect evolution. Other arthropods such as mites appear to have adopted their TPS genes from microbial sources via horizontal gene transfer. A similar scenario likely occurred in soft corals, where TPS families with closer resemblance to microbial TPSs have been discovered recently. Together, these findings will spur the identification of similar or still unknown enzymes in terpene biosynthesis in other lineages of animals. They will also help develop biotechnological applications for animal derived terpenes of pharmaceutical value or advance sustainable agricultural practices in pest management.

Emergence of terpene chemical communication in insects: Evolutionary recruitment of isoprenoid metabolism.

Insects have evolved a chemical communication system using terpenoids, a structurally diverse class of specialized metabolites, previously thought to be exclusively produced by plants and microbes. Gene discovery, bioinformatics, and biochemical characterization of multiple insect terpene synthases (TPSs) revealed that isopentenyl diphosphate synthases (IDS), enzymes from primary isoprenoid metabolism, are their likely evolutionary progenitors. However, the mutations underlying the emergence of the TPS function remain a mystery. To address this gap, we present the first structural and mechanistic model for the evolutionary emergence of TPS function in insects. Through identifying key mechanistic differences between IDS and TPS enzymes, we hypothesize that the loss of isopentenyl diphosphate (IPP) binding motifs strongly correlates with the gain of the TPS function. Based on this premise, we have elaborated the first explicit structural definition of isopentenyl diphosphate-binding motifs (IBMs) and used the IBM definitions to examine previously characterized insect IDSs and TPSs and to predict the functions of as yet uncharacterized insect IDSs. Consistent with our hypothesis, we observed a clear pattern of disruptive substitutions to IBMs in characterized insect TPSs. In contrast, insect IDSs maintain essential consensus residues for binding IPP. Extending our analysis, we constructed the most comprehensive phylogeny of insect IDS sequences (430 full length sequences from eight insect orders) and used IBMs to predict the function of TPSs. Based on our analysis, we infer multiple, independent TPS emergence events across the class of insects, paving the way for future gene discovery efforts.

Ancient origin and conserved gene function in terpene pheromone and defense evolution of stink bugs and hemipteran insects.

Insects use diverse arrays of small molecules such as metabolites of the large class of terpenes for intra- and inter-specific communication and defense. These molecules are synthesized by specialized metabolic pathways; however, the origin of enzymes involved in terpene biosynthesis and their evolution in insect genomes is still poorly understood. We addressed this question by investigating the evolution of isoprenyl diphosphate synthase (IDS)-like genes with terpene synthase (TPS) function in the family of stink bugs (Pentatomidae) within the large order of piercing-sucking Hemipteran insects. Stink bugs include species of global pest status, many of which emit structurally related 15-carbon sesquiterpenes as sex or aggregation pheromones. We provide evidence for the emergence of IDS-type TPS enzymes at the onset of pentatomid evolution over 100 million years ago, coinciding with the evolution of flowering plants. Stink bugs of different geographical origin maintain small IDS-type families with genes of conserved TPS function, which stands in contrast to the diversification of TPS genes in plants. Expanded gene mining and phylogenetic analysis in other hemipteran insects further provides evidence for an ancient emergence of IDS-like genes under presumed selection for terpene-mediated chemical interactions, and this process occurred independently from a similar evolution of IDS-type TPS genes in beetles. Our findings further suggest differences in TPS diversification in insects and plants in conjunction with different modes of gene functionalization in chemical interactions.

Sparse Epistatic Patterns in the Evolution of Terpene Synthases.

We explore sequence determinants of enzyme activity and specificity in a major enzyme family of terpene synthases. Most enzymes in this family catalyze reactions that produce cyclic terpenes-complex hydrocarbons widely used by plants and insects in diverse biological processes such as defense, communication, and symbiosis. To analyze the molecular mechanisms of emergence of terpene cyclization, we have carried out in-depth examination of mutational space around (E)-ß-farnesene synthase, an Artemisia annua enzyme which catalyzes production of a linear hydrocarbon chain. Each mutant enzyme in our synthetic libraries was characterized biochemically, and the resulting reaction rate data were used as input to the Michaelis-Menten model of enzyme kinetics, in which free energies were represented as sums of one-amino-acid contributions and two-amino-acid couplings. Our model predicts measured reaction rates with high accuracy and yields free energy landscapes characterized by relatively few coupling terms. As a result, the Michaelis-Menten free energy landscapes have simple, interpretable structure and exhibit little epistasis. We have also developed biophysical fitness models based on the assumption that highly fit enzymes have evolved to maximize the output of correct products, such as cyclic products or a specific product of interest, while minimizing the output of byproducts. This approach results in nonlinear fitness landscapes that are considerably more epistatic. Overall, our experimental and computational framework provides focused characterization of evolutionary emergence of novel enzymatic functions in the context of microevolutionary exploration of sequence space around naturally occurring enzymes.

RefCell: multi-dimensional analysis of image-based high-throughput screens based on 'typical cells'.

BACKGROUND: Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the "curse of dimensionality" and non-standardized outputs. RESULTS: Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these "typical cells" as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample. CONCLUSIONS: We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.

Mechanism of FACT removal from transcribed genes by anticancer drugs curaxins.

Human FACT (facilitates chromatin transcription) is a multifunctional protein complex that has histone chaperone activity and facilitates nucleosome survival and transcription through chromatin. Anticancer drugs curaxins induce FACT trapping on chromatin of cancer cells (c-trapping), but the mechanism of c-trapping is not fully understood. Here, we show that in cancer cells, FACT is highly enriched within the bodies of actively transcribed genes. Curaxin-dependent c-trapping results in redistribution of FACT from the transcribed chromatin regions to other genomic loci. Using a combination of biochemical and biophysical approaches, we have demonstrated that FACT is bound to and unfolds nucleosomes in the presence of curaxins. This tight binding to the nucleosome results in inhibition of FACT-dependent transcription in vitro in the presence of both curaxins and competitor chromatin, suggesting a mechanism of FACT trapping on bulk nucleosomes (n-trapping).

Rapid Bayesian Inference of Global Network Statistics Using Random Walks.

We propose a novel Bayesian methodology which uses random walks for rapid inference of statistical properties of undirected networks with weighted or unweighted edges. Our formalism yields high-accuracy estimates of the probability distribution of any network node-based property, and of the network size, after only a small fraction of network nodes has been explored. The Bayesian nature of our approach provides rigorous estimates of all parameter uncertainties. We demonstrate our framework on several standard examples, including random, scale-free, and small-world networks, and apply it to study epidemic spreading on a scale-free network. We also infer properties of the large-scale network formed by hyperlinks between Wikipedia pages.

Generalization of the Ewens sampling formula to arbitrary fitness landscapes.

In considering evolution of transcribed regions, regulatory sequences, and other genomic loci, we are often faced with a situation in which the number of allelic states greatly exceeds the size of the population. In this limit, the population eventually adopts a steady state characterized by mutation-selection-drift balance. Although new alleles continue to be explored through mutation, the statistics of the population, and in particular the probabilities of seeing specific allelic configurations in samples taken from the population, do not change with time. In the absence of selection, the probabilities of allelic configurations are given by the Ewens sampling formula, widely used in population genetics to detect deviations from neutrality. Here we develop an extension of this formula to arbitrary fitness distributions. Although our approach is general, we focus on the class of fitness landscapes, inspired by recent high-throughput genotype-phenotype maps, in which alleles can be in several distinct phenotypic states. This class of landscapes yields sampling probabilities that are computationally more tractable and can form a basis for inference of selection signatures from genomic data. Using an efficient numerical implementation of the sampling probabilities, we demonstrate that, for a sizable range of mutation rates and selection coefficients, the steady-state allelic diversity is not neutral. Therefore, it may be used to infer selection coefficients, as well as other evolutionary parameters from population data. We also carry out numerical simulations to challenge various approximations involved in deriving our sampling formulas, such as the infinite-allele limit and the "full connectivity" assumption inherent in the Ewens theory, in which each allele can mutate into any other allele. We find that, at least for the specific numerical examples studied, our theory remains sufficiently accurate even if these assumptions are relaxed. Thus our framework establishes both theoretical and practical foundations for inferring selection signatures from population-level genomic sequence samples.

Mediator binds to boundaries of chromosomal interaction domains and to proteins involved in DNA looping, RNA metabolism, chromatin remodeling, and actin assembly.

Mediator is a multi-unit molecular complex that plays a key role in transferring signals from transcriptional regulators to RNA polymerase II in eukaryotes. We have combined biochemical purification of the Saccharomyces cerevisiae Mediator from chromatin with chromatin immunoprecipitation in order to reveal Mediator occupancy on DNA genome-wide, and to identify proteins interacting specifically with Mediator on the chromatin template. Tandem mass spectrometry of proteins in immunoprecipitates of mediator complexes revealed specific interactions between Mediator and the RSC, Arp2/Arp3, CPF, CF 1A and Lsm complexes in chromatin. These factors are primarily involved in chromatin remodeling, actin assembly, mRNA 3'-end processing, gene looping and mRNA decay, but they have also been shown to enter the nucleus and participate in Pol II transcription. Moreover, we have found that Mediator, in addition to binding Pol II promoters, occupies chromosomal interacting domain (CID) boundaries and that Mediator in chromatin associates with proteins that have been shown to interact with CID boundaries, such as Sth1, Ssu72 and histone H4. This suggests that Mediator plays a significant role in higher-order genome organization.

Genome-wide profiling of nucleosome sensitivity and chromatin accessibility in Drosophila melanogaster.

Nucleosomal DNA is thought to be generally inaccessible to DNA-binding factors, such as micrococcal nuclease (MNase). Here, we digest Drosophila chromatin with high and low concentrations of MNase to reveal two distinct nucleosome types: MNase-sensitive and MNase-resistant. MNase-resistant nucleosomes assemble on sequences depleted of A/T and enriched in G/C-containing dinucleotides, whereas MNase-sensitive nucleosomes form on A/T-rich sequences found at transcription start and termination sites, enhancers and DNase I hypersensitive sites. Estimates of nucleosome formation energies indicate that MNase-sensitive nucleosomes tend to be less stable than MNase-resistant ones. Strikingly, a decrease in cell growth temperature of about 10°C makes MNase-sensitive nucleosomes less accessible, suggesting that observed variations in MNase sensitivity are related to either thermal fluctuations of chromatin fibers or the activity of enzymatic machinery. In the vicinity of active genes and DNase I hypersensitive sites nucleosomes are organized into periodic arrays, likely due to 'phasing' off potential barriers formed by DNA-bound factors or by nucleosomes anchored to their positions through external interactions. The latter idea is substantiated by our biophysical model of nucleosome positioning and energetics, which predicts that nucleosomes immediately downstream of transcription start sites are anchored and recapitulates nucleosome phasing at active genes significantly better than sequence-dependent models.

Path statistics, memory, and coarse-graining of continuous-time random walks on networks.

Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs.

Stress-response balance drives the evolution of a network module and its host genome.

Stress response genes and their regulators form networks that underlie drug resistance. These networks often have an inherent tradeoff: their expression is costly in the absence of stress, but beneficial in stress. They can quickly emerge in the genomes of infectious microbes and cancer cells, protecting them from treatment. Yet, the evolution of stress resistance networks is not well understood. Here, we use a two-component synthetic gene circuit integrated into the budding yeast genome to model experimentally the adaptation of a stress response module and its host genome in three different scenarios. In agreement with computational predictions, we find that: (i) intra-module mutations target and eliminate the module if it confers only cost without any benefit to the cell; (ii) intra- and extra-module mutations jointly activate the module if it is potentially beneficial and confers no cost; and (iii) a few specific mutations repeatedly fine-tune the module's noisy response if it has excessive costs and/or insufficient benefits. Overall, these findings reveal how the timing and mechanisms of stress response network evolution depend on the environment.

A Biophysical Approach to Predicting Protein-DNA Binding Energetics.

Sequence-specific interactions between proteins and DNA play a central role in DNA replication, repair, recombination, and control of gene expression. These interactions can be studied in vitro using microfluidics, protein-binding microarrays (PBMs), and other high-throughput techniques. Here we develop a biophysical approach to predicting protein-DNA binding specificities from high-throughput in vitro data. Our algorithm, called BindSter, can model alternative DNA-binding modes and multiple protein species competing for access to DNA, while rigorously taking into account all sterically allowed configurations of DNA-bound factors. BindSter can be used with a hierarchy of protein-DNA interaction models of increasing complexity, including contributions of mononucleotides, dinucleotides, and longer words to the total protein-DNA binding energy. We observe that the quality of BindSter predictions does not change significantly as some of the energy parameters vary over a sizable range. To take this degeneracy into account, we have developed a graphical representation of parameter uncertainties called IntervalLogo. We find that our simplest model, in which each nucleotide in the binding site is treated independently, performs better than previous biophysical approaches. The extensions of this model, in which contributions of longer words are also considered, result in further improvements, underscoring the importance of higher-order effects in protein-DNA energetics. In contrast, we find little evidence of multiple binding modes for the transcription factors (TFs) and experimental conditions in our data set. Furthermore, there is limited consistency in predictions for the same TF based on microfluidics and PBM data.

Scaling properties of evolutionary paths in a biophysical model of protein adaptation.

The enormous size and complexity of genotypic sequence space frequently requires consideration of coarse-grained sequences in empirical models. We develop scaling relations to quantify the effect of this coarse-graining on properties of fitness landscapes and evolutionary paths. We first consider evolution on a simple Mount Fuji fitness landscape, focusing on how the length and predictability of evolutionary paths scale with the coarse-grained sequence length and alphabet. We obtain simple scaling relations for both the weak- and strong-selection limits, with a non-trivial crossover regime at intermediate selection strengths. We apply these results to evolution on a biophysical fitness landscape that describes how proteins evolve new binding interactions while maintaining their folding stability. We combine the scaling relations with numerical calculations for coarse-grained protein sequences to obtain quantitative properties of the model for realistic binding interfaces and a full amino acid alphabet.

Protein folding and binding can emerge as evolutionary spandrels through structural coupling.

Binding interactions between proteins and other molecules mediate numerous cellular processes, including metabolism, signaling, and gene regulation. These interactions often evolve in response to changes in the protein's chemical or physical environment (such as the addition of an antibiotic). Several recent studies have shown the importance of folding stability in constraining protein evolution. Here we investigate how structural coupling between folding and binding--the fact that most proteins can only bind their targets when folded--gives rise to an evolutionary coupling between the traits of folding stability and binding strength. Using a biophysical and evolutionary model, we show how these protein traits can emerge as evolutionary "spandrels" even if they do not confer an intrinsic fitness advantage. In particular, proteins can evolve strong binding interactions that have no functional role but merely serve to stabilize the protein if its misfolding is deleterious. Furthermore, such proteins may have divergent fates, evolving to bind or not bind their targets depending on random mutational events. These observations may explain the abundance of apparently nonfunctional interactions among proteins observed in high-throughput assays. In contrast, for proteins with both functional binding and deleterious misfolding, evolution may be highly predictable at the level of biophysical traits: adaptive paths are tightly constrained to first gain extra folding stability and then partially lose it as the new binding function is developed. These findings have important consequences for our understanding of how natural and engineered proteins evolve under selective pressure.

Functional roles of nucleosome stability and dynamics.

Nucleosome is a histone-DNA complex known as the fundamental repeating unit of chromatin. Up to 90% of eukaryotic DNA is wrapped around consecutive octamers made of the core histones H2A, H2B, H3 and H4. Nucleosome positioning affects numerous cellular processes that require robust and timely access to genomic DNA, which is packaged into the tight confines of the cell nucleus. In living cells, nucleosome positions are determined by intrinsic histone-DNA sequence preferences, competition between histones and other DNA-binding proteins for genomic sequence, and ATP-dependent chromatin remodelers. We discuss the major energetic contributions to nucleosome formation and remodeling, focusing especially on partial DNA unwrapping off the histone octamer surface. DNA unwrapping enables efficient access to nucleosome-buried binding sites and mediates rapid nucleosome removal through concerted action of two or more DNA-binding factors. High-resolution, genome-scale maps of distances between neighboring nucleosomes have shown that DNA unwrapping and nucleosome crowding (mutual invasion of nucleosome territories) are much more common than previously thought. Ultimately, constraints imposed by nucleosome energetics on the rates of ATP-dependent and spontaneous chromatin remodeling determine nucleosome occupancy genome-wide, and shape pathways of cellular response to environmental stresses.

Biophysical fitness landscapes for transcription factor binding sites.

Phenotypic states and evolutionary trajectories available to cell populations are ultimately dictated by complex interactions among DNA, RNA, proteins, and other molecular species. Here we study how evolution of gene regulation in a single-cell eukaryote S. cerevisiae is affected by interactions between transcription factors (TFs) and their cognate DNA sites. Our study is informed by a comprehensive collection of genomic binding sites and high-throughput in vitro measurements of TF-DNA binding interactions. Using an evolutionary model for monomorphic populations evolving on a fitness landscape, we infer fitness as a function of TF-DNA binding to show that the shape of the inferred fitness functions is in broad agreement with a simple functional form inspired by a thermodynamic model of two-state TF-DNA binding. However, the effective parameters of the model are not always consistent with physical values, indicating selection pressures beyond the biophysical constraints imposed by TF-DNA interactions. We find little statistical support for the fitness landscape in which each position in the binding site evolves independently, indicating that epistasis is common in the evolution of gene regulation. Finally, by correlating TF-DNA binding energies with biological properties of the sites or the genes they regulate, we are able to rule out several scenarios of site-specific selection, under which binding sites of the same TF would experience different selection pressures depending on their position in the genome. These findings support the existence of universal fitness landscapes which shape evolution of all sites for a given TF, and whose properties are determined in part by the physics of protein-DNA interactions.

Ubiquitous nucleosome crowding in the yeast genome.

Nucleosomes may undergo a conformational change in which a stretch of DNA peels off the histone octamer surface as a result of thermal fluctuations or interactions with chromatin remodelers. Thus, neighboring nucleosomes may invade each other's territories by DNA unwrapping and translocation, or through initial assembly in partially wrapped states. A recent high-resolution map of distances between dyads of neighboring nucleosomes in Saccharomyces cerevisiae reveals that nucleosomes frequently overlap DNA territories of their neighbors. This conclusion is supported by lower-resolution maps of S. cerevisiae nucleosome lengths based on micrococcal nuclease digestion and paired-end sequencing. The average length of wrapped DNA follows a stereotypical pattern in genes and promoters, correlated with the well-known distribution of nucleosome occupancy: nucleosomal DNA tends to be shorter in promoters and longer in coding regions. To explain these observations, we have developed a biophysical model that uses a 10-11-bp periodic histone-DNA binding energy profile. The profile is based on the pattern of histone-DNA contacts in nucleosome crystal structures, as well as the idea of linker length discretization caused by higher-order chromatin structure. Our model is in agreement with the observed genome-wide distributions of interdyad distances, wrapped DNA lengths, and nucleosome occupancies. Furthermore, our approach explains in vitro measurements of the accessibility of nucleosome-covered target sites and nucleosome-induced cooperativity between DNA-binding factors. We rule out several alternative scenarios of histone-DNA interactions as inconsistent with the genomic data.

A dynamic interplay of nucleosome and Msn2 binding regulates kinetics of gene activation and repression following stress.

The transcription factor Msn2 mediates a significant proportion of the environmental stress response, in which a common cohort of genes changes expression in a stereotypic fashion upon exposure to any of a wide variety of stresses. We have applied genome-wide chromatin immunoprecipitation and nucleosome profiling to determine where Msn2 binds under stressful conditions and how that binding affects, and is affected by, nucleosome positioning. We concurrently determined the effect of Msn2 activity on gene expression following stress and demonstrated that Msn2 stimulates both activation and repression. We found that some genes responded to both intermittent and continuous Msn2 nuclear occupancy while others responded only to continuous occupancy. Finally, these studies document a dynamic interplay between nucleosomes and Msn2 such that nucleosomes can restrict access of Msn2 to its canonical binding sites while Msn2 can promote reposition, expulsion and recruitment of nucleosomes to alter gene expression. This interplay may allow the cell to discriminate between different types of stress signaling.

Path-based approach to random walks on networks characterizes how proteins evolve new functions.

We develop a path-based approach to continuous-time random walks on networks with arbitrarily weighted edges. We describe an efficient numerical algorithm for calculating statistical properties of the stochastic path ensemble. After demonstrating our approach on two reaction rate problems, we present a biophysical model that describes how proteins evolve new functions while maintaining thermodynamic stability. We use our methodology to characterize dynamics of evolutionary adaptation, reproducing several key features observed in directed evolution experiments. We find that proteins generally fall into two qualitatively different regimes of adaptation depending on their binding and folding energetics.