Messy biology and the origins of evolutionary innovations

Links between evolutionary processes and phenotypic robustness in microbes

The costs and benefits of random phenotypic heterogeneity in microbes have been vigorously debated and experimental tested for decades; yet, this conversation is largely independent from discussion of phenotypic robustness in other disciplines. In this review I connect microbial examples of stochasticity with studies on the ecological and population-genetic consequences of phenotypic variability. These topics illustrate the complexity of selection pressures on phenotypic robustness and provide inspiration that this complexity can be parsed with theoretical advances and the experimental power of microbial systems.

Protein intrinsic disorder negatively associates with gene age in different eukaryotic lineages

The emergence of new protein-coding genes in a specific lineage or species provides raw materials for evolutionary adaptations. Until recently, the biology of new genes emerging particularly from non-genic sequences remained unexplored. Although the new genes are subjected to variable selection pressure and face rapid deletion, some of them become functional and are retained in the gene pool. To acquire functional novelties, new genes often get integrated into the pre-existing ancestral networks. However, the mechanism by which young proteins acquire novel interactions remains unanswered till date. Since structural orientation contributes hugely to the mode of proteins' physical interactions, in this regard, we put forward an interesting question - Do new genes encode proteins with stable folds? Addressing the question, we demonstrated that the intrinsic disorder inversely correlates with the evolutionary gene ages - i.e. young proteins are richer in intrinsic disorder than the ancient ones. We further noted that young proteins, which are initially poorly connected hubs, prefer to be structurally more disordered than well-connected ancient proteins. The phenomenon strikingly defies the usual trend of well-connected proteins being highly disordered in structure. We justified that structural disorder might help poorly connected young proteins to undergo promiscuous interactions, which provides the foundation for novel protein interactions. The study focuses on the evolutionary perspectives of young proteins in the light of structural adaptations.

A Rational Active-Site Redesign Converts a Decarboxylase into a C=C Hydratase: "Tethered Acetate" Supports Enantioselective Hydration of 4-Hydroxystyrenes

The promiscuous regio- and stereoselective hydration of 4-hydroxystyrenes catalyzed by ferulic acid decarboxylase from Enterobacter sp. (FDC_Es) depends on bicarbonate bound in the active site, which serves as a proton relay activating a water molecule for nucleophilic attack on a quinone methide electrophile. This "cofactor" is crucial for achieving improved conversions and high stereoselectivities for (S)-configured benzylic alcohol products. Similar effects were observed with simple aliphatic carboxylic acids as additives. A rational redesign of the active site by replacing the bicarbonate or acetate "cofactor" with a newly introduced side-chain carboxylate from an adjacent amino acid yielded mutants that efficiently acted as C=C hydratases. A single-point mutation of valine 46 to glutamate or aspartate improved the hydration activity by 40% and boosted the stereoselectivity 39-fold in the absence of bicarbonate or acetate.

Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies

Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12-26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.

Development and retention of a primordial moonlighting pathway of protein modification in the absence of selection presents a puzzle

Lipoic acid is synthesized by a remarkably atypical pathway in which the cofactor is assembled on its cognate proteins. An octanoyl moiety diverted from fatty acid synthesis is covalently attached to the acceptor protein, and sulfur insertion at carbons 6 and 8 of the octanoyl moiety form the lipoyl cofactor. Covalent attachment of this cofactor is required for function of several central metabolism enzymes, including the glycine cleavage H protein (GcvH). In Bacillus subtilis, GcvH is the sole substrate for lipoate assembly. Hence lipoic acid-requiring 2-oxoacid dehydrogenase (OADH) proteins acquire the cofactor only by transfer from lipoylated GcvH. Lipoyl transfer has been argued to be the primordial pathway of OADH lipoylation. The Escherichia coli pathway where lipoate is directly assembled on both its GcvH and OADH proteins, is proposed to have arisen later. Because roughly 3 billion years separate the divergence of these bacteria, it is surprising that E. coli GcvH functionally substitutes for the B. subtilis protein in lipoyl transfer. Known and putative GcvHs from other bacteria and eukaryotes also substitute for B. subtilis GcvH in OADH modification. Because glycine cleavage is the primary GcvH role in ancestral bacteria that lack OADH enzymes, lipoyl transfer is a "moonlighting" function: that is, development of a new function while retaining the original function. This moonlighting has been conserved in the absence of selection by some, but not all, GcvH proteins. Moreover, Aquifex aeolicus encodes five putative GcvHs, two of which have the moonlighting function, whereas others function only in glycine cleavage.

Resurrecting ancestral genes in bacteria to interpret ancient biosignatures

Two datasets, the geologic record and the genetic content of extant organisms, provide complementary insights into the history of how key molecular components have shaped or driven global environmental and macroevolutionary trends. Changes in global physico-chemical modes over time are thought to be a consistent feature of this relationship between Earth and life, as life is thought to have been optimizing protein functions for the entirety of its approximately 3.8 billion years of history on the Earth. Organismal survival depends on how well critical genetic and metabolic components can adapt to their environments, reflecting an ability to optimize efficiently to changing conditions. The geologic record provides an array of biologically independent indicators of macroscale atmospheric and oceanic composition, but provides little in the way of the exact behaviour of the molecular components that influenced the compositions of these reservoirs. By reconstructing sequences of proteins that might have been present in ancient organisms, we can downselect to a subset of possible sequences that may have been optimized to these ancient environmental conditions. How can one use modern life to reconstruct ancestral behaviours? Configurations of ancient sequences can be inferred from the diversity of extant sequences, and then resurrected in the laboratory to ascertain their biochemical attributes. One way to augment sequence-based, single-gene methods to obtain a richer and more reliable picture of the deep past, is to resurrect inferred ancestral protein sequences in living organisms, where their phenotypes can be exposed in a complex molecular-systems context, and then to link consequences of those phenotypes to biosignatures that were preserved in the independent historical repository of the geological record. As a first step beyond single-molecule reconstruction to the study of functional molecular systems, we present here the ancestral sequence reconstruction of the beta-carbonic anhydrase protein. We assess how carbonic anhydrase proteins meet our selection criteria for reconstructing ancient biosignatures in the laboratory, which we term palaeophenotype reconstruction.This article is part of the themed issue 'Reconceptualizing the origins of life'.

Confronting the catalytic dark matter encoded by sequenced genomes

The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here, we aim at determining how many enzymes of uncertain or unknown function are still present in the Saccharomyces cerevisiae and human proteomes. Using information available in the Swiss-Prot, BRENDA and KEGG databases in combination with a Hidden Markov Model-based method, we estimate that >600 yeast and 2000 human proteins (>30% of their proteins of unknown function) are enzymes whose precise function(s) remain(s) to be determined. This illustrates the impressive scale of the ‘unknown enzyme problem’. We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research. Finally, we discuss the possible roles of the elusive catalysts in light of recent developments in the fields of enzymology and metabolism as well as the significance of the unknown enzyme problem in the context of metabolic modeling, metabolic engineering and rare disease research.

Stochastic noncooperative and cooperative evolutionary game strategies of a population of biological networks under natural selection

We review current static and dynamic evolutionary game strategies of biological networks and discuss the lack of random genetic variations and stochastic environmental disturbances in these models. To include these factors, a population of evolving biological networks is modeled as a nonlinear stochastic biological system with Poisson-driven genetic variations and random environmental fluctuations (stimuli). To gain insight into the evolutionary game theory of stochastic biological networks under natural selection, the phenotypic robustness and network evolvability of noncooperative and cooperative evolutionary game strategies are discussed from a stochastic Nash game perspective. The noncooperative strategy can be transformed into an equivalent multi-objective optimization problem and is shown to display significantly improved network robustness to tolerate genetic variations and buffer environmental disturbances, maintaining phenotypic traits for longer than the cooperative strategy. However, the noncooperative case requires greater effort and more compromises between partly conflicting players. Global linearization is used to simplify the problem of solving nonlinear stochastic evolutionary games. Finally, a simple stochastic evolutionary model of a metabolic pathway is simulated to illustrate the procedure of solving for two evolutionary game strategies and to confirm and compare their respective characteristics in the evolutionary process.

Phenotypic heterogeneity promotes adaptive evolution

Genetically identical cells frequently display substantial heterogeneity in gene expression, cellular morphology and physiology. It has been suggested that by rapidly generating a subpopulation with novel phenotypic traits, phenotypic heterogeneity (or plasticity) accelerates the rate of adaptive evolution in populations facing extreme environmental challenges. This issue is important as cell-to-cell phenotypic heterogeneity may initiate key steps in microbial evolution of drug resistance and cancer progression. Here, we study how stochastic transitions between cellular states influence evolutionary adaptation to a stressful environment in yeast Saccharomyces cerevisiae. We developed inducible synthetic gene circuits that generate varying degrees of expression stochasticity of an antifungal resistance gene. We initiated laboratory evolutionary experiments with genotypes carrying different versions of the genetic circuit by exposing the corresponding populations to gradually increasing antifungal stress. Phenotypic heterogeneity altered the evolutionary dynamics by transforming the adaptive landscape that relates genotype to fitness. Specifically, it enhanced the adaptive value of beneficial mutations through synergism between cell-to-cell variability and genetic variation. Our work demonstrates that phenotypic heterogeneity is an evolving trait when populations face a chronic selection pressure. It shapes evolutionary trajectories at the genomic level and facilitates evolutionary rescue from a deteriorating environmental stress.

Phenotypic heterogeneity of genetically identical cells can generate nonheritable variation in a population. Is this heterogeneity favorable for microbes? In a changing environment, the answer is a definite yes. While scholars have argued that stochastically generated variation precedes genetic changes and thereby facilitate the evolution of complex traits, this idea has remained disputed, not least because of the shortage of experimental studies. We address this long-standing and controversial issue by integrating synthetic biology, laboratory experimental evolution, and genomic analyses. We explicitly tested the mechanisms whereby phenotypic heterogeneity may promote evolvability. Our work demonstrates that phenotypic heterogeneity facilitates evolutionary rescue from deteriorating environmental stress by generating individuals with exceptionally high fitness. Remarkably, elevated phenotypic heterogeneity evolves as a direct response to stress and thereby it promotes evolution of rare combinations of mutations. These results indicate that phenotypic heterogeneity might have an important role in the evolution of key innovations.

Catalytic promiscuity and heme-dependent redox regulation of H2S synthesis

The view of enzymes as punctilious catalysts has been shifting as examples of their promiscuous behavior increase. However, unlike a number of cases where the physiological relevance of breached substrate specificity is questionable, the very synthesis of H₂S relies on substrate and reaction promiscuity, which presents the enzymes with a multitude of substrate and reaction choices. The transsulfuration pathway, a major source of H₂S, is inherently substrate-ambiguous. A heme-regulated switch embedded in the first enzyme in the pathway can help avert the stochastic production of cysteine versus H₂S and control switching between metabolic tracks to meet cellular needs. This review discusses the dominant role of enzyme promiscuity in pathways that double as sulfur catabolic and H₂S synthetic tracks.

Cell-Based Therapeutics: Making a Faustian Pact with Biology

The diversity and specialization found in biological molecules, pathways, and cells is staggering, and should be exploited for therapeutic use. Through evolution these biological systems have attained a level of functionality that would be impossible to recapitulate with de novo assembly. To adapt these systems for therapeutic applications it will be often necessary to re-engineer molecules and pathways to yield novel sensory, control, and effector modules for insertion into existing, specialized cellular chassis. However, these efforts will be greatly impeded and confounded by the noise, complexity, and context-dependency inherent in biological systems. Thus, we argue that repurposing biology for cell-based therapeutics will be an arduous process, but one that will yield great benefit, and is superior to any alternative.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

Activity of Zearalenone in the Porcine Intestinal Tract

This study demonstrates that low doses (somewhat above the No Observed Adverse Effect Level, NOAEL) of the mycoestrogen zearalenone (ZEN) and its metabolites display multispecificity towards various biological targets in gilts. The observed responses in gilts were surprising. The presence of ZEN and zearalenols (ZELs) did not evoke a response in the porcine gastrointestinal tract, which was attributed to dietary tolerance. Lymphocyte proliferation was intensified in jejunal mesenteric lymph nodes, and lymphocyte counts increased in the jejunal epithelium with time of exposure. In the distal digestive tract, fecal bacterial counts decreased, the activity of fecal bacterial enzymes and lactic acid bacteria increased, and cecal water was characterized by higher genotoxicity. The accompanying hyperestrogenism led to changes in mRNA activity of selected enzymes (cytochrome P450, hydroxysteroid dehydrogenases, nitric oxide synthases) and receptors (estrogen and progesterone receptors), and it stimulated post-translational modifications which play an important role in non-genomic mechanisms of signal transmission. Hyperestrogenism influences the regulation of the host's steroid hormones (estron, estradiol and progesteron), it affects the virulence of bacterial genes encoding bacterial hydroxysteroid dehydrogenases (HSDs), and it participates in detoxification processes by slowing down intestinal activity, provoking energy deficits and promoting antiporter activity at the level of enterocytes. In most cases, hyperestrogenism fulfils all of the above roles. The results of this study indicate that low doses of ZEN alleviate inflammatory processes in the digestive system, in particular in the proximal and distal intestinal tract, and increase body weight gains in gilts.

Functional Trade-Offs in Promiscuous Enzymes Cannot Be Explained by Intrinsic Mutational Robustness of the Native Activity

The extent to which an emerging new function trades off with the original function is a key characteristic of the dynamics of enzyme evolution. Various cases of laboratory evolution have unveiled a characteristic trend; a large increase in a new, promiscuous activity is often accompanied by only a mild reduction of the native, original activity. A model that associates weak trade-offs with “evolvability” was put forward, which proposed that enzymes possess mutational robustness in the native activity and plasticity in promiscuous activities. This would enable the acquisition of a new function without compromising the original one, reducing the benefit of early gene duplication and therefore the selection pressure thereon. Yet, to date, no experimental study has examined this hypothesis directly. Here, we investigate the causes of weak trade-offs by systematically characterizing adaptive mutations that occurred in two cases of evolutionary transitions in enzyme function: (1) from phosphotriesterase to arylesterase, and (2) from atrazine chlorohydrolase to melamine deaminase. Mutational analyses in various genetic backgrounds revealed that, in contrast to the prevailing model, the native activity is less robust to mutations than the promiscuous activity. For example, in phosphotriesterase, the deleterious effect of individual mutations on the native phosphotriesterase activity is much larger than their positive effect on the promiscuous arylesterase activity. Our observations suggest a revision of the established model: weak trade-offs are not caused by an intrinsic robustness of the native activity and plasticity of the promiscuous activity. We propose that upon strong adaptive pressure for the new activity without selection against the original one, selected mutations will lead to the largest possible increases in the new function, but whether and to what extent they decrease the old function is irrelevant, creating a bias towards initially weak trade-offs and the emergence of generalist enzymes.

Understanding how enzymes evolve is a fundamental question that can help us decipher not only the mechanisms of evolution on a higher level, i.e., whole organisms, but also advances our knowledge of sequence-structure-function relationships as a guide to artificial evolution in the test tube. An important yet unexplained phenomenon occurs during the evolution of a new enzymatic function; it has been observed that new and ancestral functions often trade-off only weakly, meaning the original native activity is initially maintained at a high level despite drastic improvement of the new promiscuous activity. It has previously been proposed that weak trade-offs occur because the native activity is robust to mutations while the promiscuous activity is not. However, the present work contradicts this hypothesis, based on the detailed characterization of mutational effects on both activities in two examples of enzyme evolution. We propose an alternative explanation: the weak activity trade-off is consistent with being a by-product of strong selection for the new activity rather than an intrinsic property of the native activity.

Lysine Biosynthesis of Thermococcus kodakarensis with the Capacity to Function as an Ornithine Biosynthetic System

We recently discovered a biosynthetic system using a novel amino group carrier protein called LysW for lysine biosynthesis via α-aminoadipate (AAA), and revealed that this system is also utilized in the biosynthesis of arginine by Sulfolobus In the present study, we focused on the biosynthesis of lysine and ornithine in the hyperthermophilic archaeon Thermococcus kodakarensis, and showed that their biosynthesis is accomplished by a single set of metabolic enzymes. We also determined the crystal structure of the LysX family protein from T. kodakarensis, which catalyzes the conjugation of LysW with either AAA or glutamate, in a complex with LysW-γ-AAA. This crystal structure is the first example to show how LysX recognizes AAA as a substrate and provides a structural basis for the bifunctionality of the LysX family protein from T. kodakarensis Based on comparisons with other LysX family proteins, we propose a mechanism for substrate recognition and its relationship with molecular evolution among LysX family proteins, which have different substrate specificities.

A conserved phosphatase destroys toxic glycolytic side products in mammals and yeast

Metabolic enzymes are very specific. However, most of them show weak side activities toward compounds that are structurally related to their physiological substrates, thereby producing side products that may be toxic. In some cases, 'metabolite repair enzymes' eliminating side products have been identified. We show that mammalian glyceraldehyde 3-phosphate dehydrogenase and pyruvate kinase, two core glycolytic enzymes, produce 4-phosphoerythronate and 2-phospho-L-lactate, respectively. 4-Phosphoerythronate strongly inhibits an enzyme of the pentose phosphate pathway, whereas 2-phospho-L-lactate inhibits the enzyme producing the glycolytic activator fructose 2,6-bisphosphate. We discovered that a single, widely conserved enzyme, known as phosphoglycolate phosphatase (PGP) in mammals, dephosphorylates both 4-phosphoerythronate and 2-phospho-L-lactate, thereby preventing a block in the pentose phosphate pathway and glycolysis. Its yeast ortholog, Pho13, similarly dephosphorylates 4-phosphoerythronate and 2-phosphoglycolate, a side product of pyruvate kinase. Our work illustrates how metabolite repair enzymes can make up for the limited specificity of metabolic enzymes and permit high flux in central metabolic pathways.

How special is the biochemical function of native proteins?

Native proteins perform an amazing variety of biochemical functions, including enzymatic catalysis, and can engage in protein-protein and protein-DNA interactions that are essential for life. A key question is how special are these functional properties of proteins. Are they extremely rare, or are they an intrinsic feature? Comparison to the properties of compact conformations of artificially generated compact protein structures selected for thermodynamic stability but not any type of function, the artificial (ART) protein library, demonstrates that a remarkable number of the properties of native-like proteins are recapitulated. These include the complete set of small molecule ligand-binding pockets and most protein-protein interfaces. ART structures are predicted to be capable of weakly binding metabolites and cover a significant fraction of metabolic pathways, with the most enriched pathways including ancient ones such as glycolysis. Native-like active sites are also found in ART proteins. A small fraction of ART proteins are predicted to have strong protein-protein and protein-DNA interactions. Overall, it appears that biochemical function is an intrinsic feature of proteins which nature has significantly optimized during evolution. These studies raise questions as to the relative roles of specificity and promiscuity in the biochemical function and control of cells that need investigation.

The role of defensive ecological interactions in the evolution of conotoxins

Venoms comprise of complex mixtures of peptides evolved for predation and defensive purposes. Remarkably, some carnivorous cone snails can inject two distinct venoms in response to predatory or defensive stimuli, providing a unique opportunity to study separately how different ecological pressures contribute to toxin diversification. Here, we report the extraordinary defensive strategy of the Rhizoconus subgenus of cone snails. The defensive venom from this worm-hunting subgenus is unusually simple, almost exclusively composed of αD-conotoxins instead of the ubiquitous αA-conotoxins found in the more complex defensive venom of mollusc- and fish-hunting cone snails. A similarly compartmentalized venom gland as those observed in the other dietary groups facilitates the deployment of this defensive venom. Transcriptomic analysis of a Conus vexillum venom gland revealed the αD-conotoxins as the major transcripts, with lower amounts of 15 known and four new conotoxin superfamilies also detected with likely roles in prey capture. Our phylogenetic and molecular evolution analysis of the αD-conotoxins from five subgenera of cone snails suggests they evolved episodically as part of a defensive strategy in the Rhizoconus subgenus. Thus, our results demonstrate an important role for defence in the evolution of conotoxins.

Nature versus design: synthetic biology or how to build a biological non-machine

We suggest that progress in synthetic biology will be achieved by abandoning the bio-machine paradigm and by using an alliance between engineering and evolution as a guiding tool.

Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes

Background

The increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical.

Results

In this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available.

Conclusions

Identification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2170-4) contains supplementary material, which is available to authorized users.

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.

Transcriptomic and metabolomic data integration

Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.

The Impact of Non-Enzymatic Reactions and Enzyme Promiscuity on Cellular Metabolism during (Oxidative) Stress Conditions

Cellular metabolism assembles in a structurally highly conserved, but functionally dynamic system, known as the metabolic network. This network involves highly active, enzyme-catalyzed metabolic pathways that provide the building blocks for cell growth. In parallel, however, chemical reactivity of metabolites and unspecific enzyme function give rise to a number of side products that are not part of canonical metabolic pathways. It is increasingly acknowledged that these molecules are important for the evolution of metabolism, affect metabolic efficiency, and that they play a potential role in human disease—age-related disorders and cancer in particular. In this review we discuss the impact of oxidative and other cellular stressors on the formation of metabolic side products, which originate as a consequence of: (i) chemical reactivity or modification of regular metabolites; (ii) through modifications in substrate specificity of damaged enzymes; and (iii) through altered metabolic flux that protects cells in stress conditions. In particular, oxidative and heat stress conditions are causative of metabolite and enzymatic damage and thus promote the non-canonical metabolic activity of the cells through an increased repertoire of side products. On the basis of selected examples, we discuss the consequences of non-canonical metabolic reactivity on evolution, function and repair of the metabolic network.

Comparative Venomics Reveals the Complex Prey Capture Strategy of the Piscivorous Cone Snail Conus catus

Venomous marine cone snails produce a unique and remarkably diverse range of venom peptides (conotoxins and conopeptides) that have proven to be invaluable as pharmacological probes and leads to new therapies. Conus catus is a hook-and-line fish hunter from clade I, with ∼20 conotoxins identified, including the analgesic ω-conotoxin CVID (AM336). The current study unravels the venom composition of C. catus with tandem mass spectrometry and 454 sequencing data. From the venom gland transcriptome, 104 precursors were recovered from 11 superfamilies, with superfamily A (especially κA-) conotoxins dominating (77%) their venom. Proteomic analysis confirmed that κA-conotoxins dominated the predation-evoked milked venom of each of six C. catus analyzed and revealed remarkable intraspecific variation in both the intensity and type of conotoxins. High-throughput FLIPR assays revealed that the predation-evoked venom contained a range of conotoxins targeting the nAChR, Cav, and Nav ion channels, consistent with α- and ω-conotoxins being used for predation by C. catus. However, the κA-conotoxins did not act at these targets but induced potent and rapid immobilization followed by bursts of activity and finally paralysis when injected intramuscularly in zebrafish. Our venomics approach revealed the complexity of the envenomation strategy used by C. catus, which contains a mix of both excitatory and inhibitory venom peptides.

Cancer cell metabolism as new targets for novel designed therapies

Metabolic processes are altered in cancer cells, which obtain advantages from this metabolic reprogramming in terms of energy production and synthesis of biomolecules that sustain their uncontrolled proliferation. Due to the conceptual progresses in the last decade, metabolic reprogramming was recently included as one of the new hallmarks of cancer. The advent of high-throughput technologies to amass an abundance of omic data, together with the development of new computational methods that allow the integration and analysis of omic data by using genome-scale reconstructions of human metabolism, have increased and accelerated the discovery and development of anticancer drugs and tumor-specific metabolic biomarkers. Here we review and discuss the latest advances in the context of metabolic reprogramming and the future in cancer research.

Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research

Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.

The Evolutionary Potential of Phenotypic Mutations

Errors in protein synthesis, so-called phenotypic mutations, are orders-of-magnitude more frequent than genetic mutations. Here, we provide direct evidence that alternative protein forms and phenotypic variability derived from translational errors paved the path to genetic, evolutionary adaptations via gene duplication. We explored the evolutionary origins of Saccharomyces cerevisiae IDP3 - an NADP-dependent isocitrate dehydrogenase mediating fatty acids ß-oxidation in the peroxisome. Following the yeast whole genome duplication, IDP3 diverged from a cytosolic ancestral gene by acquisition of a C-terminal peroxisomal targeting signal. We discovered that the pre-duplicated cytosolic IDPs are partially localized to the peroxisome owing to +1 translational frameshifts that bypass the stop codon and unveil cryptic peroxisomal targeting signals within the 3'-UTR. Exploring putative cryptic signals in all 3'-UTRs of yeast genomes, we found that other enzymes related to NADPH production such as pyruvate carboxylase 1 (PYC1) might be prone to peroxisomal localization via cryptic signals. Using laboratory evolution we found that these translational frameshifts are rapidly imprinted via genetic single base deletions occurring within the very same gene location. Further, as exemplified here, the sequences that promote translational frameshifts are also more prone to genetic deletions. Thus, genotypes conferring higher phenotypic variability not only meet immediate challenges by unveiling cryptic 3'-UTR sequences, but also boost the potential for future genetic adaptations.

Using Nonlinear Stochastic Evolutionary Game Strategy to Model an Evolutionary Biological Network of Organ Carcinogenesis Under a Natural Selection Scheme

Molecular biologists have long recognized carcinogenesis as an evolutionary process that involves natural selection. Cancer is driven by the somatic evolution of cell lineages. In this study, the evolution of somatic cancer cell lineages during carcinogenesis was modeled as an equilibrium point (ie, phenotype of attractor) shifting, the process of a nonlinear stochastic evolutionary biological network. This process is subject to intrinsic random fluctuations because of somatic genetic and epigenetic variations, as well as extrinsic disturbances because of carcinogens and stressors. In order to maintain the normal function (ie, phenotype) of an evolutionary biological network subjected to random intrinsic fluctuations and extrinsic disturbances, a network robustness scheme that incorporates natural selection needs to be developed. This can be accomplished by selecting certain genetic and epigenetic variations to modify the network structure to attenuate intrinsic fluctuations efficiently and to resist extrinsic disturbances in order to maintain the phenotype of the evolutionary biological network at an equilibrium point (attractor). However, during carcinogenesis, the remaining (or neutral) genetic and epigenetic variations accumulate, and the extrinsic disturbances become too large to maintain the normal phenotype at the desired equilibrium point for the nonlinear evolutionary biological network. Thus, the network is shifted to a cancer phenotype at a new equilibrium point that begins a new evolutionary process. In this study, the natural selection scheme of an evolutionary biological network of carcinogenesis was derived from a robust negative feedback scheme based on the nonlinear stochastic Nash game strategy. The evolvability and phenotypic robustness criteria of the evolutionary cancer network were also estimated by solving a Hamilton-Jacobi inequality - constrained optimization problem. The simulation revealed that the phenotypic shift of the lung cancer-associated cell network takes 54.5 years from a normal state to stage I cancer, 1.5 years from stage I to stage II cancer, and 2.5 years from stage II to stage III cancer, with a reasonable match for the statistical result of the average age of lung cancer. These results suggest that a robust negative feedback scheme, based on a stochastic evolutionary game strategy, plays a critical role in an evolutionary biological network of carcinogenesis under a natural selection scheme.

Cone snail venomics: from novel biology to novel therapeutics

Peptide neurotoxins from cone snails called conotoxins are renowned for their therapeutic potential to treat pain and several neurodegenerative diseases. Inefficient assay-guided discovery methods have been replaced by high-throughput bioassays integrated with advanced MS and next-generation sequencing, ushering in the era of 'venomics'. In this review, we focus on the impact of venomics on the understanding of cone snail biology as well as the application of venomics to accelerate the discovery of new conotoxins. We also discuss the continued importance of medicinal chemistry approaches to optimize conotoxins for clinical use, with a descriptive case study of MrIA featured.

Steroid promiscuity: Diversity of enzyme action. Preface

This Special Issue on the topic of Steroid and Sterol Signaling: Promiscuity and Diversity, dwells on the growing realization that the 'one ligand, one binding site' and 'one enzyme, one reaction' concepts are out of date. Focusing on cytochromes P450 (CYP), hydroxysteroid dehydrogenases (HSDs), and related enzymes, the Special Issue highlights that a single enzyme can bind to diverse substrates, and in different conformations, and can catalyze multiple different conversions (and in different directions), thereby, generating an unexpectedly wide spectrum of ligands that can have subtly different biological actions. This article is part of a Special Issue entitled 'Steroid/Sterol Signaling' .

Biological messiness vs. biological genius: Mechanistic aspects and roles of protein promiscuity

In contrast to the traditional biological paradigms focused on 'specificity', recent research and theoretical efforts have focused on functional 'promiscuity' exhibited by proteins and enzymes in many biological settings, including enzymatic detoxication, steroid biochemistry, signal transduction and immune responses. In addition, divergent evolutionary processes are apparently facilitated by random mutations that yield promiscuous enzyme intermediates. The intermediates, in turn, provide opportunities for further evolution to optimize new functions from existing protein scaffolds. In some cases, promiscuity may simply represent the inherent plasticity of proteins resulting from their polymeric nature with distributed conformational ensembles. Enzymes or proteins that bind or metabolize noncognate substrates create 'messiness' or noise in the systems they contribute to. With our increasing awareness of the frequency of these promiscuous behaviors it becomes interesting and important to understand the molecular bases for promiscuous behavior and to distinguish between evolutionarily selected promiscuity and evolutionarily tolerated messiness. This review provides an overview of current understanding of these aspects of protein biochemistry and enzymology.

Bioinformatics-Aided Venomics

Venomics is a modern approach that combines transcriptomics and proteomics to explore the toxin content of venoms. This review will give an overview of computational approaches that have been created to classify and consolidate venomics data, as well as algorithms that have helped discovery and analysis of toxin nucleic acid and protein sequences, toxin three-dimensional structures and toxin functions. Bioinformatics is used to tackle specific challenges associated with the identification and annotations of toxins. Recognizing toxin transcript sequences among second generation sequencing data cannot rely only on basic sequence similarity because toxins are highly divergent. Mass spectrometry sequencing of mature toxins is challenging because toxins can display a large number of post-translational modifications. Identifying the mature toxin region in toxin precursor sequences requires the prediction of the cleavage sites of proprotein convertases, most of which are unknown or not well characterized. Tracing the evolutionary relationships between toxins should consider specific mechanisms of rapid evolution as well as interactions between predatory animals and prey. Rapidly determining the activity of toxins is the main bottleneck in venomics discovery, but some recent bioinformatics and molecular modeling approaches give hope that accurate predictions of toxin specificity could be made in the near future.

Genomic and experimental evidence for multiple metabolic functions in the RidA/YjgF/YER057c/UK114 (Rid) protein family

Background

It is now recognized that enzymatic or chemical side-reactions can convert normal metabolites to useless or toxic ones and that a suite of enzymes exists to mitigate such metabolite damage. Examples are the reactive imine/enamine intermediates produced by threonine dehydratase, which damage the pyridoxal 5'-phosphate cofactor of various enzymes causing inactivation. This damage is pre-empted by RidA proteins, which hydrolyze the imines before they do harm. RidA proteins belong to the YjgF/YER057c/UK114 family (here renamed the Rid family). Most other members of this diverse and ubiquitous family lack defined functions.

Results

Phylogenetic analysis divided the Rid family into a widely distributed, apparently archetypal RidA subfamily and seven other subfamilies (Rid1 to Rid7) that are largely confined to bacteria and often co-occur in the same organism with RidA and each other. The Rid1 to Rid3 subfamilies, but not the Rid4 to Rid7 subfamilies, have a conserved arginine residue that, in RidA proteins, is essential for imine-hydrolyzing activity. Analysis of the chromosomal context of bacterial RidA genes revealed clustering with genes for threonine dehydratase and other pyridoxal 5'-phosphate-dependent enzymes, which fits with the known RidA imine hydrolase activity. Clustering was also evident between Rid family genes and genes specifying FAD-dependent amine oxidases or enzymes of carbamoyl phosphate metabolism. Biochemical assays showed that Salmonella enterica RidA and Rid2, but not Rid7, can hydrolyze imines generated by amino acid oxidase. Genetic tests indicated that carbamoyl phosphate overproduction is toxic to S. enterica cells lacking RidA, and metabolomic profiling of Rid knockout strains showed ten-fold accumulation of the carbamoyl phosphate-related metabolite dihydroorotate.

Conclusions

Like the archetypal RidA subfamily, the Rid2, and probably the Rid1 and Rid3 subfamilies, have imine-hydrolyzing activity and can pre-empt damage from imines formed by amine oxidases as well as by pyridoxal 5'-phosphate enzymes. The RidA subfamily has an additional damage pre-emption role in carbamoyl phosphate metabolism that has yet to be biochemically defined. Finally, the Rid4 to Rid7 subfamilies appear not to hydrolyze imines and thus remain mysterious.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1584-3) contains supplementary material, which is available to authorized users.

A directed-overflow and damage-control N-glycosidase in riboflavin biosynthesis

Plants and bacteria synthesize the essential human micronutrient riboflavin (vitamin B2) via the same multi-step pathway. The early intermediates of this pathway are notoriously reactive and may be overproduced in vivo because riboflavin biosynthesis enzymes lack feedback controls. In the present paper, we demonstrate disposal of riboflavin intermediates by COG3236 (DUF1768), a protein of previously unknown function that is fused to two different riboflavin pathway enzymes in plants and bacteria (RIBR and RibA respectively). We present cheminformatic, biochemical, genetic and genomic evidence to show that: (i) plant and bacterial COG3236 proteins cleave the N-glycosidic bond of the first two intermediates of riboflavin biosynthesis, yielding relatively innocuous products; (ii) certain COG3236 proteins are in a multi-enzyme riboflavin biosynthesis complex that gives them privileged access to riboflavin intermediates; and (iii) COG3236 action in Arabidopsis thaliana and Escherichia coli helps maintain flavin levels. COG3236 proteins thus illustrate two emerging principles in chemical biology: directed overflow metabolism, in which excess flux is diverted out of a pathway, and the pre-emption of damage from reactive metabolites.

Inhibitory activities of short linear motifs underlie Hox interactome specificity in vivo

Hox proteins are well-established developmental regulators that coordinate cell fate and morphogenesis throughout embryogenesis. In contrast, our knowledge of their specific molecular modes of action is limited to the interaction with few cofactors. Here, we show that Hox proteins are able to interact with a wide range of transcription factors in the live Drosophila embryo. In this context, specificity relies on a versatile usage of conserved short linear motifs (SLiMs), which, surprisingly, often restrains the interaction potential of Hox proteins. This novel buffering activity of SLiMs was observed in different tissues and found in Hox proteins from cnidarian to mouse species. Although these interactions remain to be analysed in the context of endogenous Hox regulatory activities, our observations challenge the traditional role assigned to SLiMs and provide an alternative concept to explain how Hox interactome specificity could be achieved during the embryonic development.

DOI:http://dx.doi.org/10.7554/eLife.06034.001

In all animals, it is important that cells are correctly organised into tissues and organs. This organisation starts in the embryo, and cells are instructed to perform different roles depending on their position within the body.

A family of proteins called the Hox proteins coordinates the organisation of the cells in the animal embryo by binding to and controlling the expression of specific genes. To properly control their target genes, Hox proteins need to interact with other proteins called transcription factors that can also bind to the genes. However, only a few of these transcription factors have been identified so far, and it is not clear how Hox proteins are able to interact with them.

Here, Baëza, Viala, Heim et al. identified several more transcription factors that can bind to the Hox proteins in fruit fly embryos. The experiments show that Hox proteins are able to bind to many transcription factors that are very different from each other. Baëza, Viala, Heim et al. also show that two short sections within the Hox proteins known as short linear motifs are important for controlling these interactions. A fly Hox protein that was missing these motifs was able to interact with new transcription factors. This inhibitory role was found in Hox proteins from mice and sea anemones, suggesting that these motifs may play the same role in all animals.

Baëza, Viala, Heim et al.'s findings challenge the traditional view of the role of the short linear motifs in interactions between proteins. Also, the findings provide an alternative explanation for how the Hox proteins are only able to interact with particular transcription factors in animal embryos. The next step will be to find out whether the inhibitory role of short linear motifs could more generally apply to many other protein families.

DOI:http://dx.doi.org/10.7554/eLife.06034.002

Autocatalytic sets in E. coli metabolism

Background

A central unsolved problem in early evolution concerns self-organization towards higher complexity in chemical reaction networks. In theory, autocatalytic sets have useful properties to help model such transitions. Autocatalytic sets are chemical reaction systems in which molecules belonging to the set catalyze the synthesis of other members of the set. Given an external supply of starting molecules – the food set – and the conditions that (i) all reactions are catalyzed by at least one molecule, and (ii) each molecule can be constructed from the food set by a sequence of reactions, the system becomes a reflexively autocatalytic food-generated network (RAF set). Autocatalytic networks and RAFs have been studied extensively as mathematical models for understanding the properties and parameters that influence self-organizational tendencies. However, despite their appeal, the relevance of RAFs for real biochemical networks that exist in nature has, so far, remained virtually unexplored.

Results

Here we investigate the best-studied metabolic network, that of Escherichia coli, for the existence of RAFs. We find that the largest RAF encompasses almost the entire E. coli cytosolic reaction network. We systematically study its structure by considering the impact of removing catalysts or reactions. We show that, without biological knowledge, finding the minimum food set that maintains a given RAF is NP-complete. We apply a randomized algorithm to find (approximately) smallest subsets of the food set that suffice to sustain the original RAF.

Conclusions

The existence of RAF sets within a microbial metabolic network indicates that RAFs capture properties germane to biological organization at the level of single cells. Moreover, the interdependency between the different metabolic modules, especially concerning cofactor biosynthesis, points to the important role of spontaneous (non-enzymatic) reactions in the context of early evolution.

Graphical Abstract

Expanding the enzyme universe: accessing non-natural reactions by mechanism-guided directed evolution

High selectivity and exquisite control over the outcome of reactions entice chemists to use biocatalysts in organic synthesis. However, many useful reactions are not accessible because they are not in nature's known repertoire. In this Review, we outline an evolutionary approach to engineering enzymes to catalyze reactions not found in nature. We begin with examples of how nature has discovered new catalytic functions and how such evolutionary progression has been recapitulated in the laboratory starting from extant enzymes. We then examine non-native enzyme activities that have been exploited for chemical synthesis, with an emphasis on reactions that do not have natural counterparts. Non-natural activities can be improved by directed evolution, thus mimicking the process used by nature to create new catalysts. Finally, we describe the discovery of non-native catalytic functions that may provide future opportunities for the expansion of the enzyme universe.

Implications of the small number of distinct ligand binding pockets in proteins for drug discovery, evolution and biochemical function

Coincidence of the properties of ligand binding pockets in native proteins with those in proteins generated by computer simulations without selection for function shows that pockets are a generic protein feature and the number of distinct pockets is small. Similar pockets occur in unrelated protein structures, an observation successfully employed in pocket-based virtual ligand screening. The small number of pockets suggests that off-target interactions among diverse proteins are inherent; kinases, proteases and phosphatases show this prototypical behavior. The ability to repurpose FDA approved drugs is general, and minor side effects cannot be avoided. Finally, the implications to drug discovery are explored.

Lost in transcription: transient errors in information transfer

Errors in information transfer from DNA to RNA to protein are inevitable. Here, we focus on errors that occur in nascent transcripts during transcription, epimutations. Recent approaches using novel cDNA library preparation and next-generation sequencing begin to directly determine the rate of epimutation and allow analysis of the epimutational spectrum of transcription errors, the type and sequence context of the errors produced in a transcript by an RNA polymerase. The phenotypic consequences of transcription errors have been assessed using both forward and reverse epimutation systems. These studies reveal that transient transcription errors can produce a modification of cell phenotype, partial phenotypic suppression of a mutant allele, and a heritable change in cell phenotype, epigenetic switching in a bistable gene network.

Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.

Metabolic engineering to enhance the value of plants as green factories

The promise of plants to serve as the green factories of the future is ever increasing. Plants have been used traditionally for construction, energy, food and feed. Bioactive compounds primarily derived from specialized plant metabolism continue to serve as important scaffold molecules for pharmaceutical drug production. Yet, the past few years have witnessed a growing interest on plants as the ultimate harvesters of carbon and energy from the sun, providing carbohydrate and lipid biofuels that would contribute to balancing atmospheric carbon. How can the metabolic output from plants be increased even further, and what are the bottlenecks? Here, we present what we perceive to be the main opportunities and challenges associated with increasing the efficiency of plants as chemical factories. We offer some perspectives on when it makes sense to use plants as production systems because the amount of biomass needed makes any other system unfeasible. However, there are other instances in which plants serve as great sources of biological catalysts, yet are not necessarily the best-suited systems for production. We also present emerging opportunities for manipulating plant genomes to make plant synthetic biology a reality.

Methods for integration of transcriptomic data in genome-scale metabolic models

Several computational methods have been developed that integrate transcriptomic data with genome-scale metabolic reconstructions to infer condition-specific system-wide intracellular metabolic flux distributions. In this mini-review, we describe each of these methods published to date with categorizing them based on four different grouping criteria (requirement for multiple gene expression datasets as input, requirement for a threshold to define a gene's high and low expression, requirement for a priori assumption of an appropriate objective function, and validation of predicted fluxes directly against measured intracellular fluxes). Then, we recommend which group of methods would be more suitable from a practical perspective.