Messy biology and the origins of evolutionary innovations

Functional analysis of single enzymes combining programmable molecular circuits with droplet-based microfluidics

The analysis of proteins at the single-molecule level reveals heterogeneous behaviours that are masked in ensemble-averaged techniques. The digital quantification of enzymes traditionally involves the observation and counting of single molecules partitioned into microcompartments via the conversion of a profluorescent substrate. This strategy, based on linear signal amplification, is limited to a few enzymes with sufficiently high turnover rate. Here we show that combining the sensitivity of an exponential molecular amplifier with the modularity of DNA-enzyme circuits and droplet readout makes it possible to specifically detect, at the single-molecule level, virtually any D(R)NA-related enzymatic activity. This strategy, denoted digital PUMA (Programmable Ultrasensitive Molecular Amplifier), is validated for more than a dozen different enzymes, including many with slow catalytic rate, and down to the extreme limit of apparent single turnover for Streptococcus pyogenes Cas9. Digital counting uniquely yields absolute molar quantification and reveals a large fraction of inactive catalysts in all tested commercial preparations. By monitoring the amplification reaction from single enzyme molecules in real time, we also extract the distribution of activity among the catalyst population, revealing alternative inactivation pathways under various stresses. Our approach dramatically expands the number of enzymes that can benefit from quantification and functional analysis at single-molecule resolution. We anticipate digital PUMA will serve as a versatile framework for accurate enzyme quantification in diagnosis or biotechnological applications. These digital assays may also be utilized to study the origin of protein functional heterogeneity.

Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

The Boltzmann distributions of molecular structures predict likely changes through random mutations

New folded molecular structures can only evolve after arising through mutations. This aspect is modeled using genotype-phenotype maps, which connect sequence changes through mutations to changes in molecular structures. Previous work has shown that the likelihood of appearing through mutations can differ by orders of magnitude from structure to structure and that this can affect the outcomes of evolutionary processes. Thus, we focus on the phenotypic mutation probabilities φqp, i.e., the likelihood that a random mutation changes structure p into structure q. For both RNA secondary structures and the HP protein model, we show that a simple biophysical principle can explain and predict how this likelihood depends on the new structure q: φqp is high if sequences that fold into p as the minimum-free-energy structure are likely to have q as an alternative structure with high Boltzmann frequency. This generalizes the existing concept of plastogenetic congruence from individual sequences to the entire neutral spaces of structures. Our result helps us understand why some structural changes are more likely than others, may be useful for estimating these likelihoods via sampling and makes a connection to alternative structures with high Boltzmann frequency, which could be relevant in evolutionary processes.

Tracing the birth of structural domains from loops during protein evolution

The structures and functions of proteins are embedded into the loop scaffolds of structural domains. Their origin and evolution remain mysterious. Here, we use a novel graph-theoretical approach to describe how modular and non-modular loop prototypes combine to form folded structures in protein domain evolution. Phylogenomic data-driven chronologies reoriented a bipartite network of loops and domains (and its projections) into 'waterfalls' depicting an evolving 'elementary functionome' (EF). Two primordial waves of functional innovation involving founder 'p-loop' and 'winged-helix' domains were accompanied by an ongoing emergence and reuse of structural and functional novelty. Metabolic pathways expanded before translation functionalities. A dual hourglass recruitment pattern transferred scale-free properties from loop to domain components of the EF network in generative cycles of hierarchical modularity. Modeling the evolutionary emergence of the oldest P-loop and winged-helix domains with AlphFold2 uncovered rapid convergence towards folded structure, suggesting that a folding vocabulary exists in loops for protein fold repurposing and design.

The non-deterministic genotype-phenotype map of RNA secondary structure

Selection and variation are both key aspects in the evolutionary process. Previous research on the mapping between molecular sequence (genotype) and molecular fold (phenotype) has shown the presence of several structural properties in different biological contexts, implying that these might be universal in evolutionary spaces. The deterministic genotype-phenotype (GP) map that links short RNA sequences to minimum free energy secondary structures has been studied extensively because of its computational tractability and biologically realistic nature. However, this mapping ignores the phenotypic plasticity of RNA. We define a GP map that incorporates non-deterministic (ND) phenotypes, and take RNA as a case study; we use the Boltzmann probability distribution of folded structures and examine the structural properties of ND GP maps for RNA sequences of length 12 and coarse-grained RNA structures of length 30 (RNAshapes30). A framework is presented to study robustness, evolvability and neutral spaces in the ND map. This framework is validated by demonstrating close correspondence between the ND quantities and sample averages of their deterministic counterparts. When using the ND framework we observe the same structural properties as in the deterministic GP map, such as bias, negative correlation between genotypic robustness and evolvability, and positive correlation between phenotypic robustness and evolvability.

Evolution of SLiM-mediated hijack functions in intrinsically disordered viral proteins

Viruses and their hosts are involved in an ‘arms race’ where they continually evolve mechanisms to overcome each other. It has long been proposed that intrinsic disorder provides a substrate for the evolution of viral hijack functions and that short linear motifs (SLiMs) are important players in this process. Here, we review evidence in support of this tenet from two model systems: the papillomavirus E7 protein and the adenovirus E1A protein. Phylogenetic reconstructions reveal that SLiMs appear and disappear multiple times across evolution, providing evidence of convergent evolution within individual viral phylogenies. Multiple functionally related SLiMs show strong coevolution signals that persist across long distances in the primary sequence and occur in unrelated viral proteins. Moreover, changes in SLiMs are associated with changes in phenotypic traits such as host range and tropism. Tracking viral evolutionary events reveals that host switch events are associated with the loss of several SLiMs, suggesting that SLiMs are under functional selection and that changes in SLiMs support viral adaptation. Fine-tuning of viral SLiM sequences can improve affinity, allowing them to outcompete host counterparts. However, viral SLiMs are not always competitive by themselves, and tethering of two suboptimal SLiMs by a disordered linker may instead enable viral hijack. Coevolution between the SLiMs and the linker indicates that the evolution of disordered regions may be more constrained than previously thought. In summary, experimental and computational studies support a role for SLiMs and intrinsic disorder in viral hijack functions and in viral adaptive evolution.

Intrinsic protein disorder uncouples affinity from binding specificity

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins often function by molecular recognition, in which they undergo induced folding. Based on prior generalizations, the idea prevails in the IDP field that due to the entropic penalty of induced folding, the major functional advantage associated with this binding mode is "uncoupling" specificity from binding strength. Nevertheless, both weaker binding and high specificity of IDPs/IDRs rest on limited experimental observations, making these assumptions more speculations than evidence-supported facts. The issue is also complicated by the rather vague concept of specificity that lacks an exact measure, such as the K_d for binding strength. We addressed these issues by creating and analyzing a comprehensive dataset of well-characterized ID/globular protein complexes, for which both the atomic structure of the complex and free energy (ΔG, K_d ) of interaction is known. Through this analysis, we provide evidence that the affinity distributions of IDP/globular and globular/globular complexes show different trends, whereas specificity does not connote to weaker binding strength of IDPs/IDRs. Furthermore, protein disorder extends the spectrum in the direction of very weak interactions, which may have important regulatory consequences and suggest that, in a biological sense, strict correlation of specificity and binding strength are uncoupled by structural disorder.

Phenotypic mutations contribute to protein diversity and shape protein evolution

Errors in DNA replication generate genetic mutations, while errors in transcription and translation lead to phenotypic mutations. Phenotypic mutations are orders of magnitude more frequent than genetic ones, yet they are less understood. Here, we review the types of phenotypic mutations, their quantifications, and their role in protein evolution and disease. The diversity generated by phenotypic mutation can facilitate adaptive evolution. Indeed, phenotypic mutations, such as ribosomal frameshift and stop codon readthrough, sometimes serve to regulate protein expression and function. Phenotypic mutations have often been linked to fitness decrease and diseases. Thus, understanding the protein heterogeneity and phenotypic diversity caused by phenotypic mutations will advance our understanding of protein evolution and have implications on human health and diseases.

The sphingolipid anteome: implications for evolution of the sphingolipid metabolic pathway

Modern cell membranes contain a bewildering complexity of lipids, among them sphingolipids (SLs). Advances in mass spectrometry have led to the realization that the number and combinatorial complexity of lipids, including SLs, is much greater than previously appreciated. SLs are generated de novo by four enzymes, namely serine palmitoyltransferase, 3-ketodihydrosphingosine reductase, ceramide synthase and dihydroceramide Δ4-desaturase 1. Some of these enzymes depend on the availability of specific substrates and cofactors, which are themselves supplied by other complex metabolic pathways. The evolution of these four enzymes is poorly understood and likely depends on the co-evolution of the metabolic pathways that supply the other essential reaction components. Here, we introduce the concept of the 'anteome', from the Latin ante ('before') to describe the network of metabolic ('omic') pathways that must have converged in order for these pathways to co-evolve and permit SL synthesis. We also suggest that current origin of life and evolutionary models lack appropriate experimental support to explain the appearance of this complex metabolic pathway and its anteome.

Mechanisms of protein evolution

How do proteins evolve? How do changes in sequence mediate changes in protein structure, and in turn in function? This question has multiple angles, ranging from biochemistry and biophysics to evolutionary biology. This review provides a brief integrated view of some key mechanistic aspects of protein evolution. First, we explain how protein evolution is primarily driven by randomly acquired genetic mutations and selection for function, and how these mutations can even give rise to completely new folds. Then, we also comment on how phenotypic protein variability, including promiscuity, transcriptional and translational errors, may also accelerate this process, possibly via "plasticity-first" mechanisms. Finally, we highlight open questions in the field of protein evolution, with respect to the emergence of more sophisticated protein systems such as protein complexes, pathways, and the emergence of pre-LUCA enzymes.

The origin of RNA interference: Adaptive or neutral evolution?

The origin of RNA interference (RNAi) is usually explained by a defense-based hypothesis, in which RNAi evolved as a defense against transposable elements (TEs) and RNA viruses and was already present in the last eukaryotic common ancestor (LECA). However, since RNA antisense regulation and double-stranded RNAs (dsRNAs) are ancient and widespread phenomena, the origin of defensive RNAi should have occurred in parallel with its regulative functions to avoid imbalances in gene regulation. Thus, we propose a neutral evolutionary hypothesis for the origin of RNAi in which qualitative system drift from a prokaryotic antisense RNA gene regulation mechanism leads to the formation of RNAi through constructive neutral evolution (CNE). We argue that RNAi was already present in the ancestor of LECA before the need for a new defense system arose and that its presence helped to shape eukaryotic genomic architecture and stability.

Where does RNA interference come from? This Essay describes a new step-by-step evolutionary model of how RNA interference might have originated in early eukaryotes through neutral events from the molecular machinery present in prokaryotes.

Information Fragmentation, Encryption and Information Flow in Complex Biological Networks

Assessing where and how information is stored in biological networks (such as neuronal and genetic networks) is a central task both in neuroscience and in molecular genetics, but most available tools focus on the network’s structure as opposed to its function. Here, we introduce a new information-theoretic tool—information fragmentation analysis—that, given full phenotypic data, allows us to localize information in complex networks, determine how fragmented (across multiple nodes of the network) the information is, and assess the level of encryption of that information. Using information fragmentation matrices we can also create information flow graphs that illustrate how information propagates through these networks. We illustrate the use of this tool by analyzing how artificial brains that evolved in silico solve particular tasks, and show how information fragmentation analysis provides deeper insights into how these brains process information and “think”. The measures of information fragmentation and encryption that result from our methods also quantify complexity of information processing in these networks and how this processing complexity differs between primary exposure to sensory data (early in the lifetime) and later routine processing.

Adaptation dynamics between copy-number and point mutations

Together, copy-number and point mutations form the basis for most evolutionary novelty, through the process of gene duplication and divergence. While a plethora of genomic data reveals the long-term fate of diverging coding sequences and their cis-regulatory elements, little is known about the early dynamics around the duplication event itself. In microorganisms, selection for increased gene expression often drives the expansion of gene copy-number mutations, which serves as a crude adaptation, prior to divergence through refining point mutations. Using a simple synthetic genetic reporter system that can distinguish between copy-number and point mutations, we study their early and transient adaptive dynamics in real time in Escherichia coli. We find two qualitatively different routes of adaptation, depending on the level of functional improvement needed. In conditions of high gene expression demand, the two mutation types occur as a combination. However, under low gene expression demand, copy-number and point mutations are mutually exclusive; here, owing to their higher frequency, adaptation is dominated by copy-number mutations, in a process we term amplification hindrance. Ultimately, due to high reversal rates and pleiotropic cost, copy-number mutations may not only serve as a crude and transient adaptation, but also constrain sequence divergence over evolutionary time scales.

The Moderately (D)efficient Enzyme: Catalysis-Related Damage In Vivo and Its Repair

Enzymes have in vivo life spans. Analysis of life spans, i.e., lifetime totals of catalytic turnovers, suggests that nonsurvivable collateral chemical damage from the very reactions that enzymes catalyze is a common but underdiagnosed cause of enzyme death. Analysis also implies that many enzymes are moderately deficient in that their active-site regions are not naturally as hardened against such collateral damage as they could be, leaving room for improvement by rational design or directed evolution. Enzyme life span might also be improved by engineering systems that repair otherwise fatal active-site damage, of which a handful are known and more are inferred to exist. Unfortunately, the data needed to design and execute such improvements are lacking: there are too few measurements of in vivo life span, and existing information about the extent, nature, and mechanisms of active-site damage and repair during normal enzyme operation is too scarce, anecdotal, and speculative to act on. Fortunately, advances in proteomics, metabolomics, cheminformatics, comparative genomics, and structural biochemistry now empower a systematic, data-driven approach for identifying, predicting, and validating instances of active-site damage and its repair. These capabilities would be practically useful in enzyme redesign and improvement of in-use stability and could change our thinking about which enzymes die young in vivo, and why.

Soil chemistry determines whether defensive plant secondary metabolites promote or suppress herbivore growth

Plant secondary (or specialized) metabolites mediate important interactions in both the rhizosphere and the phyllosphere. If and how such compartmentalized functions interact to determine plant-environment interactions is not well understood. Here, we investigated how the dual role of maize benzoxazinoids as leaf defenses and root siderophores shapes the interaction between maize and a major global insect pest, the fall armyworm. We find that benzoxazinoids suppress fall armyworm growth when plants are grown in soils with very low available iron but enhance growth in soils with higher available iron. Manipulation experiments confirm that benzoxazinoids suppress herbivore growth under iron-deficient conditions and in the presence of chelated iron but enhance herbivore growth in the presence of free iron in the growth medium. This reversal of the protective effect of benzoxazinoids is not associated with major changes in plant primary metabolism. Plant defense activation is modulated by the interplay between soil iron and benzoxazinoids but does not explain fall armyworm performance. Instead, increased iron supply to the fall armyworm by benzoxazinoids in the presence of free iron enhances larval performance. This work identifies soil chemistry as a decisive factor for the impact of plant secondary metabolites on herbivore growth. It also demonstrates how the multifunctionality of plant secondary metabolites drives interactions between abiotic and biotic factors, with potential consequences for plant resistance in variable environments.

Recombination facilitates genetic assimilation of new traits in gene regulatory networks

A new phenotypic variant may appear first in organisms through plasticity, that is, as a response to an environmental signal or other nongenetic perturbation. If such trait is beneficial, selection may increase the frequency of alleles that enable and facilitate its development. Thus, genes may take control of such traits, decreasing dependence on nongenetic disturbances, in a process called genetic assimilation. Despite an increasing amount of empirical studies supporting genetic assimilation, its significance is still controversial. Whether genetic assimilation is widespread depends, to a great extent, on how easily mutation and recombination reduce the trait's dependence on nongenetic perturbations. Previous research suggests that this is the case for mutations. Here we use simulations of gene regulatory network dynamics to address this issue with respect to recombination. We find that recombinant offspring of parents that produce a new phenotype through plasticity are more likely to produce the same phenotype without requiring any perturbation. They are also prone to preserve the ability to produce that phenotype after genetic and nongenetic perturbations. Our work also suggests that ancestral plasticity can play an important role for setting the course that evolution takes. In sum, our results indicate that the manner in which phenotypic variation maps unto genetic variation facilitates evolution through genetic assimilation in gene regulatory networks. Thus, we contend that the importance of this evolutionary mechanism should not be easily neglected.

Fuzzy protein theory for disordered proteins

Why proteins are fuzzy? Constant adaptation to the cellular environment requires a wide range of changes in protein structure and interactions. Conformational ensembles of disordered proteins in particular exhibit large shifts to activate or inhibit alternative pathways. Fuzziness is critical for liquid–liquid phase separation and conversion of biomolecular condensates into fibrils. Interpretation of these phenomena presents a challenge for the classical structure-function paradigm. Here I discuss a multi-valued formalism, based on fuzzy logic, which can be applied to describe complex cellular behavior of proteins.

Three mutations repurpose a plant karrikin receptor to a strigolactone receptor

Uncovering the basis of small-molecule hormone receptors' evolution is paramount to a complete understanding of how protein structure drives function. In plants, hormone receptors for strigolactones are well suited to evolutionary inquiries because closely related homologs have different ligand preferences. More importantly, because of facile plant transgenic systems, receptors can be swapped and quickly assessed functionally in vivo. Here, we show that only three mutations are required to turn the nonstrigolactone receptor, KAI2, into a receptor that recognizes the plant hormone strigolactone. This modified receptor still retains its native function to perceive KAI2 ligands. Our directed evolution studies indicate that only a few keystone mutations are required to increase receptor promiscuity of KAI2, which may have implications for strigolactone receptor evolution in parasitic plants.

Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity

Any descriptive language is necessarily metaphoric and interpretative. Two somewhat overlapping-but not identical-languages have been thoroughly employed in the last decade to address the issue of regulatory complexity in biological systems: the terminology of network theory and the jargon of electric circuitry. These approaches have found many formal equivalences between the layout of extant genetic circuits and the architecture of man-made counterparts. However, these languages still fail to describe accurately key features of biological objects, in particular the diversity of signal-transfer molecules and the diffusion that is inherent to any biochemical system. Furthermore, current formalisms associated with networks and circuits can hardly face the problem of multi-scale regulatory complexity-from single molecules to entire ecosystems. We argue that the language of economic theory might be instrumental not only to portray accurately many features of regulatory networks, but also to unveil aspects of the biological complexity problem that remain opaque to other types of analyses. The main perspective opened by the economic metaphor when applied to control of microbiological activities is a focus on metabolism, not gene selfishness, as the necessary background to make sense of regulatory phenomena. As an example, we analyse and reinterpret the widespread phenomenon of catabolite repression with the formal frame of the consumer's choice theory.

Arginine Modulates Carbapenem Deactivation by OXA-24/40 in Acinetobacter baumannii

The resistance of Gram-negative bacteria to β-lactam antibiotics stems mainly from β-lactamase proteins that hydrolytically deactivate the β-lactams. Of particular concern are the β-lactamases that can deactivate a class of β-lactams known as carbapenems. Carbapenems are among the few anti-infectives that can treat multi-drug resistant bacterial infections. Revealing the mechanisms of their deactivation by β-lactamases is a necessary step for preserving their therapeutic value. Here, we present NMR investigations of OXA-24/40, a carbapenem-hydrolyzing Class D β-lactamase (CHDL) expressed in the gram-negative pathogen, Acinetobacter baumannii. Using rapid data acquisition methods, we were able to study the "real-time" deactivation of the carbapenem known as doripenem by OXA-24/40. Our results indicate that OXA-24/40 has two deactivation mechanisms: canonical hydrolytic cleavage, and a distinct mechanism that produces a β-lactone product that has weak affinity for the OXA-24/40 active site. The mechanisms issue from distinct active site environments poised either for hydrolysis or β-lactone formation. Mutagenesis reveals that R261, a conserved active site arginine, stabilizes the active site environment enabling β-lactone formation. Our results have implications not only for OXA-24/40, but the larger family of CHDLs now challenging clinical settings on a global scale.

Recognition of non-CpG repeats in Alu and ribosomal RNAs by the Z-RNA binding domain of ADAR1 induces A-Z junctions

Adenosine-to-inosine (A-to-I) editing of eukaryotic cellular RNAs is essential for protection against auto-immune disorders. Editing is carried out by ADAR1, whose innate immune response-specific cytoplasmic isoform possesses a Z-DNA binding domain (Zα) of unknown function. Zα also binds to CpG repeats in RNA, which are a hallmark of Z-RNA formation. Unexpectedly, Zα has been predicted — and in some cases even shown — to bind to specific regions within mRNA and rRNA devoid of such repeats. Here, we use NMR, circular dichroism, and other biophysical approaches to demonstrate and characterize the binding of Zα to mRNA and rRNA fragments. Our results reveal a broad range of RNA sequences that bind to Zα and adopt Z-RNA conformations. Binding is accompanied by destabilization of neighboring A-form regions which is similar in character to what has been observed for B-Z-DNA junctions. The binding of Zα to non-CpG sequences is specific, cooperative and occurs with an affinity in the low micromolar range. This work allows us to propose a model for how Zα could influence the RNA binding specificity of ADAR1.

ADAR1 is an interferon-induced enzyme that catalyzes editing of adenine to inosine across the transcriptome as part of the immune response. Here the authors establish how ADAR1 recognizes non-CpG RNA sequences to facilitate the formation of A-Z junctions.

Chemical Discovery in the Era of Metabolomics

Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.

Promiscuous activity of 3-isopropylmalate dehydrogenase produced at physiological level affords Escherichia coli growth on d-malate

Promiscuous activities of enzymes may serve as starting points for the evolution of new functions. However, most experimental examples of promiscuity affording an observable phenotype necessitate the artificial overexpression of the target enzyme. Here, we show that 3-isopropylmalate dehydrogenase (IPMDH), an enzyme involved in leucine biosynthesis, has a secondary activity on d-malate, which is sufficient for d-malate assimilation under physiological conditions where the enzyme is upregulated. In vitro, the turnover constant (k_cat ) of IPMDH for d-malate is about 30-fold lower than the k_cat for 3-isopropylmalate, yet sufficiently high to support the growth on d-malate. From an evolutionary perspective, our results highlight the possibility of phenotype emergence triggered by arbitrary changes in environmental conditions and prior to any mutational event.

Carbohydrate supramolecular chemistry: beyond the multivalent effect

(Hetero)multivalency acts as a multichannel switch that shapes the supramolecular properties of carbohydrates in an intrinsically multifactorial biological context.

Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes

Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g., just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12 percent in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.

Location, location! cellular relocalization primes specialized metabolic diversification

Specialized metabolites are structurally diverse and cell- or tissue-specific molecules produced in restricted plant lineages. In contrast, primary metabolic pathways are highly conserved in plants and produce metabolites essential for all of life, such as amino acids and nucleotides. Substrate promiscuity - the capacity to accept non-native substrates - is a common characteristic of enzymes, and its impact is especially apparent in generating specialized metabolite variation. However, promiscuity only leads to metabolic diversity when alternative substrates are available; thus, enzyme cellular and subcellular localization directly influence chemical phenotypes. We review a variety of mechanisms that modulate substrate availability for promiscuous plant enzymes. We focus on examples where evolution led to modification of the 'cellular context' through changes in cell-type expression, subcellular relocalization, pathway sequestration, and cellular mixing via tissue damage. These varied mechanisms contributed to the emergence of structurally diverse plant specialized metabolites and inform future metabolic engineering approaches.

MANET 3.0: Hierarchy and modularity in evolving metabolic networks

Enzyme recruitment is a fundamental evolutionary driver of modern metabolism. We see evidence of recruitment at work in the metabolic Molecular Ancestry Networks (MANET) database, an online resource that integrates data from KEGG, SCOP and structural phylogenomic reconstruction. The database, which was introduced in 2006, traces the deep history of the structural domains of enzymes in metabolic pathways. Here we release version 3.0 of MANET, which updates data from KEGG and SCOP, links enzyme and PDB information with PDBsum, and traces evolutionary information of domains defined at fold family level of SCOP classification in metabolic subnetwork diagrams. Compared to SCOP folds used in the previous versions, fold families are cohesive units of functional similarity that are highly conserved at sequence level and offer a 10-fold increase of data entries. We surveyed enzymatic, functional and catalytic site distributions among superkingdoms showing that ancient enzymatic innovations followed a biphasic temporal pattern of diversification typical of module innovation. We grouped enzymatic activities of MANET into a hierarchical system of subnetworks and mesonetworks matching KEGG classification. The evolutionary growth of these modules of metabolic activity was studied using bipartite networks and their one-mode projections at enzyme, subnetwork and mesonetwork levels of organization. Evolving metabolic networks revealed patterns of enzyme sharing that transcended mesonetwork boundaries and supported the patchwork model of metabolic evolution. We also explored the scale-freeness, randomness and small-world properties of evolving networks as possible organizing principles of network growth and diversification. The network structure shows an increase in hierarchical modularity and scale-free behavior as metabolic networks unfold in evolutionary time. Remarkably, this evolutionary constraint on structure was stronger at lower levels of metabolic organization. Evolving metabolic structure reveals a 'principle of granularity', an evolutionary increase of the cohesiveness of lower-level parts of a hierarchical system. MANET is available at http://manet.illinois.edu.

Multivalent glycoligands with lectin/enzyme dual specificity: self-deliverable glycosidase regulators

Multivalent mannosides with inherent macrophage recognition abilities, built on β-cyclodextrin, RAFT cyclopeptide or peptide dendrimer cores, trigger selective inhibition of lysosomal β-glucocerebrosidase or α-mannosidase depending on valency and topology, offering new opportunities in multitargeted drug design.

Twenty Years of "Lipid World": A Fertile Partnership with David Deamer

"The Lipid World" was published in 2001, stemming from a highly effective collaboration with David Deamer during a sabbatical year 20 years ago at the Weizmann Institute of Science in Israel. The present review paper highlights the benefits of this scientific interaction and assesses the impact of the lipid world paper on the present understanding of the possible roles of amphiphiles and their assemblies in the origin of life. The lipid world is defined as a putative stage in the progression towards life's origin, during which diverse amphiphiles or other spontaneously aggregating small molecules could have concurrently played multiple key roles, including compartment formation, the appearance of mutually catalytic networks, molecular information processing, and the rise of collective self-reproduction and compositional inheritance. This review brings back into a broader perspective some key points originally made in the lipid world paper, stressing the distinction between the widely accepted role of lipids in forming compartments and their expanded capacities as delineated above. In the light of recent advancements, we discussed the topical relevance of the lipid worldview as an alternative to broadly accepted scenarios, and the need for further experimental and computer-based validation of the feasibility and implications of the individual attributes of this point of view. Finally, we point to possible avenues for exploring transition paths from small molecule-based noncovalent structures to more complex biopolymer-containing proto-cellular systems.

Metabolite Repair Enzymes Control Metabolic Damage in Glycolysis

Hundreds of metabolic enzymes work together smoothly in a cell. These enzymes are highly specific. Nevertheless, under physiological conditions, many perform side-reactions at low rates, producing potentially toxic side-products. An increasing number of metabolite repair enzymes are being discovered that serve to eliminate these noncanonical metabolites. Some of these enzymes are extraordinarily conserved, and their deficiency can lead to diseases in humans or embryonic lethality in mice, indicating their central role in cellular metabolism. We discuss how metabolite repair enzymes eliminate glycolytic side-products and prevent negative interference within and beyond this core metabolic pathway. Extrapolating from the number of metabolite repair enzymes involved in glycolysis, hundreds more likely remain to be discovered that protect a wide range of metabolic pathways.

Robustness by intrinsically disordered C-termini and translational readthrough

During protein synthesis genetic instructions are passed from DNA via mRNA to the ribosome to assemble a protein chain. Occasionally, stop codons in the mRNA are bypassed and translation continues into the untranslated region (3′-UTR). This process, called translational readthrough (TR), yields a protein chain that becomes longer than would be predicted from the DNA sequence alone. Protein sequences vary in propensity for translational errors, which may yield evolutionary constraints by limiting evolutionary paths. Here we investigated TR in Saccharomyces cerevisiae by analysing ribosome profiling data. We clustered proteins as either prone or non-prone to TR, and conducted comparative analyses. We find that a relatively high frequency (5%) of genes undergo TR, including ribosomal subunit proteins. Our main finding is that proteins undergoing TR are highly expressed and have a higher proportion of intrinsically disordered C-termini. We suggest that highly expressed proteins may compensate for the deleterious effects of TR by having intrinsically disordered C-termini, which may provide conformational flexibility but without distorting native function. Moreover, we discuss whether minimizing deleterious effects of TR is also enabling exploration of the phenotypic landscape of protein isoforms.

The causes of evolvability and their evolution

Evolvability is the ability of a biological system to produce phenotypic variation that is both heritable and adaptive. It has long been the subject of anecdotal observations and theoretical work. In recent years, however, the molecular causes of evolvability have been an increasing focus of experimental work. Here, we review recent experimental progress in areas as different as the evolution of drug resistance in cancer cells and the rewiring of transcriptional regulation circuits in vertebrates. This research reveals the importance of three major themes: multiple genetic and non-genetic mechanisms to generate phenotypic diversity, robustness in genetic systems, and adaptive landscape topography. We also discuss the mounting evidence that evolvability can evolve and the question of whether it evolves adaptively.

Procyanidin A2, an anti-diabetic condensed tannin extracted from Wendlandia glabrata, reduces elevated G-6-Pase and mRNA levels in diabetic mice and increases glucose uptake in CC1 hepatocytes and C1C12 myoblast cells

To reduce the global burden of diabetes in an affordable way great attention has been paid to the search for functional foods and herbal remedies. One of the most popularly used functional foods in the North Eastern region of India is tender shoots of Wendlandia glabrata DC. In the current study identification of active anti-diabetic constituent of the tender shoots of W. glabrata was guided through α-glucosidase inhibition and procyanidin A2 was identified with IC₅₀ 0.27 ± 0.01 μg mL⁻¹ making it potential source for postprandial management of DM type 2. The study has also demonstrated procyanidin A2 as a potent anti-diabetic agent that exhibits significant glucose-6-phosphatase inhibitory activities and downregulated mRNA level in diabetic mice as well as increases glucose uptake in hepatocytes and myoblast cells. This study revealed that easily available tender shoots of W. glabrata could be used to make specific dietary recommendations for consumption for affordable management of diabetes.

Wendlandia glabrata and procyanidin A2 isolated thereof are exhibited significant anti-diabetic effect.

Structure and evolution of the 4-helix bundle domain of Zuotin, a J-domain protein co-chaperone of Hsp70

The J-domain protein Zuotin is a multi-domain eukaryotic Hsp70 co-chaperone. Though it is primarily ribosome-associated, positioned at the exit of the 60S subunit tunnel where it promotes folding of nascent polypeptide chains, Zuotin also has off-ribosome functions. Domains of Zuotin needed for 60S association and interaction with Hsp70 are conserved in eukaryotes. However, whether the 4-helix bundle (4HB) domain is conserved remains an open question. We undertook evolutionary and structural approaches to clarify this issue. We found that the 4HB segment of human Zuotin also forms a bundle of 4 helices. The positive charge of Helix I, which in Saccharomyces cerevisiae is responsible for interaction with the 40S subunit, is particularly conserved. However, the C-termini of fungal and human 4HBs are not similar. In fungi the C-terminal segment forms a plug that folds back into the bundle; in S. cerevisiae it plays an important role in bundle stability and, off the ribosome, in transcriptional activation. In human, C-terminal helix IV of the 4HB is extended, protruding from the bundle. This extension serves as a linker to the regulatory SANT domains, which are present in animals, plants and protists, but not fungi. Further analysis of Zuotin sequences revealed that the plug likely arose as a result of genomic rearrangement upon SANT domain loss early in the fungal lineage. In the lineage leading to S. cerevisiae, the 4HB was subjected to positive selection with the plug becoming increasingly hydrophobic. Eventually, these hydrophobic plug residues were coopted for a novel regulatory function—activation of a recently emerged transcription factor, Pdr1. Our data suggests that Zuotin evolved off-ribosome functions twice—once involving SANT domains, then later in fungi, after SANT domain loss, by coopting the hydrophobic plug. Zuotin serves as an example of complex intertwining of molecular chaperone function and cell regulation.

Exploiting enzyme promiscuity to shape plant specialized metabolism

The amazing variability of plant metabolism and its rapid divergence during evolution pose fundamental questions as to the driving forces, mechanisms, and players in metabolic differentiation. This review examines concepts that help us understand adaptive pathway evolution, with a particular emphasis on plant specialized metabolism, previously often termed secondary metabolism. Following a general introduction to pathway and metabolite evolution, the focus is directed to enzyme promiscuity and its classification. Promiscuous enzymes (or substrates), 'silent' elements of the metabolome, and the 'underground metabolism' may be used and combined to evolve 'new' metabolic pathways. It appears that new pathways rarely appear from scratch, but instead emerge from 'floppy' enzymes and elements of a 'messy' metabolism, and in this way a range of metabolites is generated, some of which may provide benefits to the plant.

A Single Mutation Unlocks Cascading Exaptations in the Origin of a Potent Pitviper Neurotoxin

Evolutionary innovations and complex phenotypes seemingly require an improbable amount of genetic change to evolve. Rattlesnakes display two dramatically different venom phenotypes. Type I venoms are hemorrhagic with low systemic toxicity and high expression of tissue-destroying snake venom metalloproteinases. Type II venoms are highly neurotoxic and lack snake venom metalloproteinase expression and associated hemorrhagic activity. This dichotomy hinges on Mojave toxin (MTx), a phospholipase A2 (PLA2) based β-neurotoxin expressed in Type II venoms. MTx is comprised of a nontoxic acidic subunit that undergoes extensive proteolytic processing and allosterically regulates activity of a neurotoxic basic subunit. Evolution of the acidic subunit presents an evolutionary challenge because the need for high expression of a nontoxic venom component and the proteolytic machinery required for processing suggests genetic changes of seemingly little immediate benefit to fitness. We showed that MTx evolved through a cascading series of exaptations unlocked by a single nucleotide change. The evolution of one new cleavage site in the acidic subunit unmasked buried cleavage sites already present in ancestral PLA2s, enabling proteolytic processing. Snake venom serine proteases, already present in the venom to disrupt prey hemostasis, possess the requisite specificities for MTx acidic subunit proteolysis. The dimerization interface between MTx subunits evolved by exploiting a latent, but masked, hydrophobic interaction between ancestral PLA2s. The evolution of MTx through exaptation of existing functional and structural features suggests complex phenotypes that depend on evolutionary innovations can arise from minimal genetic change enabled by prior evolution.

The metabolite repair enzyme Nit1 is a dual-targeted amidase that disposes of damaged glutathione in Arabidopsis

The tripeptide glutathione (GSH) is implicated in various crucial physiological processes including redox buffering and protection against heavy metal toxicity. GSH is abundant in plants, with reported intracellular concentrations typically in the 1-10 mM range. Various aminotransferases can inadvertently transaminate the amino group of the γ-glutamyl moiety of GSH to produce deaminated glutathione (dGSH), a metabolite damage product. It was recently reported that an amidase known as Nit1 participates in dGSH breakdown in mammals and yeast. Plants have a hitherto uncharacterized homolog of the Nit1 amidase. We show that recombinant Arabidopsis Nit1 (At4g08790) has high and specific amidase activity towards dGSH. Ablating the Arabidopsis Nit1 gene causes a massive accumulation of dGSH and other marked changes to the metabolome. All plant Nit1 sequences examined had predicted plastidial targeting peptides with a potential second start codon whose use would eliminate the targeting peptide. In vitro transcription/translation assays show that both potential translation start codons in Arabidopsis Nit1 were used and confocal microscopy of Nit1-GFP fusions in plant cells confirmed both cytoplasmic and plastidial localization. Furthermore, we show that Arabidopsis enzymes present in leaf extracts convert GSH to dGSH at a rate of 2.8 pmol min^-1 mg^-1 in the presence of glyoxalate as an amino acceptor. Our data demonstrate that plants have a dGSH repair system that is directed to at least two cellular compartments via the use of alternative translation start sites.

Towards a Stochastic Paradigm: From Fuzzy Ensembles to Cellular Functions

The deterministic sequence → structure → function relationship is not applicable to describe how proteins dynamically adapt to different cellular conditions. A stochastic model is required to capture functional promiscuity, redundant sequence motifs, dynamic interactions, or conformational heterogeneity, which facilitate the decision-making in regulatory processes, ranging from enzymes to membraneless cellular compartments. The fuzzy set theory offers a quantitative framework to address these problems. The fuzzy formalism allows the simultaneous involvement of proteins in multiple activities, the degree of which is given by the corresponding memberships. Adaptation is described via a fuzzy inference system, which relates heterogeneous conformational ensembles to different biological activities. Sequence redundancies (e.g., tandem motifs) can also be treated by fuzzy sets to characterize structural transitions affecting the heterogeneous interaction patterns (e.g., pathological fibrillization of stress granules). The proposed framework can provide quantitative protein models, under stochastic cellular conditions.

An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling

Many components of signaling pathways are functionally pleiotropic, and signaling responses are marked with substantial cell-to-cell heterogeneity. Therefore, biochemical descriptions of signaling require quantitative support to explain how complex stimuli (inputs) are encoded in distinct activities of pathways effectors (outputs). A unique perspective of information theory cannot be fully utilized due to lack of modeling tools that account for the complexity of biochemical signaling, specifically for multiple inputs and outputs. Here, we develop a modeling framework of information theory that allows for efficient analysis of models with multiple inputs and outputs; accounts for temporal dynamics of signaling; enables analysis of how signals flow through shared network components; and is not restricted by limited variability of responses. The framework allows us to explain how identity and quantity of type I and type III interferon variants could be recognized by cells despite activating the same signaling effectors.

The promiscuous estrogen receptor: Evolution of physiological estrogens and response to phytochemicals and endocrine disruptors

Many actions of estradiol (E2), the principal physiological estrogen in vertebrates, are mediated by estrogen receptor-α (ERα) and ERβ. An important physiological feature of vertebrate ERs is their promiscuous response to several physiological steroids, including estradiol (E2), Δ⁵-androstenediol, 5α-androstanediol, and 27-hydroxycholesterol. A novel structural characteristic of Δ⁵-androstenediol, 5α-androstanediol, and 27-hydroxycholesterol is the presence of a C19 methyl group, which precludes the presence of an aromatic A ring with a C3 phenolic group that is a defining property of E2. The structural diversity of these estrogens can explain the response of the ER to synthetic chemicals such as bisphenol A and DDT, which disrupt estrogen physiology in vertebrates, and the estrogenic activity of a variety of plant-derived chemicals such as genistein, coumestrol, and resveratrol. Diversity in the A ring of physiological estrogens also expands potential structures of industrial chemicals that can act as endocrine disruptors. Compared to E2, synthesis of 27-hydroxycholesterol and Δ⁵-androstenediol is simpler, leading us, based on parsimony, to propose that one or both of these steroids or a related metabolite was a physiological estrogen early in the evolution of the ER, with E2 assuming this role later as the canonical estrogen. In addition to the well-studied role of the ER in reproductive physiology, the ER also is an important transcription factor in non-reproductive tissues such as the cardiovascular system, kidney, bone, and brain. Some of these ER actions in non-reproductive tissues appeared early in vertebrate evolution, long before the emergence of mammals.

Chemical Diversity of Metal Sulfide Minerals and Its Implications for the Origin of Life

Prebiotic organic synthesis catalyzed by Earth-abundant metal sulfides is a key process for understanding the evolution of biochemistry from inorganic molecules, yet the catalytic functions of sulfides have remained poorly explored in the context of the origin of life. Past studies on prebiotic chemistry have mostly focused on a few types of metal sulfide catalysts, such as FeS or NiS, which form limited types of products with inferior activity and selectivity. To explore the potential of metal sulfides on catalyzing prebiotic chemical reactions, here, the chemical diversity (variations in chemical composition and phase structure) of 304 natural metal sulfide minerals in a mineralogy database was surveyed. Approaches to rationally predict the catalytic functions of metal sulfides are discussed based on advanced theories and analytical tools of electrocatalysis such as proton-coupled electron transfer, structural comparisons between enzymes and minerals, and in situ spectroscopy. To this end, we introduce a model of geoelectrochemistry driven prebiotic synthesis for chemical evolution, as it helps us to predict kinetics and selectivity of targeted prebiotic chemistry under “chemically messy conditions”. We expect that combining the data-mining of mineral databases with experimental methods, theories, and machine-learning approaches developed in the field of electrocatalysis will facilitate the prediction and verification of catalytic performance under a wide range of pH and Eh conditions, and will aid in the rational screening of mineral catalysts involved in the origin of life.

Medical Systems Biology

The aim of this volume is to encourage the use of systems-level methodologies to contribute to the improvement of human-health . We intend to motivate biomedical researchers to complement their current theoretical and empirical practice with up-to-date systems biology conceptual approaches. Our perspective is based on the deep understanding of the key biomolecular regulatory mechanisms that underlie health, as well as the emergence and progression of human-disease . We strongly believe that the contemporary systems biology perspective opens the door to the effective development of novel methodologies to the improvement of prevention . This requires a deeper and integrative understanding of the involved underlying systems-level mechanisms. In order to explain our proposal in a simple way, in this chapter we privilege the conceptual exposition of our chosen framework over formal considerations. The formal exposition of our proposal will be expanded and discussed later in the next chapters.

A pathway for every product? Tools to discover and design plant metabolism

The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. Here we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals.

Metabolite–Enzyme Coevolution: From Single Enzymes to Metabolic Pathways and Networks

How individual enzymes evolved is relatively well understood. However, individual enzymes rarely confer a physiological advantage on their own. Judging by its current state, the emergence of metabolism seemingly demanded the simultaneous emergence of many enzymes. Indeed, how multicomponent interlocked systems, like metabolic pathways, evolved is largely an open question. This complexity can be unlocked if we assume that survival of the fittest applies not only to genes and enzymes but also to the metabolites they produce. This review develops our current knowledge of enzyme evolution into a wider hypothesis of pathway and network evolution. We describe the current models for pathway evolution and offer an integrative metabolite–enzyme coevolution hypothesis. Our hypothesis addresses the origins of new metabolites and of new enzymes and the order of their recruitment. We aim to not only survey established knowledge but also present open questions and potential ways of addressing them.